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Explaining Positive Contributionsin Public Goods Experiments

By Steve Scalet1

Casual observations of our economic life suggest that we sometimes contribute to public goodsenterprises even when we recognize the pull to free ride on the contributions of others. Standardeconomic theory (specifically, Nash equilibrium theory) predicts that agents will not contribute topublic goods projects because of this free riding problem. Experimentalists have activelypursued whether the anecdotal observations of economic life withstand carefully controlledreplication in a laboratory setting. They do. These public goods experiments, based on avoluntary contributions mechanism (VCM), have consistently yielded positive contributions inthe face of dominant strategy Nash equilibrium prediction of zero contributions. As a resultexperimentalists in public goods have focused their efforts on the question, “Why do agentscontribute in the voluntary contribution mechanism?” This paper examines the experimentalistresponse to this question.

1 Comments welcome. I would like to thank David Schmidtz for providing many helpful

comments. I would also like to thank participants of the seminar on nonclassical experimentaleconomics held at the University of Arizona, Spring 1996, including Steve Backerman, Joe Bial,Mark Isaac, Eric Schorvitz, Roelant Verbeek, and Duncan James. All errors remain my own.

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Outline

I. What Needs to be Explained: Establishing a BaselineII. The Explanations Experimentalists are Proposing

A. Alternative Strategies/The Failure of Backward InductionB. Alternative PreferencesC. Saving the Nash Prediction: Learning/Errors/ConfusionD. Incomplete Information and the Failure to Test the TheoryE. Factors Affecting Contribution Rate

III. Public Goods Experiments and Experimental Methodology: Locating ProblemsA. Different Functions of ExperimentsB. Theories of Agent Behavior and Levels of ExplanationC. Environment, Institution, and ExplanationD. The Problem of Controlling Preferences

IV. Kindness or Confusion: The Case of a Public Goods ExperimentA. A Summary of Andreoni’s ExperimentB. A Critique of Andreoni’s Experiment

1. Missed Alternative Hypotheses2. (Un)reasonably Inferring Motives3. Control Problems

V. Three Questions and Their AnswersA. What Have We Learned from this Class of Experiments?B. What are the Most Serious Problems that these Experiments Must Overcome?C. What are Some Undeveloped Areas for Future Public Goods Experimental Research?

1. The Option to Cooperate2. Creating Community3. Framing Affects4. Collapsing the Preference/Strategy Distinction5. Enlarging the Vision of Performance Criteria

VI. Conclusion

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Explaining Positive Contributions in Public Goods Experiments

by Steve Scalet

Dominant strategy Nash equilibrium predicts that agents will not contribute in a finitelyrepeated voluntary contributions public good mechanism. Yet the experimental evidence ofpositive contributions has motivated an active research program whose primary goal has been toexplain why these contributions occur. This paper offers a critical analysis of these experiments.The first two sections examine the structure of the explanations experimentalists have recentlyproposed. Section III identifies the most important explanatory problems facing public goodsexperiments. Section IV illustrates these problems through a critique of a recent experimentaldesign. Section V suggests directions for future research. I. What Needs to be Explained? Establishing a Baseline

There has been some consensus about the stylized facts (See Ledyard (1995) and Andreoni(1995)).2 Through a repeated play voluntary contributions mechanism (VCM), subjects initiallycontribute about halfway between the Pareto-efficient and free riding level. These contributionsdecline with repetition. This public goods design has the following general features. Smallgroups of four or five individuals are each given an endowment which they can distribute in partor whole to a public good. These contributions are added together, multiplied by some factor, andthen redistributed equally to each person. Each person knows his own endowment, how much hecontributes, and the group contributions at the end of each round. Subjects do not know theindividual contributions of any other subject. The experiment is repeated for approximately tenrounds.3 This experiment presents a social dilemma or public goods problem because individualrationality (modeled on the logic of backward induction) dictates no contributions to the publicgood although everyone would be better off (receive higher earnings) if everyone contributed.4

2 Reference what the notion of “stylized facts” refers to and where it is used in relation to

the public goods experiments--probably check Ledyard piece.

3 See Ledyard. This design largely due to Isaac, McCue and Plott (1985) and Isaac andWalker (1988).

4 The literature on social dilemmas and public goods is well-developed. For a recentassessment, see collection of articles in Public Goods and Market Failures: A CriticalExamination Edited by Tyler Cowen Transaction Publishers, 1992.

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This paper focuses on variations of this baseline treatment which preserve the characteristics of asocial dilemma.5

Although these stylized facts -- positive contributions and decline with repetition -- haveproven to be robust benchmarks, experimentalists face the continual problem of identifying what“really” needs to be explained. Early public goods experiments attempted to resolve an empiricaldispute: Is free riding a practical problem? Economic theory predicts massive underprovision orno provision of public goods yet everyday experience suggests that people do contributesignificantly to public goods enterprises. Field experiments are unable to address this issuebecause they have the endemic problem of uncontrolled preferences, making impossible areliable measure of efficiency. Experimentalists entered the scene with a method to documentcontribution levels relative to optimal allocations. The essential characteristic of theseexperiments was the concept of induced valuation as a control on preferences. Theseexperiments were designed as ideal or “friendly” tests of economic theory: if the prediction offree riding was to apply in any environment, it would apply here.

As Isaac, McCue and Plott remark (1985), their experiments were premised on thehypothesis that other experimental work by noneconomists did not provide strict tests of theeconomic theory because of various contaminating features.6 Had the economists’ VCMexperiments produced complete free riding behavior, no further “explanation” of the resultswould have been necessary: standard neoclassical theory, with the appropriate homo economicusassumptions (with an hypothesis of backward induction), would have provided the explanation.The problem has been the persistent experimental finding that people contribute at least somepositive amount in almost any environment. The friendly tests were not friendly enough. Thequestion became: Did this indicate a problem with experimental controls or a problem with thebasic theory?

Without resolving these issues, experimentalists turned to identifying factors which affectcontribution rates.7 It is often unclear whether these factors are intended to be explanatory oradditional data to be explained. Various experiments have shown (in varying degrees) that

5 Thus, this paper excludes any institutional variations, such as provision point

experiments, which change the basic incentive structure of the standard VCM described above.

6 This strategy is something of standard procedure in experimental economics. ConsiderHolt and Davis: “laboratory methods can be used to provide a minimal test of a theory. If thetheory does not work under the controlled ‘best shot’ conditions of the laboratory, the obviousquestion is whether it will work under any circumstances” (Experimental Economics, 16)

7 Compare to Roth’s history of public goods experiments where, in part, he says, “In thecourse of these [public goods] experiments, the debate has thus shifted from whether or not freeriding occurs, to how much and under what conditions it occurs, to what mechanisms andenvironments may be most vulnerable to its effects” (35).

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numbers, marginal per capita return (MPCR), communication, economics training, experience,culture, beliefs, gender, homogeneity in payoffs and endowments, and many others factors, dosometimes matter. Do these factors explain why people contribute, or do they provide anempirical regularity that must then be explained by further theory? Communication and cultureprovide good examples. The level of communication or cultural background of subjects may beused to explain why positive contributions occur: they explain the stylized facts. On the otherhand, communication and cultural background may become part of the baseline facts to beexplained by further theory. Public goods experiments need to clarify this fundamental point.8

This issue will be examined in Section III.In what follows, I begin with the most basic of baselines: the occurrence of positive

contributions. There is good reason to include other results, such as decline with repetition, orimprovement with communication and increasing MPCR, but I begin as simply as possible toprepare the way for these other considerations and to examine the general topic of providing anadequate explanation.9

II. The Explanations Experimentalists are Proposing

How do we explain positive contributions in the repeated-play VCM? This is the basic questionof the paper. This section classifies the current experimentalist responses to this question.

A. Alternative Strategies/The Failure of Backward Induction

8 Consider for example, an experimental design by Kachelmeier and Shehata. They write

that their work is part of “a recent trend beyond simple documentation of the voluntarycontributions phenomenon toward identification of factors that can systematically affect andhence help to explain the level of group cooperation. This study continues that trend, and is thefirst to our knowledge to incorporate the possibility of cultural differences” (my italics, 4).

9 Ledyard, for example, appropriately identifies repetition as a legitimate feature of abaseline. He writes, “That decay in contributions occur with repetition in environments with azero dominant strategy is indisputable. What explains the phenomena remains to be found.Follow up research is needed” (Ledyard, 45 (Note: page reference for the Ledyard review refer tothe California Institute of Technology Working Paper 861)). Compare with Palfrey and Prisbey(working paper), who document “general findings” to include aggregate contribution ratesbetween 20 and 50 percent, some contribution by nearly all players, a strong negative relationshipbetween marginal rate of substitution and contribution rate, and fall in contribution withrepetition.

