[Paleopsych] Vernon L. Smith: Constructivist and Ecological Rationality in Economics

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Vernon L. Smith: Constructivist and Ecological Rationality in Economics
The American Economic Review, 2003 June, pp. 465-508

[This article is a fabulous one. It brings together the concepts of 
Friedrich Hayek and his notion of dispersed knowledge, the separate 
concepts of rationality in the classical SSSM (Standard Socioeconomic 
Science Model, which is NOT the SSSM (Standard Social Science Model, with 
its avoidance of Darwin, whose collapse last year I reported) and the 
rationality of brains with shaply limited abilities to know and to 
calculate, experimental economics (the work in which garnered Smith the 
Nobel Prize in economics), game theory, and neurobiology.

[Read it as closely as you wish, though it assumes a fairly high level of 
understanding of work in game theory and economics. The talk itself is 
given at http://nobelprize.org/economics/laureates/2002/smith-lecture.pdf 
and appears to be shorter that what you see below. I tried mightily to 
modify the output of Adobe's PDF --> TXT converter of the American 
Economic Review article to something more readable. If you'd like the AER 
PDF, send me an e-mail.]

This article is a revised version of the lecture Vernon L. Smith delivered 
in Stockholm, Sweden, on December 8, 2002, when he received the Bankof 
Sweden Prize in Eco­nomic Sciences in Memory of Alfred Nobel.

The title was suggestedto the author in the paper by Joel Norman, "Two 
Visual Systems and Two Theories of Per­ception: An Attempt to Reconcile 
the Constructivist and Ecological Approaches" (Behavioral and Brain 
Sciences, 2002). After .nishing this paper I found that my use of the term 
below had been used byGerd Gigerenzer et al. (1999), for application to 
"fast and frugal decision making," by individuals: "A heuristic is 
ecologically rational to the de­gree thatitis adapted to the structure 
ofan environment" (p. 13). Hayek, in the citations below, identi. es both 
kinds of rationality.

* Department of Economics and Law, Interdisciplinary Center for Economic 
Science, George Mason University, 4400 UniversityDrive, MSN 1B2, Fairfax, 
VA 22030. Doing experimental economics has changed the way I thinkabout 
economics. There are many reasons for this, but one of the most prominent 
is that designing and conducting experi­ments forces you to think through 
the process rules and procedures of an institution. Few, like Einstein, 
can perform detailed and imaginative mental experiments. Most of us need 
the challenge of real experiments to discipline our thinking. In this 
paper I hope to indicate how my thinking has been changed in some detail. 
Iam indebted to Sid Siegel for technical and conceptual inspiration; to 
John Hughes, Stan Reiter, andthe Purdue Universityfacultyfrom 1955 to 1967 
for warm, tolerant support beginning with my .rst experiment; to Charles 
Plott, Charles Holt, and Martin Shu­bik for many valuable encounters over 
the years on institu­tional and experimental issues; to students, 
visitors, the current ICES team, and especially to my growing but 
tol­erating family who have made all these years the best years of my 
life.

When we leave our closet, and engage in the common affairs oflife, 
(reason's) con­clusions seem to vanish, like the phan­toms ofthe nighton 
the appearance of the morning;and 'tis dif. cult for us to retain even 
that conviction, which we had at­tained with dif.culty... (David Hume, 
1739, 1985, p. 507).

... we must constantly adjust our lives, our thoughts and our emotions, in 
order to live simultaneouslywithin different kinds of orders according to 
different rules. If we were to apply the unmodi.ed, un­curbed rules 
(ofcaring intervention to do visible "good") of the ... small band or 
troop, or ... our families ... to the (ex­tendedorder of cooperation 
throughmar­kets), as our instincts and sentimental yearnings often make us 
wish to do, we would destroy it. Yet ifwe were to always apply the 
(competitive) rules of the ex­tendedorder to our more intimate group­ings, 
we would crush them (Friedrich A. Hayek, 1988, p. 18; italics are his, 
paren­thetical reductions are mine).

We have become accustomed to the idea that a naturalsystem like the human 
body or an ecosystem regulates itself. To ex­plain the regulation, we 
lookfor feedback loops rather than a centralplanningand directing body. 
But somehow our intui­tions about self­regulation do not carry over to the 
arti. cial systems of human society. (Thus) ... the ... disbelief always 
expressed by (my) architecture students (about)... medieval cities as 
marvelously patterned systems that had mostly just "grown" in response to 
myriads of indi­vidualdecisions. To my studentsa pattern implieda planner 
... . The idea that a city could acquire its pattern as "naturally" as a 
snow. ake was foreign to them (Herbert Alexander Simon, 1981, 1996, p. 
33).

Historically, a recurrent theme in economics is that the values to which 
people respond are notcon.ned to thoseone wouldexpectbasedon the narrowly 
de.ned canons of rationality. These roots go back to Adam Smith (1759, 
1776), who examined the moralsympathiesthat characterize natural human 
sociality.1Contrary to vulgar impressions, in Smith’s view, each 
indi­vidual de. ned and pursued his own interest in his own way, and 
individuals were mischaracterized

1 Economistsare largelyuntouched by Smith’s .rst great work, which was 
eclipsed by the Wealthof Nations. Thus, one of the profession’s best­known 
historian of economic thought, "found these two works in some measure 
basically inconsistent" (Jacob Viner, 1991, p. 250). For a contrary 
interpretation seeSmith (1998). Many of the references herein to my own 
andcoauthored work have been reprinted in Smith (1991, 2000).

by the metaphor, "economic man." (cf., Hayek, 1991, p. 120). This careless 
scholarship fails to recognize the key proposition articulated by the 
Scottish philosophers: to do good for others, does not require deliberate 
action to further the perceived interest ofothers. As Mandeville so 
succinctlyput it, "The worst of all the multitude did something for the 
common good." (See Mandeville’s poem, "The Grumbling Hive" or "Knaves 
Turned Honest," 1705; quoted in Hayek, 1991, p. 82.) Manycontemporary 
schol­ars, and not only popular writers, have reversed Mandeville’s 
proposition, and argued that the standard socioeconomic science model 
(SSSM) requires,justi.es, and promotes sel.sh behav­ior.2On the contrary, 
because enforceable rights can never cover every margin of decision, 
op­portunism in all relational contracting and ex­change across time are 
costs, not bene.ts, in achievinglong­term value from trade;an ideol­ogy of 
honesty3 means that people play the game of "trade," rather than "steal," 
although crime may often pay the rational lawbreaker who always chooses 
dominant strategies. Nor does nonsel. sh behavior in ordinary market 
transactions prevent those transactions from promoting specialization and 
creating wealth.

Cultures that have evolved markets have enor­mously expandedresource 
specialization, created commensurate gains from exchange, and are 
wealthier than those that have not. This proposi­tion says nothing about 
the necessity of human sel. shness--the increased wealth of particular 
in­dividuals can be used for consumption, invest­ment, to pay taxes, for 
Macarthur Fellows, gifted to the symphony, the Smithsonian, or the poor.4

2 That A implies B in no sense allows the reverse state­ment. But why 
would we economists confuse necessary with suf. cient conditions?The 
textfrom Hume provides the answer. No one can consistentlyapplyrational 
logical prin­ciples to everything he or she does; if there are cognitive 
costs in every application then the effort cost will often exceed the 
bene. ts (Smith and Ferenc Szidarovszky, 2003). Theorists live by 
provingtheorems, and when in this mode we rarely make such errors. A 
missingchapter in the study of bounded rationality is its application to 
understanding, and accepting with a little humility, the severe 
limitations it imposes on our developmentof economic theory.

3 Douglass Cecil North (1981) has emphasized the im­portance of ideology 
in promoting economic growth. 4In the Potlatch, some wealth-- created in 
part by pri­vate property rights in .shing grounds--was publicly 
de­stroyed. Markets economize on the need for virtue, but do not eliminate 
it.

Researchin economic psychology5has prom­inently reported examples where 
"fairness" considerations are said to contradict the ratio­nality 
assumptions of the standardsocioeconomic science model. But 
experimentaleconomists have reported mixed results on rationality: people 
are often better (e.g., in two­person anonymous inter­actions), in 
agreement with (e.g., in .ow supply and demand markets), or worse (e.g., 
in asset trading), in achieving gains for themselves and others than is 
predicted by rational analysis. Pat­terns in these contradictions and con. 
rmations provide important clues to the implicit rules or norms that 
people may follow, and can motivate new theoretical hypotheses for 
examination in both the .eld and the laboratory. The pattern of results 
greatly modi.es the prevailing, and I be­lieve misguided, rational SSSM, 
and richly modernizes the unadulterated message of the Scottish 
philosophers.

I. On Two Forms of Rationality

The organizing principle throughout this pa­per is the simultaneous 
existence of two rational orders. I shall try to make the case that both 
orders are distinguishingcharacteristics of what we are as 
socialcreatures; that both are essential to understanding and unifying a 
large body of experience from socioeconomic life and the ex­perimental 
laboratory, and in charting relevant new directions for economic theory as 
well as experimental­empirical programs.

A. Constructivist Rationality

The .rst concept of a rational order derives from the SSSM going back to 
the seventeenth century. The SSSM is an example of what Hayek has called 
constructivist rationality (or "constructivism"), which stems particularly

5I will use the term "economic psychology" generally to refer to cognitive 
psychology as it has been applied to economic questions, and to a third 
sub.eld of experimental methods in economics recently 
product­differentiated as "behavioral economics" (Sendhil Mullainathan and 
Richard H. Thaler, 2001), and further differentiated into "behavioral game 
theory" (Colin F. Camerer, 2002); the original foun­dations were laid byW. 
Edwards, DannyKahneman, Anatol Rapoport, Paul Slovic, and Amos Tversky to 
name some of the most prominent.

from Descartes (also Bacon and Hobbes),6 who believed and argued that all 
worthwhile social institutions were and should be created bycon­scious 
deductiveprocesses of human reason.7In economics the SSSM leads to 
rational predic­tive models of decision that motivate research hypotheses 
thatexperimentalists have been test­ing in the laboratory since the 
mid­twentieth century. Although the test results tend to be con.rming in 
impersonal market exchange, the results are famously and recalcitrantly 
mixedin "personal exchange," notably in a great variety of two­person 
extensive­form games where some half of the people attempt and frequently 
succeed when risking cooperation, even when anonymously paired.8These 
results have moti­vated constructivist extensions of game theory based on 
other­regarding, in addition to own­regarding, preferences (e.g., Gary E 
Bolton, 1991; Matthew Rabin, 1993), and on "learning"--the idea that the 
predictions of the SSSM might be approached over time by trial­and­error 
adaptation processes (Ido Erev and

6 In the nineteenth century, Bentham and John Stuart Mill were among the 
leading constructivists. Bentham (and the utilitarians) sought to "... 
remake the whole of... (Brit­ish) ... law and institutions on rational 
principles" (Hayek, 1960, p. 174). Mill introduced the much­abused 
construc­tivist concept of(but not the name)"natural monopoly." To Mill it 
was transparently wasteful and duplicative to have two or more mail 
carriers operating on the same route. He is the intellectual father of the 
U.S. and other postal mo­nopolies around the world, their resistance to 
innovation, and their demise in the face of the privatization movement in 
some countries and the growth of superior substitutes in others. Mill 
could not imagine that it would be ef.cient for two cities to be connected 
by two parallel railroad tracks (Mill, 1899, Vol. 1, pp. 131, 141- 42; 
Vol. 2, p. 463). Mill died in 1873. I would conjecture that by that date, 
or soon thereafter, men with grade­school educations had become rich 
constructing the .rst parallel­route railroads. These emergent 
contradictions to constructivist natural monopoly are examples ofwhat we 
shall call ecological rationality, as detailed below.

7 "... Descartes contended that all the useful human in­stitutions were 
and ought to be deliberate creation(s) of conscious reason ... a capacity 
of the mind to arrive at the truth by a deductive process from a few 
obvious and un­doubtable premises" (Hayek, 1967, p. 85).

8 Behavioral economists have made a cottage industry of showing that the 
SSSM assumptions seem to apply almost nowhere to real decisions. This is 
because their research program has been a deliberate search in the tails 
of distri­butions for "Identifyingthe ways in which behavior differs from 
the standard model ..." (Mullainathan and Thaler, 2001, Vol. II, p. 1094), 
a search that can only succeed. Alvin E. Roth, 1998; Camerer and Teck­Hua 
Ho, 1999).

An alternative and perhaps complimentary explanation of some of these 
contradictions to theory is that people may use social­grown norms of 
trust and reciprocity9 (including eq­uity, meaning to each according to 
his justly earned dessert; i.e., equality ofopportunity, not outcome)to 
achieve cooperative states superior to individuallyrational defection 
outcomes. We will report some experimental tests designedto separate 
competing preference and reciprocity theories of behavior in personal 
exchange. Al­though reciprocity seems to be a leader in the comparisons we 
summarize, its strength is not uniform across alltests, andmuch remains to 
be learned aboutthe hidden recesses ofmeaningin human behaviorandthe 
circumstances in which cooperative or noncooperativebehavior is 
man­ifest.10Technically, the issue is how most pro­ductively to model 
agent "types" byextending game theory so that types are an integral part 
of its predictive content, rather than merely im­ported as an ex post 
technical explanation of experimental results. For example, moves can 
signal types, andeffect decision,which explains why game form matters, 
andwhy payoffs avail­able, but forgone, can effect outcomes. These 
elements must be part ofthe internal structure of the theory such that 
outcomes become predic­tions conditional on the elementarycharacteris­tics 
of players who read each other’s intentions. If successful, many of the 
basic results in game

9 Dissatis.ed with the utilitarian approach because its predictions fail 
to account for the observed importance of instructions/procedures, we 
began investigating the reci­procity hypothesis in Elizabeth Hoffman et 
al. (1994). Me­chanically, utilities can serve as intermediate 
placeholders for reciprocal trust, but, as surface indicators, serve 
poorly to generate new hypotheses designed to understandinterac­tive 
processes. Good theory must be an engine for generat­ing testable 
hypotheses, and utility theory runs outof fuel quickly. Utility values are 
seen as providing the ultimate "given" data, and the conversation stops.

10I am reminded of a department head from Hewlett­Packard visitingour lab. 
Inaively assumed that he would be most interested in demonstrations of 
some of our market experiments. Not so. He was more interested in the 
"trust" experiments. Why?He saw the HP management problem as one 
ofgettingteams to cooperate internallyby buildingtrust and 
trustworthiness, while being vigorous competitors ex­ternally. Could the 
trust games serve as a measurement and teaching tool for helpingto solve 
this problem? This nicely illustrates the tension in Hayek’s two­worlds 
quote in the text. theory would become special cases of the ex­tended 
theory.

In market experiments--where cooperation can occur through the 
coordination function of prices produced by, but simultaneously result­ing 
from, interaction with individual choice behavior--the results are more 
commonly in accord with standard competitive models that maximize group 
welfare. This professionalvic­tory is hollowed by the failure of standard 
the­ory to predict the "surprisingly"11 weak conditions under which the 
results obtain.12 Thus, for tractability, Cartesian rationalism 
provisionally assumes or "requires" agents to possess complete payoff and 
other information-- far more than could ever be given to one mind. In 
economics the resulting exercises are be­lieved to sharpen economic 
thinking, as if-then parables. Yet, these assumptions are unlikelyto 
approximate the levelofignorance thathas con­ditioned either individual 
behavior, or our evolved institutions, as abstract norms or rules 
independent of particular parameters, which have survived as part of the 
world of experi­ence.13 The temptation is to ignore this reality because 
it is poorly understood, and does not yieldto our familiar but inadequate 
modeling tools, and to proceed in the implicit belief that our parables 
capture what is most essential

11Robert B. Wilson (1992, p. 256)discusses an ef. ­ciency theorem, and 
suggests that the phenomenon is "per­haps unsurprising." It is, nowadays, 
but few believe it; also, theory has lagged well behind the evidence, and 
yields inadequate testable insight into the process dynamics oper­ating in 
different institutions.

12I want to acknowledge correspondence with Charles Plott andaddthe 
following:Althoughthis is a giant victory for the economic theory of 
markets it simultaneously dem­onstrates that the theory is incomplete. The 
unexpectedly weak conditions under which the results obtain are good news 
for market performance, but not such good news for the scienti. c 
community because itdemonstrates that we do not understand why markets 
work as they do. You do not have to have large numbers of agents, each an 
insigni. cant part ofthe whole--three or four buyers andas manysellers are 
entirely adequate in a wide range of economic environ­ments; they do not 
have to have complete or perfect or common information--each can have only 
private informa­tion; nor is it required that individuals make decisions 
systematically or be economically sophisticated.

13Throughout the paper I will use "environment" to mean the collection of 
agentvalues (preferences) that de.ne the gains from trade; "institution" 
to refer to the language (messages), rules of message exchange, and 
contract in a market; and "behavior" for agent message choices 
condi­tional on the environment and institution (Smith, 1982).

about what we observe. Having sharpened our understanding on Cartesian 
complete informa­tion parables we carry these tools into the world for 
applicationwithoutall the necessary caveats that re. ect the tractability 
constraints imposed by our bounded professional cognitive capaci­ties as 
theorists.

In summary, constructivism uses reason to deliberately create rules of 
action, and create human socioeconomic institutions that yield outcomes 
deemed preferable, given particular circumstances, to those producedby 
alternative arrangements. Although constructivism is one of the crowning 
achievements of the human intellect, it is important to remain sensitive 
to the fact that human institutions and most deci­sion makingis not guided 
primarily, ifat all, by constructivism. Emergent arrangements, even if 
initially constructivist in form, must have sur­vival properties that take 
accountofopportunity costs and environmentalchallenges invisible to our 
modeling efforts.

