We propose a theory of asset pricing based on heterogeneous agents who continually adapt their expectations to the market that these expectations aggregatively create. And we explore the implications of this theory computationally using our Santa Fe artificial stock market. Asset markets, we argue, have a recursive nature in that agents ’ expectations are formed on the basis of their anticipations of other agents ’ expectations, which precludes expectations being formed by deductive means. Instead traders continually hypothesize—continually explore—expectational models, buy or sell on the basis of those that perform best, and confirm or discard these according to their performance. Thus individual beliefs or expectations become endogenous to the market, and constantly compete within an ecology of others ’ beliefs or expectations. The ecology of beliefs co-evolves over time. Computer experiments with this endogenous-expectations market explain one of the more striking puzzles in finance: that market traders often believe in such concepts as technical trading, “market psychology, ” and bandwagon effects, while academic theorists believe in market efficiency and a lack of speculative opportunities. Both views, we show, are correct, but within different regimes. Within a regime where investors explore alternative expectational models at a low rate, the market settles into the rational-

This article argues that the agent-based computational model permits a distinctive approach to social science for which the term “generative ” is suitable. In defending this terminology, features distinguishing the approach from both “inductive ” and “deductive ” science are given. Then, the following specific contributions to social science are discussed: The agent-based computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agent-based modeling provides a powerful way to address certain enduring—and especially interdisciplinary—questions. It allows one to subject certain core theories—such as neoclassical microeconomics—to important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give rise—or “map up”—to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agent-based (“bottom up”) models. The agent-based approach may well have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agent-based modeling offers powerful new forms of hybrid theoretical-computational work; these are particularly relevant to the study of non-equilibrium systems. The agentbased approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation. This interpretation raises important foundational issues in social science—some related to intractability, and some to undecidability proper. Finally, since “emergence” figures prominently in this literature, I take up the connection between agent-based modeling and classical emergentism, criticizing the latter and arguing that the two are incompatible. � 1999 John Wiley &

A model of population rule learning is formulated and estimated using experimental data. When predicting the population distribution of choices and accounting for the number of parameters, the population rule learning model is much better than aggregation of individually estimated rule learning models. Further, rule learning is a statistically significant and important phenomena even when focusing on population statistics, and is much better than one-rule learning dynamics. 2001 Elsevier Science B.V. All rights reserved. JEL classification: C15; C52; C72 Keywords: Rules; Learning; Games; Experimental; Testing 1. Introduction Recent learning research in one-shot games can be divided into two domains: (i) population learning or evolutionary dynamics as typified by replicator dynamics, 1 and (ii) individual learning. 23 The first domain focuses on how the population distribution of play changes over time, while the second domain focuses on how an individual's behavior changes over...

This paper, in a nutshell, disputes the adequacy of the standard model for describing games with perfect information and proposes a model which is adequate for this purpose. We show that standard models fail to capture an important structural aspect of strategic thinking and therefore leave unformalized many intuitive arguments that depend on it. The model we propose captures this aspect by giving a fuller and more faithful account of strategic thinking. Using this model we re-examine the relation between rationality and backward induction and give formal expression to statements about the reasoning of players in games with perfect information }statements that cannot be formalized in the standard model

Players in a game are “in equilibrium ” if they are rational, and accurately predict other players ’ strategies. In many experiments, however, players are not in equilibrium. An alternative is “cognitive hierarchy ” (CH) theory, where each player assumes that his strategy is the most sophisticated. The CH model has inductively defined strategic categories: step 0 players randomize; and step k thinkers best-respond, assuming that other players are distributed over step 0 through step k � 1. This model fits empirical data, and explains why equilibrium theory predicts behavior well in some games and poorly in others. An average of 1.5 steps fits data from many games. I.