I am not arguing that a baseline which includes only the fact of positive contributions isappropriate for developing new experimental designs. It is a useful starting point to examine thestructure and methodology of the experimental literature.

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Nash-equilibrium theory predicts that agents will behave as if they were calculating bybackward induction from the end-point. This claim may be challenged by proposing alternativestrategies consistent with positive contributions. First, agents may try to influence future playwith current play. For example, I may invest a token in the public goods based on the belief thatother players will take this contribution as a signal for them to contribute in future rounds.10

Second, agents may hold fairness heuristics. My decision may reflect a rule that I follow, such as,“I will give a significant portion to public goods as long as others do”. We might propose thatpeople follow “norms of fairness”.11 Third, agents may exhibit adaptive behavior.12 An agentmay adapt his strategy as play proceeds independent from concerns about fair play. Proponents ofadaptation might suggest that strategies evolve from habits rather than reason. Fourth, agents mayprocess information according to various capacities as developed in the “bounded rationality”literature.13 All of these “alternative strategies” hypotheses hold in common a rejection of theNash strategy of backward induction to explain positive contributions.

Note two features of this general class of hypotheses. First, alternative strategyhypotheses are easily entangled with claims about preferences. Take the norms of fairness. Onthe one hand, following a norm of fairness is adopting an alternative strategy to backwardinduction. On the other hand, the norm may be modeled simply as one among many preferencesof a utility maximizing agent. Thus, fairness claims may be about preference structure rather thanstrategy. Experimentalists arguing for fairness heuristics must separate out these twoexplanations or explain why the traditional division in economics between preferences andstrategies is not tenable. (Further discussion in Section III).

10 See especially Isaac, Walker, Williams (1994) for tests on signaling which they

conjecture as a plausible explanation as early as Isaac, McCue, Plott (1985).

11 See especially Kahneman, Knetsch and Thaler (1986) Marwell and Ames (1979).Andreoni (working paper, 1996) includes Guth (1988), Bolton (1991), Frank (1985). Sugden(1984) suggests that agents might follow a principle of reciprocity to explain contributions. Hecontrasts his view with Margolis’ (1982) theory of altruism which explains contributions by theextent to which agents are motivated by others’ welfare. Sugden offers a theory of constrainedcooperation. Agents pursue self-interest within the constraint of following a moral rule, wherethis rule -- the principle of reciprocity -- depends not on what the agent wishes others tocontribute (Sugden cross-references Laffont (1975), Collard (1978) and Harsanyi (1980) on this“Kantian principle), but on what others actually do contribute.

12 See especially Miller and Andreoni (1991) and Miller (1996). Andreoni (workingpaper, 1996) includes Roth and Erev (1994), Binmore, Gale and Samuelson (1995) in thiscategory.

13 See, for example, Herbert Simon’s “Theories of Bounded Rationality” in Decision andOrganization where he discusses search models and satisficing rather than maximizing theories.

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Second, public goods experimentalists often make claims about agents’ cognitiveprocesses without the “as if” addendum. They hypothesize that people contribute because theyactually follow rules of fairness (rather than contributing as if they follow rules of fairness).Thus, if the fairness hypotheses is consistent with the data, other sources of evidence, such asinterviews, will be thought to increase the hypotheses’ legitimacy. Yet many economists tend tobe skeptical both about interviews and direct claims about what is going on inside the head ofsubjects. These points remains controversial in the public goods experimental literature.Andreoni’s (1995) “Results” section includes outcomes of subject interviews regarding “verbaldescriptions of their strategies” (898). Conversely, Ledyard states that interviews are “unreliableso it is important to find ways to avoid their use” (62).14

This dispute is related to the great deal of unease with which the literature classifies therationality of these various strategies. Nash strategy is almost always referred to as rationalbehavior. But what about the other alternative strategies? The literature commonly regards anynon-Nash strategy as non-rational.15 There is a problem: it is hard to consider purposefulwelfare-improving strategies as non-rational. If an agent believes signaling is effective, attemptsto signal just because she believe this will benefit her and the group, it actually works, and sheuse this strategy consistently whenever in this type of situation, it’s hard not to consider this to bea rational strategy. Similarly, a decision to follow norms of fairness might be regarded as aclassic example of instrumental rationality. An agent has a goal and behaves to bring about itssatisfaction.

These two subtexts in the experimental literature regarding strategies are noteworthy forthe following reason: if proponents of alternative strategies are serious about the rejection ofNash strategy as a good predictor for contributing in public goods, then a convincing explanationmust include a more carefully developed discussion of how these alternative strategies contrastwith the rationality claims of background induction. Herein lies potential for a fruitful exchangebetween philosopher’s work in rationality theory and experimentalists’ push to explain positivecontributions.

14 Why does Ledyard believe that interviews are unreliable? He tell us in a footnote: “As

a classroom exercise, I have often asked students to describe their strategy after an experiment. Inthe overwhelming majority of cases the data generated in that experiment reject the subjects’ ownhypotheses about their own behavior. I now tend to ignore any expost anecdotal evidence fromsurveys” (62).

There is some irony in Ledyard using an anecdote to henceforth reject the scientificviability of anecdotes.

15 For example, Andreoni reviews the “non-rational approaches to altruism” whichreplace “rational models with ‘rules’ or heuristics’ that people follow” (working paper, 1996, p.1).

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B. Alternative preferencesIf agents are altruistic, then they may be maximizing and producing the positive

contributions that experimentalists observe. If we conceive of the homo economicus assumptionin two parts, maximizers, and own-money-reward maximizers, then this second class ofhypotheses rejects the own-money-reward claim.16 The persistent finding of positivecontributions have driven experimentalists to devise a variety of “altruistic-like” preferencestructures. Perhaps the most common notion of altruism is to posit that agents have a preferencefor other agents’ utility: as other peoples’ utility goes up, so does mine. Or perhaps an “impure”altruist gets utility from personally contributing. Andreoni calls this “warm-glow” giving or“impure altruism” because the agent doesn’t really care (or only care) about others’ utility, butrather, feeling good about giving. This type of preference might explain the cases of peoplegiving to causes where they know that the contribution does not do much good, but it feels goodto give in any case. There are other possibilities.17

Here again, it is important to distinguish between those who posit a “rule of fairness”from the current stock of claims about altruistic preferences. The general distinction is this: if thehypothesis entails a rejection of the maximizing Nash strategy, such as claims about reciprocityand signaling, then the hypothesis is an alternative strategy. Claims under the label of“alternative preferences”, however, are typically distinguished by their attempt to save themaximizing postulate, partly on the view that this claim is the more basic to economic theory. Anexcellent example of this latter approach is Andreoni’s paper “Giving According to GARP”(working paper, 1996). He purports to rationalize the data on positive contributions by assumingthat agents have a preference for others’ utility. He represents agent choice through astraightforward quasiconcave utility function.18

16 It’s not entirely clear which assumption the rejection of which causes more damage to

traditional economic theory. The maximizing assumption is often taken to be more basic and theown-money-reward claim added on for decoration. Standard micro theory, for example, is aboutconstrained optimization leaving open preference content. In practical applications, however, aninteresting turn-about occurs. The maximizing claim is taken only approximately (we satisfice)and the own-money reward claim is held dear. A recent book about privatizing Russia claimedthat the basic economic motive of getting rich was the organizing principle around which thereform effort was orchestrated. Reformers assumed Russians wanted to get rich, not that theywere maximizers. Boycko, Maxim Anrei Shleifer and Robert Vishny Privatizing Russia MIT,1995).

17 See Palfrey and Rosenthal (1988), McKelvey ad Palfrey (1992), Palfrey and Prisbey(1992), Dawes (1980), Andreoni and Miller (1990), Andreoni (1989).

18 Some research aims at both strategy and preference, such as the work of Offerman,Sonnemans, and Schram: “Not only an altruism component is needed in the utility function . . .

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C. Saving the Nash Prediction: Learning/Errors/ConfusionThese hypotheses are distinguished from the first two in that they attempt to vindicate the

basic organizing principle of homo economicus and Nash equilibrium play. Advocates of the“learning hypotheses” claim that positive contributions occur because players are learning how tobecome Nash players.19 Advocates of the “error hypotheses” claim that players deviate from theNash outcome because they are making errors, which are best seen as noise in the data.20

Advocates of the “confusion hypotheses” claim that subjects are confused about the relevantinformation or how to calculate this information.21 These hypotheses are closely related toalternative strategies and are distinguished by their “vindication” status. For example, twohypotheses both labeled “learning” may differ in that one predicts convergence to zerocontributions (and thus saves the free-riding prediction) while the other proposes an algorithmthat does not predict zero contributions. The former supports the Nash prediction slightlymodified to account for dynamic considerations; the latter is an alternative strategy. The samedistinction can be made about the confusion hypotheses. They may support theories of boundedrationality at odds with the logic of backward induction, or simply support a footnote qualifyinghow agents become clear-headed about the rational Nash strategy. In general, these hypothesesexplain deviations from no contributions as non-purposiveful behavior due to factors which “areessentially impossible to measure” (Palfrey and Prisbey, p. 2).