B. Limitations and Distractions of Constructivist Rationality

Since our theories and thought processes about social systems involve the 
conscious and deliberate use of reason, it is necessary to con­stantly 
remindourselves that human activity is diffused and dominated by 
unconscious, auto­nomic, neuropsychologicalsystems that enable people to 
function effectively without always calling upon the brain’s scarcest 
resource-- attentional and reasoning circuitry. This is an important 
economizing property of how the brain works. If it were otherwise, no one 
could get through the day under the burden of the self­conscious 
monitoring and planning of ev­ery trivial action in detail.14 Also, no one 
can express in thoughts, let alone words, all that he or she knows, 
anddoes not know but might call upon, or need to discover, for some 
purposive action. Imagine the strain on the brain’s re­sources if at the 
supermarket a shopper were required to explicitly evaluate his preferences 
for every combination of the tens of thousands of grocery items that are 
feasible for a given

14 "If we stopped doing everythingfor which we do not know the reason, or 
for which we cannot provide ajusti. ­cation ... we would probably soon be 
dead" (Hayek, 1988, p. 68).

budget. Such mental processes are enormously opportunity­costly and 
implicitly our brain knows, if our conscious mind does not know, that we 
must avoid incurring opportunity costs that are not worth the bene. t.15 
The challenge ofanyunfamiliar action or problemappears .rst to trigger a 
search bythe brain to bring to the conscious mind what one knows that is 
related to the decision context. Context triggers auto­biographic 
experiential memory, which ex­plains why context surfaces as a nontrivial 
treatment, particularly in small group experi­ments. The brain (including 
the entire neuro­physiological system) takes over directly in the case of 
familiar, mastered tasks, and plays the equivalent of lightening chess 
when the "ex­pert" trades, plays Beethoven’s Fifthpiano con­certo, or 
connects with a 95­mile/hour fastball--all without self­aware "thinking" 
by the mind.

We fail utterly to possess natural mechanisms for reminding ourselves of 
the brain’s off­line activities and accomplishments. This important 
proposition has led Michael S. Gazzaniga (1998)to askwhy the brain fools 
the mind into believing it is in control.16 Andto Hayek, who

15Expected utility theory is for teaching (as Wassily Leontief once 
suggested), but also for the constructivist modeling of consistent choice. 
It seems inadequate for the prediction, or the ecological understanding, 
ofbehavior. Its inadequacy for prediction has been plainly emphasized in 
the many contributions of Amos Tversky and Daniel Kah­neman (see their 
1987 paper for an excellent summary statement), some of which have been 
quali. ed and reinter­preted in the work of Gigerenzer et al. (1999). The 
results are far more encouraging in the context of markets, where subjects 
are not consciously maximizing. See, e.g., Smith (1991, 2000)and Plott 
(2001). Joyce Berg et al. (1994), in a signi. cant paper, .nd that the 
measurement of risk aver­sion varies with the type of market institution 
or procedure used in extracting risk measures from decisions. See Smith 
and Szidarovszky (2003) for a constructivist utilitarian treatmentof 
decision whose reward outcome requires cog­nitive costs to be incurred: 
objective rationality is not sub­jectively rational, and therefore it is 
not optimal for the individualto apply the objectively "optimal" 
prescriptions.

16 "By the time we thinkwe know something--it is part ofour conscious 
experience--the brain has already done its work. It is old news to the 
brain, but fresh to ‘us.’ Systems built into the brain do their work 
automatically and largely outside ofour conscious awareness. The brain 
.nishes the work half a second before the information it processes reaches 
our consciousness. ... We are clueless about how all this works and gets 
effected. We don’t plan or articulate these actions. We simply observe the 
output. ... The brain begins to cover for this ‘done deal’ aspect ofits 
functioning thoroughly understood this proposition, what was the "fatal 
conceit"? "The idea that the abil­ity to acquire skills stems from 
reason." The constructivist mindmakes a fatal "error," blind­ing itself to 
understanding, as we are warned, "one should never suppose that our reason 
is in the higher critical position andthatonly those moral rules are valid 
that reason endorses" (Hayek, 1988, p. 21). But the anthropocentric 
(­morphic) mind routinely makes this signi. ­cant error.

Most ofour operating knowledge17 we do not remember learning. Natural 
language is the most prominent example, but also music and virtually 
everything that constitutes our devel­opmentalsocialization. We learn the 
rules ofa language and of ef. cient social intercourse, 
withoutexplicitinstruction, simply byexposure to family and extended 
family social networks (Jerome Kagan and Sharon Lamb, 1987; Alan Page 
Fiske, 1991;Kagan, 1994;Steven Pinker, 1994). That the brain is capable of 
off­line subconscious learning is shown by experiments withamnesiacs who 
are taught a new task. They learn to perform well, but memory of having 
learned the task escapes them (Barbara J. Knowlton et al., 1996).

C. Ecological Rationality

These considerations lead to the secondcon­cept of a rational order, as an 
undesigned eco­logical system that emerges out of cultural and biological 
evolutionary18 processes: homegrown

by creating in us the illusion that the events we are experi­encingare 
happeningin real time--notbefore our conscious experience ofdeciding to do 
something" (Gazzaniga, 1998, pp. 63-64).

17 Hayek (1967, p. 44) notes that "... modern English usage does not 
permit generallyto employthe verb ‘can’ (in the sense of the German 
konnen) to describe all those instances in which an individualmerely 
‘knows how’ to do a thing... (including)... the capacity to act according 
to rules which we may be able to discover but which we need not be able to 
state in order to obey them."

18Manyrecognize that evolutionary processes are nec­essarily 
coevolutionary,butthere is also deep denial of this, and bias, that all is 
due to "culture" (which is even more poorly understood than biology), 
leading Pinker (2002)to investigate why. Heritable abstract function can 
become dormant, atrophied, or malfunctional in the absence of 
ini­tializing input on a developmental time schedule for the brain’s 
vision, language, and socialization circuitry. That these processes are 
coevolutionary is evidentin the study of twins (Nancy L. Segal, 1999). 
Deconstructivist reports ar­gue that these studies exhibit many of the 
usual data and statistical identi.cation problems (Arthur S. Goldberger, 
1979), but the need is for positive revisionist analysis.

principlesofaction, norms, traditions, and"mo­rality."19 Ecological 
rationality uses reason-- rationalreconstruction--to examine the behavior 
of individuals based on their experience and folkknowledge, who are 
"naive" in their ability to apply constructivist tools to the decisions 
they make;to understandthe emergentorder in human cultures; to discover 
the possible intel­ligence embodiedin the rules, norms, and insti­tutions 
of our cultural and biological heritage that are createdfrom human 
interactions but not bydeliberatehuman design. People follow rules without 
being able to articulate them, but they can be discovered. This is the 
intellectualheri­tage ofthe Scottish philosophers, who described and 
interpreted the social and economic order they observed.

An eighteenth­century precursor of Herbert Simon, David Hume was concerned 
with the limits of reason, the bounds on human under­standing, and with 
scalingbackthe exaggerated claims of Cartesian constructivism. To Hume, 
rationality was phenomena that reason discov­ers in emergent institutions. 
Thus, "the rules of morality... are not conclusions of(our) reason." 
(Hume, 1985, p. 235). Adam Smith developed the idea ofemergent order for 
economics. Truth is discovered in the form of the intelligence embodied in 
rules and traditions that have formed, inscrutably, outofthe 
ancienthistory of human social interactions. This is the antithesis of the 
anthropocentric beliefthat ifan observed social mechanism is functional, 
somebody in the unrecordedpast must have used reason con­

19 "Morality" refers to any maxim of cohesive social behavior that 
survives the test of time, and is prominently represented by the great 
"shalt not" prohibitions of the leading world religions: thou shalt not: 
(1) steal, (2) covet the possessions of others, commit (3)murder, (4) 
adultery, or (5) bear false witness. The .rst two de.ne and defend 
property rights in the product of one’s labor, and all re­sources 
accumulated bysuch labor, enabling the emergence of the extended order of 
mind through markets. The last three commandments protect the sanctity of 
social exchange--the external order of the mind. These modest exclusionary 
constraints leave an immense scope for free­dom within their bounds. 
Corollaries, like the Buddhist live­and­let­live version of the golden 
rule, are explicit in this respect:"do not unto others as you would have 
them not do unto you." sciously to create it to serve its perceived 
in­tended purposes.20

In experimental economics the eighteenth­century Scottish tradition is 
revealed in the ob­servation ofemergent order in numerous studies of 
existing market institutions such as the con­tinuous double auction 
(CDA).21To paraphrase Adam Smith, people in these experiments are led to 
promote group welfare­enhancing social ends that are not part of their 
intention. This principle is supported by hundreds of experi­ments whose 
environments and institutions (sealed bid, posted offer, and others 
besides CDA) may exceed the capacity offormalgame­theoretic analysis to 
articulate predictive mod­els. But they do not exceed the functional 
capacity of collectives of incompletely in­formed human decision makers, 
whose auto­nomic mental algorithms coordinate behavior through the rules 
of the institution--social algorithms--to generate high levels of 
mea­sured performance.

Acknowledging and investigating the work­ings of unseen processes are 
essential to the 20In cultural and biological evolution, order arises from 
mechanisms for generating variation to which is applied mechanisms for 
selection. I am indebted to Todd Zywicki, who, at a recent Liberty Fund 
conference on "Hayek, Ex­periment and Freedom," observed that reason is 
good at providing variation, but not selection. Constructivism is indeed 
an engine for generating variation, but is far too limited in its ability 
to comprehend and apply all the rele­vant facts to serve the process of 
selection, which is better left to ecological processes. 21What 
experimentalists have unintentionally brought to the table is a 
methodology for objectively testing the Scottish­Hayekian hypotheses under 
scienti. c controls. This answers the question Milton Friedman is said to 
have raisedconcerningthe validity of Hayek’s theory/reasoning: "How would 
you know?" (I am unable to .nd/provide the reference). Remarkably, Hayek 
stated on the very edge of recognizing what experiments could do for 
testing his the­ory, then dismissed it. Thus,"We can test it (‘competition 
as a discovery procedure’) on conceptual models, and we might conceivably 
test it in arti. cially created real situa­tions, where the facts which 
competition is intended to discover are already known to the observer. But 
in such cases it is of no practical value, so that to carry out the 
experiment would hardly be worth the expense" (Hayek, 1984, p. 255;also 
see Smith, 2002, pp. 95-96). Economic historians, e.g., 
North(1981),andpolitical economists, e.g., Elinor Ostrom (1990), have long 
explored the intelligence and ef. cacy embodied in emergent socioeconomic 
institu­tions that solve, or fail to solve, problems of growth and 
resource management. They study "natural" ecological ex­periments from 
which we have learned immeasurably.

growthofour understandingof social phenom­ena, and enable us to probe 
beyond the anthro­pocentric limitations ofconstructivism.

Both kinds of rationalityhave in. uenced the design and interpretation of 
experiments in economics. Thus, if people in certain contexts make choices 
that contradict our formaltheory of rationality, rather than conclude that 
they are irrational, some ask why, reexamine main­tained hypotheses 
including all aspects of the experiments--procedures, payoffs, context, 
in­structions, etc.--and inquire as to what new concepts and experimental 
designs can help us to better understandthe behavior. What is the 
subjects’ perceptionofthe problem that they are trying to solve?

Finally, understanding decision requires knowl­edge beyond the 
traditionalbounds of econom­ics,22 a challenge to which Hume and Smith 
were not strangers.23 This is manifest in the recentstudies of the 
neuralcorrelates ofstrate­gic interaction (McCabe calls it 
neuroeconom­ics) using fMRI and other brain­imaging technologies. That 
researchexplores the neuro­correlates of intentions or "mind reading," and 
other hypotheses aboutinformation,choice, and own versus other payoffs in 
determining inter­active behavior.

The above themes will be illustrated and dis­cussed in a wide variety of 
examples drawn from economics, law, experimental economics, and 
psychology. I will begin with impersonal exchange through markets, drawing 
on the learning from experiments and .eld observa­tions to illustrate how 
the contrast between con­structive and ecological rationality informs 
learning from observation. Then I willexamine 
personalexchange,particularlyin the context of two­person extensive­form 
games, asking why constructivist models are of limited success in 
predicting behavior in single­play games, even when subjects are 
anonymously matched.

22I importune students to read narrowly within econom­ics, but widelyin 
science. Within economics there is essen­tially only one model to be 
adapted to every application: optimization subject to constraints due to 
resource limita­tions, institutional rules, and/or the behavior ofothers, 
as in Cournot­Nash equilibria. The economic literature is not the best 
place to .ndnew inspiration beyond these traditional technical methods of 
modeling.

23Thus, for Hayek, "an economist who is onlyan econ­omist cannot be a good 
economist."

II. Impersonal Exchange: The Extended Order of the Market

A. How Are the Two Concepts of a Rational Order Related?

Constructivism takes as given the social structures generated by emergent 
institutions that we observe in the world, and proceeds to model it 
formally. An example would be the Dutch auction or its alleged isomorphic 
equiv­alent, the sealed­bidauction (William Vickrey, 1961; Paul Milgrom 
and Robert J. Weber, 1982). Constructivist models need notask why or how 
an auction institutionarose or what were the ecologicalconditions that 
createdit, or why there are so many distinctauction institutions. In some 
cases it is the other way around. Thus, revenue equivalence theorems show 
that the standard auctions generate identical expected outcomes, which, if 
taken literally, leave no modeled economicreason for choosingbetween them.

More generally, using rational theory, one represents an observed 
socioeconomic situation withan abstract interactive game tree. 
Contrar­ily, the ecological concept of rationality asks from whence came 
the structure capturedby the tree? Why this social practice, from which we 
can abstract a particular game, andnot another? Were there other practices 
and associated game trees that lacked survival properties and were 
successfully invaded by what we observe? There is a sense in which 
ecological systems, whether cultural or biological, must necessarily be, 
or are in the process of becoming, rational: they serve the .tness needs 
of those who unin­tentionally created them through their interac­tions. 
Constructivist mental models are based on assumptions about behavior, 
structure, and the value­knowledge environment. These as­sumptions might 
be correct, incorrect, or irrel­evant, and the models may or may not lead 
to rational action in the sense of serving well the needs ofthose to whom 
the models apply. As theorists the professional charge for which we are 
paid is to formulate and prove theorems. A theorem is a mapping from 
assumptions into testable or observable implications. The de­mands 
oftractabilityloom large in this exercise, and to get anything much in the 
way of results it is necessary to consider both the assumptions andtheir 
implications as variables. Few game theorists, buildingon the assumption 
thatagents always choose dominant strategies, believed this to 
characterize the behavior ofall agents in all situations. Hence, the 
near­universal justi. ­cation oftheory as an exercise in "understand­ing." 
But the temptation is to believe that our "castles in the sky" (as W. 
Brock would say) have directmeaningin our worlds ofexperience andproceedto 
impose them where itmaynotbe ecologically rational to do so.

To understandwhat is--the tip of the knowl­edge iceberg--requires 
understandingofa great deal that is not. In the laboratory we can not 
onlyrationally reconstruct counterfactuals, as in economic history, but 
also use experiments to test and examine their properties. Let us lookat 
two contemporary examples.

1. Deregulating Airline Routes.--Airline route deregulation brought an 
unanticipated re­organizationofthe network, calledthe hub­and­spoke 
system. (See, e.g., George Donahue, 2002). This is an 
ecologicallyrationalresponse, apparently anticipated bynone of the 
construc­tivist arguments for deregulation, and predicted byno one. Nor 
could it have been uncovered, I submit, in 1978by surveys of airline 
managers, or by marketing surveys of airline customers. Unknown to both 
managers and customers was the subsequently revealed decision preferences 
of customers who unknowingly favored fre­quencyof daily departure 
andarrival times--a preference that had to be discovered through market 
experimentation. Nonstop service be­tween secondary cities was simply not 
sustain­able in a deregulatedworldof free choice. The onlyway to achieve 
ef. ciency, both the demand for frequency of service and pro.table load 
factors, among secondary cities was for the .ights to connect through 
hubs. Hence, the hy­pothesis that a rational ecologicalequilibrium emerged 
to dominate repeated constructivist at­tempts, by business entrants and 
start­ups, to satisfy an incompatible set of constraints pro­vided by the 
microstructure of demand, pro.t­ability, and technology.

Might it have been otherwise if airport run­way rights, or "slots," had 
been an integral part of the deregulation of airline routes, and the 
time­of­day spotpricing ofslots had emerged to re.ect hub congestion 
costs? (Stephen Rassenti et al., 1982). We do not know, but the effectof 
this hypotheticalcounterfactualon the viability of hub bypass could be 
assessed in laboratory experiments. As in allstudies of what is not, the 
challenge is to estimate the parameters that would implement the 
appropriate economic environment.

2. The California Energy Crisis.--Asecond, and very troubling, example is 
the circum­stances leadingto the California energycrisis. As in other 
regions of the country and the world, deregulation was effected as a 
planned transition with numerous political compro­mises. In California it 
took the form of deregu­lating wholesale markets and prices while 
continuing to regulate retail prices at .xed hourly rates over the daily 
and seasonalcycles in consumption. The utilities negotiated an in­crease 
in these average retail rates to meet the revenue requirements of capital 
investments that were "stranded" (i.e., werebelieved to be unable to 
recover their costs under competi­tion). This preoccupationwith the past, 
and with average revenue/cost thinkingbyregulators and regulated alike, 
ill­prepared the state for the consequences of having no dynamic 
mecha­nisms for prioritizing the end use consumption of power.

As expected, traditionalvolatilityin the mar­ginal cost of 
generatedelectricity was immedi­atelytranslatedinto volatileintra­day 
wholesale prices. What was not expected was that a com­binationoflow 
rainfall(reducingPaci. c North­west hydroelectric output), growth in 
demand, unseasonably hot weather, generators down on normal maintenance 
schedules, etc., caused the temporary normal daily peakingof prices to be 
greatly accentuated, and to be muchmore last­ingthan had occurred earlier 
in the Midwestand South. Events of small probability happen at about the 
expected frequency, and since there are many such events the unexpectedis 
not that unlikely. Constructivist planning failed to pro­vide for retail 
competition to experiment with programs allowingconsumers to save money by 
enablingtheir lower priorityuses of power to be interrupted in times of 
supply stress. Interrupt­ible deliveries are a direct substitute for both 
energy supply and energy reserves, and are an essential means of assuring 
adequate capacity and reserves that cover all the various supply 
contingencies faced bythe industry.

Because of the regulatory mandate that all demand must be served at a .xed 
price, the planning did notallow for the earlyintroduction of demand 
responsive retail prices and technol­ogies to enable peak consumption to 
be re­duced. Instead of mechanism design we had .xed retail price "design" 
to generate average revenue that was supposed to cover average cost, and 
it failed. The regulatory thought pro­cess is as follows: the function of 
price is to provide revenue, and the function of revenue is to cover cost. 
But this is the antithesis of the market function of price. For neither 
manage­ment nor the regulators was itnaturalto thinkin terms of pro.ting 
from selling less power. Yet that was precisely the route by which the 
Cali­fornia distributors could have avoided the loss of an estimated $15 
billion: every peak kilo­watt­hour not sold at the average retail rate 
wouldhave saved upto ten times that amountof energy cost. Static 
technology, and the utter fantasy that all load can always be served, was 
protected from innovation bythe legally fran­chised local wires monopoly. 
An entrant could not seekto win customers by offering discounts for 
switching from peak to off­peak consump­tion, and, at the entrant’s 
investment risk, in­stalling the required control devices on end­use 
appliances. This legacy--long entrenched, and jealously sheltered by local 
franchised monop­olies after deregulation--gave California dis­patchers no 
alternative but to trap people in elevators and shut down high­end 
computer programming facilities at critical times of peak power shortage.

