© 2015, Springer Science+Business Media New York. We experimentally investigate the disposition of decision makers to use case-based reasoning as suggested by Hume (An enquiry concerning human understanding, 1748) and formalized by case-based decision theory (Gilboa and Schmeidler in Q J Econ 110:605–639, 1995). Our subjects face a monopoly decision problem about which they have very limited information. Information is presented in a manner which makes similarity judgements according to the feature matching model of Tversky (Psychol Rev 84:327–352, 1977) plausible. We provide subjects a “history” of cases. In the $$2\times 2$$2×2 between-subject design, we vary whether information about the current market is given and whether immediate feedback about obtained profits is provided. The results provide support for the predictions of case-based decision theory, particularly when no immediate feedback is provided.

We study how learning shapes behavior towards risk when individuals are not assumed to know, or to have beliefs about, probability distributions. In any period, the behavior change induced by learning is assumed to depend on the action chosen and the payoff obtained. We characterize learning processes that, in expected value, increase the probability of choosing the safest actions and provide sufficient conditions for them to converge to the choices of risk averse expected utility maximizers. We provide a learning theoretic motivation for long run risk choices, such as those in expected utility theory with known payoff distributions. © 2012 Elsevier Inc.

Decision makers are often described as seeking higher expected payoffs and avoiding higher variance in payoffs. We provide some necessary and some sufficient conditions for learning rules, that assume the agent has little prior and feedback information about the environment, to reflect such preferences. We adopt the framework of Börgers, Morales and Sarin (2004, Econometrica) who provide similar results for learning rules that seek higher expected payoffs. Our analysis reveals that a concern for variance leads to quadratic transformations of payoffs to appear in the learning rule. © 2012.

We investigate the impact of reputation in a laboratory experiment. We do so by varying whether the past choices of a long-run player are observable by the short-run players. Our framework allows for reputation to have either a beneficial or a harmful effect on the long-run player. We find that reputation is seldom harmful and its beneficial effects are not as strong as theory suggests. When reputational concerns are at odds with other-regarding preferences, we find the latter overwhelm the former.

The key element of models of contest is the contest success function (CSF) which specifies the winning probabilities of agents. The existing axiomatizations of CSFs assume that contestants can make only one type of investment. This paper generalizes these axiomatizations to the case where each agent can have multiple types of investments. This allows us to provide a unified framework to extend and interpret the results of Skaperdas (Econ Theory 7:283-290, 1996) and Clark and Riis (Econ Theory 11:201-204, 1998), and rationalize some seemingly ad hoc CSFs used by applied researchers. © 2008 Springer-Verlag.

This paper studies a model of memory. The model takes into account that memory capacity is limited and imperfect. We study how agents with such memory limitations, who have very little information about their choice environment, play games. We introduce the notion of a Limited Memory Equilibrium (LME) and show that play converges to an LME in every generic normal form game. Our characterization of the set of LME suggests that players with limited memory do (weakly) better in games than in decision problems. We also show that agents can do quite well even with severely limited memory, although severe limitations tend to make them behave cautiously. Copyright ©2004 by the authors. All rights reserved.

In the payoff assessment model of choice (Sarin and Vahid, 1999), only the assessment of the chosen strategy is updated. We extend that model to allow the agent to also update the assessments of strategies that the agent thinks are similar to the chosen strategy. We use this model to explain observed behaviour in a recent experiment. Statistical tests cannot distinguish between the payoff distributions generated by the model and the observed payoff distributions in almost every period. © Royal Economic Society 2004.

We use the simple dynamic model developed in Sarin and Vahid (1999, Games and Economic Behavior, 28, 294-309) to explain the experiments reported in Erev and Roth (1998, American Economic Review, 88, 848-881). In an earlier paper (Sarin and Vahid (1998, mimeo, Texas a & M University and Monash University)) we showed that the model predicted behavior in repeated coordination games remarkably well, and better than equilibrium theory or reinforcement learning models. In this paper we show that the same one-parameter model can also explain behavior in games with a unique mixed strategy Nash equilibrium better than alternative models. Hence, we obtain further support for the simple dynamic model. Journal of Economic Literature Classification Numbers: C7, D8. © 2001 Academic Press.

Continuous approximations that are ordinary differential equations (ODEs) or stochastic differential equations (SDEs) are often used to study the properties of discrete stochastic processes. We show that different ways of taking the continuous limit of the same model may result in either an ODE or a SDE and study the manner in which each approximates the discrete model. We compare the asymptotic properties of the continuous equations with those of the discrete dynamics and show that they tend to provide a better approximation when a greater amount of variance of the discrete model is preserved in the continuous limit. Journal of Economic Literature Classification numbers: C6, C7, D8 © 2000 Academic Press.

We study a model in which a decision maker has limited memory. For a large class of decision rules that may be used to select among strategies, the decision maker converges to play the maxmin strategy. The result arises not because of any inherent caution on the part of the decision maker, but because the decision maker's memory eventually contains only bad payoffs from the strategies no longer considered the best. Hence, a new rationale is provided for maxmin behavior. Journal of Economic Literature Classification Numbers : C72, D83. © 2000 Academic Press.

This article considers a simple model of reinforcement learning. All behavior change derives from the reinforcing or deterring effect of instantaneous payoff experiences. Payoff experiences are reinforcing or deterring depending on whether the payoff exceeds an aspiration level or falls short of it. Over time, the aspiration level is adjusted toward the actually experienced payoffs. This article shows that aspiration level adjustments may improve the decision maker's long-run performance by preventing him or her from feeling dissatisfied with even the best available strategies. However, such movements also lead to persistent deviations from expected payoff maximization by creating ̈probability matching̈ effects.

We develop a model which reflects the tendency of people to simplify the decision problems they face. The decision maker chooses among alternate strategies only on the basis of the payoff she assesses she would obtain from them, and these assessments do not explicitly take into account her subjective judgements regarding the likelihood of alternate states of the world. At each stage, the decision maker chooses the strategy that she assesses to give the highest payoff. She updates her assessments adaptively. We show that such behavior leads to maxmin choices. We also consider the decision maker who experiences shocks. Journal of Economic Literature Classification Numbers: C7, D8. © 1999 Academic Press.

Players do not have knowledge of the objective game. They know the available strategies and have scalar valued non-probabilistic payoff assessments. They act myopically, always choosing the strategy they consider best. They update these assessments in the direction of the received payoff. It is shown that such players converge to choose their maxmin strategies when facing a game against nature. In the Prisoner's Dilemma, however, the players need not converge to their maxmin strategy, and often end up cooperating. ©1999 Elsevier Science B.V. All rights reserved.

This paper considers a version of R. R. Bush and F. Mosteller's (1951,Psych. Rev.58, 313-323; 1955, "Stochastic Models for Learning," Wiley, New York) stochastic learning theory in the context of games. We show that in a continuous time limit the learning model converges to the replicator dynamics of evolutionary game theory. Thus we provide a non-biological interpretation of evolutionary game theory.Journal of Economic LiteratureClassification Numbers: C72, D83. © 1997 Academic Press.