D. Incomplete Information and the Failure to Test the TheoryA final category of explanations are all those views which assert that a certain amount of

slack exists between the experimental design and the elements of Nash theory. Advocates of thisview divide into two camps: (1) those claiming that the experiments are not really testing thefree-riding hypothesis because the conditions in the lab do not conform to the full informationrequirement; (2) those claiming more generally that positive contributions are the result ofexperimental procedures which lack appropriate control. I will comment on each in turn.

also an equilibrium concept is needed which relaxes the assumption of accurate expectations (theusual assumption of the Bayesian Nash Equilibrium)” (18).

19 See Andreoni (1988)

20 See Palfrey and Prisbey (1992 and working paper “Altruism, Reputation, and Noise inLinear Public Goods Experiments”). In the working paper, they write in their conclusion,“subjects exhibit statistical fluctuations in their decision making, that manifests itself as randomnoise in the data. This explanation is consistent with the analysis we conduct, both at theaggregate level and at the individual level” (p. 12).

21 See Andreoni (1995) and Saijo and Nakamura (1992).

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Consider (1). The first claim has lost most of its foothold, and where it has not, the claimreduces to an alternative strategy. To see this, recall that early experiments by economists wereskeptical of the sociology and psychology experimental literature that so conflicted with the free-riding prediction. By carefully expunging invalidating factors and constructing an environment tomore closely match the game-theoretic assumptions, the economics literature produced resultsmore agreeable to the free-riding prediction without matching it. One explanation for theremaining discrepancy is that the game-theoretic prediction assumes that each agent believes thatthe other agents will behave rationally on a model of backward induction from the finiteendpoint. But this assumption may not be replicated in the lab (Who knows what agents believeabout other agents?). Thus, the occurrence of positive contributions would not constitute a test ofthe theory. This attempt to save the Nash equilibrium framework has lost most of its footholdbecause the view reduces to a position that says the theory isn’t testable. After fifteen years of‘best shot’ tests of the theory, it would appear that a defense of Nash equilibrium theory on thegrounds that the theory has not been tested condemns the theory as useless, at least for thoseinterested in testable implications of economic theory.22

Alternatively, game theorists might develop models which suspend the full informationassumption and derive testable implications about contribution levels. Kreps (1982) showed howpositive contributions might be considered “rational”, given certain (albeit unrealistic)assumptions about how other people strategize. Both this model and other game-theoretichypotheses about contribution rates that build from the Kreps model are best seen as alternativestrategies insofar as they challenge the Nash equilibrium prediction of zero contributions andascribe to agent strategies which differ from the logic of backward induction.23

Consider (2). This second claim — that positive contributions are due to various controlproblems — is no explanation at all; rather, it serves as a warning about what can be inferredfrom the results. Addressing these control problems through experiments will lead to twopossibilities: vindication of the Nash-equilibrium prediction (which has not occurred), oralternative explanations based on hypotheses about (A) alternative strategies, (B) alternativepreferences, or (C) errors. This neat explanatory division is no accident. The bases of thesedistinctions and further discussion of control problems is the topic Section III.

E. Factors Affecting Contribution Rate

22 cf. Cox and Isaac (1986): “We hold the view that a theory of economic agents is

usefully tested whenever the assumptions of the theory which could reasonably be directlyobservable in a group of economic agents are captured as closely as possible in the laboratory”(662).

23 For example, Andreoni (1988) considers the Kreps model as a “strategies hypothesis”(p. 293-294).

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One final caveat on classification. The discussion above provides a schema throughwhich experimentalists try to explain why positive contributions occur.24 In addition, the factorslisted below are sometimes offered as explanations, sometimes as the data to be explained, andsometimes as treatment variables to discriminate between theories of agent behavior. Theyinclude: MPCR, homogeneity in pay-offs and endowments, subject knowledge and beliefs, agentgender, levels of communication, subject experience, subject training in economics, attitudesabout group solidarity, levels of trust, subjects’ beliefs about degrees of risk, unanimity,sequencing, revision, moral suasion, real time adjustments, presentation affects, and culturalpredispositions.25

III. Experimental Methods and Public Goods Experiments: Locating Problems

The expansive experimental literature on public goods provides a rich forum to revisitissues of methodology in experimental design and to examine public goods experiments in termsof these issues. Ledyard calls this framework “mechanism design”. Its development in the past25 years is due to Hurwicz (1972), Plott (1979), Kiser and Ostrom (1982), Wilde (1980), Smith(1982), Radner (1987), Groves and Ledyard (1987) and Myerson (1991). Smith’s seminaldiscussion is the focus of this section. In “Microeconomic Systems as an Experimental Science”his aim is to describe the method and function of experiments in economics.

A microeconomic system consists of an environment and an institution. The environmentincludes the numbers of economic agents, the number of commodities and resources and thecharacteristics of the agent. On Smith’s model agent characteristics include individuals’ utilityfunctions and both knowledge and commodity endowments. The institution defines the rights of

24 More precisely, they serve a single explanatory goals, although the initial baseline may

include more than facts about positive contributions, such as evidence of decline with repetition,evidence of decline with experienced subjects, etc.

Consider, for example, three comments by Ledyard in his review of other experiments,each of which indicate a different baseline: (1) “The last observation [in the Dawes’ et al.experiments] concerns the most obvious and least informative result: relevant talking matters alot, but although four different types of communication were tried the data provide littleinformation as to why” (24); (2) “The fact that repetition is an important treatment [in Isaac, Plotand McCue experiments] is good to know but there is no way to know why it is from this paper.Are subjects learning? If so, are they learning how to compute dominate strategies or how tointerpret the payoff tables or whether the others are ‘fair’ or . . . ?” (32); (3) “Homogeneousinterest, little or rough information, face to face discussions in small groups, no experience, smallnumbers and high marginal payoffs from contributing will all cause an increase in contributions.Why and how often this all works remains a mystery” (my italics, footnote omitted, 73)

25 Ledyard (1995) provides the most comprehensive discussion of these factors to date.

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communication and exchange. Smith describes these features as specifying the language ofcommunication, allocation rules, and adjustment process rules to provide for the beginning,continuation and termination of messages.26

The section focuses on three topics: (1) the difference between testing theories of agentbehavior and comparing the performance of institutions, (2) the significance of theories of agentbehavior, environmental factors, and institutional factors for explaining positive contributionsand (3) the importance and manner of control over preferences.

A. Different Functions of ExperimentsThe theorist develops models of behavior to explain and predict outcomes which arise

from a particular institutional and environmental setting. Agent behavior functions generate themessage-sending behavior of an agent. This function lists the allocation-determining message ofan agent characterized within a specified environment, given the property rights of all agentsdefined by the institution. The message is “allocation-determining” because an institution existsto specify the allocations which result, given the message. The observable elements in thissystem are the number of agents, their commodity and resource endowments, the institutionassigning property rights in exchange, the messages chosen, and the final allocations. Theunobservable elements are agents’ preference structure, their knowledge endowments, and theagent message behavior functions.27 In a well-designed experiment hypothesis testing of theoriesof agent behavior are possible because preference and knowledge endowments are controlled andall other relevant aspects of the system are observed. The behavioral model, if a good one,predicts what messages agents will choose in S. That is, a good behavioral theory enables thederivation of the agent behavior functions. In short, hypothesis testing is one function ofmicroeconomic laboratory experiments.

Experiments may also be designed to compare the performance of institutions. Aperformance criterion, such as a suitably devised measure of efficiency, will define what countsas “better” and “worse” outcomes. Thus, institutions may be classified by how well they producegood outcomes. Smith acknowledges the legitimacy of both tasks (hypothesis testing andcomparative performance of institutions), but emphasizes institutional evaluation. In his essayunder the section titled “What Would We Like to Know?”, Smith writes, “. . . we would like toknow enough about the economic environment, and about agent behavior in the presence ofalternative institutions, to be able to classify institutions according to the mapping they provide

26 We can summarize the system, S, as follows: S = {e (ui, Ti, wi), I (Mi, hi, ci, gi)},where, for agent i in environment e, ui is his utility, Ti is his knowledge endowment, wi is hiscommodity endowment; for agent i in institution I, Mi is his message space, hi are his allocationrules, ci are his cost imputation rules and gi are his adjustment process rules; or simply, S = {e,I}.