All power delivery systems are vulnerable to a combination of unfavorable 
events that will produce short supplies at peak demand. Con­structivism 
alone, without competitive trial­and­error ecologicalexperimentationwith 
retail delivery technologies and consumer prefer­ences, cannot design 
mechanisms that process all the distributed knowledge that individuals 
either possess or will discover, and that is rele­vant to .nding an ef. 
cient mix of both demand and supply responsiveness.24

24This is illustrated bya November 6, 2002 press release by Puget 
SoundEnergy. "PSE Proposes to End Pilot Time­of­Use Program Ahead of 
Schedule: PSE’s time­of­use (TOU) program was created in 2000 duringthe 
energycrisis and was intended to provide .nancial incentives for 
cus­tomers to shift some of their electricity consumption to less 
expensive, off­peak times of the day. The program was restructured in July 
2002to re. ect a calmer energy market.

Economic Systems Design. 25--What can we learn from experiments about how 
demand re­sponsiveness could impact energy shortages as in the California 
crisis? Rassenti et al. (2003) measure this impact by creating a market in 
which a modest and achievable 16 percent of peak retail demand can be 
interrupted voluntar­ily at discount prices by wholesale energy 
pro­viders. In the experiments, demand cycles throughfour levels each 
"day" and is expressed in the wholesale market with two contrasting 
experimental treatments: (1) robot buyers who reveal all demand at the 
spot market clearing price; (2) four pro.t­motivated human buyers who are 
free to bid strategically in the market to obtain the lowest available 
prices. In each case bids to supply power are entered by .ve 
pro.t­motivated human suppliers. In the passive­demand treatment prices 
average much above the benchmark competitiveequilibrium, and are very 
volatile. In the treatment with human buy­ers, prices approach the 
competitive equilib­rium, and price volatility becomes miniscule. By 
empowering wholesale buyers, in addition to sellers, to bidstrategicallyin 
their own inter­est, even though 84 percent of peak demand is "must 
serve," buyers are able to effectivelydis­cipline sellers and hold prices 
at competitive levels.26

Since that time it has resulted in most participants’ bills being slightly 
higher than on .at rates. ... Reynolds (a PSE spokesman, said) ‘However, 
when exploring new territory, you need to be able to recognize when the 
program is not working as you had hoped... and begin a rigorous analysis 
ofthe program andhow it could be successfully restructured for the future 
energy marketplace.’ " It is because no one knows what will work best that 
you have to open retailing up to the .eld experiment called "free entry 
and exit." One experimental possibility is a contract that would share the 
inherently unknown and unpredictable savings with the customer. When a 
total cannot be known in advance, use a proportionalityrule. New Zealand’s 
tradable .sh catch quo­tas were originally speci.ed in quantities, and 
were rede­signed as proportions of the changing quantity available (oral 
communication with Maurice McTigue, 2002). An advantage of laboratory 
experiments is that these kinds of errors are exposed, and corrective 
alternatives tested, at very low cost.

25I have never been comfortable with this label because it is reminiscent 
of the idea that we can engineer best social arrangements, which the 
reader will see is not my interpre­tation. See footnote 20.

26The most widely agreed­upon design failure in the California crisis was 
the rule preventing the distribution utilities from engaging in long­term 
contracts to supply power (Wilson, 2002, p. 1332). Beware this simplistic 
pop­ular explanation:it is a two­wrongs­make­a­rightargument: yes, of 
course, given that you were going to protect the monopoly power ofthe 
distribution utilities to tie the rental of the wires to the metering and 
sale of energy at a .xed regulated price, then one way to protect them 
temporarily from the consequent wholesale price volatility might be to 
encourage long­term contracts at a .xed average delivery cost. But 
suppliers willwant higher prices and/or short­term contracts if they 
anticipate shortages--you cannot get blood outofa turnip; long contracts 
work to lower cost only to the extent that suppliers are surprised by high 
spot prices, but when it comes time to renegotiate expiring contracts they 
will not replicate the error. California discovered this when they 
intervened to sign long­term contracts, and encoun­tered high prices. This 
whole argument turns the design problem on its head. You must (1) remove 
the legal power of the local wires monopoly to prevent competing energy 
suppliers from contracting with customers to discount off­peak energy, 
charge premiums for peak energy, and install the supporting control 
devices; (2)let this competition de­termine the dynamic price structure, 
and investment re­quired to implement it; (3) simultaneously, let .nancial 
instruments evolve to hedge whatever risk is left over as prices become 
less volatile. Financial instruments can hedge price volatility,notload 
volatility. Only demand­responsive interruptible loads can relieve supply 
stress and provide the demand­side reserves that reduce the risk of lost 
load. No one mind or collective can anticipate and plan the needed mix of 
technologies to enable the market to manage de­mand. Therefore it is 
essential to remove all entry barriers, and allow .rms to experiment 
through competition to dis­cover and innovate ef. cient ways of organizing 
retail de­livery systems. Claims that short­run retail demand is 
"notoriously" inelastic miss the point: how would you know if 
loan­sheddingtechnologyis in.exible? Competition and incentives to 
innovate have never been part ofthe structure.

This example illustrates the use ofthe labo­ratory in economic systems 
design. In these exercises we can test­bed alternative market auction 
rules andthe effect oftransmission con­straintson generator supply 
behavior(Steven R. Backerman et al., 2001), vary the degree of 
marketconcentration, or "power" in a noncon­vex environment (Michael J. 
Denton et al., 2001), compare the effectof more or less stra­tegic demand 
responsiveness (Rassenti et al., 2003), study network and multiple market 
ef­fects also in a nonconvex environment (Mark Olson et al., 2003), and 
test­bed markets to inform, but not .nalize, market liberalization policy 
(Rassenti et al., 2002). For a survey of many examples, see McCabe et al. 
(1991).

The two types of rational order are bothex­pressedin the 
experimentalmethodology devel­opedfor economic systems design. This branch 
of experimentaleconomics uses the labas a test bed to examine the 
performance of proposed new institutions, and modi. es their rules and 
implementation features in the lightofthe test results. The proposed 
designs are initially con­structivist, although most applications, such as 
the design ofelectricitymarkets or auctions for spectrum licenses, are far 
too complicated for formalanalysis (Jeffrey Banks et al., 2003;Ras­senti 
et al., 2003).

But when a design is modi.ed in the lightof test results, the modi.cations 
tested, modi.ed again, retested, and so on, one is using the laboratory to 
effect an evolutionary adaptation as in the ecological concept ofa 
rationalorder. If the .nalresult is implemented in the .eld, it certainly 
undergoes further evolutionary change in the light ofpractice, 
andofoperational forces not tested in the experiments because they were 
unknown, or beyondcurrentlaboratory technol­ogy.27 In fact this 
evolutionary process is es­sential if institutions, as dynamic social 
tools, are to be adaptive and responsive to changing conditions. How can 
such .exibility be made part of their design? We do not know because no 
one can foresee what changes will be needed.

Market Institutionsand Performance.--Non­cooperative or Cournot­Nash 
competitive equi­librium (CE)theory has conventionallyoffered two speci. 
cations concerningthe preconditions for achievinga CE:(1)agents require 
complete, or "perfect," information on the equations de­.ning the CE; also 
common knowledge--all must know that all know that all know that they have 
this information. In this way all agents have common expectations of a CE 
and their behavior must necessarily produce it; (2) an­other tradition, 
popularly articulated in text­books, and showing, perhaps, more 
sensitivity for plausibility,has argued for a weaker require­mentthat 
agents needonly be price­takers in the market.

27People often ask, What are the limits of laboratory investigation? I 
think any attempt to de. ne such limits is very likely to be bridged by 
the subsequent ingenuity and creativity (the primary barriers at any one 
time) of some experimentalist. Twenty­. ve years ago I could not have 
imagined being able to do the kinds of experiments that today have become 
routine in our laboratories. Experimen­talists also include many of us who 
see no clear border separating the lab and the .eld.

The alleged"requirement" ofcomplete, com­mon, or perfect information is 
vacuous: I know of no predictive theorem stating that when agents have 
such information their behavior produces a CE, andin its absence their 
behavior fails to produce a CE. If such a theorem existed, it could help 
us to design the experiments that could test these dichotomous 
predictions. I sug­gest that the idea that agents need complete 
information is derived from introspective error: as theorists we need 
complete information to calculate the CE. But this is not a theory of how 
information or its absence causes agent behav­ior to yield or not a CE. It 
is simplyan unmo­tivated statement declaring, without evidence, that every 
agentis a constructivist in exactlythe same sense as are we as theorists. 
Andthe claim that it is "as if" agents had complete informa­tion, helps 
not a wit to understand the well­springs ofbehavior. What is missing are 
models of the process whereby agents go from their initial circumstances, 
and dispersed informa­tion, using the algorithms ofthe institution to 
update their status, andconverge (or not) to the predictedequilibrium.28

28The inherent dif. culty in equilibrium modeling of the CDA is shown by 
the fact that so few have even attempted. Wilson (1987), 
characteristically, has had the courage and competence to log progress. 
Daniel Friedman (1984) uses an unconventional no­congestion assumption to 
.nesse Nash­Cournot analysis, concluding ef. ciency and a .nal competitive 
clearing price. Wilson (1987) uses standard assumptions of what is common 
knowledge--number of buyers (sellers), each with inelastic 
demand(supply)for one unit, preferences linear in payoffs, no risk 
aversion or wealth effects, valuations jointly distributed, and agent 
ca­pacity to "compute equilibrium strategies and select one equilibrium in 
a waythat is common knowledge" (p. 411). This is an abstract 
as­if­all­agents­were­game­theorists con­structivist modelofa thought 
process that no game theorist would or does use when participatingin a 
CDA. The model itself generates its own problems, such as degeneracy in 
the endgame when there is only one buyer and seller left who can feasibly 
trade--a problem that is not a problem for the subjects, who do not know 
this, and see imperfectly in­formed buyers and sellers still attempting to 
trade and thereby disciplining price. Extra marginal traders provide 
opportunity cost endgame constraints on price. Agents need have no 
understandingof opportunity cost in order for their behavior to be shaped 
by it. Wilson recognizes these con­siderations: "The crucial de. ciencies, 
however, are inescap­able consequences of the game­theoretic formulation" 
(Wilson, 1987, p. 411). We are squarely up against the 
limitations--perhaps the dead­end ultimate consequences-- of Cartesian 
constructivism. We have not a clue, any more than the so­called "naive" 
subjects in experiments, how it is that our brains so effortlessly solve 
the equilibration prob­

As a theory the price­takingparable is also a nonstarter: who makes price 
if all agents take price as given?If it is the Walrasian auctioneer, why 
have such processes been found to be so inef. cient? (Corrine Bronfman et 
al., 1996).

Hundreds ofexperiments in the past 40 years (Smith, 1962, 1982; Douglas D. 
Davis and Charles A. Holt, 1993, 1995;John H. Kageland Roth, 1995;Plott, 
2001)demonstrate that com­plete information is not necessary for a CE to 
form out of a self­ordering interaction between agent behavior and the 
rules of information exchange and contract in a variety of different 
institutions,but most prominentlyin the contin­uous bid/ask double auction 
(CDA).29 That complete information also may not be suf. cient for a CEis 
suggested(the samples are small)by comparisons showing that convergence is 
slowed or fails under complete information in certain environments (Smith, 
1976, 1980).

An interesting contribution by Dhananjay K. Gode and Shyam Sunder 
(hereafter GS; see Shyam Sunder, 2003, and the references itcon­tains) is 
to demonstrate that an important com­ponent of the emergentorder observed 
in these marketexperiments derives from the institution, not merely the 
presumedrationalityof the indi­viduals. Ef. ciency is 
necessarilyajointproduct ofthe rules ofthe institutionandthe behaviorof 
agents. What Sunder and his coauthors have shown is that in the 
double­auctionmarket for a single commodity (we know not yet how far it 
can be generalized),ef. ciency is higheven with "zero" intelligence robot 
agents, each of whom chooses bids (asks)completely at random from all 
those thatwillnotimpose a loss on the agent. Thus, agents who are 
notrational constructivist pro.t maximizers, and use no learning or 
up­dating algorithms, achieve most of the possible social gains from trade 
using this institution. Does this example illustrate in a small way those 
"super­individualstructures within which individuals found great 
opportunities ... (and that)... could take account of more factual 
cir­cumstances than individualscould perceive,and in consequence... is in 
some respects superior to, or 'wiser' than, human reason ... "? (Hayek, 
1988, pp. 77, 75). We do not know if the GS results generalize to multiple 
market settings as discussed in the next paragraph. Ross M. Miller (2002), 
however, has shown that in a very elementarytwo­market 
environment--intertemporally separated mar­kets for the same 
commodity--the GS results are quali. ed. Complex price dynamics, 
includ­ing "bubbles," appear, and there is loss of ef.­ciency, although 
the loss is not substantial. On average the decline is apparently from 
around 94 percent to 88 percent.

lem in interacting with other brains though the CDA (and other) 
institutions. We model not the rightworld to capture this important 
experimental .nding. 29See Jon Ketcham et al. (1984) for a comparison of 
CDA with the posted offer (PO)retail pricing mechanism. CDA converges more 
rapidly andis more ef. cient than PO. So why does not CDA invade and 
displace PO? It is the high cost of training every retail clerk to be an 
effective negotiator for the .rm. Institutions re.ect the .ne structure of 
opportunitycost, and the loss of exchange ef.ciency in PO is more than 
offset by the distributional productive ef. ciency of the mass 
retailinginnovation of the 1880’s that led price policy to be centralized. 
As I write, those policies are being modi. ed on the Internet where prices 
can be adjusted to the opportunity cost characteristics of buyers, such as 
how many other Internet sites they have visited (Cary Deck andBart Wilson, 
2002).Institutional changes in response to innovations like mass retailing 
are part of the emergence of an ecologically rational equilibrium.

In multiple market trading in nonlinear inter­dependent 
demandenvironments, each individ­ual’s maximum willingness­to­pay for a 
unitof commodity A depends on the price of B, and vice versa, and in this 
more complex economy double auction, markets also converge to the vector 
of CE prices and trading volumes. A two­commodity example is reported in 
Smith (1986), based on nonlinear demand (CES pay­off function)and linear 
supply functions found in Arlington Williams and Smith (1986); also see 
Williams et al. (2000). In these experiments, numerical tables based on 
the preference and cost information de.ning the general­equilibrium 
solution of four nonlinear equations in two prices andtwo quantities are 
dispersedamong the undergraduate subjects. They buy and sell units 
ofeachof the two commodities in a series of trading periods. Prices and 
trading volume converge, after several trading periods, to the CE de. ned 
by the nonlinear equations. The subjects would not have a clue as to how 
to solve the equations mathematically. The exper­imenter applies the tools 
of constructivist rea­son to solve for the benchmark CE, butin repeat play 
this "solution" emerges from the spontane­ous order created by the 
subjects trading under the rules ofthe double­auction market institu­tion. 
Numerous other experiments with many simultaneousinterdependentmarkets 
show sim­ilar patterns ofconvergence (Plott, 1988, 2001).

The Iowa Electronic Market.--What evi­dence do we have that the laboratory 
ef.ciency properties ofcontinuous double auction trading apply also in the 
.eld? One of the best sources of evidence, I believe, is found in the Iowa 
Electronic Market (IEM) used widely around the world (Robert Forsythe et 
al., 1992, 1999). These markets are used to study the ef. cacy of futures 
markets in aggregatingwidelydispersed information on the outcomes of 
political elec­tions, or any well­de. ned extra­laboratory event, such as 
a change in the discount rate by the Fed. The "laboratory" is the 
Internet. The "subjects" are all who log on and buy an initial portfolio 
of claims on the .nalevent outcomes; they consist of whomever logs in, and 
are not anykind ofrepresentativeor"scienti.c" sample as in the polls with 
which they are paired. The institution is the open­book double auction.

In the IEM, traders make a market in shares representing pair­mutuel 
claims on the popular vote (or winner­take­all) outcome of an elec­tion, 
referendum, etc. For example, the .rst IEM was on the 1988 presidential 
election. Each person wanting to trade shares deposits a minimum sum, $35, 
with the IEM and receives a trading account containing $10 cash for 
buy­ingadditionalshares, and ten elemental portfo­lios at $2.50 each, 
consisting of one share of each of the candidates--Bush, Dukakis, Jackson, 
and"rest­of­. eld." Tradingoccurs continuously in an open­book bid­ask 
market for several months, and everyone knows that the market will be 
called (tradingsuspended) in November on election day, when the 
dividendpaidon each share is equal to the candidate’s fraction of the 
popular vote times $2.50. Hence if the .nal two candidates and all others 
receive popular vote shares (53.2 percent, 45.4 percent, 1.4 percent), 
these proportions (times $2.50) represent the payoff to a trader for each 
share held. Conse­quently, at any time t, normalizing on $1, the price of 
a share(4$2.50) re. ects the market expectationofthat candidate’s share 
ofthe total vote. A price, $0.43, means the market predicts that the 
candidate will poll 43 percent of the vote. Other forms of contract that 
can be traded in some IEMs include winner­take­all, or num­ber of seats in 
the House, and so on.

The IEM data set includes 49 markets, 41 worldwide elections, and 13 
countries. Several results standout:the closingmarket prices, pro­duced 
bya nonrepresentativesample of traders, show lower average absolute 
forecasting error (1.5 percent) than the representative exit poll samples 
(1.9 percent); in the subset of 16 na­tional elections, the market 
outperforms the polls in 9of 15 cases; in the course ofseveral months 
preceding the election outcome, the market predictions are consistently 
much less volatile than the polls; generally, larger and more active 
markets predict better than smaller, thinner markets; surveys ofthe market 
traders show that their share holdings are biased in favor of the 
candidates they themselves prefer.

