Gintis Moral Sentiments and Material Interests The Foundations of Cooperation in Economic Life (MIT, 2005)

2.For an overview of the evidence from field studies, see Bromley (1992) and Ostrom (1990). Laboratory experiments designed to replicate common pool resource environments reveal extensive sanctioning behavior that is broadly consistent with the findings from field studies (Ostrom, Walker, and Gardner 1992); see also Fehr and Fischbacher (this volume, chapter 6).

3.A mathematical analysis of this model with proofs of all claims made in the text may be found in the appendix.

4.It is not essential to the argument that the norm prescribe behavior that is optimal in this sense, only that it result in greater payoffs for the group than would be observed under opportunistic extraction.

5.The section to follow draws on our considerably more extensive survey (Sethi and Somanathan 2003). Other evolutionary models of reciprocity that rely on the power of commitment include Gu¨th and Yaari (1992), Gu¨th (1995), Sethi (1996), Huck and Oechssler (1999), and Friedman and Singh (1999). Gintis (2000) and Sethi and Somanathan (2001) analyze models in which both commitment (the power to influence the actions of others) and parochialism (the conditioning of one’s behavior on the composition of one’s group) play a role.

6.This is, of course, analogous to Hamilton’s argument that an altruistic gene will spread in a population if individuals share a sufficiently high proportion of their genes on average with those with whom they interact (Hamilton 1964).

7.This model of partial assortation on the basis of signaling is due to Frank (1987, 1988); see also Guttman (2002). For a model in which prior cooperative acts are themselves used as signals, see Nowak and Sigmund (1998).

8.See Bowles and Gintis (2004) for a model along these lines.

9.Gintis (2000) models this effect in an empirically motivated model of public goods provision.

10.See Frank (1987), Robson (1990), Guttman (2002) and Smith and Bliege Bird (this volume, chapter 4) for further discussion and variations on the theme of signaling.

11.Models in which stable mixtures of reciprocators and pure cooperators can arise include Axelrod (1986) and Sethi and Somanathan (1996); see Gale, Binmore, and Samuelson (1995) for similar findings in a different context. A discussion of conformist transmission and its implications may be found in Boyd and Richerson (1995). The model of structured populations mentioned here is due to Boyd and Richerson (2000); see also Boyd et al. (this volume, chapter 7).

12.Important contributions include Rabin (1993), Levine (1998), Fehr and Schmidt (1999), Bolton and Ockenfels (2000), Falk and Fischbacher (1998), Dufwenberg and Kirchsteiger (1998), and Charness and Rabin (2002); see also Falk and Fischbacher (this volume, chapter 6).

References

Axelrod, R. 1986. ‘‘An Evolutionary Approach to Norms.’’ American Political Science Review 80:1095–1111.

Bester, H., and W. Gu¨th. 1998. ‘‘Is Altruism Evolutionarily Stable?’’ Journal of Economic Behavior and Organization 34:193–209.

Bolton, G. E., and A. Ockenfels. 2000. ‘‘ERC: A Theory of Equity, Reciprocity and Competition.’’ American Economic Review 90:166–193.

Bromley, D., ed. 1992. Making the Commons Work. San Francisco: ICS Press.

Bowles, S. and H. Gintis. 2004. ‘‘The Evolution of Strong Reciprocity,’’ Theoretical Population Biology 65:17–28.

Boyd, R., H. Gintis, S. Bowles, and P. J. Richerson. 2004. Chapter 7, this volume.

Boyd, R., and P. J. Richerson. 1985. Culture and the Evolutionary Process. Chicago: University of Chicago Press.

Boyd, R., and P. J. Richerson. 2000. ‘‘Group Beneficial Norms Can Spread Rapidly in a Structured Population.’’ Mimeo. University of California at Los Angeles.

Charness, G., and M. Rabin. 2002. ‘‘Understanding Social Preferences with Simple Tests.’’

Quarterly Journal of Economics 117:817–869.

Dufwenberg, M., and G. Kirchsteiger. 1998. ‘‘A Theory of Sequential Reciprocity.’’ Center Discussion Paper 9837. Tilburg University.

Falk, A., and U. Fischbacher. 1998. ‘‘A Theory of Reciprocity.’’ Mimeo. University of Zu¨rich.

Falk, A., and U. Fischbacher. 2004. Chapter 5, this volume.