27 Smith (259).

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from environments into outcomes” (259). In this respect hypothesis testing for theories of agentbehavior is a means to reach the real prize of evaluating institutions. Not surprisingly, Smith’sexperimental designs in public goods were devised to evaluate institutions.28 This essay dealswith experiments that, generally speaking, are quite different in structure and function. This pointdeserves emphasis.

Consider Ledyard’s literature review of public goods experiments. His introductoryremarks include the claim that “At an even more basic level, public goods raise issues about thevery nature of humans” (2). He believes that public goods experiments can help us understand“the fundamental nature of humankind” (13); and he organizes his review of the literature by theextent to which previous experiments address the question, “Are agents in a public goodssituation selfish or altruistic?” (9).

Why ask this question in the first place? It appears that explaining why positivecontributions occur has prompted inquiries about basic human nature. The interestingmethodological point is that the basic explanatory question motivates an experimental design totest hypothesis of agent behavior rather than the evaluate the comparative performance ofinstitutions.29 The experimental question drives the structure of the experimental design. And thisquestion — Why do positive contributions occur? — is thought to require testing about thefundamental nature of human beings.

28 See, for example, his “An Experimental Comparison of Three Public Good Decision

Mechanisms”, “Incentive Compatible Experimental Processes for the Provision of PublicGoods”, and “The Principle of Unanimity and Voluntary Consent in Social Choice” in his Papersin Experimental Economics (1991).

29 This distinction is not uncommon. In addition to Ledyard’s and Smith’s discussion,consider a Palfrey and Rosenthal article (1988) which experimentally tested an altruismhypothesis: “Key questions in the analysis of social dilemmas include: (1) How can incentives tomake voluntary contributions be structured to achieve ‘better’ allocations of public goods? (2) Isthere a model which can be used to accurately predict or explain behavior under a broad varietyof incentive structures without requiring a separate assumption to explain behavior under eachvariation in incentives? These two questions, one normative and the other positive, remainlargely unanswered” (309-310). Although I probably disagree with his claim that (2) is a“positive” question, these two questions correspond exactly with the distinction raised in thissection.

Despite its currency, the distinction is not always clearly made. Ledyard, for example,states an “ultimate” goal of experiments in one section as follows: “We would ultimately like tobe able to say: you give me the details of the environment and a complete description of aninstitution, then my model of behavior will predict what will happen” (4). In a later section,however, he identifies the “issue” as institutional performance: “The issue is not so much honestrevelation of preferences as it is what level of public goods will be provided by subjects and howis that affected by environment and mechanism” (38).

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Put in this light, the experiments sound a bit suspicious.30 Can these experiments tell uswhether people are altruistic or selfish? What exactly are these experiment trying to accomplish?A response to these questions falls into two parts: addressing a methodological worry about therelation between theories of agent behavior and the task of social explanation (section IIIBbelow), and addressing the practical worry about the nature and extent of controlled preferencesin the laboratory (section IIIC-D below).

B. Theories of Agent Behavior and Levels of ExplanationLet’s return to the basic issue of explaining positive contributions. Smith’s

microeconomic system permits three explanatory sources: theories of agent behavior,environmental factors, and institutional factors. Public goods experiments have focused primarilyon the first source, or they have tried to explain away the discrepancy between data and theory.Consider if, contrary to the results, zero contributions had occurred in nearly all public goodsexperiments. Then, the explanation would have been that agents act as if or actually reason byway of the logic of backward induction, which just is a theory of agent behavior. Because publicgood outcomes vary as they do, the first step was to try to “explain” these discrepancies byconsidering various control problems. In effect, experimentalists attempted to eliminate the needfor explaining positive contributions by eliminating the phenomena to be explained. Althoughnot yet completely abandoned, I believe this route has met with limited success (See remarks insection IID above). Ledyard’s review amply illustrates the pervasive fact of positive contributionsin the face of the most stringent tests. Thus, alternative theories of behavior are now prevalent toexplain why these contributions occur.

But what exactly does a “theory of behavior” refer to? Consider this passage from Isaacand Cox (1986):

At one level is the core ‘theory’ (or theorem or model or conjecture) which is ‘proven’ by theconsistency of its (formal or informal) internal logic. At another level is the operationalizedtheory which is purported to have interesting predictive power in an actual economicenvironment. . . . Unfortunately, the economics profession has been slow on appreciating thedifference between proving a theorem and testing an operationalized theory about humanbehavior (662-663).

30 Perhaps Plott conveys this suspicion when he writes, “So, without inquiring into the

ultimate motivations of individuals, one can study the possible consequences of organization(e.g., monopoly, competition) which result from a fixed pattern of preferences” (my italics, p.140 in Collective Decision Making). Perhaps Isaac conveys this suspicion when he writes,“Unfortunately, we are not as sure as others in knowing that data from experiments such as thesecan necessarily separate among these possibilities” (working paper). “These possibilities” refersto theories of agent behavior such as signaling, altruism and warm-glow.

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Experimentalists, such as Ledyard, readily recognize this distinction between core andoperationalized theory. Consider, for example, Ledyard’s proposal to address whether altruism isat work in explaining the relationship between MPCR and contribution rate. He attempts tooperationalize three “theories” by generating equations testable in the laboratory consistent witheach theory.31 I will use his discussion as a platform to relate the distinctions between core andoperationalized theories to the problem of providing adequate explanations.

First, equations themselves are not explanatory. Simply put, knowing that the dataconform to the relations expressed in an equation does not explain anything. The equations mustcombine with a relevant interpretation; or said again, the equations must operationalize a theorywhich serves to interpret the various relations. For example, in the case of Ledyard’s equations,the first expression operationalizes, “a theory based on successful group effort. The idea is thatthose who contribute are happy to do so if at least those who do are better off than at the initialendowment” (52). The second equation operationalizes a theory “based on the idea that subjectstrade off selfish payments against altruism” (52). The third equation operationalizes “a thirdtheory, based on the idea that subjects care about fairness or equality. . . .” (53).

Second, note that the above interpretations have a similar character: they appeal to what isgoing on inside the heads of the agent, whether they are “happy”, “selfish”, or “care aboutfairness”. This feature might be stated generally as an appeal to human folk psychology.Microeconomic explanations tend to rely on claims about the beliefs, preferences and cognitivecapacities of the individual agent. This general feature applies in public goods experiments: toexplain why positive contributions occur relies on inferences about what subjects are thinking.32

31 See Ledyard in Section 3.3.2 “Numbers” pp. 51-53.

32 This claim is not without controversy, especially in the writings of Vernon Smith.Smith discusses this claim at length in Theory, Experiment and Economics (pp. 162-167), as wellas other writings, such as “Experimental Economics: A Reply” in Papers in ExperimentalEconomics, (p. 288), and “Economics in the Laboratory” (p 118). In “Theory, Experiment andEconomic” Smith discusses the implications of the gap between decision theory and actualcognitive processes, even if theoretical predictions match outcomes. His discussion warrants anentire commentary in itself, but I will restrict this footnote to a clarification. The following twoclaims are quite different: (1) “economic agents can achieve efficient outcomes which are notpart of their intention” (p. 118); (2) choice is determined by “rule-governed behavior” such that“the ‘rules’ are apparently programmed into the instincts (genes)” (p. 288). The first claimcaptures the essence of Adam Smith’s invisible hand: aggregate outcomes (of efficiency) neednot be caused by individual intentions for efficiency. Individual choice may be caused by selfishmotives. The second claim is far more radical. Choice is not a function of human intentions.Choice is caused by instinctual rule-governing subconscious activities. Smith references animalbehavior which corresponds with equilibrium predictions but do not presumably involve any

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Thus, if theories of agent behavior are to serve an explanatory role, then they willcombine propositions about human cognitive processes with empirically distinct predictionsabout the messages agents will choose in a particular environment and institutional setting. Agenbehavior theories in public goods experiments are not just equations or conjectures aboutaltruism and selfishness. They combine both elements: (a) theorems, models, or conjecturesabout human intentions and motivations (b) their corresponding testable implications in alaboratory environment, often in equation form. Thus, it appears that experimental theories ofagent behavior combine the notions of core and operationalized theories discussed by Isaac andCox.