In view of this last result why do markets outperform the polls? Forsythe 
et al. (1992) argue that it’s their marginal trader hypothesis. Those who 
are active in price "setting," that is, in entering limit bids or asks, 
are found to be less subject to this bias, than those traders ac­cepting 
(selling and buying "at market") the limit bids and asks. Polls record 
unmotivated, representative, average opinion. Markets record motivated 
marginal opinion that cannot be de­scribed as "representative." This 
analysis helps to provide a good mechanical, if notultimate, understanding 
of how human interaction with the rules of a bid/ask CDA yield ef. cient 
predictions.30

30 Other markets besides the IEM are known to have ef. cient 
information­aggregating properties. Pari­mutuel racetrack markets are an 
example where, interestingly, the environmentis much like the IEM: the 
settlements occur at a well­de. ned end­state known to all agents, unlike 
stock market trading where expectations .oat continuously with no clear 
value revelation endpoint. "The racetrack betting market is surprisingly 
ef. cient. Market odds are remarkably good estimates ofwinning 
probabilities. This implies (sic) that racetrack bettors have considerable 
expertise, and that the markets shouldbe taken seriously" (Thaler 
andWilliam T. Ziemba, 1988, p. 169). It is surprising to behavioral 
economists because their methodologyis restricted to look­ing for 
deviations from the standard model. What is unusual here is that in 
racetracks they have found reportable evi­dence for ef. cient outcomes.For 
those who follow the experimental economics, IEM and similar 
controlled­environmentmarket studies, ef. ciency is notonly common­place 
(if notuniversal), it cannotbe attributed to agents with "considerable 
expertise." The agents are mostly naive, al­though they get repeat 
interaction experience, which, from the evidence, clearly gives them 
expertise enough. But, as in the IEM and experimental markets, racetrack 
markets are not perfect: there are inef. ciencies in the "place" and 
"show" options and the favorite­long­shot bias, with the

We have seen that markets economize on information, understanding, the 
number of agents, and individualrationality. Can they also economize on 
the need for externalintervention to protect particular interests, ifall 
are empow­ered by the trading institution to act in their 
individualinterests?

B. Strategy­proofness: Theory and Behavior

Preferences are private and unobservable, and institutions have to rely on 
the messages reported by agents, not their true preferences. This follows 
from the fact that no one mindhas all the information known together by 
all those in the market. It is therefore possible for an agent to affect 
prices andoutcomes in a market by strategically misreporting preferences. 
This prospect has motivated the literature seeking strategy­proof 
mechanisms: "An allocation mechanism is strategy­proof if every agent’s 
utility­maximizing choice of what preferences to report depends only on 
his own preferences andnot on his expectationsconcerning the pref­erences 
that other agents will report" (Mark Satterthwaite, 1987, p. 519). This 
requires each agent to have a dominantstrategyto report true preferences, 
and has led to impossibility theo­rems establishing the nonexistence of 
such a mechanism under certain conditions.

In view of such negative theoretical results and the narrow conditions 
under which solu­tions have been investigated, it is important to ask what 
people actually do in experimental environments in which the experimenter 
in­duces preferences privately on individual sub­jects. We know what is 
impossible, but what is possible in more open­ended systems than are 
modeled by theory? Is it possible that when all are free to choose from a 
large space of strategies, ecologically rational strategies will emerge 
that immunize against strategic manip­ulation? Given that information is 
inherently latter more pronounced in the last two races of the day. 
Various hypotheses have been offered to explain these inef. ciencies,but 
more signi.cantis that computer pro­grams have been written to arbitrage 
(yielding returns of some 11 percent per bet), the place, show, and 
long­shot inef. ciencies. (It is my understandingthat goodpro.ts have been 
accumulated on these programs, so far without neu­tralizing the arbitrage 
opportunities--let the good times roll!) dispersed, has society evolved 
institutions in which forms of behavior arise that result in practical 
ifnotuniversal solutions to the prob­lem ofstrategy­proofness?

The double auction is a well­known example yielding CE in a wide range of 
economic envi­ronments including small numbers. Are there other examples, 
and if there are, what are the strategic behavioral mechanisms that people 
adopt to achieve strategy­proofness?

One example is the sealed­bid­offer auction: in each contract period the 
submitted bids are orderedfrom highest to lowest, the offers (asks) from 
lowest to highest, with the intersection (cross) determining the uniform 
clearing price andvolume exchanged (see TimothyCason and Friedman, 1993; 
Friedman, 1993; and Wilson, 1993). Also see Smith etal. (1982)for 
compar­ative studies of different versions of the sealed­bid­offer 
mechanism andthe continuousdouble auction.

In experiments with stationary supply and demand, initiallybothbuyers 
andsellers greatly underreveal their true individual willingness to buy or 
sell. Volume is very low (10-15 percent ofoptimal), the market is inef. 
cient andeach agent can see that at the initial clearing price they are 
leaving money on the table. In repeat play they increase revelation, but 
mostly of units near the last period’s clearing price. As volume increases 
and the clearing price closes in on the CE, the realized inverse demandand 
supply become very .at near the true clearing price with many tied or 
nearly tied bids and asks that exceed the capacity of any single buyer or 
seller. At this steady state, and given this behavior, if anyone withholds 
purchases or sales she is denied an allocation as other more competitively 
traded units substitute for hers. This results in a "behavioral 
strategy­proof equilibrium." Such is the power of motivated, privately 
informed agents in trial­and­error re­peat interaction.

These experimentalresults make it plain that the theoretical condition for 
a strategy­proof equilibrium--that each agent have a dominate strategy to 
reveal true willingness­to­pay or willingness­to­accept for allunits, 
andnotjust units near the margin--is much too strong. The above 
description from blind two­sided auc­tions, however, also shows that there 
is a social cost to the achievement ofa strategy­proofequi­librium: blind 
two­sided auctions converge more slowlyto the competitiveequilibriumthan 
continuous double auctions, andupon converg­ing, may not be quite as ef. 
cient if agents occasionally attempt manipulation, are disci­plined, and 
return to the full exchange volume.

A second example is the uniform price dou­ble auction (UPDA), a real­time 
continuous feedback mechanism clearing all trades at a single price in 
each trading period. This is a "designer market" inventedby 
experimentalists who asked, "Can we combine the continuous information 
feedback advantages of the double auction with the uniform price (zero 
within­period volatility)advantages of the sealed­bid­offer auction?" As 
we have seen above, with blind bidding several repeat interactions are 
re­quired to reach optimality, with many lost trades in the process. Can 
we accelerate the price discoveryprocess bycontinuouslyfeeding back 
information on the tentative state of the market, and allowing bids (asks) 
to be adjusted within each period?

This institution is made possible by high­speed computer and communication 
technol­ogy. It comes in several .avors, or variationson the rules. In 
allversions ateach time, t # T 5 time market is "called" (closed), the 
tentative clearing price, pt, is displayed and each agent knows the 
acceptance state of all her bids (asks). This allows bids andasks to be 
adjusted in realtime. See the chapter by McCabe et al. (1993, pp. 311-16) 
for a report of 49 UPDA experiments comparing these different versions 
with the continuous double auction. UPDA ex­hibits even more 
underrevelation of demand and supply than the blind two­sided auction 
discussed above, but ef. ciency tends to be much higher, especially in the 
.rst periods, and, in one form (endogenous close, open book, the "other 
side" rule with conditional time priority), exceeds that of the continuous 
double auction.

Experiments using UPDA in a randomly .uctuating supply and demand 
environment routinelyexhibit ef. ciencies of95-100percent, sometimes 
withas little as 5-10 percentof the available surplus revealed. This is 
shown in Table 1 for summary data from UPDA experi­mentup43. Most agents 
enter bids (asks) equal to or near the clearing price as it is 
continuously displayed in real time. It is ofcourse true, hy­pothetically, 
that if all agents reveal their true demand or supply with the exception 
of one intra­marginal buyer or seller, then that agent

TABLE1--SUMMARY OFRESULTS: UPDA EXPERIMENT UP 43; 5, 5

Notes: (Pe, Qe) 5 equilibrium price and quantity. (Pr, Qr) 5 realized 
price and quantity. Eff% 5 ef. ciency, % max surplus. Rev% 5 %of surplus 
revealed. can manipulate the price to his or her advantage. But this 
parable is irrelevant. The relevantques­tion is what behavior is manifest 
when every agent has the potential for manipulating the price. Without 
knowledge or understandingof the whole, and without design or intention, 
the participants use the rules at their disposal to achieve three 
properties observed by the exper­imenter: (1)highef. ciency, (2) maximum 
indi­vidual pro.t given the behavior of all other agents, and(3)protection 
from manipulation by their protagonists.31This ecologicalresult 
illus­trates the perceptive insight ofHayek (1988, pp. 19-20). "Rules 
alone can unite an extended

31Space prevents me from dealing fully with the many importantissues 
raised when a subset ofagents have asym­metric advance information on 
product quality or value characteristics. The analysis shows that such 
conditions generate market failure or inef. ciency. Some of these 
prob­lems, however, arise because the analysis is inadequate in examining 
both sides of the market, and the implications of the information content 
of prices. Experiments have estab­lishedthat constructivist inef. ciency 
is often alleviated by one of several ecologically rational response 
mechanisms: competition among sellers for reputations, quality (brand) 
signaling,product warranties, andthe aggregationofprivate asymmetric 
information into public price patterns that self­correct the alleged 
problems. See, e.g., Plott and Louis Wilde (1982); Plott and Sunder (1982, 
1988); Miller and Plott (1985); Camerer and Keith Weigelt (1988).

order ... . Neither all ends pursued, nor all means used, are known or 
need be known to anybody, in order for them to be taken account of within 
a spontaneous order. Such an order forms of itself... ."

C. Gresham’s Law: If It Isn’t Cournot­Nash, Why Is It a Law?

In this section Ihave given manyexamples of institutions in whichthe CE 
theoryof markets predicts their observed behavior. Do we have contrary 
examples? Yes. Gresham’s Law: bad money drives out good. This "law," while 
sometimes claimed to be an observed phenom­ena in countries all over the 
world, is not a Cournot­Nash equilibrium.32 If currencies A and B are both 
available, A having an intrinsic worth while B is worthless .at money, 
then the theory predicts that A will drive out B. This is because each 
agent believes other agents are rational, and will accept only A in 
exchange. Each agent will therefore avoid getting stuck with the inferior 
B by accepting only A, which becomes the dominant circulating medium of 
exchange, while B is "horded." Experiments have con.rmed that if both 
types of money are initially available, subjects use onlythe superior 
currency (an interest­bearing consol) as a me­dium of exchange. But in 
treatments in which subjects .rst experience a history ofusing .at money, 
it being the only medium of exchange available, and then the consol is 
introduced, subjects continue trading withthe .at money, hording the 
interest­bearing consol (Gabriele Camera etal., 2003). This is entirely 
rational if each agent believes others will accept the .at money in 
exchange and this belief is supported by experience. Think of Gresham’s 
Law as a belief equilibrium in which theory alone is un­able to predict 
when it might occur (Ledyard, 1986).

Complementingthese results, another exper­imentalstudyshows that when .at 
money is the only currency, it will be used even under the 32 Hayek (1967, 
p. 318) notes that Gresham’s Law is not due to Gresham nor is it a "law" 
in the theoretical sense, and "... as a mere empirical rule is practically 
worthless." In the 1920’s when people started using dollars and other hard 
currencies in substitution for the depreciating mark, the claim emerged 
that Gresham’s Law was wrong--that it was the other way around.

conditionthat it is abandonedand replaced with a new .at money issue at 
the end of a .nite horizon. In this studythe realeconomy is found to 
suffer some loss in ef. ciency relative to the use of "backed" (commodity) 
money, but the economy does not collapse even in short hori­zon 
treatments. Collapse in real sector ef.­ciency is observed only when a 
"government" sector prints .at money to purchase real goods from the 
private sector. Moreover, additional experimental tests show thatthe 
collapse cannot be due to the resulting in.ation, but to interfer­ence 
with the real price discovery of markets when some agents are able to 
crowd out private real purchases with printing press money33 (Deck et al., 
2001).

D. Psychology and Markets

Psychologists and "behavioral economists" who study decision behavior 
almost uniformly report results contrary to rational theory (Robin Hogarth 
and Melvin Reder, 1987). It was not always so,34but the focus on 
"anomalies," be­ginning in the 1970’s, converted the emerging discovery 
enterprise into a search for contradic­tions between reports of behavior 
andthe cari­catures35 of mainstream theory that constitute much of its 
core. Psychologists, to their credit,

33This is demonstrated by comparison experiments in which there are no 
government agents, but .at money is in. ated each period by the average 
rate that is observed in those experiments with government agents present.

34 "Prior to 1970 or so, most researchers in judgment and decision­making 
believedthatpeople are pretty good decision­makers. ... Since then, 
however, opinionhas taken a decided turn for the worse, though the decline 
was not in any sense demanded by experimental results. Subjects did not 
sud­denly become any less adept at experimental tasks nor did 
experimentalists begin to grade their performance against a tougher 
standard. Instead, researchers began selectively to emphasize some results 
at the expense of others." "The view that people are irrational is real in 
the sense that people hold it to be true. But the reality is mostly in the 
rhetoric" (Lola Lopes, 1991, pp. 66, 80).

35I say "caricatures" because economics has long of­fered much in the way 
of theoretical exceptions to the core neoclassical model of 
self­interested market competition: externalities in choice, public 
goodeffects, and "anomalies" in choice under uncertainty 
requiringexplanation (Friedman and L. J. Savage, 1948; Harry Markowitz, 
1952). But it is the neoclassical assumption of self­interested agents 
that hasbeen the most productive of theoretical results and therefore is a 
prominent and easy target of criticism.

have maintained an intensive program examin­ingthe behavioralnature 
ofthese contradictions to the classical model. For example, Sidney Siegel 
(1959) and Lawrence E. Fouraker and Siegel (1963) reported both 
con.rmations and contradictions, and used the pattern to propose improved 
models. Similarly, in prospect theory Kahneman and Tversky (1979) have 
proposed modi.cations in both the utility and probability 
weightingfunctions of standard expected utility theory.36 Research 
strategies that focus on the study of errors, however, can distort 
profes­sional beliefs, to say nothing of popular repre­sentations, if the 
primary emphasis is on the failures, to the exclusion ofthe predictive 
suc­cesses, ofthe theory.37

E. Psychology, Economics, and the Two Kinds of Rationality

Curiously, the image ofeconomists and psy­chologists as protagonists 
obscures their under­lyingagreement on foundations. Bothrely upon 
constructivism:(1)to the extentthatmarkets are

36Their most important contributions in prospect theory were in empirical 
tests demonstrating the relevance of two ideas suggested originally by 
Markowitz (1952): the idea that the theory applies to changes in wealth 
(income) rela­tive to the individual’s current asset state, andthat people 
are risk preferring in losses and risk averse in gains. This much is 
consistent with standard expected utility theory, which requires onlythat 
the prizes ofchoice can be ordered, and therefore applies either to wealth 
or income. Which prizes the theory is best applied to has always seemed to 
me to be inherently a subject for empirical determination. If applied to 
wealth, the theory starts to infringe on preference theory over time, long 
recognized as especially dif. cult modeling terrain.

37As Isee it experimental market economics and behav­ioral economics are 
in principle complementary. Experi­mental economists study market 
performance (rationality) given individual valuations, while cognitive 
psychologists study the valuations (rationality)of individuals. If the 
ob­jects traded are prospects the appropriate valuations are their "cash 
values," whether based on expected utility, pros­pect theory (Kahneman and 
Tversky, 1979), or some other representation. Thus Plottand Jonathan T. 
Uhl (1981)study experimental markets in which the items traded are 
gam­bles, and reportconvergence to a CEde. ned by demand and supplybased 
on the expected values ofthe gambles. But the connective interface between 
rationality at the individual and at the market level and how institutions 
modulate the interface has not been well explored. Markets do their thing 
with whatever are the values--rational, irrational, or 
nonrational--thatare provided by individuals. rational38 or irrational,39 
this derives directly andonly from the rationality or irrationality of 
agents;40 (2) individual rationality is a self­aware, calculating process 
of maximization;41

(3) predominantly both are reluctant to allow that naive, unsophisticated 
agents can achieve socially optimalends withoutcomprehensionof the whole, 
as well as their individual parts, implemented by deliberate action (there 
is no "magic," and no room for the GS zero intelli­gence traders); (4) 
consequently, psychologists test the rationality of individual decisions 
largely byaskingfor subject responsesto choice problems to discover how 
they "reason." Rather than challenge this constructivist view, 
econo­mists, subject to the identical vision (how do agents consciously 
think?), are critical of the question­response survey methods used in 
cog­nitive psychology: the stakes are zero or too low,42and the subjects 
are too unsophisticated, inexperienced, or untrained to allow a serious 
researcher to .nd out how "real agents really think." Many psychologists 
appear to .nd irra­tionality everywhere, and many economists appear to see 
the .ndings as everywhere irrel­evant. To these economists, how agents 
think indeed exhausts the core of empirical econom­

38For example, the double­auction markets discussed above.

39Experimental asset markets bubble and crash on the long path 
ofexperience to equilibrium (Smith et al., 1988; Porter and Smith, 1994). 
For a new study of subject expe­rience and asset bubbles see Martin 
Dufwenberg et al. (2003).

40 Thus, even a "... monopolist ... has to havea fullgeneral­equilibrium 
model of the economy" (Kenneth Arrow, 1987, p. 207). Also see footnote 30 
above on racetrack market ef. ciency, and the inference that the bettors 
must therefore have considerable expertise. Thus, market rationality is 
au­tomatically assumed to derive entirely from individual ra­tionality.

41 Here is a particularly clear statement of decision as rational 
constructivist action: "Incentives do not operate by magic: they work by 
focusing attention and by prolonged deliberation" (Tversky and Kahneman, 
1987, p. 90).

42The use of cash or other reward medium in decision behavior experiments 
is listed by Ralph Hertwig and An­dreas Ortmann (2001)as one ofthe key 
differences between psychology and economics experiments. The controversy 
over paying subjects, however, is rapidly being eroded as cognitive 
psychologists and experimental economists join with 
neurobiologists--including those who are informed on animal behavior 
models--and subjects are paid salient re­wards (Gregor Thut et al., 1997; 
Hans C. Breiter et al., 2001;McCabe et al., 2001). ics; psychologists 
merely "fail" to properly im­plement their investigation of this core.43 
In point of fact, opinion surveys can provide important insights: 
sometimes survey .ndings can be tested more rigorously with 
reward­motivated choices in the laboratory or the .eld and are found to 
have predictive content (e.g., the asymmetry between losses and gains in 
wealth). Sometimes what people actually do completely contradicts what 
they say, and sometimesyou cannot .nd out by asking be­cause the agents 
themselves do not know what they will do or are doing. For example:

Comparisons of riskpreferences under low and high monetary stakes have 
shown that actual reward levels have a statistically signi.cant effect on 
decision, but that the qualitative conclusions from hypothetical choice 
re­sponse surveys are not refuted by studies using very high stakes--the 
accumulated payoffs average three times subjects’ normal monthly living 
expenses (Steven J. Kachelmeier and Mohamed Shehata, 1992; also see Hans 
P. Binswanger, 1980, for similar .ndings).