In this paper, we present an abstract formal model of decision-making in a social setting that covers all aspects of the process, from recognition of a potential for cooperation through to joint decision. In a multi-agent environment, where self-motivated autonomous agents try to pursue their own goals, a joint decision cannot be taken for granted. In order to decide effectively, agents need the ability to (a) represent and maintain a model of their own mental attitudes, (b) reason about other agents' mental attitudes, and (c) influence other agents' mental states. Social mental shaping is advocated as a general mechanism for attempting to have an impact on agents' mental states in order to increase their cooperativeness towards a joint decision. Our approach is to specify a novel, high-level architecture for collaborative decision-making in which the mentalistic notions of belief, desire, goal, intention, preference and commitment play a central role in guiding the individual agent's and the group's decision-making behaviour. We identify preconditions that must be fulfilled before collaborative decision-making can commence and prescribe how cooperating agents should behave, in terms of their own decision-making apparatus and their interactions with others, when the decision-making process is progressing satisfactorily. The model is formalized through a new, many-sorted, multi-modal logic.

In Bagwell (1995) it is claimed that, in models of commitment, "the firstmover advantage is eliminated when there is a slight amount of noise associated with the observation of the first-mover's selection." We show that the validity of this claim depends crucially on the restriction to pure strategy equilibria. The game analyzed by Bagwell always has a mixed equilibrium that is close to the Stackelberg equilibrium when the noise is small. Furthermore, an equilibrium selection theory, that combines elements from the theory of Harsanyi and Selten (1988) with elements from the theory of Harsanyi (1995), actually selects this "noisy Stackelberg equilibrium." Journal of Economic Literature Classification Number: C72. Copyright c fl1997 by Academic Press. This material has been published in Games and Economic Behavior, 21, 282308, the only definitive repository of the content that has been certified and accepted after peer review. Copyright and all rights therein are retained by Academic ...

ABSTRACT 1 Results on the formation of multi-agent teams are reviewed and extended. Conditions are specified under which it is individually rational for agents to spontaneously form coalitions in order to engage in collective action. In a cooperative setting the formation of such groups is to be expected. Here we show that in non-cooperative environments—presumably a more realistic context for a variety of both human and software agents—self-organized coalitions are capable of extracting welfare improvements. The Nash equilibria of these coalitional formation games are demonstrated to always exist and be unique. Certain free rider problems in such group formation dynamics lead to the possibility of dynamically unstable Nash equilibria, depending on the nature of intra-group compensation and coalition size. Yet coherent groups can still form, if only temporarily, as demonstrated by computational experiments. Such groups of agents can be either long-lived or transient. The macroscopic structure of these emergent 'bands ' of agents is stationary in sufficiently large populations, despite constant adaptation at the agent level. It is argued that assumptions concerning attainment of agent-level (Nash) equilibrium, so ubiquitous in conventional economics and game theory, are difficult to justify behaviorally and highly restrictive theoretically, and are thus unlikely to serve either as fertile design objectives or robust operating principles for realistic multi-agent systems.

. Traditional decision theory has assumed that agents have complete, consistent and readily available beliefs and preferences. Obviously, even if an expert system has complete and consistent beliefs, it cannot have them readily available. Moreover, some beliefs about beliefs are not even approximately computable. It is shown that if all players have complete and consistent beliefs, they can compute approximate beliefs about beliefs of any order by considering events arbitrarily close in some well-defined sense to the ones in question. 1. Introduction In traditional decision sciences (see, for example [8]) decision makers are usually not assumed to be restricted in their thinking in any way. They have consistent beliefs and preferences which are available throughout the decision making process. The widely accepted Bayesian approach to decision making under uncertainty maintains that, whenever an agent lacks information about the value of a certain variable, s/he still has some "subjecti...

In a self-confirming equilibrium, each player correctly forecasts the actions that opponents will take along the equilibrium path, but may be mistaken about the way that opponents would respond to deviations. This paper develops a refinement of self-confirming equilibrium in which players use information about opponents' payoffs in forming beliefs about the way that opponents play off of the equilibrium path. We show that this concept is robust to payoff uncertainty. We also discuss its relationship to other concepts, and show that it is closely related to assuming almost common certainty of payoffs in an epistemic model with independent beliefs. Journal of Economic Literature Classification Numbers C72, D84. 2 1.