When theories of agent behavior serve an explanatory role, however, there is one notableaddition: their explanatory power increases as they become predictive over a widening range ofenvironments and institutions. As with the distinction between core and operationalized theories,experimentalists readily recognize the principle that good explanations are unifying. For example,consider “standard economic theory” in the context of public goods. The operationalized theoryis a prediction of zero contributions. The behavioral interpretation is that individual reasonaccords with a particular conception of rationality based on a process of backward induction froma finite endpoint.33 The unifying theory is taught in every introductory microeconomics course:constrained utility maximization. The unifying theory serves to explain and predict economicbehavior across all interesting environmental and institutional settings. This package (umbrellatheory, behavioral interpretation, specific prediction) constitutes an explanation for outcomes.Unfortunately, it doesn’t seem to work in public goods experiments. Thus, various conjectureshave been operationalized and tested in an experimental setting. These theories, illustrated inLedyard’s discussion above, provide competing explanations for positive contributions in thesense that they include core and operationalized theories. However, they are not unifying. Thiscurrent lack of a unifying theory explains in part the relative lack of impact of the public goodsanomaly on the wider economics profession. Consider the behavioral interpretation of concernsfor fairness, perhaps the most widely referenced theory to explain positive contributions. Thisexplanation will have a rather limited impact in economics without a wider theory within whichthe fairness claims make sense. Current alternative theories to explain public goods contributionsare too local.34

cognitive or calculating capacities to the animal. (p. 288). If (2) is true, interviews are uselessbecause conscious deliberations do not explain what causes behavior. It’s unclear in “Economicsin the Laboratory” (p. 118) that Smith recognizes this distinction between (1) and (2). The claimappended to this footnote is controversial for those who believe (2) is plausible.

33 This formulation is neutral about the “as if” qualification to these claims.

34 cf. discussion by Kahneman, Knetch and Thaler, p. s299. Also note that Smith iscareful to leave open whether such a unifying theory is possible. Perhaps behavioral anomaliescannot be part of unifying explanation. He writes, “. . . there may not exist in all contexts (or in

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This discussion is somewhat abstract by necessity because public goods experiments arebeset with an abstract problem. The alternative hypotheses on the table to explain positivecontributions lack a unifying theory. Even if various conjectures (“agents care about fairness”)prove consistent with the results, the (current) limited applicability of these conjectures seversthe link between theories of agent behavior and the explanatory role these theories might serve.This abstract concern is the single most serious obstacle facing all experimentalists in publicgoods who are trying to break ranks with standard theory. What is on the horizon line? Twentyyears of experimental evidence suggests that the anomalies of positive contributions will not goaway. Whether error theories can successfully explain this discrepancy is doubtful yet certainlynot a closed topic. Perhaps a unifying theory with the most prospects for successfullysupplementing or altering the base behavioral assumptions of homo economicus is thecontinuing refinement of evolutionary theory as a means to categorize and catalogue humanbehavior in a wide variety of social experience, including economic experience. But it is not thepoint of the paper to pursue this suggestion.35

Before turning to environmental and institutional factors, at least one other feature ofcurrent theories of agent behavior is noteworthy; namely, how limited they are as potentialexplanations within the methodological framework described above. Theories of agent behaviorare constrained within an experimental framework of controlled preferences. Thus, alternativetheories of behavior differ only by the form of calculations over a stock of possibly varying butexogenous preferences. For the most part preference formation remains a closed subject in thepublic goods experimental literature. I will discuss this point next.

C. Environment, Institution, and Explanation36

The previous section is premised on the view that testing theories of agent behavior arethe primary means for explaining positive contributions. On this view the manipulation ofenvironmental factors are more naturally seen as uncovering empirical regularities that, in turn,must be explained by a theory of agent behavior. A changing ratio of male and female subjects,endowment structures, or MPCR might reveal interesting variations in contribution rates. A

any) a satisfactory theory or hypothesis allowing derivation of the Bi functions” (258). Also seeNancy Cartwright for general criticisms on the goal of unification in science.

35 For a recent treatment of the explanatory potential of evolutionary theory, see AllanGibbard Wise Choice, Apt Feelings: A Theory of Normative Judgment Harvard University Press,1990. cf. discussion by Smith on the role of theorizing in science in “Microeconomic Systems asan Experimental Science” (pp. 271-272) and his “Economics in the Laboratory” (p. 118).

36 This section continues the aim of identifying general theoretical worries in publicgoods experiments. See Ledyard for the most comprehensive discussion to date of particularenvironmental and institutional treatments.

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viable theory of agent behavior will explain why these changes occur. In effect, theseexperiments aim to refine the question with which this paper deals rather than provide theanswer. Call this the “standard view”: that treating environmental factors establishes regularitiesfor further explanation.

This function, while certainly legitimate, faces a considerable ambiguity. Someenvironmental features, such as agent preferences and knowledge endowments, can have the“feel” of providing bona fide explanations for positive contributions. For example, suppose anagent contributes because she prefers that others are made better off, as long as she does not feelput-out by acting on this preference.37 She reports that this preference is based on her moralbeliefs about fairness, and when pressed, she derives her notions of fairness from religiousconvictions. Doesn’t this “story”, if true, provide an explanation for her contributions? Moregenerally, is it not possible that general models of fairness based on agents’ other-regardingpreferences explain contribution rates? If so, then the explanation rests on claims aboutpreference structure, a feature of the environment. But on the standard view environmentalfeatures serve to establish regularities, not provide explanations. The issue identifies a tensionbetween hypotheses about fairness and altruism and Smith’s microeconomic system. Aspresented in his 1982 article, Smith’s microeconomic system entails a distinction betweentheories of agent behavior and preference structure. Thus, theories of altruism for the laboratorymust also distinguish between (a) how agents calculate over preferences (strategy), and (b) thecontent of the preferences that agents hold (environment). The public goods literature, however,has not devised critical experiments to distinguish strategy from preference structure.

One of two conclusions apply. Either experimental research is in an embryonic stage if itcannot distinguish strategy and environment, or the distinction is not viable. Smith writes, “Thedistinction between that which we label ‘behavior’ and that which is called an ‘agent’scharacteristic’ (environment) will not, nor need it be, a priori. Part of the function of experimentsis to increase our understanding of the issues involved in being or not being able to make thisdistinction” (153). Public goods experiments are especially fruitful testing grounds for eitherengaging this distinction or demonstrating that it is no longer useful. I offer severalconsiderations to support the view that public goods experiments undermine the usefulness ofthis distinction.

First, the question of “usefulness” depends on whether the distinction furthers either ofthe two goals described above: (a) explaining why outcomes occur and (b) devising betterinstitutions. Whether the distinction is useful for (b) is a topic I will not pursue. But it is unclear,to say the least, how this distinction is useful for (a). In fact, it appears to be a handicap. Considerthe discussion about explanation developed above. Explaining why positive contributions occur

37 This view, labeled “exploitation aversion” by Weiman and suggested by Andreoni (and

others), attempts to explain decay in contribution rate by frustrated altruism rather than clarityabout the dominant strategy.

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depends on a story about agents’ cognitive processes -- what people desire, prefer, believe, andhow they reason. These cognitive processes do not neatly map a distinction between strategy andpreferences. It is no surprise that this distinction has led to a gap in the experimentalist literature:“our theories are woefully weak on dynamic process analysis” (Smith, Papers in ExperimentalEconomics, 375). The methodology limits the types of theories of agent behaviorexperimentalists will be inclined to develop.

In effect, Smith’s microeconomic system splits the person in half. She partly resides inthe exogeneous environment; partly as the autonomous choosing agent. The split per se seemsappropriate; gender, for example, appears to be a paradigm environmental feature. But an agents’preferences or knowledge endowment are part of the full panoply of cognitive processes fromwhich we typically offer explanations. Thus, the distinction between environment and behavioron which nearly all experimental methodology is based does not cut neatly against the goal ofexplaining positive contributions. The public goods experimental literature will continue to forcethe question: Of what use is this distinction for explaining positive contributions?38

Finally, a few brief comments on institutional factors. Variations on institutional featuresmight provide “explanations” of the outcomes in a general sense which is captured in theprinciple that institutions matter.39 In this sense an institution-free economics (that is, most ofeconomic theory in the last two hundred years) does not explain why economic activity occurs asit does. Experimental economics joins with the economic historian in the belief thatunderstanding institutional structures is important for explaining economic interactions. Beyondthis general claim, however, the connection (and problems) between theories of behavior,institutions, and explanation, are quite similar to the connections (and problems) betweentheories of behavior, environment, and explanation. Consider the standard view in this context:institutional variations serve to test the robustness of theories of agent behavior. But what aboutculture, customs, and communication? Might not cultural differences or types of communicationexplain why positive contributions occur? Yet these factors are distinct from theories of agentsbehavior. Where do these factors fit in the explanatory nexus? Some experimentalists have raised

38Non-economist experimentalists often do not utilize this distinction. For example,

Offerman, Sonnemans, and Schram review psychological explanations of positive contributionsby differentiating types of human dispositions, or “value orientations”. Aquino, Steisel and Kayemphasize rather complex theories of human motivation which do not seem to rely on adistinction between environment and behavior. Offerman, Sonnemans, and Schram go on todevise tests using the language of these distinctions, but it is clear that this procedure is not thenorm in the psychology literature. The problem, of course, is devising theories on this basiswhich can be generalized to explain a wide range of observed behavior. This has yet to be done.But it’s not clear that these tests are any less “scientific” of “controlled” for the lack of makingthis distinction.