Consider the double auction in classroom demonstration experiments:in 
debrie. ngs af­terwards students deny that there is any kind of 
quantitative model that could predict their market price and exchange 
volume, or that they were able to maximize their pro.ts; but a participant 
with an envelope containing the predictions provided in advance, opens it 
showing that this consensus is false. The dis­persed private value/cost 
information is ag­gregated into prices thatare at the equilibrium andeach 
agentis maximizinghis or her pro.t given the behavior ofall others. Here 
there is indeed a kindof"magic," but only, I think, in not being well 
understood or modeled at the game­theoretic level of individual choice.44

43 Kahneman clearly does not see people as irrational except in the narrow 
context used in economic modeling based on dominant choice. In fact he 
describes his empirical .ndings contradicting the SSSM as having been 
easy, thanks to the implausibility to any psychologist of the SSSM. See 
the Nobel Foundation interview of the 2002 Nobel Laureates in economics at 
http://www.nobel.se/ economics/laureates/2002/kahneman­interview.html.

44 At the macro­market level, convergence, and cases of stable and 
unstable equilibrium, are well predicted by the classical Walrasian 
adjustment model, but the paths taken, including jumps across alternative 
unstable equilibria, are

Our bounded rationality as economic theo­rists is far more constraining on 
economic science, than the bounded rationality of pri­vately informed 
agents is constraining on their ability to maximize the gains from 
exchange. In asset trading, participant survey responses re. ect the 
disconnect between their informa­tion on fundamental value and their 
puzzling experience of a price bubble and crash gen­eratedon the long path 
to the rational expec­tations equilibrium (T. Schwartz and J. S. Ang, 
1989). Opinion polls administered to the IEM trad­ers show the same 
judgment biases that psy­chologists and political scientists .nd in public 
opinion polls, but these biases did not interfere with the market’s 
ability to predict the popular vote outcomes (Forsythe et al., 1992). In 
preference reversal survey experiments subjects report many inconsistent 
choices: gamble A is preferredto B buta subject will sell A for less than 
B. Arbitraging the sub­jects’ cash­motivated choices quicklyreduces these 
inconsistencies (Yun Peng Chu and RueyLingChu, 1990, p. 906), andit has 
been shown that the inconsistencies are unbiased random errors under some, 
but not all, con­ditions (James C. Cox and DavidM. Grether, 1996); also 
see Barry Soper and Gary Gigio­lotti, 1993, where choice intransitivity is 
studied directly and the errors are found to be random. Kahneman et al. 
(1986; hereafter KKT) pro­vide many examples in which respondents are 
asked to rate the fairness,45 on a four­

not well predicted by the model. See the outstanding sum­mary by Plott 
(2001). The disconnect with choice behavior is evident in the following: 
Walrasian dynamics makes ad hoc assumptions about price adjustments in 
response to excess demandsayingnothing about the correspondingpay­off 
motivation of the agents who drive the price changes. Walrasian dynamics 
is a story about the taˆtonnement mech­anism in which there are no 
disequilibrium trades, whereas Plott’s (2001)summary is about continuous 
double­auction trading witha great many disequilibrium trades.

45The descriptor "fairness" has so many meanings in different contexts 
that I believe it is best to avoid the term entirelyin experimental 
science except where it is explicitly modeled and the model tested in 
environments where sub­jects make decisions on the basis of the de. 
ningparameters of the model; then the descriptor "fair" and its ambiguity 
can be avoided altogether. This is the way it is used in the utilitarian 
de. nitions by Robert Franciosi et al. (1995), in Ernst Fehr and Klaus M. 
Schmidt (1999), and Bolton and Axel Ockenfels (2000). Of course it is 
appropriate to use the point scale, of elementary business actions in 
competitive environments. In one case a hardware store raises the price 
ofsnow shov­els from $15 to $20 after a snowstorm. Eighty­two percent of 
the respondents con­sider this action either unfair or very unfair. 
Franciosi et al. (1995, pp. 939- 40)substitute the words "acceptable" for 
"fair" and "unac­ceptable" for "unfair"46and add one additional sentence 
to this KKT example: "The store does this to prevent a stock out for its 
regular customers since another store has raised its price to $20." Now 
only 32percent rate the action unfavorably. This exercise suggests the 
possible sensitivity of survey results to emotive words and/or perceived 
"justi. ca­tion" in terms of impersonal market forces.

Note that it is in private information environ­ments, where the market is 
aggregating infor­mation far beyond the reach of what each individual 
knows, and is able to comprehend, that the solicited opinions are so far 
off the mark. The surveys yield no useful understand­ing because the 
subjects have none to relate. In the complete information asset market, 
subjects are aware of its fundamental value structure, and come to have 
common expectations through an experiential process of repetition; i.e., 
initialcommon information is not suf.cient to induce common 
expectations.47 They play myopically and their expressed baf.ement 
("prices rise without cause") re. ects this myo­

descriptor if the purpose is to see how its instructional use might have 
an emotive affect on behavior. The emotive content of "fairness" is clear 
in the important work of Edward E. Zajac (1995), who has also examined the 
rhetoric of fairness arguments as self­interest serving in the Florida, 
2000, election controversy (Zajac, 2002).

46 KKT state that "... the phrase ‘it is fair,’ is simply an abbreviation 
for a substantial majority of the population studied thinks it is fair" 
(KKT, 1986,p. 201). But their main interest is in whether .rm behavior is 
affected by commu­nitynorms. Whether or not an action is "acceptable" 
would seem to be just as important in determining .rm behavior as whether 
or not it is "fair." If the two terms map into different attitudes, then 
there is inherent ambiguity in spec­ifying the effect on .rm behavior.

47This interpretation is consistent with asset­trading ex­periments using 
undergraduates, small businesspersons, corporation managers, and 
over­the­counter traders (Smith et al., 1988, Porter and Smith, 1994). 
Exceptions using inexperienced subjects, to my knowledge, have only been 
observed with advanced graduate students (McCabe and Smith, 2000).

pia. These comments suggest that much insight might be obtained from the 
systematic study of the conditions under which surveyresults are robustly 
informative and the conditions where they are not.

F. Fairness: An Experimental Market Test

In developing a descriptive theory48 of the "reference transaction," KKT 
state that what is considered "fair" may change: "Terms of ex­change that 
are initially seen as unfair may in time acquire the status of a reference 
transac­tion" (KKT, 1991, p. 203). This paves the way for the adaptation 
of "fairness beliefs" to changes in the competitive equilibrium. 
Al­thoughthe competitivemodelis the one thathas static predictive content, 
its prediction is silent as to how long it will take to respond to a 
change in parameters. KKT’s arguments are not predictive, but they tella 
story about why mar­kets might be sluggish in respondingto change. How 
good is their story?

Franciosi et al. (1995) state a preference modelofoptimal choice that 
allows for a utili­tarian trade­off between own consumption and 
"fairness." For example, the utility of two com­modities (x, z)is given 
by: u(x, z)5 z 1 ax 2 (b/2)x2 2 ax[(p/p0) 2 1], in which the sell­er’s 
pro.t, p, relative to a reference pro. t, p0, appears as an "externality" 
in the buyer’s utility function. The usual maximization subject to an 
income constraint yields the inverse demand equation:p 5 a 2 bx 2 
a[(p/p0)2 1]. Thus, for a . 0 any change in the environment that increases 
a .rm’s pro.t relative to the reference pro.t has an external effect that 
lowers the buyer’s inverse demand for units x. If a 5 0, then we have the 
standard own­maximizingthe­ory. Consequently, Franciosi et al. (1995) can 
test the hypothesis, never using the word "fair­ness," that if subject 
buyers have a utilitarian concern for pro.ts not being increased relative 
to a baseline then after a change from the base­line this should alter the 
observed equilibrium relative to the standard predicted equilibrium withno 
externaleffect, a 5 0. In a posted offer market giving KKT their best shot 
(sellers can­not see each other's posted prices, andtherefore cannot 
knowingly emulate or undercut each other's prices), Franciosi etal. 
(1995).nd that when a 5 0 (implemented by either no disclo­sure, or by 
marginal cost­justifying disclosure) the market converges quickly to the 
new com­petitive equilibrium. When a . 0 (implemented by pro.t p and 
p0disclosure)prices converge more slowly, but precisely, to the new 
equilib­rium. Hence, under conditions most favorable to a "fairness" 
effect, the response dynamics is changed,butnotthe equilibriumas predicted 
by the standard competitive model. The discipline of the market swamps all 
but a transient "fair­ness" effect. If, realistically,sellers can see each 
others prices, I would predict a much smaller "fairness" effect, if any.

48This methodology is driven by the untenable belief that general theories 
can be derived directly from observa­tions if youjust have enoughdata (see 
Smith, 2002, and the references therein). "Perhaps the most important 
lesson learned from these studies is thatthe rules of fairness cannot be 
inferred either from conventional economic principles or from intuition 
and introspection. In the words of Sherlock Holmes in ‘The Adventure of 
the Copper Beaches’: ‘Data! Data! Data! I cannot make bricks without clay’ 
" (KKT, 1986, pp. 115-16). Neither can a predictive theory of"fair­ness" 
be inferred from any amount of the KKT data. If N "fairness" rules are 
discovered by trial and error modi. ca­tions in the survey questionnaires, 
you cannot reject the hypothesis that there is an N 1 1 variation that 
will identify a new one. More data will not help, as the fairness concept 
is used here as a word that provides no effective means of 
modifyingstandard theory to correct for its predictive .aws.

III. Personal Social Exchange

One of the most intriguing discoveries of experimental economics is that 
(1) as we have seen, people invariably behave noncoopera­tively in small 
and large group "impersonal" market exchange institutions; (2) many (up to 
half in single play; over 90 percent in repeat play) cooperate in 
"personal" exchange (two­person extensive­form games); (3)yet in both 
economic environments all interactions are be­tween anonymous players. In 
this section Ishall attempt to summarize some of the most com­pelling 
evidence of cooperation in personal ex­change--in the .eld as well as the 
laboratory-- and review some of the test results designed to discriminate 
among the more prominentpredic­tive hypotheses for modeling cooperative 
be­havior. Whatever might be the most useful way to model and explain 
cooperation, unaided by market incentives, my working hypothesis 
throughoutis that it is a product of an unknown mix of cultural and 
biological evolution, with the biologyprovidingabstract function de. ning 
potential, and culture shaping the emergent forms that we observe. But to 
motivate the whole exercise in thought, I will begin with a discussion 
ofsome persistent cross­cultural so­cial practices from business, law, 
anthropology, and American economic history.

How might a social rule (practice, norm) emerge, become a cultural .xture, 
and be widely emulated? I will use a parable to illus­trate how a rule for 
"bargaining in good faith" might become established.

In bargaining over the exchange price be­tween a buyer and seller, suppose 
the seller begins byannouncinga selling price, the buyer responds with a 
lower buying price, the seller reduces his asking price, and so on. In 
this concessionary process it is considered bad form for the buyer (or 
seller), once having made a concession, to return to a lower (or higher) 
price. This violates a principle of"bargainingin good faith" (see Siegel 
and Fouraker, 1960, p. 20). How might this come about? One can sup­pose 
that those who failto bargain in goodfaith would be less likely to be 
sought out by others for repeat transactions. Such behavior raises 
transactions cost by increasing the time it takes to complete a sale. 
Trading pairs would be expected to self­select, tending to isolate the 
more time­consuming bargainers, and it would take them longer to .ndthose 
willingto tolerate the time cost of bargaining. Such practices-- 
inherently economizing in this parable--might then become part of a 
cultural norm, powerful enoughto be codi.edultimately in contractlaw andin 
stock exchange rules. Proposition:in this manner collectives discover law 
in those rules that persist long enough to become entrenched practices. In 
this example the emergent rule reduces transaction cost, leaving open the 
clas­sical question of how equilibrium can be char­acterized in bilateral 
bargaining.

A. Spontaneous Order Without the Law49

The early "law­givers" didnotmake the law they "gave"; they studied social 
traditions and

49Experimental studies have inquired as to whether emergent norms 
ofcooperation and constructivist incentive schemes are substitutes, the 
latter crowding out the former. See Iris Bohnet et al. (2001) and Fehr and 
Simon Ga¨chter (2002) for studies suggesting that they are substitutes 
(for­mal rules undermine informal cooperative norms), and Ser­ gio G. 
Lazzarini et al. (2002) for new results suggesting that they are 
complements-- contracts facilitate the self­enforcement of relational 
elements beyond contractibility. I would hypothesize that both must be 
true: constructivist rules ultimately must pass .tness tests of ecological 
ratio­nality. Formal rules that are incompatible with informal rules will 
be modi.ed or eliminated; those that are compat­ible willpersist. Hence, 
at any time slice in history, both must necessarilybe observedacross 
allsocioeconomicexperiments.

informal rules andgavevoice to them, as God’s, or natural, law.50 The 
common lawyer, Sir Edward Coke, championed seventeenth­century social 
norms as law commandinghigher author­ity than the king. Remarkably, these 
forces pre­vailed, paving the way for the rule of law in England.51 
Similarly, the cattlemen’s associa­tions, land clubs, and mining districts 
in the American West allfashionedtheir own rules for 
establishingpropertyrights andenforcingthem: the brandon the hindquarters 
ofhis calf was the cattlemen’s indelible ownership signature on his 
property, enforcedbygunmen hiredthrough his cattle club;52squatter’s 
rightswere defended ably (possession is nine points of the law?) by the 
land clubs composedof those brave enough to settle wilderness lands in 
advance ofveterans exercising their land script claims, and of set­

50 "... ([A]ll early (my insertion: as in Sumar with the beginning of 
writing) ‘law­giving’ consisted in efforts to record and make known a law 
that was conceived as unal­terably given. A ‘legislator’ might endeavor to 
purge the law of supposed corruptions, or to restore it to its pristine 
purity, but it was not thought that he could make new law. ... But if 
nobody had the power or intention to change the law ... this does not 
meanthat law did notcontinue to develop" (Hayek, 1973, p. 81).

51What allowed the rule of "natural" or found law to prevail in England 
"... was the deeply entrenched tradition of a common law that was not 
conceived as the product of anyone’s will but rather as a barrier to all 
power, including that of the king--a tradition which Edward Coke was to 
defend against King James I and Francis Bacon, and which Matthew Hale at 
the end of the seventeenth century mas­terly restated in opposition to 
Thomas Hobbes" (Hayek, 1973, p. 85; also see pp. 167, 173-74).

52These voluntary private associations for sharing the cost ofa common 
good--policing--were subsequently un­dermined by statehood, and the 
publicly .nanced local sheriff as the recognized monopolylaw enforcement 
of. cer. This observation contradicts the myththat a central function of 
government is to "solve" the free­rider problem in the private 
provisionofpublic goods. Here we have the reverse: the incentive of the 
cattlemen’s clubs was to free ride on the general taxpayer, assigningthe 
sheriff the task of enforcing property rights in cattle. The same 
free­ridingoccurs with school busingprograms, andin

publicly provided education itself in which government .nancing need not 
require gov­ernment provision. tlers under the Homestead Act; mining 
claims were de.ned, established, and defended by the guns of the mining 
clubbers, whose rules were later to become part of public mining law 
(Terry L. Anderson andPeter J. Hill, 1975;John Umbeck, 1977). For over a 
century, the Maine lobstermen have establishedrights, usedthreats, then 
force, to defend exclusive individual lobster­.shing territories in the 
ocean (James Acheson, 1975). Eskimo polar bear hunting teams awardedthe 
upperhalfofthe bear’s skin (prized for its long mane hairs used to line 
women’s boots)to that person who .rst .xed his spear in the prey (Peter 
Freuchen, 1961). Extant hunter­gatherers have evolved sharingcustoms for 
the products of communal hunting and gathering. For example, the Ache of 
Eastern Paraguay share the volatile products of the hunt widely within the 
tribe, while the low variance products of gatheringare sharedonly within 
the nuclear family (Hillard Kaplan and Kim Hill, 1985; Kristen Hawkes, 
1991).

B. Ellickson Out­Coases Coase

Using the rancher/farmer parable, Ronald H. Coase (1960)argued that if 
there were no costs of transacting, then theoretically ef. ciency could 
not depend on who was liable for dam­ages to crops caused by stray 
animals. Legal liability gives the rancher an incentive to em­ploycost­ef. 
cient measures to control straying cattle. But if she were not liable, 
then in a world ofzero transactions cost, victims would be led in their 
own interest to negotiate a settlement paying the rancher to undertake the 
same ef.­cient control measures induced by legal liabil­ity. In so doing, 
trespass victims save the costof crop damage, assumed to be more than the 
cost of cattle control--otherwise it is inef. cient to control them. The 
externality is internalizedby market negotiation incentives. Curiously, 
the Coase Theorem--that in the absence of trans­actions cost ef. 
ciencydoes notdepend upon the locus of liability--was controversial. It 
was clearly intended as a kindly spoof of oversim­pli. ed theories that, 
in particular, ignored trans­actions cost.53 The real problem, addressed

53Later game­theoretic formulations have allowed that with two or more 
alternatives there may exist "standoff equilibria" that stall agreement in 
Coase bargaining (see

brilliantlyby Coase, was to deal with the ques­tion of ef. cient liability 
rules in a world of signi. cant transactions cost. He then proceeded to 
use the transactions cost framework to exam­ine the problem of social cost 
in a variety of legal precedents andcases.54

In the beginning Shasta County California was governed by "open range" 
law, meaning that in principle ranchers are not legally liable for damages 
resulting from their cattle acciden­tally trespassing on unfenced land. 
Then, in 1945 a California law authorized the Shasta County Board of 
Supervisors to substitute a "closed range" ordinance in subregions of the 
county. Dozens of conversions have occurred since this enabling law. Under 
a closed range law the rancher is strictly liable--even if not 
negligent--for damage caused by his livestock. Robert C. Ellickson (1991), 
out­Coased Coase by, in effect, asking, "Given that this county applies 
the polar legal rules used in Coase’s illustration, how do neighbors in 
Shasta County actually handle the problem of stray cattle?" The answer: 
"Neighbors in fact are strongly inclined to cooperate, but theyachieve 
cooper­ative outcomes not by bargaining from legally established 
entitlements,55 as the parable sup­poses, but rather developing and 
enforcing adaptive norms of neighborliness that trump formal legal 
arrangements. Although the route chosen is not the one that the parable 
anticipates, the end reached is exactly the one that Coase predicted: 
coordination to mutual advantage without supervision by the state"56 
(Ellickson, Roger B. Myerson, 1991, p. 506). These cases may limit 
extensions of the Coase Theorem, butdo not, Ithink,detract from its 
essential message that the locus of liability was irrelevant.