Fehr, E., and U. Fischbacher. 2004. Chapter 6, this volume.

Fehr, E., and K. M. Schmidt. 1999. ‘‘A Theory of Fairness, Competition, and Cooperation.’’ Quarterly Journal of Economics 114:817–868.

Frank, R. H. 1987. ‘‘If Homo economicus Could Choose His Own Utility Function, Would He Want One with a Conscience?’’ American Economic Review 77:593–604.

Frank, R. H. 1988. Passions within Reason: The Strategic Role of the Emotions. New York: W. W. Norton.

Friedman, D., and N. Singh. 1999. ‘‘On the Viability of Vengeance.’’ Mimeo. University of California at Santa Cruz.

Gale, J., K. Binmore, and L. Samuelson. 1995. ‘‘Learning to be Imperfect: The Ultimatum Game.’’ Games and Economic Behavior 8:56–90.

Gintis, H. 2000. ‘‘Strong Reciprocity and Human Sociality.’’ Journal of Theoretical Biology 206:169–179.

Gu¨th, W. 1995. ‘‘An Evolutionary Approach to Explaining Cooperative Behavior by Reciprocal Incentives.’’ International Journal of Game Theory 24:323–344.

Gu¨th, W., and M. Yaari. 1992. ‘‘Explaining Reciprocal Behavior in Simple Strategic Games: An Evolutionary Approach.’’ In U. Witt (ed.) Explaining Forces and Change: Approaches to Evolutionary Economics. Ann Arbor: University of Michigan Press.

Guttman, J. M. 2003. ‘‘Repeated Interaction and the Evolution of Preferences for Reciprocity.’’ Economic Journal 113:631–656.

Hamilton, W. D. 1964. ‘‘The Genetical Evolution of Social Behavior.’’ Journal of Theoretical Biology 7:1–16.

Hardin, G. 1968. ‘‘The Tragedy of the Commons.’’ Science 162:1243–1248.

Huck, S., and J. Oechssler. 1999. ‘‘The Indirect Evolutionary Approach to Explaining Fair Allocations.’’ Games and Economic Behavior 28:13–24.

Levine, D. K. 1998. ‘‘Modeling Altruism and Spitefulness in Experiments.’’ Review of Economic Dynamics 1:593–622.

Nowak, M. A., and K. Sigmund. 1998. ‘‘Evolution of Indirect Reciprocity by Image Scoring.’’ Nature 393:573–577.

Ostrom, E. 1990. Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge: Cambridge University Press.

Ostrom, E., J. Walker, and R. Gardner. 1992. ‘‘Covenants with and without a Sword: SelfGovernance Is Possible.’’ American Political Science Review 86:404–417.

Rabin, M. 1993. ‘‘Incorporating Fairness into Game Theory and Economics’’ American Economic Review 83:1281–1302.

Robson, A. 1990. ‘‘Efficiency in Evolutionary Games: Darwin, Nash, and the Secret Handshake.’’ Journal of Theoretical Biology 144:379–396.

Sethi, R. 1996. ‘‘Evolutionary Stability and Social Norms.’’ Journal of Economic Behavior and Organization 29:113–140.

Sethi, R., and E. Somanathan. 1996. ‘‘The Evolution of Social Norms in Common Property Resource Use.’’ American Economic Review 86:766–788.

Sethi, R., and E. Somanathan. 2001. ‘‘Preference Evolution and Reciprocity.’’ Journal of Economic Theory 97:273–297.

Sethi, R., and E. Somanathan. 2003. ‘‘Understanding Reciprocity.’’ Journal of Economic Behavior and Organization 50:1–27.

Smith, E. A., and R. B. Bird. 2004. Chapter 4, this volume.

9.1Introduction

The extensive empirical research presented in this volume and elsewhere (see reviews by Bowles 1998; Frey and Jegen 2001; E. Ostrom 1998, 2000) challenges the assumption that human behavior is driven in all settings entirely by external material inducements and sanctions. Instead of assuming the existence of a single type of ‘‘profit maximizing’’ or ‘‘utility maximizing’’ individual, a better foundation for explaining human behavior is the assumption that multiple types of individuals exist in most settings. Among the types of individuals likely to be present in any situation are ‘‘rational egoists,’’ who focus entirely on their own expected material payoffs. Neoclassical economics and non-cooperative game theory have usually assumed that rational egoists are the only type of player that scholars need to assume in order to generate useful and validated predictions about behavior. Substantial research in nonmarket experimental settings now provides strong evidence that in addition to rational egoists, many settings also involve ‘‘strong reciprocators,’’ who are motivated by both intrinsic preferences and material payoffs. As discussed in this volume, strong reciprocators will frequently adopt strategies of conditional cooperation and conditional punishment in settings where individuals can observe each other’s behavior.