39 See Smith (1994), p. 116.

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concerns about the distinction between environment and institution.40 But the more pressingissue in public goods experiments is the distinction between (or collapse of) theories of agentbehavior, and environmental or institutional factors.

What lessons are to be taken from the preceding two subsections? The aim of explanationis vague. This is no defect. It is not to be remedied by constructing necessary and sufficientconditions for adequate explanations. Different explanations are suitable for different purposes,and the general inquiry, “What explains positive contributions?” can be engaged on severallevels. Experimentalists demand theories whose interpretations entail testable hypotheses. Yetthere is a sense that experimental work in public goods has yet to provide any systematicexplanation for what they find. The problem, I believe, lies not in establishing the “robustness”of the empirical regularities, although continued work will surely reveal new and interestingpatterns. Instead, the problem lies in the failure to link the data with viable unifying explanations.This problem begins at the foundation of experimental methodology and emerges in full bloomin the experimental literature in public goods. This literature is asking the right question: afundamental explanatory question. Its continued pursuit is liable to change the foundations ofexperimental design.

D. The Problem of Controlling Preferences “Control over preferences is the most significant element distinguishing laboratoryexperiments from other methods of economic inquiry” (Smith, 262). The comments aboveaddressed rather abstract theoretical worries surrounding public goods experiments. This sectionaddresses the practical concern of controlling preferences. Without control over preferences,efficiency claims about institutional performance are speculative and evidence for theories ofagent behavior (e.g., a maximizing hypothesis) will be confounded by untested auxiliaryassumptions about preferences.41

In tandem with the distinction between strategies and environment, nearly allexperimental methodology and certainly those experiments in the public goods literature, areorganized about a fundamental premise: an experiment is “valid” only if researchers are able toreasonably infer from the structure of the experiment that preferences have been controlled.

40 cf. Plott: “How seriously should one take the ‘fundamental equation’? [preferences +

institutions + physical possibilities = outcomes] It provides a useful, maintained hypothesis but itwill probably experience a great deal of evolution. . . . a lot of work must be done on determiningexactly what goes under the title of an ‘institution’ Are customs and ethics to be regarded asinstitutions? What about organizations such as coalitions? These are embarrassing questionswhich suggest that the ‘fundamental equation’ is perhaps not as fundamental as we would like”(160 Plott “The Application of Laboratory Experimental Methods to Public Choice”)

41 Efficiency is defined in terms of preferences. Thus, without knowledge of persons’preferences, there can be no knowledge of a system’s efficiency.

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Experimentalists have also adopted, by near universal assent, Smith’s theory of inducedvaluation as the means to attain control.42 Specifically, induced valuation links preferences withmonetary rewards in accord with several precepts, the satisfaction of which constitute sufficientconditions for a valid controlled microeconomic experiment. These precepts are nonsatiation,saliency, dominance, and privacy.

These precepts entail that it must be reasonable to infer that agents’ preferences are amonotone increasing function of the reward medium (nonsatiation) and that the amount of themedium rewarded is related to the agents’ choices (saliency). Paying individuals money based ontheir experimental decisions is thought to satisfy these precepts with the addition of two furtherconstraints. First, money payments should dominate any subjective costs associated withparticipating in the activity (dominance) and each subject ought to be informed only about his orher own payoff alternatives (privacy). This last precept -- privacy -- deserves some emphasis. Thepoint of induced valuation is for the experimenter to reasonably infer that the primarymotivations of the agents are captured by the programmed and observable induced values. If so,then the experimenter can know something about what is otherwise unobservable, the agents’preferences. Privacy serves to ensure that individual choice is not influenced by “invidious,egalitarian, or altruistic cannons of taste” (266).43 By restricting what each agent knows aboutthe payoffs of other agents, the influence of these tastes (i.e., preferences) on choice will benullified. An agent presumably cannot exercise (non-induced) tastes for others’ welfare if the

42 Consider Kim and Walker: “This method of ‘induced valuation’, pioneered by Smith

(1975) is probably the only way to ensure that the experimenter knows the optimal level of x”(18).

43 It may be useful to include the full passage from which this excerpt was taken:

Nonsatiation and saliency are sufficient conditions for the existence of an experimentalmicroeconomy, that is, motivated individuals acting within the framework of an institution, butthey are not sufficient for a controlled microeconomic experiment. For this we also must havedominance and privacy, since individuals may experience important subjective costs (or values)in transacting, and my bring invidious, egalitarian, or altruistic cannons of taste to the laboratoryfrom every day social economy. Precepts 1-4 permit us to study laboratory microeconomicenvironments in which real economic agents exchange real messages through real property rightinstitutions that yield outcomes redeemable in real money

Insofar as we are only interested in testing hypotheses derived from theories, we are done,that is, Precepts 1-4 are sufficient to provide rigorous controlled tests of our ability as economiststo model elementary behavior” (266-267).

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agent does not know how the other agents are doing. Thus, only the controlled economic agentwalks through: the autonomous own-reward nonsatiating agent.44

Control over preferences is a challenge in public goods experiments precisely becausethese four precepts do not clearly ensure the control that seems more easily attained in otherinstitutional settings, such as auction mechanisms. If an agent has altruistic cannons of taste, thestructure of a VCM clearly enables the agent to exercise this taste, by contributing. Withoutknowing the payoffs to each individual, the nature of voluntary public goods institutions allows asingle individual to contribute with full knowledge that it will benefit others generally. In thiscase “privacy” offers little reason to believe that autonomous own-reward nonsatiating agentsinhabit the laboratory. This is a serious practical problem. Independent from the conceptualworries above, the structure of the VCM institution makes it unlikely that the four precepts(privacy especially) sufficiently control preferences.

IV. Kindness or Confusion: Andreoni’s Public Goods’ Experiment

A. A Summary of Andreoni’s ExperimentThis section illustrates and develops the general problems identified in Section III through

an analysis of a public goods experiment designed by a leader in the field: James Andreoni andhis article “Cooperation in Public-Goods Experiments: Kindness or Confusion?”

Andreoni expresses the hope that laboratory experiments serve to increase knowledgeabout “individual motives for giving” (891). He wishes to distinguish the motivations ofkindness and confusion and determine the extent to which they explain contributions to publicgoods. Andreoni describes “kindness” as any hypothesis which can be interpreted as subjectshaving tastes for cooperation. He describes “confusion” as an hypothesis “that the experimentershave somehow failed to convey adequately the incentives to the subjects” or “that subjects havesomehow not grasped the true incentives” (893).

The experiment includes three conditions. The Regular condition is the standard public-goods experiment. The Rank condition is played like the standard experiment, except that themonetary payoffs depend not on the experimental earnings but on how the subjects rank incomparison to the other subjects. The subject with the highest experimental earnings in a roundreceives $.95, the second-highest earns $.87, the third-highest $.80, and so forth, according to afixed schedule known by all participants in the Rank condition. In the case of ties the subjectsare awarded the average of the payoffs associated with their rank. For example, a two-way tie forhighest earnings means that both players will receive (.95+.87)/2 and the next highest earningswill receive the third rank payoff of $.80. As Andreoni states, this alternative pay-off scheme

44 Smith emphasizes in a footnote that the nonsatiation precept is not designed to ensure

that agents are only self-interested but that experimenters know what agents’ preferences are. Theexperimenter, in fact, may induce interdependent preferences (264).

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makes a zero-sum game out of the standard positive-sum public goods game. It preserves thedominant strategy equilibrium of complete free-riding and, according to Andreoni, offers noincentive to contribute. He writes, “Not only are there no monetary gains from cooperation, thepotential for kindness or altruism would also appear to be largely eliminated. The incentives forany reciprocal altruism have surely been removed” (894). Thus, any contribution that occurs inthe Rank condition indicates confusion about the incentives, not a taste for kindness. The resultsof the Rank condition -- where contributions are explained as confusion -- can then be comparedto the results of the Regular condition -- where contributions are explained as either kindness orconfusion. Given a symmetry between experiments (except for the different payoff structures),then variations in contribution levels between experiments sorts out the extent of contributionsdue to kindness and the extent due to confusion.