54Coase (1974) also noticed that the lighthouse was frequently cited by 
theorists as an example of a "pure" public good. As was his style (to 
confront the casual para­bles oftheory that .nessed certain costs by .at), 
his re­sponse in effect was, "Well, let’s see what people have done who 
actually operate lighthouses, or who use the services of lighthouses." It 
turned out that early lighthouses were pri­vate enterprise, not 
government, solutions to a public good problem, and the alleged 
inevitability of free­riding was solved by the owner who contracted with 
port authorities to collect lighthouse fees when ships arrived portside to 
load or unload cargo.

55These are the outside options or threat points in game theory.

56The same results emerge in laboratory experiments reported by Hoffman 
and Matthew L. Spitzer (1985). 1991, p. 4). Thus, Shasta County citizens, 
in­cluding judges, attorneys, and insurance adjus­tors, do not have full 
working knowledge of formal local trespass law.57Citizens notifyowners and 
help catchthe trespassinganimal; use men­tal accounting(reciprocity)to 
settle debts, e.g., a rancher, whose cattle have strayedmaytellthe victim 
to come down and take some hay, or if your goat eats my tomato plants, you 
offer to help me replant them; use negativegossip, com­plain to of. cials, 
submit informal claims for money (but not through a lawyer) to punish 
deviant neighbors; rarely use lawyers to seek monetary compensation; share 
the building of fences, most often bya rule ofproportionality-- you pay 
more if you have more animals than your neighbor; ignore fence law as 
irrelevant; and do not change fence obligations with the plantingofcrops. 
Finally, contrary to the Coa­sian parable, the main cost of trespass is 
not from crop damage, but from highwaycollisions that killanimals and 
damage property.

C. Extensive Form Interactions Between Anonymously Paired Individuals

Cooperation has also emerged in anonymous two­person extensive­form games 
in laboratory experiments. Although such behavior is con­trary to rational 
prescriptions, it is not inconsis­tent with our examples of spontaneous 
order without externally imposed law. Why do we study anonymous 
interactions in the laboratory?The model ofnonrepeated game theory is 
about strangers without a historyor a future (Robert W. Rosenthal, 1981), 
but ano­nymity has long been used in small group ex­periments to control 
for the unknown complexities of natural social intercourse (Sie­geland 
Fouraker, 1960). It is well documented that face­to­face interaction 
swamps subtler procedural effects in yielding cooperative out­comes 
(Hoffman and Spitzer, 1985; Kagel and Roth, 1995). But more important, I 
believe it is this condition that provides the greatest scope for 
exploring the human instinct for social ex­change, and how it is affected 
by context, re­

57 Under open range the animal owner is liable for in­tentional trespass, 
trespass ofa lawfulfence, andtrespass by goats, whatever the 
circumstances, suggestingthe hypothe­sis that goat behavior had long been 
recognized in pastoral norms and now captured in codi. ed law. ward, and 
procedural conditions that vary elements of social distance. Again, 
studying what is not helps us to understand what is.

D. Perception and the Internal Order of the Mind: Why Context Matters

Two decision tasks, represented by the same abstract game tree, may lead 
to different re­sponses because they occur in different con­texts. Why? 
The answer may be found in the process by which we perceive the external 
world. Hayek (1952)58 was a pioneer in devel­oping a theory of perception, 
which anticipated recent contributionsto the neuroscience of per­ception. 
It is natural for our minds to suppose that experience is formed from the 
receipt of sensory impulses that re. ect unchanging at­tributes 
ofexternalobjects in the environment. Instead, Hayek proposed that our 
current per­ception results from a relationship between ex­ternal impulses 
and our past experience of similar conditions. Categories formed in the 
mind are based on the relative frequency with which current and past 
perceptions coincide. Memory consists of external stimuli that have been 
modi.ed by processing systems whose organizationis conditionedbypast 
experience59 (Hayek, 1952, pp. 64, 165). There is a "constant dynamic 
interaction between perception and memory, which explains the ... identity 
of pro­cessing and representational networks of the cortex that modern 
evidence indicates." "Al­though devoid of mathematical elaboration, 
Hayek’s model clearly contains most of the elements of those later network 
models of as­sociative memory ..." (Joaquin M. Fuster, 1999, pp. 88- 89).

Hayek’s model captures the idea that, in the internal order of the mind, 
perception is self­organized:abstract function combines withex­perience to 
determine network connectivityand expansion.60Loss can occur either from 
lack of

58The Sensory Order was notpublished until 1952when Hayek revised a 
manuscript, originally written in the 1920’s, entitled in English 
translation, "Contributions to a Theory of How Consciousness Develops" 
(noted in corre­spondence to me by Bruce Caldwell).

59The interdependence between perception andmemory is revealed by the 
different descriptions of the same event by two eyewitnesses (Gazzaniga et 
al., 1998, pp. 484- 86).

60Built into your brain is the maintained hypothesis that the world around 
you is stationary. Look at the wall and function or the stimulus of 
developmental ex­perience. Block or distort sensory input, and function is 
impaired; impair function by brain lesions or inherited de.ciency, and 
develop­ment is compromised.

This model is consistent with the hypothesis that the mind is organized by 
interactive mod­ules (circuits) that are specialized for vision, for 
language learning, for socialization, and a host ofother functions(see 
Leda Cosmides andJohn Tooby, 1992; Pinker, 1994). In this view, mind is 
the unconscious product of coevolution be­tween the biological and 
cultural development of our brains that distinguished us from other 
primates. It was what made reason possible. Our folk predilection for 
believing in the "blank slate" concept of mind (Pinker, 2002) makes plain 
that this interpretation of mind is just as consonant with our direct 
experience as was once the idea that the earth is .at, or that witches had 
to be destroyed. In each case to escape from the folk perception requires 
the falsifyingindirect evidence, based on reason, to become partofour 
"felt" experience. Construc­tivist rationality then becomes ecologically 
rational.

E. Experimental Procedures

The experiments I will report will show how social contextcan be 
importantin the interactive decision behavior we observe. This 
possibilityfol­lows from the autobiographical character of memoryand the 
manner in which past encoded experience interacts with current sensory 
input in creating memory. I will be reporting the results of 
decision­making in single play, two­person, sequential­move game trees. 
Subject in­structions do not use technical and role­

move your eyes back and forth with head still. The wall does not move. Now 
press your eyeball with your .nger through the eyelid from the side. The 
wall moves as you jiggle your eyeball. Why the difference? When you .ex 
the eye muscles and move your eyes back and forth, a copy of the signal 
goes to the occipital cortex to offset apparent movementofthe wall so that 
the net perception is that ofa stationary wall. This stabilizing 
self­ordered system for seeing also makes you vulnerable to optical 
illusions of motion. Moving your eyes back and forth between the tunnel 
gate and your airplane as it docks, you ambiguously "sense" that either 
the gate or the plane, or both, is in motion. The ambiguity is resolved 
only when the gate, or plane, stops.

suggestive words like "game," "play," "players," "opponent," and "partner" 
(except where variations on the instructions are used as systematic 
treatments to identify their effect);61 rather, reference is made to the 
"decision tree," "decision maker 1" (DM1) or 2 (DM2), and "your 
counterpart," etc. The purpose is to pro­vide a baseline context, which 
avoids emotive words that might trigger unintended meanings by the 
experimenter.62 I do not mean that the baseline is "neutral," a concept 
that is not clearly de.nable, given thatcontext effects can dependon 
autobiographical experience. The ef­fect of instructionalvariation on 
decision is an empirical matter and any particular set of in­structions 
must always be considered a treat­ment unless the observations are shown 
to be robust to changes in the instructions. All obser­vations must be 
seen as ajoint productof ex­perimental procedures and the theoretical 
hypotheses, implemented by particular parame­ters that it is our intention 
to test. This is not unique to laboratory observations, but a 
charac­teristic also of .eld observations, and the whole of science (see 
Smith, 2002, for examples from physics, astronomy, andexperimental 
econom­ics). It is therefore important to understandhow procedures as well 
as different parameteriza­tions (games, payoffs) affect behavior.

Subjects are recruited in advance for an eco­nomics experiment. Upon 
arrival at the ap­pointed time they register, receive a show­up fee, and 
are assigned to a private computer terminalin a large room with 
40stations. Com­monly there are 11 other people, well spaced throughout 
the room, in the experiments re­ported below. After everyone has arrived, 
each person logs into the experiment, reads through the instructions for 
the experiment, responds to instructional questions, andlearns that he or 
she is matched anonymously with another person in the room, whose identity 
willnever be known,

61See Terence Burnham et al. (2000), discussed below, for a study 
comparing the effect of using "partner" and "opponent" in a trust game.

62It is not meaningful or helpful to talk about "experi­menter effects." 
There are instructional and procedural ef­fects, includingthe presence or 
absence ofan experimenter, what he/she knows or does not know (as in 
double blind behavioral experiments), and what he/she does or does not do. 
All of the elementary operations used to implement an experiment are 
treatments that may or may not have a signi. cant effect on observed 
outcomes. and vice versa. This does not mean that a sub­ject knows nothing 
about their matched coun­terpart. For example, it mayappear evident that 
he or she is another "like" person, such as an undergraduate, or an 
industry executive with whom one may feel more­or­less an in­group 
identity. Obviously, each person imports into the experiment a host of 
different past experi­ences and impressions that are likely to be 
as­sociated with the currentexperiment.

F. The Contextof Decision: The Ultimatum Game Example

Consider the ultimatum game, a two­stage, two­person game with the 
following abstract form: for each pair the experimenter makes a .xed sum 
of money, m, available (e.g., m will be 10 one­dollar bills, or 10 
ten­dollar bills); Player 1 moves .rst offering 0 # x # m units of the 
money to Player 2, Player 1 retaining m 2 x;Player 2 then responds by 
either accept­ing the offer, in which case Player 1 is paid m 2x, and 
Player 2 is paid x, or rejecting the offer, in whichcase each player 
receives nothing.

Below I report ultimatum results from four different 
instructional/procedural treatments (contexts) that have the same 
underlying ab­stract game structure. In each case imagine that you are 
Player 1. See Hoffman et al. (1994; hereafter HMSS) for instructional 
details, and for references to the literature and the origins of the 
ultimatum game.

Divide $10.--You and your counterpart have been provisionallyallocated$10, 
and randomly assigned to positions. Your task as Player 1is to divide the 
$10 by .lling out a form that will then go to your counterpart who will 
acceptor reject it.

Contest Entitlement.--The 12 people in the room each answer the same 10 
questions on a general knowledgequiz. Your score is the num­ber of 
questions answered correctly; ties are broken in favor of the person who 
.rst .nished the quiz. The scores are ranked from 1(highest) through 12 
(lowest). Those ranked 1-6 are in­formed that they have earned the right 
to be Player 1, the other six will be Players 2.

Exchange.--Player 1 is a seller, and Player 2 is a buyer. A table lists 
the buyer and seller pro.t for each price $0, $1, $2, ... , $10charged by 
the seller, and the buyer chooses to buy or not buy. The pro.t of the 
seller is the price chosen;the pro. tof the buyer is ($10 2 price). Each 
receives nothing if the buyer refuses to buy.

Contest/Exchange.--This treatment combines Contest withExchange; i.e., the 
sellers andbuy­ers in Exchange are selected by the contest scoring 
procedure. In one version the total amount is 10one­dollar bills, and in 
the second it is 10 ten­dollar bills.

Whatever the context there is a game­theoretic concept ofequilibrium 
(subgame per­fect) that yields the same prediction in all four treatments 
(Reinhard Selten, 1975): Player 1 offers the minimum unit ofaccount, $1 
($10)if m 5 $10 ($100), andPlayer 2 accepts the offer. This follows from 
the assumption that each player is self­interested in the narrow sense of 
always choosing the largest of two immediate payoffs for herself; that 
this condition is com­mon knowledge for the two players; and that Player 1 
applies backward induction to the de­cision problem faced by Player 2, 
conditional on Player 1’s offer. Thus Player 1 reasons that 
anypositivepayoff is better than zero for Player 2andtherefore, Player 
1need onlyoffer x 5 $1 ($10).

One dif. culty with this analysis is that, de­pending on context, the 
interaction may be in­terpreted as a social exchange between the two 
anonymously matched players who in day­to­day experience read intentions 
into the actions ofothers (S. Baron­Cohen, 1995). Suppose the situation is 
perceived as a social contract as follows: if Player 2 has an entitlement 
to more than the minimum unitofaccount, then an offer of less than the 
perceived entitlement(say, only $1, or even $2-$3) may be rejected by some 
Players 2. Player 1, introspectively anticipating this possible mental 
state of Player 2, might then offer $4 or $5 to insure acceptance of his 
offer. Alternatively, Player 1 might enjoy (get utility from) giving money 
to his counterpart. The point is simply that there are alternative models 
to that of subgame perfection that pre­dict choices in the ultimatum game, 
and these alternatives leave wide latitude for the possibil­ity of context 
affecting the behavior of both players. Abstract game theory can embrace

TABLE 2--MEAN PERCENTAGE OFFERED BY TREATMENT IN ULTIMATUM GAMES

these alternatives through the arti.ce of "types"-- utilities, or beliefs 
states suchas trust, trustworthiness, reciprocation, etc. Ultimately the 
predictive success of such models depends on relating task descriptions 
de. ning context to autobiographical characteristics of individuals that 
are then identi.ed by types that in turn determine behavior. The point 
that needs em­phasis is that it is easy to go from types (tradi­tionally 
utility or beliefs about states) to game­theoretic choice;the hard part is 
to relate types to characteristics of the individual’s memory­sensory 
system. Given the directions ofneuro­science andthe learning from brain 
imaging, I do not think this is an impossible order.

Observe thatin "Divide $10" the original$10 is allocated imprecisely to 
both players. More­over, a common de. nition of the word "divide" 
(Webster) includes the separation of some di­visible quantity into equal 
parts. Finally, ran­dom devices are recognized as a standard mechanism for 
"fair" (equal)treatment. Conse­quently, the instructions might be 
interpretedas suggesting that the experimenter is engaged in the "fair" 
treatment of the subjects cueing them to be "fair" to each other.

As an alternative, Contest deliberately intro­duces a pre­game procedure 
that requires Player 1 to "earn" the right to be the .rst mover. This may 
cue some insipient norm of just desserts based on the pre­game quiz. In 
Exchange the ultimatum game is imbed­ded in the gains from exchange from a 
transac­tion between a buyer and a seller. In an exchange, both the buyer 
and the seller are made better off, and buyers in our culture may accept 
the right of a seller to move.rstby quotinga price. Contest/Exchange 
combines the implicit property right norm of a seller with a mecha­nism 
for earning the property right.

Table 2 summarizes the results from two different studies ofultimatum game 
bargaining with stakes of either 10 one­dollar or 10 ten­dollar bills for 
eachofNpairs of players, where N varies from 23 to 33 subject pairs.

1. Comparing Divide $10 with Divide $100 under the random entitlement we 
observe a trivial difference in the amount offered be­tween the low stakes 
(43.7 percent) and the tenfold increase in the stakes (44.4 percent). 
Also, there is no signi. cant difference in the percentage rate at 
whichoffers are rejected, 8.3 percent, and 3.7 percent, respectively.

2. When Exchange is combined with an earned entitlement the increase in 
stakes lowers the offer percentage from 30.8 percent for $10 stakes to 
27.8 percent for $100 stakes, but this difference is within the normal 
range of sampling error usingdifferentgroups ofsub­jects and is not signi. 
cant. Surprisingly, however, this miniscule decline in the mean offer 
causes the rejection rate to go up from 12.5 percent to 21.7percent. Three 
of four subject Players 1 offering $10 are rejected, and one offer of $30 
is rejected in the game with $100 stakes. As has been shown in 
trust/punishment games, this behavior is as­sociated with a strong human 
propensity to incur personal cost to punish those who are perceived as 
cheaters, even under strict anonymity.

3. We note that comparing the Divide $10/ Random entitlement condition 
withthe Ex­change entitlement, the offer percentage declines from 43.7 
percent to 37.1 percent, and comparing the former to the Earned 
en­titlement the decline is from 43.7 percent to 36.2 percent, both 
reductions being statisti­callysigni. cant. Even more signi. cant is the 
reduction from 43.7 percent to 30.8 percent when the Earned and Exchange 
entitlements are combined. Moreover, in all four of these comparisons the 
rejection rate is null or modest (0 to 12.5 percent).

4. The small proportion of the offers that were rejected, except when the 
stakes were $100 in the Earned/Exchange context and the mean offers 
declined to a low of 27.8 per­cent, indicates thatPlayers 1read their 
coun­terparts well, and as the context is altered, normally offer a suf. 
cient amount to avoid being rejected. The one exception shows clearly that 
pushing the edge, even if it seems justi.ed by the higher stakes, may 
invite an escalation ofrejections.

These data indicate that context is important in the ultimatum game: the 
percentage offered varies by over a third as we move from the highest 
(44percent)to the lowest (28 percent) measured effect. Also see Hoffman et 
al. (2000). Like variation is reported in cross­cultural exper­iments: a 
comparison of two hunter­gather and .ve modern cultures reveals variation 
from a high of 48 percent (Los Angeles subjects) to a low of 26 percent 
(Machiguenga subjects from Peru) (Joseph Henrich, 2000). These 
compari­sons used care in attempting to control for in­structional 
differences across different languages, but this is inherently 
problematic, given the nature ofperception, in that one cannot be sure 
thatthe instructions, translations, payoffs, or the proce­dures for 
handling the subjects,control adequately for context across cultures. In 
each culture one needs to vary the instructions/procedures and ob­serve 
the sampling distribution of outcomes, then compare the sample 
distributions across cultures. These instructionaltreatment effects call 
into question the extent to which one can de.ne what is meant by 
"unbiased" instructions. If results are robust with respect to 
instructional changes, this can only be established empiri­cally. Without 
such studies no claims can be made concerning the relative "neutrality" of 
instructions. The main lesson is that, because of the nature of perception 
and memory, context should matter, and in the ultimatum game the variation 
of observed results with systematic instructional changes designed to 
alter context shows clearly that context can and does matter. 
Experimenters, subject to the same perception/ memory variations, are 
likely to disagree as to what is "neutral."