Laboratory experiments of social dilemmas, trust games, dictator games, and ultimatum games repeatedly find higher-than-predicted cooperative behavior that cannot be explained by theories assuming the existence of only rational egoists. ‘‘It is a well known fact in the experimental literature that in games like the trust game, there is always a 30–40 percentage of individuals who act in a purely egoistic way’’ (Frey and Benz 2001, 9). This leaves 60 to 70 percent of the other

individuals who tend to follow more complex strategies involving some levels of trust and reciprocity. Furthermore, the proportion of different types of individuals is likely to change over time due to the self-selection of individuals into diverse types of situations and due to endogenous changes in preferences and expectations over time as a result of the patterns of interactions and outcomes achieved (see E. Ostrom and Walker 2003).

A considerable body of contemporary policy analysis is, however, based on the earlier widely accepted presumption that all individuals are strictly rational egoists motivated entirely by external payoffs. When rational egoists find themselves in a wide diversity of collectiveaction situations, the predicted result is a deficient equilibrium of zero or very low contributions to joint outcomes. Consequently, centrally designed and externally implemented material incentives—both positive and negative—are seen as universally needed to overcome these Pareto-deficient equilibria. Leviathan is alive and well in our policy textbooks. The state is viewed as a substitute for the shortcomings of individual behavior and the presumed failure of community. Somehow, the agents of the state are assumed to pay little attention to their own material self-interest when making official decisions and to know and seek ‘‘the public interest.’’

For contemporary policy analysis to have a firm empirical foundation, it is necessary to adopt a broader theory of human behavior that posits multiple types of individuals—including rational egoists as well as strong reciprocators—and examines how the contexts of collective action affect the mix of individuals involved.

In section 9.3, I will briefly review the evidence regarding intrinsic motivations. The evidence shows that in some settings (particularly those where individuals lose a sense of control over their own fate), providing external inducements to contribute to collective benefits may actually produce counterintentional consequences. External incentives may ‘‘crowd out’’ behaviors that are based on intrinsic preferences so that lower levels of contributions are achieved with the incentives than would be achieved without them (Frey 1994, 1997). External incentives may also ‘‘crowd in’’ behaviors based on intrinsic preferences and enhance what could have been achieved without these incentives.

In section 9.4, I will then discuss the delicate problem of designing institutions that enhance cooperation rather than crowding it out. Instead of relying on the state as the central, top-down substitute for all

public problem solving, it is necessary to design complex, polycentric orders that involve both public governance mechanisms and private market and community institutions that complement each other (see McGinnis 1999a, 1999b, 2000). Reliance primarily on national governments crowds out public and private problem solving at regional and local levels (and radical decentralization would crowd out public problem solving at regional and national levels). Effective institutional designs create complex, multi-tiered systems with some levels of duplication, overlap, and contestation. The policy analyst’s penchant for neat, orderly hierarchical systems needs to be replaced with a recognition that complex polycentric systems are needed to cope effectively with complex problems of modern life.

9.2 Testing the Predictions of the Standard Model of Rational

Choice

One of the great advantages of contemporary game theory and formal models of collective-action theory is that they generate clear predictions of expected behavior in specific types of situations. Given precise models of collective-action situations and clear predictions of expected behavior, it is possible to set up experimental laboratory designs that enable one to test the empirical veracity of the predictions. With the substantial methodological advances in conducting experimental laboratory research (Smith 1982; Plott 1979), this method has become a useful tool for social scientists in the testing of theories and the replication of findings by multiple scholars in diverse cultures. Experimental research related to the theory of collective action has generated very clear predictions that have repeatedly been challenged in the lab. Let us briefly discuss two related sets of predictions and results.

9.2.1 Predictions and Empirical Results from Linear Public Good Games

When individuals are in a one-shot linear public good situation, each individual can choose between contributing nothing to the provision of a benefit that all will share or contributing some portion of a given endowment of assets. Each individual is predicted to contribute zero assets. When the game is repeated a finite number of times, each individual would contribute zero assets in the last round, and because of backward induction, each individual is predicted to contribute zero assets in each and every round leading up to the final round.