Andreoni notes, however, a potentially important informational asymmetry between thetwo conditions: Rank subjects know how they rank, Reg subjects do not. Were Reg and Rank theonly treatments, then explaining different contribution levels may be confounded by theinformation asymmetry. Andreoni controls this factor by running a RegRank condition, which isthe Reg condition with the information content of the Rank condition (i.e., subjects are informedof their rank but paid according to experimental earning of the Reg condition). A comparisonbetween Reg and RegRank tests the affect of an information differential on contribution levels,and comparing RegRank and Rank tests the original research question of separating confusionfrom kindness. The difference observed in the latter comparison indicates the levels ofcontributions due to kindness. Said again, any significant difference between these twoconditions indicates that not all of the contributions in the Regular condition can plausibly beexplained as confusion. The experiments are attractive partly for their simplicity and the carewith which Andreoni attempts to control the relevant factors.

Andreoni discovered that subjects in the Regular condition are more cooperative thansubjects in the Rank condition. Subjects in RegRank are somewhere in between. Thecontribution levels of Regular condition subjects were nearly identical to the empirical regularityestablished in the baseline: Cooperation in the first round is around fifty percent and steadilydeclines to around 25 percent in the last round. In the Rank condition cooperation was about 33percent in the first round, steadily decaying to five percent in the last round. In the RegRankcondition, cooperative levels began at about 46 percent and ended in round 10 at nine percent.

In sum, Andreoni explains positive contribution levels from the data by attributingroughly half of the contributions to kindness and half to confusion. Rather than review the sum ofAndreoni’s analysis, I include this short passage from the “Results” section,

The measures of kindness and confusion in Table 2 [“Percentage of Subject ContributingZero to the Public Good per Round] suggest an interesting pattern. Over rounds 1-6 thetotal amount of cooperation is rather stable. However, over the same period the amountof confusion is declining rapidly, and the amount of kindness is increasing. After round

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6, confusion is rather stable, but kindness falls. This points to a possible explanation forthe ‘decay’ phenomenon often observed in public-goods experiments. When individualswho start off confused finally learn the dominant strategy, it appears that they may firsttry to cooperate but then eventually turn to free-riding. This could suggest that, for somesubjects, kindness may depend on reciprocity” (footnote omitted, 898).

B. A Critique of Andreoni’s Experiment1. Missed Alternative Hypotheses. Andreoni describes two main hypotheses to explain

positive contributions: “kindness” or “confusion”. This classification faces a dilemma: Eitherthey are not mutually exhaustive hypotheses, or they are and then an interpretation of “kindness”to describe the results is injudicious at best. On the first horn, Andreoni states that kindnessserves to capture claims that people have “tastes for cooperation”. This formulation identifies aclaim about preference structure without identifying a claim about alternative strategies, such assignaling or any non-Nash strategy. Thus, if kindness refers to preference structure, thenAndreoni is ignoring many alternative hypotheses about strategizing to explain the data. On thesecond horn, Andreoni’s two hypotheses may be interpreted as mutually exhaustive: confusionand not-confusion. On this reading any difference between RegRank and Rank indicates thatpositive contributions cannot be explained as confusion, leaving open whether kindness, warm-glow, or some other self-regarding strategy is at play.

If Andreoni’s experiments are interpreted on the second horn, his mistake is a misleadinguse of the label “kindness” to describe hypotheses that may having nothing to do with kindmotives. The problem is not without incident: he closes his article by suggesting that“experimental research should shift to include studies of preferences for cooperation” (900)without noting that the results may be interpreted as following from a non-Nash strategy. Thus,either the hypothesis of “kindness” is a misleading label (second horn) or Andreoni has missedalternative hypotheses (first horn).

I’d like to shift focus from the “kindness” hypothesis to the “confusion” hypothesis. Thishypothesis is ambiguous between two main claims: that “confusion” identifies (a) a controlproblem for the experimenter or (b) a thinking problem for the agents. In the latter case, does thisstate of confusion indicate problems with the agents’ knowledge endowment (as part of theenvironment) or the agents’ cognitive abilities (as part of a strategic hypothesis)? He writes, “Insummary, this paper goes beyond showing that subject tend to cooperate too much in free-ridingexperiments; it identifies the part of this cooperation that needs explanation with behavioralmodels, and the part that may be due to methodological issues in experiments” (900).Presumably, Andreoni is saying that contributions in the Rank condition signify a controlproblem for the experimenter. But they may reveal a need for a theory of bounded rationalitywhich might involve both the agents’ knowledge endowment and cognitive processing.Andreoni’s discussion does not acknowledge these alternative explanations.

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These comments are not aimed to discredit Andreoni’s design, but to make explicit acertain amount of slack between two vaguely formulated hypotheses, the experimental test, andthe interpretation of the results. The results do not yield a verdict about why agents contribute inRank: perhaps it’s a control problem, perhaps it’s a thinking problem, perhaps its a problemabout what people know. The results do not yield a verdict about why there are differences incontributions between RegRank and Rank: perhaps it’s kindness, perhaps it’s an alternativestrategy. We do not know.

2. (Un)Reasonably Inferring Motives. What can we know? I’d like to distinguish twoissues: controlling for preferences and discovering ultimate motivations. Andreoni appears to beinterested in the latter when he describes himself as attempting to isolate subject “motives”. Thisview of the experimental project is in keeping with Ledyard’s claim that public goodsexperiments are inquiries about “the fundamental nature of humankind”. Yet we are reminded ofPlott’s admonishment: “So, without inquiring into the ultimate motivations of individuals, onecan study the possible consequences of organization (e.g., monopoly, competition) which resultfrom a fixed pattern of preferences” (my italics, 140).

How are we to differentiate ultimate motivations from control on preferences? Considerthe difference between Andreoni’s project and Plott’s idea. Andreoni wishes to attribute a motiveto an agent, justified in virtue of yielding a reasonable explanation of the choices.45 And Plottwishes to postulate a stable (indeed, fixed) relationship between preferences (as they areexpressed in willingness to pay schedules) and choices. For Plott, if an agent is willing to pay$10 for a shirt, it doesn’t matter why the agent is willing to pay this money.46 What matters (forPlott) is how the institution coordinates these preferences. Ultimate motives do not matter. Thus,the difference between ultimate motivations and controlling for preferences seems to be this.Controlled preferences enable the experimenter to know what the subject wants or prefers. (OnSmith’s preceptual basis, the preferences correspond to the induced money values imposed by theexperimenter.) Ultimate motivations explain why the agent has the preferences she has.

If this distinction is viable, then we can try to understand Andreoni’s as a control onpreferences and not a discovery of ultimate motivations. Andreoni tries to infer what the agentprefers rather than explain why the agents has the preference she has. Knowledge aboutpreference structure, assuming the causal link between preference and choice, then allow Plottand Andreoni (and others) to get on with the business of evaluating institutions. Perhaps an agentfree rides because he wants to give the money to his dying grandmother. That is not a preference

45 Andreoni writes, “. . . the experiments will subtract off the social, cultural, and strategic

incentives for subjects to cooperate, leaving confusion as the most reasonable explanation forcooperation” (893).

46 In Plott’s words, it doesn’t matter in economics whether “(a) I think it is pretty; (b) mywife thinks it is pretty; or (c) I want to eat it” (140).

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for altruism. That is an ultimate motivation. Perhaps an agent contributes in the Rank conditionto help the other players earn more money, but then lets grandmother whither away for recentlylost funds. That is a preference for altruism with dubious ultimate motivations. Perhaps we nearlyall contribute some money because feelings of cooperation are ingrained as part of survivaladaptive features of most human beings. That is probably a preference for altruism. But it may bea strategy for reciprocity. And the evolutionary explanation is an addendum: a type of ultimatemotivation, but one that, if explanatory, certainly matters for economics.

Current experimental designs aimed at discovering motivations might clarify this vexingissue of “ultimacy”. As a start, experimentalists can bring some clarifty be defining selfishness oraltruism relative to a context, such that a finding of altruism might reveal less about thefundamental nature of humankind, and more about motivational dispositions defined in relationto the other group members. Otherwise, stories about dying grandmothers become an intractableheadache.

3. Control Problems. Public goods experiments are demonstrating how claims aboutcontrolled preference rest on thin ice. First, consider Andreoni’s “strangers” condition. In accordwith previous research Andreoni assigns the subjects randomly to new groups after eachiteration. He writes, “This is important in order to avoid the possibility of reputation-building”(895). This condition attempts to control for one possible strategy of repeated-play games:building a reputation to affect contribution rates. Andreoni might view this as a “fifth precept”for a valid controlled microeconomic public goods experiment. Unfortunately, this control facesproblems. As long as I know that the “stranger” from the random re-assignment has been inprevious rounds, I still might attempt signaling, given the common knowledge that the othershave played before. It’s not at all clear that the random assignment really controls for reputationeffects. The treatment, it seems, does not enable a reasonable inference that reputation effects areadequately controlled. Perhaps a treatment where random assignments are coupled with subjectsplaying for the first time would attain better control. The point is negative: it’s unclear thatreputation effects are controlled.