G. Dictator Games With and Without Gains from Exchange

The ultimatum game is converted into a dic­tator game byremoving the 
rightof the second mover to veto the offer of the .rst. Forsythe et al. 
(1994; hereafter FHSS) note that if the ob­served tendency toward equal 
split of the prize is due primarily to "fairness"--a socialnorm of just 
division--then it should be of little conse­quence if this right is 
eliminated. But if it is the prospectofrejection-- however 
irrational--that tempers the amount offered by Player 1, then the outcomes 
should be materially affected by removing the right of rejection, which 
converts the ultimatum game into what is called the dictator game. Thus a 
signi. cant reduction in the mean percent offered in the dictator game 
wouldbe consistentwith the secondhypothesis, while no signi. cant 
reduction would be consis­tent with the .rst. Comparing the results in 
Table 3, column 1 with those for Divide $10, Random entitlement in Table 
2, we see that the mean dictator offer is only 23.3 percent com­pared with 
the mean ultimatum offer of 43.7 percent. FHSS conclude that fairness 
alone can­notaccountfor behaviorin the ultimatumgame. This is correct, 
but, equallyof interest, why are dictators giving away nearly a quarter of 
their endowment? This research puzzle was picked up by HMSS who 
conjectured thatsuch gener­

TABLE3--DICTATOR GIVING: WITH AND WITHOUT GAINS FROM EXCHANGE AND SOCIAL 
HISTORY

ositymight be, at least in part, a consequence of the incompleteness of 
anonymity. In all the games prior to the HMSS studythe members of each 
player pair were anonymous with respect to each other butnotwithrespect to 
the exper­imenter who knew every person’s decision. Hence, they introduced 
a "double blind" treat­ment category (two versions) in which the pro­tocol 
made it transparent that no one, including the experimenter, could learn 
the decisions of any player. Data from the secondversion, Dou­ble Blind 2, 
are reported in Table 3. In this treatment mean dictator offers decline to 
only 10.5 percent. Consequently, context--in this case social 
connectedness or distance--has an important affect on dictator 
transfers.63 These issues are explored more fully in Hoffman et al. 
(1996b), who vary social distance by varying the instructional and 
protocol parameters that de.ne various versions of single­and 
double­blinddictator games. Also reported in Table 3is the percent given 
by the top 50 percent ofthe most generous dictators: 38.3 percent for 
Single Blind and 21 percent for Double Blind. Berg et al. (1995, hereafter 
BDM)modify the dictator game to introduce gains from "ex­

63These double blind procedures and treatment effects have been replicated 
by two other investigations (Catherine C. Eckel and Philip J. Grossman, 
1996, and Burnham, 1998). Bolton et al. (1998), usinga different double 
blind procedure failed to replicate the results, suggesting that 
procedures matter and interact with the double blind con­dition.

change."64Their investmenttrust two­stage dic­tator game also uses the 
Double Blind 2 protocol: dictators in room Asend any portion of their $10 
(0 to $10)to their random counter­part in room B. People in bothrooms know 
that if $x is sent byanyone, it is tripled, so that the counterpart 
receives $3x. Thus, the most gener­ous offer, $10, yields a gain of $30. 
The coun­terpart can then respond by sending any part (0 to $3x)of the 
amountreceived backto his or her matchedsender. Now an exchangewithgains 
to both parties is possible, and BDM ask if this context is a signi. cant 
treatment. Note that the analysis of the game is no different than the 
one­stage dictator game: by the principle of backward induction Player 1 
can see that Player 2’s interest is to keep all the money received, and 
therefore nothing should be sent. The fact thatthe sender’s transfer 
willbe tripled is irrelevant. But it is not irrelevant if both players see 
the interactionas an exchange based on trust by Player 1 and 
trustworthiness by Player 2.

In Table 3 sender Players 1 now give 51.6 percent when the transfer is 
tripled, compared with 23.3 percent when it is not. Furthermore, the top 
50 percent of the givers send 74.4 per­cent of the money, up from 38.3 
percent. This shows how the tripledpie shifts the distribution 64For a 
recent extension and replication of the BDM .ndings see Madan Pillutla et 
al. (2003). Also see Ortmann et al. (2000). toward larger transfers by 
Players 1. But on average the senders do not quite break even: an average 
of 27.2 percent of the amount received byPlayers 2is returned to Players 
1(break even would be 33.3 percent since x is tripled). In the social 
history treatment the instructions and protocolare the same as described 
above except that the second treatment group is shown the 
distributionofamounts transferredand returned for the .rst group. 
Comparingthe social history with the baseline mean percent given and 
re­turned reveals the effect ofbeingexposedto the decision data ofthe .rst 
group. Social history does not cause a reduction in transfers, which 
actually increases marginally from 51.6 percent to 53.6 percent. The 
average percent returned increases from 27.2percent to 35.5percent,just 
above the break­even level.

These results are not explicable bythe canons of traditional game theory 
that assumes self­interested (in the sense of always choosing larger 
payoffs) types. Byintroducinggainsfrom the investment by Player 1, who can 
only ben­e.t if Player 2 perceives the process as an exchange calling for 
payment for services ren­dered, dictator giving more than doubles. And the 
effect ofsocial history does not precipitate a decline in investment nor 
in the return to Play­ers 2--in fact both increase slightly. The same 
behaviors have been observed in chimpanzee and capuchin monkey communities 
(Frans B. M. deWaal, 1989, 1997). Should such trust­ingand trustworthy 
behaviors be diminishedin human communities characterized by the maxim 
that "the rules ofmoralityare not ... con­clusions of (our) reason?"

H. Trust Games

Ultimatum and dictator games have been studiedextensively, butare much too 
simple to allow an adequate understandingofsome ofthe underlying behavior 
manifest in them. It is tempting to overinterpret them in terms of a mixed 
utility for own and other reward. The potential for greatly expanding what 
can be learned is illustrated in Table 3 where BDM extendedthe dictator 
game to a two­stage game with gains from voluntary exchange. We turn 
therefore to a somewhat richer class of two­person extensive­form trust 
games in which equilibrium play, cooperation, and the prospect ofdefection 
can be studiedin a richer parameter space than the ultimatum game.65

Figure 1 is a typical"trust" game tree.66 Play starts at the topofthe 
tree, node x1, withPlayer 1 who can move right, which stops the game 
yielding the upper payoff to Player 1, $10, and the lower payoffto Player 
2, $10, or move down in which case Player 2chooses a move at node x2. If 
Player 2’s move is right, Player 1receives $15, and Player 2, $25. This is 
the cooperative (C) outcome. If, however, Player 2 moves down, the payoffs 
to 1 and 2are, respectively, $0and $40. This is the defection (D) outcome, 
in which Player 2 defects on Player 1’s offer to cooperate. The 
subgame­perfect equilibrium (SPE) is $10 for each player. This follows 
be­cause at node 1 Player 1 can apply backward inductionbyobservingthat if 
play reaches node x2, Player 2’s dominant choice is to defect. Seeing that 
this is the case, Player 1’s dominant choice is to move right at the top 
of the tree, yielding the SPE outcome.

These game­theoretic assumptions are very strong. As we see from the above 
discussion, however, they have the dubious meritof allow­ing "unambiguous" 
predictions to be made about behavior.67 We particularly want to note that 
if every player is exactly like every other player, and is strictly 
self­interested, there is no room for "mind reading," or 
inferringintentions from actions, and no room for more sophisti­cated and 
subtle action in the self­interest.

To illustrate, suppose that you have been throughthe standard economics 
course in game

65Space does not permitexamining also the effect of beingable to punish 
defection. See McCabe et al. (1996)for a more complete report of trust 
games with and without punishment of defection and witha wide variety of 
match­ing protocols.

66 As indicated above, the word "trust" never appears in the instructions. 
Of interest, however, is that the subjects use this word when you ask them 
open­ended questions about their analysis and perceptions of the game. 
"Its all a ques­tion of whether you can trust your partner." Neither do we 
use the word "partner."

67Except see Smith (2002), wherein it is shown that if, in addition to the 
research hypothesis derived from game theory (e.g., Cournot­Nash or SPE), 
there is one auxiliary hypothesis (e.g., payoffs are adequate, types have 
been accuratelyde. ned, or subjects are sophisticated), then either the 
theoretical hypothesis is not falsi.able, or it has no predictive content. 
The belief, however, persists that the predictions of game theory are 
sharp andunambiguous (see, e.g., Camerer et al., 2001).

theory and that you are in the position of Player 2 in Figure 1. 
Consequently, you expect Player 1 to move right at the top of the tree. He 
does not. He moves down, and it is your turn. Surely he did not move down 
because he prefers $0 to $10, or expects you to defect. He must thinkthat 
you thinkthat he wants you to choose C. What­ever else can he have in 
mind? Maybehe cannot do backward induction, or thinks you are not 
self­interested. So how are you going to re­spond? He is making it 
possible for you to increase your payoff by 150percent, and his by 50 
percent, compared with the SPE. He is not even asking for the larger share 
ofthe pie that his action has created! According to reciprocity theory, 
ifyou choose C, you will reciprocate his inferred intentions, and complete 
an ex­change--exactlyin the same way that you trade favors, services, and 
goods across time with your closer friends andassociates in life (except 
for those who are victims of antisocial person­ality disorder, or 
sociopaths, and are unable to maintain social relations based on 
reciproc­ity),68 also in the same way that you do not hesitate to leave a 
"tip" ("to insure prompt­ness"?) for good service at a restaurant even in 
a foreign city. Without a conscious thoughtyou often say, "I owe you one," 
in response to an acquaintance’s favor. So you might choose C with hardly 
a thought, or, since he will never know your identity, upon closer re. 
ection, you may thinkthat it just makes no sense not to take the $40. 
Although you are nota clinical socio­path, here is an opportunityto cuta 
corner and no one can know. As Player 1 in Figure 1 are you certain that 
you wouldwant to play SPE?69

68 "Sociopaths, who comprise only 3-4 percent of the male population and 
less than 1 percent of the female population, are thought to account for 
20 percent of the UnitedStates prison population andbetween 33 percent and 
80 percentof the population of chronic criminaloffenders. Furthermore, 
whereas the "typical" U.S. burglar is esti­mated to have committed a 
median .ve crimes per year before being apprehended, chronic 
offenders--those most likely to be sociopaths--report committing upwards 
of 50 crimes per year and sometimes as many as two or three hundred. 
Collectively, these individuals are thought to ac­count for over 50 
percent of all crimes in the U.S." (see Linda Mealy, 1995, p. 523, and pp. 
587-99 for references and caveats; also David T. Lykken, 1995).

69This thought process may explain why, in data re­ported by Giorgio 
Coricelli et al. (2000)comparing faculty with undergraduate subjects, the 
faculty take much longer (and earn less money) than the undergraduates to 
decide

FIGURE1. INVEST$10 TRUSTGAME: FREQUENCY OF PLAY

In regard to this reciprocity analysis of the game, we should note that 
the game in Figure 1 is a much reduced­form version of the BDM game: 
thinkof Player 1 as sending $10, which becomes $30;Players 2can either 
split the $30 equally with Player 1, givingthe imputation C, or Player 2 
can keep it all, yielding the D outcome. But there is another difference, 
one of context. In the experiment using Figure 1 the subjects play an 
abstract game, one that is not embedded in a BDM­type story about sending 
$10 upstairs, which becomes $30, and the re­ceiver can either keep it all 
or split the gain made possible by the sender. But given the BDM outcomes 
reported above we shouldnot be too surprised that some subject pairs might 
endat C.

The outcomes are shown in Figure 1 for 24 undergraduate subjects: 50 
percent move down, and of these 75 percent "reciprocate." whether to offer 
cooperation, and whether to defect. Yet given knowledge of game theory, 
and knowing that one’s counterpart has the equivalent knowledge, what is 
there to think about?

Why So Much Cooperation?--Mycoauthors and I have interpreted the outcome 
Cas due to reciprocity. But there are other interpretations; e.g., that 
the subjects are game­theoreticallyun­sophisticated or have nonsel. sh 
preferences. The effectof subjects on outcomes is an empir­icalmatter, 
andis most important, butcannotbe pursued here in depth. It is essential 
to programs concerned with extending game theory to "player types." 
Subject background diversity and resulting choice behavior can help inform 
the identi.cation andclassi.cation of "types," whether reciprocators, 
sophisticates, or utilitar­ian (see McCabe and Smith, 2001; McCabe et al., 
2001).

Is It the Subjects? Undergraduate’s Versus Graduates.--In the above trust 
game nearly half the Players 1forgo the sure thing, SPE, and 
three­quarters of the responses are cooperative. We have often heard 
suchresults dismissedas a consequence of usingnaive subjects. (This 
dis­missal has the logicalimplication that the orig­inal 
theoreticalhypothesis is either not falsi. able or has no predictive 
content. See footnote 67.) McCabe andSmith(2000) examinedthis expla­nation 
using advanced graduate students from the population, a sample of whom 
participated in the $100 Exchange/Entitlement version of the ultimatum 
game reported in Table 2, show­ing almost identical results for graduate 
and undergraduate students. They used the trust game shown in Figure 1. 
For comparison, McCabe and Smith (2000)used the undergrad­uate data shown 
in Figure 2. In both groups 50 percent of Players 1offer cooperation, 
while 75 percent of the undergraduate and 64 percent of the graduate 
student Players 2 reciprocate. However naive undergraduates are alleged to 
be, these tests suggest that graduate students with training in economic 
theory are capable of showingvery similar behaviorin this extensive­form 
game, andin the ultimatum game reported in Table 2.

Is It Utility for Other?--Bolton (1991), Rabin (1993), Fehr and Schmidt 
(1999), and Bolton and Ockenfels (2000) have proposed usefulpreference 
models ofdecision that aim at accounting for behavior in a variety of 
experi­ments, most particularlyultimatum and dictator games. The idea 
behindthese models is that we can explain cooperation in bargaininggames 
by

FIGURE2. INVEST$10 TRUST GAME COMPARING UNDERGRADUATES (U) AND GRADUATES 
(G)

saying that people have a taste for altruism or for "fair" outcomes (or a 
distaste for "unfair" outcomes), where fairness is understoodas pay­off 
equity, as in Franciosi et al. (1995). The hypothesis is that subjects 
seek to maximize these adjusted utilities. It is only the intrinsic 
propertiesofoutcomesthatare assumed to drive behavior; what alternatives 
the players facedat a previous decision node are irrelevant. This means 
that intentions, as re.ected in the move choices, are assumedto be super. 
uous in the interactions between the parties. The former approach identi. 
es utilitytypes. The latter iden­ti.es types who signal intentions, who 
are into reading move signals, and risk misidentifying reciprocity versus 
defection types. The impor­tant testable distinctions are that the former 
are immune to instructional procedures and to path dependency--the 
opportunity costs of forgone options; this constitutes the core of the 
research program of my coauthors andme. This program leads naturally to an 
understand­ing of its own limitations as well as that of the utilitarian 
program, i.e., both types may be needed.

Other­regarding preference models are un­able to account for our previous 
data demon­strating that procedures and context variables matter. In the 
ultimatum game data reported above where the context is variedfrom "Divide 
$10" to "Context/Exchange," ostensibly the utilities to the participants 
are the same under each ultimatum condition. However, behavior varies 
dramatically. These models also cannot explain the results reported above 
in the single versus double blind protocol in dictator games, and the 
dramatic change in dictator behavior in the BDM investment trust game. 
Clearly, the behavioris muchmore variable than is expected from 
outcome­based utility models.

An altruistic utility interpretation of cooper­ation can be invokedin 
trust games like that in Figure 1: Player 2 may move down because her 
utility for reward is increasing in bothown and other payoff. Figure 3 is 
a trust game that enables us to distinguish subjects who cooper­ate from 
motivations of altruism, and those whose cooperation derives from 
reciprocity in an exchange. The game starts at the top, node x1, with 
Player 1 who can move right, which stops the game yielding the upper 
payoff to Player 1, $7, andthe lower payoff to Player 2, $14, or move down 
in which case Player 2 chooses a move at node x2. If the move is right, 
each player gets $8. If Player 2 moves down, Player 1can then move rightat 
node x3yielding $10 for each, or down, yielding $12 for Player 1 and $6 
for Player 2. The subgame­perfect equilibrium (SPE) is $8 for each player. 
This follows because at node x1, Player 1 can apply backward induction by 
observing that if play reaches node x3, Player 1 will want to move down. 
But Player 2, also usingbackward induc­tion will see that at node x2 he 
should move right. Since right at node x2 yields a higher payoff to Player 
1, at node x1 Player 1 will conclude that he should move down. Hence, the 
SPE outcome would prevail by the logic of self­interestedplayers who 
always choose dom­inantstrategies, andapply the principleofback­ward 
induction.

If Player 1 has other­regarding preferences (altruism) and is willing to 
incur some cost to greatly increase the payoff to Player 2, Player 1 may 
move right at x1. His payoff of $7 is only one­eighth smaller than his 
payoff at the SPE, and yields $14 for Player 2. Hence, at a cost of $1 to 
himself, Player 1 can increase his coun­

FIGURE3. TRUST GAME WITH ALTRUISM: FREQUENCY OFPLAY

terpart Player 2’s payoff by $6. Player 1 need have onlya modest 
preference for an increase in Player 2’s welfare to induce him to move 
right because of the 6 to 1 return for the other player over the cost to 
Player 1.

At x2, Player 2 maymove down signalingto Player 1 that such a move enables 
both to pro.t (gains from exchange), provided that at x3 Player 1 
cooperates by reciprocatingPlayer 2’s favor. Alternatively at x3 Player 1 
can defect (D) on the offer to cooperate by choosing his dominate 
strategy, and move down.

The outcome frequencies for the trust game (N 5 26pairs) are 
entereddirectlyon the tree in Figure3.The .rst result-- overwhelmingly 
decisively--is that no Player 1 chooses the A

FIGURE4A. VOLUNTARYTRUST GAME

(altruistic) outcome;all choose to pass to Player 2 seeking a higher 
payoff for themselves, and being content to give Player 2 a smaller payoff 
than is achieved at A, dependingupon the .nal outcome of the move 
sequence. Secondly, 46 percent offer to cooperate (down), and 50 per­cent 
reciprocate.