Not only do we have evidence from many field settings that individuals do contribute to the provision of public goods (see, for example, Loveman 1998; Kaboolian and Nelson 1998), there is similar evidence from a large number of carefully controlled laboratory experiments. Between 40 to 60 percent of subjects in a one-shot linear public good situation contribute assets to the provision of a public good (Dawes, McTavish, and Shaklee 1977; Isaac, Walker, and Thomas 1984; Davis and Holt 1993; Ledyard 1995; Offerman 1997). About the same percentage of subjects contribute tokens in the first round of a finitely repeated public good experiment. The rate of contribution, however, decays over time, approaching but never reaching the predicted zero level (Isaac and Walker 1988). Because of the decay toward zero contributions in the experiment’s last ten rounds, an initial reaction by theorists was that it took subjects ten rounds to learn the rational way to play the game. Subsequent experiments extended the pre-announced time horizon to 20, 40, and 60 repetitions. These showed that subjects tended to keep cooperation levels varying in the 30 to 50 percent range for long sequences of time and that the decay toward zero contributions did not occur a few rounds prior to the announced final round (Isaac, Walker, and Williams 1994).

9.2.2 Predictions and Empirical Evidence Related to Secondand Third-Level Social Dilemmas

Not only is there a clear prediction concerning the lack of provision in public good situations, participants are viewed as helpless in getting out of such situations. An effort to arrive at an agreement for determining how much of a public good should be provided and how the costs of provision should be shared would take time and effort to achieve. Once achieved, everyone would benefit whether or not they had contributed to the design of such an agreement. Thus, the prediction is that no one would participate in the effort to extract themselves from the initial dilemma. Furthermore, monitoring compliance to such an agreement and sanctioning those who did not give their agreed-upon share would be costly for those who might think about undertaking such an activity. Again, everyone would benefit from such activities whether or not they had contributed. Thus, no one is expected to invest any of their own resources in monitoring and sanctioning activities. But this is not what is found in many settings.

For example, in experiments where subjects are offered an opportunity to pay a fee in order to assess a fine against someone else, subjects

are willing to expend their own resources to punish non-cooperators (E. Ostrom, Walker, and Gardner 1992; Fehr and Ga¨chter 1998; Yamagishi 1986). Similar to field settings, subjects in a lab are rather indignant and angry at others who do not do their share in protecting a common-pool resource or providing a public good. These subjects give up costly resources to sanction noncooperators. And, when individuals agree upon their own sanctioning system, they do not need to use it extensively—as the compliance rate with a self-imposed harvesting limit and sanctioning system is extremely high (E. Ostrom, Gardner, and Walker 1994).

Cardenas, Stranlund, and Willis (2000) report on a common-pool resource experiment conducted in the Colombian countryside with campesinos who frequently have to deal with resource problems in their everyday life. In one of the experimental conditions, the campesino subjects were given a choice of withdrawal levels from the resource that would be monitored by an external observer. The externally imposed rule was that the subjects should harvest at an optimal level for group returns, or face a realistic but low level of monitoring and a sanction imposed by the outside observer. The subjects in this experimental condition actually increased their withdrawal levels. This is in marked contrast to their own behavior in those experiments where the subjects could talk on a face-to-face basis and no rule was imposed. What was remarkable about this experiment was that subjects, who were simply allowed to communicate with one another on a face-to-face basis, were able to achieve a higher joint return than the subjects who had an optimal but imperfectly enforced rule imposed on them. As the authors conclude:

We have presented evidence that indicates that local environmental policies that are modestly enforced, but nevertheless are predicted by standard theory to be welfare-improving, may be ineffective. In fact, such a policy can do more harm than good, especially in comparison to allowing individuals collectively to confront local environmental dilemmas without intervention. We have also . . . presented evidence that the fundamental reason for the poor performance of external control is that it crowded out group-regarding behavior in favor of greater self-interest. (Cardenas, Stranlund, and Willis 2000, 1731)

It has also been found that individuals in both experimental and field settings are willing to invest substantial time and energy in designing and adapting rules so that they can achieve collective outcomes. In field settings, the time and effort may be substantial (Lam