Second, and this consideration is more troubling, Smith’s nonsatiation assumptionsubstantively affects the behavioral interpretation of Andreoni’s results. Perhaps the “confused”contributions in the Rank condition are due to altruistic motives. If this explanation were correct,however, then agents would prefer less of the reward medium than more. Control would be lost.This comment is revealing for both the experiment and Vernon Smith’s framework. Smith’spreceptual hypotheses include behavioral assumptions that may substantively color the set ofpossible interpretations. If we assume nonsatiation, then it is impossible to attribute altruism inthe Rank Condition. In public goods experiments this innocuous assumption is not so innocuous.Perhaps agents prefer others to do well even at their own expense. When Andreoni interviewedthe subjects, he found that one “subject indicated that he chose the dominant strategy for the firsthalf of the [Rank] experiment, but then switched to giving all his endowment to the public goodto ‘give others a chance’” (898).

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It’s still an open question whether the theory of induced valuation and Smith’s preceptualbasis enable control over preferences in public goods experiments. This section has outlined twospecific cases in which there is doubt that control has been attained. As I have suggested in thesection “The Problem of Controlling Preferences”, the problem may be endemic to the structureof public goods experiments.

V. Three Questions and Their Answers

A. What have we learned from this class of experiments?

Positive contributions occur in almost any public goods environment. These contributionsdecline with repetition. Communication increases contributions. Increasing marginal per capitareturn increases contributions. Other factors seem to matter but are not well documented. Theseare the summary findings from Ledyard’s review of the literature. He conjectures that Nash-equilibrium cannot explain the data. A blanket hypothesis of altruism cannot explain the data.Instead, he suggests that players are of three different “types”, dedicated and occasionallyconfused Nash players, generally selfish players susceptible to altruism, framing, decision costs,and other strategies, and finally, the X factor, players whose behavior is inexplicable. What havewe learned? The most simple tests create remarkably robust regularities: positive contributionsand decline with repetition. No current theory can adequately explain this behavior. That, Ibelieve, is the current state of affairs.

B. What are the most serious problems that these experiments must overcome?

The most serious problems are the most fundamental problems, providing a unifying explanationfor positive contributions and the intrinsic problem of controlling preferences with a publicgoods mechanism. The balance of this essay has examined several issues related to these twoproblems.

C. What are some undeveloped areas for future public goods experimental research?

In this final section I offer series of conjectures for new experimental designs and theoreticalresearch on the voluntary contributions mechanism. They are a natural extension of earlierdiscussions in this paper.

1. The Option to Cooperate. The institutional setting of a repeated play VCM allowsagent choice to be influenced by a variety of considerations not influential in other institutions,such as the double auction with privacy. Subject access to information is paramount. Smithwrites, “The condition of complete information gives the freest play to expectations based onsocial norms and beliefs” (166). Camerer, Loewenstein and Weber (1989) coined the “curse of

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knowledge” as the result that less information can be “better” for the subject than moreinformation, in the sense that subjects more quickly converge to a Nash equilibrium with morelimited information about the characteristics of the other agents. The parallel to the public goodssetting has not been recognized sufficiently. The very design of a VCM provides informationabout how others (as a group) will do depending on one’s own contribution. As discussed above,this simple fact is the root problem for achieving control over preferences. This featureeffectively negates the privacy condition, increases informational content and allows freer playfor social norms. In some cases, information is thought to induce a curse of knowledge. In publicgoods cases, however, this knowledge is not a curse. It makes everybody better off. Increasedinformation enables an “option to cooperate.”

The repeated play VCM enables an option to cooperate because it increases theinformation available to subjects. But why might subjects exercise this option? Smith’sobservation is on target: increased information enables choice to be affected by social norms.Much of the literature has focused on the notions of altruism, fairness and reciprocity as ways togive content to these norms. Whether instilled as a survival adaptive trait, inculcated throughupbringing or chosen through deliberative reasoning, economists have been reticent to includethese social characteristic into their models. Hypotheses of altruism, reciprocity and others are allexamples to capture a basic agent characteristic of wanting to engage in and promote socialcooperation. This admission need not deny the primacy or relevance of self-interest. Rather, thepublic goods results indicate that more complex trade-offs occur among various motives andstrategies. With the special context of complete information markets and public goodsmechanisms, agents are able to choose an option to cooperate. Other market mechanisms withlimited information (e.g., auction mechanisms) do not enable agents the opportunity to cooperate.If contributions are rooted in basic motives to foster social cooperation, then decline withrepetition is due to “exploitation aversion”, “frustrated altruism”, “exploitation attraction” andother motivations that are current in the experimental literature. Just as we wish to increase ourmoney holdings, we are wish to cooperate with others. Public goods mechanisms enable theexercise of both tendencies.

2. Framing Affects and other atypical factors. If choice is affected by social norms, thenperhaps these norms are responsive to a variety of factors not usually examined in economics oridentified by Smith’s microeconomic system. The role of framing may be chief among theseaffects. Kahneman, Knetch and Thaler write, “Judgments of fairness are influenced by framingand other factors considered irrelevant in most economic treatments” (s299). In recentexperimental work on framing affects Andreoni writes, “cooperation in public goods games issignificant, and the positive frame of the game appears to be an important factor in this result.This level of cooperation is not a mere experimental fluke, but appears to be a fundamental partof human interactions in these games” (10).

The option to cooperate is triggered as people know more about others. The repeated playVCM enables this option through unavoidable information leaks, which, in turn, allow social

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norms to slip into an experimental setting. That is, social norms explain choice. The question foreconomists is not to deny that social norms are relevant but to explain how this works. Framingaffects appear to be a central issue. Perhaps other factors not usually considered importanteconomic variables are at play. The causal influences to establish the connection between socialnorm and choice may be complicated. These complications signal an increasing role forexperimental work to explain important aspects of economic behavior.

3. Collapsing the Preference/Strategy Distinction. The disposition to cooperative refers tobeliefs and motivations that probably do not correspond to the traditional distinction betweenpreference and strategy captured in Smith’s microeconomic system. As Smith remarks, empiricalresearch may revise this distinction. The increasing evidence that framing matters providesincreasing pressures to examine the dynamics of preference formation and to re-examine thenotion that preferences and strategy are separable in the fashion of current experimental designs.

4. Enlarging the Vision of Performance Criteria. Experiments might serve at least twodifferent goals: to explain behavior or to devise better institutions (See Section III). This paperhas focused on experiments which attempt to explain behavior. These experiments, however, aresuggest at least one insight for institutional evaluation. Rabin captures the point nicely: “Welfareeconomics should be concerned not only with efficient allocation of material goods, but also withdesigning institutions such that people are happy about the way they interact with others” (Rabin,1283). Experimentalists ought to take seriously welfare affects of market interactions.47 Smith’stheory of induced valuation may be unable to provide sufficient control on preferences in thepublic goods experiments to capture how the welfare of agents are affected. We need to find away to test whether people are better off when they engage in cooperative ventures that work.

VI. Conclusion

No one denies that agents contribute in the standard repeated play VCM. The debate iswhether these results are significance. Perhaps the goal of a unifying explanation for economicbehavior is not possible. Experimentalists pursuing framing affects are inclined toward this view.Perhaps these contributions are anamalous. Experimentalists supporting an error theory areinclined toward this view. Yet this commentary suggests that these results preface new avenuesof experimental research in public goods. Herbert Simon writes,

47 After debriefing subjects in a public goods experiment run from a course taught by this

instructor, I found that several subjects expressed disturbance at the inability to attain the Pareto-optimum. They viewed the results as a failure at cooperation and a revelation about theselfishness of the “other” players, despite the fact that their own contribution levels declined at asimilar rate as the “others”. They were clearly not happy about the social interactions.

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What motivates human choice is an empirical question, and neoclassical conclusionsderived from the dubious assumption that economic motives dominate must bereexamined. In several areas of economics, current doctrines will have to undergosevere revision as motives other than economic gain. . . assume their appropriateplace in theory. . . . Such an economics will have little to say a priori but will reasonfrom numerous painfully gathered facts (159-160).

The repeated play VCM is surely an appropriate forum to gather the evidence. We do not yetknow the long-term influence of public goods experiments on the wider discipline, but basiceconomic theory must continue to take account of the growing body of unexplained evidence thatagents contribute in the voluntary contributions mechanism.

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