I. Utility Versus Exchange: Does Opportunity Cost Matter?

If reciprocity is perceived as an exchange in which each player gains 
relative to the default outcome (SPE), then the cooperative outcome must 
yieldan increase in the size of the prize to be splitbetween the two 
players (see McCabe et al., 2003). Also, Player 2 must believe that (i) 
Player 1 made a deliberate choice to make this outcome possible, and (ii) 
incurred an opportu­nity cost in doing so, i.e., gave up a smaller certain 
payoff riskinga still smaller payoff if C is not attained. It then becomes 
credible to Player 2that Player 1 did a favor for Player 2, and reasonably 
can expect reciprocal action in return. Notice that our argument is in the 
form

FIGURE4B. INVOLUNTARY TRUST GAME

of a constructivist theory that need not charac­terize the subjects’ 
reasoning, even if it has predictiveaccuracy; i.e., 
constructiverationality may predict emergent ecologically 
rationalout­comes, just as CE theory predicts market out­comes notpart 
ofthe consciousintentionsofthe agents. McCabe et al. (2001), however, 
report fMRI brain­imaging data supporting the hy­pothesis that subjects 
who cooperate use the "mind reading" circuit modules in their brains (see 
Baron­Cohen, 1995). This circuitry is not activatedin subjects who choose 
not to cooper­ate (SPE). In responding to postexperiment 
questionsaskingthem to write their impressions oftheir decisions, subjects 
frequentlyreport that the experiment is all about whether you can trust 
your counterpart. They do not refer to returning a favor, to reciprocity, 
an exchange, fairness, etc., suggestingthat if their actions are driven by 
reciprocity motives, such are not part of a self­aware reasoning process.

Reciprocity reasoning motivated the alterna­tive game trees shown in 
Figure 4 designed to test reciprocity against utility interpretations of 
choice. In Figure 4A, ifPlayer 1moves down at the topthe potential prize 
increases from $40to $50. Player 2 can defect at a cost to Player 1, and 
can clearly infer that Player 1 deliberately enabled the outcome to 
increase from ($20, $20)to ($25, $25). But in Figure 4B Player 2 can see 
that Player 1 was presented with no voluntary choice to move down. 
Consequently, Player 1 incurred no opportunitycost to enable Player 2 to 
achieve C. Player 1 did nothing intentionally for Player 2, and according 
to rec­iprocity reasoning, Player 2 incurredno implied debt that neededto 
be repaid. Player 2can thus move down with impunity. Consequently, 
reci­procity theory predicts a greater frequency of right moves by Players 
2 in Figure 4A than in Figure 4B. Since only outcomes matter, both 
own­andother­regardingutilitytheories predict no difference in Player 2’s 
choices between Figures 4A and 4B. In fact, as shown by the frequency data 
on the trees, a third of the Play­ers 2on the right play C, whereas nearly 
twice that manychoose C in the left game.

Intentionshave also been foundto matter in a study of ultimatum 
bargaining: "... we show that identical offers in an ultimatum game 
trig­ger vastlydifferent rejection rates dependingon the other offers 
available to the proposer" (Armin Falk et al., 1999, p. 2).

J. Extensive­Versus Normal­Form Games

A fundamental principle of game theory is that rational behavior is 
invariant to the form-- extensive or normal--of the game. Behavior in the 
extensive andnormal forms has been com­pared byAndrew Schotter et al. 
(1994), Amnon Rapoport (1997), and McCabe et al. (2000). All three reject 
the invariance principle, but in the .rst study the rationality principles 
they pro­posedto explain the invariance either failedto predict the 
differences, "or they were notwhat we expected" (Schotter et al., 1994, 
pp. 446- 47). Rapoport provides two versions of the "Battle­of­the­Sexes" 
game to show how order­of­play information in the extensive form al­lows 
players to better coordinate their actions. McCabe et al. (2000) argue 
that the important principle that allows better coordination "de­rives 
from the human capacity to read another person’s thoughts or intentions by 
placing themselves in the position and information state ofthe other 
person" (p. 4404). This"mindread­ing" to detect intentions underlies 
reciprocity.

($7, $14)  0 26 5 0.0 0 245 0.0 12 7 26 5 0.46 245 0.29 ($8, $8)  6 12 5 
0.50 1 75 0.14 ($12, $6)  6 12 5 0.50 6 75 0.86

We summarize here the .ndings of McCabe et al. (2000)for the trimmed 
version of the game they study, which is the game we have depicted in 
Figure 3. In the extensive form of the game in Figure 3, Player 2 sees the 
move of Player 1 before Player 2 chooses to move. In this form of the 
game, intentions can be clearly communicated along the lines storiedabove. 
In the normal (or strategic) form of the same game each player chooses a 
move at each node without knowing whether thatnode willactuallybe 
reachedin the move sequence. Decisions are thus contingent on the node 
beingreachedandmay be irrelevant in determining the payoffs. In the normal 
form, therefore, we can present the game as an n 3 m matrix ofthe n 5 
3strategies ofPlayer 1--right at node x1, right or down at node x3, and 
the m 5 2 strategies of Player 2, right or down at node x2. Players 1 and 
2 each simultaneously choose among these alternatives not knowing the 
choice of the other.

McCabe et al. (2000) predict that Players 2 willmove down at x2, with 
higher frequency in the extensive than the normal form. They also predict 
higher rates of cooperation by Players 1 (andlower defection rates) in the 
extensive than normal forms. Neither own­or other­regarding utility 
theorycan support these predictions. The data are shown in Table 4: 46 
percent of the Players 1 offer to cooperate in the extensive form, only 29 
percent in the normal form. Sim­ilarly, they observe a 50 percent 
cooperative rate byPlayers 2in the extensive form, but only 14 percent in 
the normal form.

These results and that of others cited above imply that the extensive and 
normal forms are not played as if they were the same games. Players’ moves 
signalintentionsthat are not the same when actually experienced in 
extensive form as when imaginedin a mental experiment corresponding to the 
normal form. I would ar­gue that experience and its memory in life is an 
extensive process that encodes context along with outcome. The brain is 
not naturally adapted to solve all sequential move problems byreducing 
them to a single strategyvector as in a highly structured game. 
Apparently, we have a built­in tendency to wait, observe, then decide--a 
process that conserves cognitive re­sources by applyingthem only to 
contingencies that are realized, and avoids the need for revi­sion, given 
the inevitable surprises in the less structured games of 
life.70Constructivist mod­eling glosses over distinctions of which we are 
unaware that govern the ecologyofchoice. Ex­perimental designs conditioned 
only by con­structivist thinking, ill­prepare us to collect the data that 
can inform needed revisions in our thinking. It is both cost effective and 
faithfulto game­theoretic assumptions in experiments to collect move data 
from each subject under all contingencies, but it distorts 
interpretability if game forms are not equivalent. The assump­tions of 
game theory, such as those leadingto the logical equivalence of the two 
game forms, shouldnot be imposedon experimental designs, thereby 
constraining our understanding of be­havior beyond those assumptions.

K. Neuroeconomics

Neuroeconomics is concerned with studying the connections between how the 
mind/brain works--the internal order of the mind--and behavior in (1) 
individual decision making, (2) social exchange, and (3) institutions such 
as markets. The working hypothesis is that the brain has evolved 
different, but interdependent, adaptive mechanisms for each of these tasks 
involving experience, memory, and perception. The tools include 
brain­imagingtechnology and the existence of patients with localized brain 
lesions associated with speci.c loss of certain mental functions. Decision 
making has drawn the attention of

70 Any such natural process must be deliberately over­come, 
constructively, in situations where nature serves us poorly. 
neuroscientists who study the deviant behavior of neurological patients 
with speci.c brain le­sions, suchas front lobe (ventromedialprefron­tal) 
damage.

Such patients have long been known to be challenged by tasks involving 
planning and coordination over time, although they score normallyon 
batteries of psycholog­ical tests (Antonio R. Damasio, 1994). A land­mark 
experimental study of such patients (compared with controls) in individual 
decision­making under uncertainty is that of Antoine Bechara et al. 
(1997). Starting with (. ctional) endowments of $2,000, each subject on 
each trialdraws a card from one offour freely chosen decks (A, B, C, D). 
In decks (A, B) each card has a payoff value of $50, whereas in decks (C, 
D), each is worth $100.

Also, the $100 decks contain occasional large negative payoff cards, while 
the $50deckshave much smaller negative payoff cards. All this must be 
learned from single card draws in a sequence of trials, with a running 
tally of cumulative payoff value. A subject performs much better by 
learning to avoid the $100decks in favor of the $50decks. By period 60, 
normal control subjects have learned to draw onlyfrom the $50(C, D) decks, 
while the brain­damaged subjects continue to sample disadvantageously the 
$100 (A, B) decks. Furthermore, the controlsubjects shift to the (C, D) 
decks before they are able to articu­late why, in periodic questioning. 
Also, they pre­register emotional reactions to the (A, B) decks as 
measured by real­time skin conductiv­ity test (SCT)readings. But the 
brain­damaged patients tend to verbally rationalize continued sampling of 
the (A, B) decks, and some types (with damaged amygdala) register no SCT 
re­sponse. Results consistent with those of Be­chara et al. (1997)have 
been reported byVinod Goel et al. (1997) who study patient perfor­mance in 
a complex .nancial planning task.

Over 50 years ago experiments with animal behavior demonstrated that 
motivation was based on relative or forgone reward-- opportunity cost--and 
not on an absolute scale of values generated by the brain. Thus David J. 
Zeaman (1949)reported experiments in which rats were trained to run for a 
large reward­motivatedgoal. When shifted to a small reward, the rats 
re­sponded by running more slowly than they wouldto the small rewardonly.

Acontrolgroup began witha smallreward andshiftedto a large one, andthese 
rats immediately ran faster than if the large reward alone was applied. 
Monkeys similarly respondto comparisons of differential rewards. It is now 
establishedthat orbitofrontal cortex (just above the eyes) neuron activity 
in monkeys enable them to discriminate between rewards that are directly 
related to the animals’ relative, as distinct from absolute, preference 
amongrewards suchas cereal, apple,andraisins (in order of increasing 
preference in monkeys) (Leon Tremblay and Schultz, 1999). Thus suppose A 
is preferred to B is preferred to C based on choice response. Then 
neuronal activity is greater for A than for B when the subject is viewing 
A and B, and similarlyfor B and C when comparing B and C. But the 
activ­ity associated with B is much greater when comparedwith C than when 
it is compared with A. This is contrary to what one would expect to 
observeif A, B, and C arecodedon a .xed properties scale rather than a 
relative scale (Tremblay and Schultz, 1999, p. 706).

These studies have a parallel signi.cance for humans. Prospect theory 
proposes that the eval­uationofa gamble depends noton the totalasset 
position but focuses myopically on the oppor­tunitycost, gain or loss, 
relative to one’s current asset position. There is also asymmetry--the 
effectofa loss looms larger than the effect ofa gain of the same magnitude 
(Kahneman and Tversky, 1979).Barbara A. Mellers et al. (1999) have shown 
that the emotional response to the outcome ofa gamble depends on the 
perceived value and likelihoodof the outcome andon the forgone outcome. It 
feels better (less bad) to receive $0from a gamble when you forgo 1$10 
than when you forgo 1$90. (Theyuse the term "counterfactual" rather than 
"opportunity cost" to refer to the alternative that might have 
pre­vailed.) Thus, our ability to form opportunity cost comparisons 
receive importantneurophys­iological support from our emotional circuitry. 
Breiter et al. (2001)use these same principlesin the design of a 
functional magnetic imaging (fMRI) study of human hemodynamic re­sponses 
to both the expectation andexperience ofmonetary gains and losses under 
uncertainty. They observedsigni.cantactivation responses in the amygdala 
and orbital gyrus, with both activations increasing with the expected 
value of the gamble. There was also some evidence that the right 
hemisphere is predominantly ac­tive for gains, and the left for losses--a 
partic­ularly interesting possibility inviting deeper examination, perhaps 
by imaging split­brain subjects in the same task. Also see Kip Smithet al. 
(2002).

The effect of paying subjects is informed by Thut et al. (1997)who compare 
brain activation under monetary rewards with the feedbackofan "OK" 
reinforcement in a dichotomous choice task. The monetary rewards yielded a 
signi. ­cantly higher activationofthe orbitofrontal cor­tex and other 
related brain areas (see also Schultz, 2000, 2002).

The neuralcorrelates of individual decision­making were extended, by 
McCabe et al. (2001), to an fMRI study of behavior in two­person strategic 
interactions in extensive­form trust games like those in Figures 1 to 4. 
The prior hypothesis, derived from reciprocity the­ory, the theory­of­mind 
literature, and sup­ported by imaging results from individual studies of 
cued thought processes (P. C. Fletcher et al., 1995), was that cooperators 
would show greater activation in the prefrontal cortex (speci.cally BA­8) 
and supporting cir­cuitry, than noncooperators. The control, for 
comparison with the mental processes used when a subject is playing a 
human, is for the subject to play a computer knowing the pro­grammed 
response probabilities and therefore havingno needto interpretmoves as 
intentions. The predicted activations were signi. cantly greater, relative 
to controls, for cooperators than noncooperators, and are consistent with 
the rec­iprocity interpretation of behavior discussed above.

IV. Conclusions

Cartesian constructivism applies reason to the design ofrules for 
individualaction, to the design of institutions that yield socially 
optimal out­comes, and constitutes the standard socioeco­nomic science 
model. But most ofour operating knowledge, and ability to decide 
andperform is nondeliberative. Our brains conserve attentional, 
conceptual, and symbolic thoughtresources be­cause they are scarce, and 
proceeds to delegate most decision­making to autonomic processes 
(including the emotions) that do not require conscious attention. Emergent 
arrangements, even if initially constructivist, must have survival 
properties that incorporate opportunity costs andenvironmental challenges 
invisible to con­structivist modeling. This leads to an alternative, 
ecological concept of rationality: an emergent order based on 
trial­and­error cultural and bio­logical evolutionary processes. It yields 
home­and socially grown rules of action, traditions and moral principles 
that underlie property rights in impersonalexchange, and socialcohe­sion 
in personalexchange. To studyecological rationality we use rational 
reconstruction--for example, reciprocity or other­regarding 
prefer­ences--to examine individual behavior, emer­gent order in human 
culture and institutions, and their persistence, diversity, and 
develop­ment over time. Experiments enable us to test propositions derived 
from these rational reconstructions.

The study of both kinds of rationality has been prominent in the work of 
experimental economists. This is made plain in the many direct tests of 
the observable implications of propositions derived from economic and game 
theory. It is also evident in the great variety of experiments that have 
reached far beyond the theory to ask why the tests have succeeded, failed, 
or performed better (under weaker con­ditions) than was expected. What 
have we learned, not as.nal truth, but as compelling working hypotheses 
for continuing examination?

1. Markets constitute an engine of productiv­ity by supporting resource 
specialization throughtrade andcreatinga diverse wealth of goods 
andservices.

2. Markets are rule­governed institutions pro­viding algorithms that 
select, process, and order the exploratory messages of agents who are 
better informedas to their personal circumstances than that of others. As 
pre­cautionary probes by agents yield to con­tracts, each becomes more 
certain ofwhat must be given in order to receive. Out of this interaction 
between minds through the intermediary of rules the process aggre­gates 
the dispersed asymmetric informa­tion, converging more­or­less rapidly to 
competitive equilibria if they exist. Each experimental market carries its 
own unique mark with a different dynamic path.

3. Allthis information is captured in the static or time­variable supply 
and demand envi­ronment and must be aggregated to yield ef. cient 
clearingprices. We can never fully understand how this process works in 
the world because the required information is not given, or available, to 
any one mind. Thus, for many, the arguments of the Scot­tish philosophers 
and of Hayekare obscure and mystical. But we can design experi­ments in 
whichthe information is not given to any participant, then compare market 
outcomes with ef. cient competitive out­comes and gauge a market 
institution’s performance.

4. The resulting order is invisible to the par­ticipants, unlike the 
visible gains they reap. Agents discover what they need to know to achieve 
outcomes optimal against the con­straining limits imposed byothers.

5. Rules emerge as a spontaneousorder--they are found--not deliberately 
designed by one calculating mind. Initially constructiv­ist 
institutionsundergo evolutionary change adapting beyond the circumstances 
that gave them birth. What emerges is a form of "social mind" that solves 
complex organi­zation problems without conscious cogni­tion. This "social 
mind" is born of the interaction among all individuals through the rules 
of institutions that have to date survived cultural selection processes.

6. This process accommodates trade­offs be­tween the cost 
oftransacting,attending,and monitoring, andthe ef.ciencyofthe allo­cations 
so that the institution itself gener­ates an order of economy that .ts the 
problem it evolved to solve. Hence, the hundreds of variations on the .ne 
structure of institutions, each designedwithout a de­signer to accommodate 
disparate condi­tions, but all of them subservient to the reality of 
dispersed agent information.

7. We understand little about how rule sys­tems for social interaction and 
markets emerge, but it is possible in the laboratory to do variations on 
the rules, and thus to study that which is not.

8. Marketsrequire enforcement--voluntaryor involuntary--of the rules of 
exchange. These are: the right possession, it’s trans­ference by consent, 
and the performance of promises (Hume). Voluntary enforcement occurs when 
people in the market reward good services with gratuities or "tips," an 
example, perhaps, of an emergent cultural norm in which people recognize 
that tips are part of an informal exchange. If self­or 
community­enforcement conditions are not present, the result is unintended 
conse­quences for the bad, as markets are com­promisedor may fail. The 
game of "trade" must not yield to the game of "steal."

9. Reciprocity, trust, and trustworthiness are important in personal 
exchange where for­mal markets are not worth their cost, yet there are 
gains from exchange to be cap­tured. They are also important in 
contract­ing as not every margin of gain at the expense of other can be 
anticipated and formalized in written contracts.

10. People are not required to be sel.sh;rather the pointofthe Scottish 
philosophers was that people did not have to be good to produce good. 
Markets economize on in­formation, understanding, rationality, num­bers of 
agents, andvirtue.

11. Markets in no way need destroythe foun­dation upon which they probably 
emerged--social exchange between family, friends, andassociates. This is 
supported in the studies reported by Henrich (2000). Thus, individuals can 
be habitual socialex­changers and vigorous traders as well, but as in 
Hayek’s "two worlds" text, the eco­logically rational coexistence of 
personal and impersonal exchange is not a self­aware Cartesian construct. 
Consequently, there is the ever­present danger that the rules of "personal 
exchange" will be ap­plied inappropriately to govern or modify the 
extended order ofmarkets. Equallydan­gerous, the rules of 
impersonalmarketex­change may be applied inappropriately to our cohesive 
social networks.

12. New brain­imaging technologies have mo­tivatedneuroeconomic studies of 
the inter­nal order of the mindand its links with the spectrum of human 
decision from choice among.xedgambles to choice mediatedby market and 
other institutionalrules. We are onlyat the beginningof this enterprise, 
but its promise suggests a fundamental change in how we think, observe, 
and model deci­sion in all its contexts.

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