Larry Samuelson Publications

We offer an approach to cooperation in repeated games of private monitoring in which players construct models of their opponents' behavior by observing the frequencies of play in a record of past plays of the game in which actions but not signals are recorded. Players construct models of their opponent's behavior by grouping the histories in the record into a relatively small number of analogy classes for which they estimate probabilities of cooperation. The incomplete record and the limited number of analogy classes lead to misspecified models that provide the incentives to cooperate. We provide conditions for the existence of equilibria supporting cooperation and equilibria supporting high payoffs for some nontrivial analogy partitions.

We examine the evolutionary selection of attitudes toward aggregate risk in an age structured population. Aggregate shocks perturb the population's consumption possibilities. Consumption is converted to fertility via a technology that exhibits first increasing and then decreasing returns to scale, captured in the simplest case by a fertility threshold. We show that evolution will select preferences that exhibit arbitrarily high aversion to aggregate risks with even very small probabilities of sufficiently low outcomes. These findings complement the familiar result that evolution will select for greater aversion to aggregate than idiosyncratic risks by identifying circumstances under which the difference can be extreme.

Decision theory can be used to test the logic of decision making---one may ask whether a given set of decisions can be justified by a decision-theoretic model. Indeed, in principal-agent settings, such justifications may be required---a manager of an investment fund may be asked what beliefs she used when valuing assets and a government may be asked whether a portfolio of rules and regulations is coherent. In this paper we ask which collections of uncertain-act evaluations can be simultaneously justified under the maxmin expected utility criterion by a single set of probabilities. We draw connections to the the Fundamental Theorem of Finance (for the special case of a Bayesian agent) and revealed-preference results.

“Crowds” are often regarded as “wiser” than individuals, and prediction markets are often regarded as effective methods for harnessing this wisdom. If the agents in prediction markets are Bayesians who share a common model and prior belief, then the no-trade theorem implies that we should see no trade in the market. But if the agents in the market are not Bayesians who share a common model and prior belief, then it is no longer obvious that the market outcome aggregates or conveys information. In this paper, we examine a stylized prediction market comprised of Bayesian agents whose inferences are based on different models of the underlying environment. We explore a basic tension—the differences in models that give rise to the possibility of trade generally preclude the possibility of perfect information aggregation.

People reason about uncertainty with deliberately incomplete models, including only the most relevant variables. How do people hampered by different, incomplete views of the world learn from each other? We introduce a model of “model-based inference.” Model-based reasoners partition an otherwise hopelessly complex state space into a manageable model. We nd that unless the differences in agents’ models are trivial, interactions will often not lead agents to have common beliefs, and indeed the correct-model belief will typically lie outside the convex hull of the agents’ beliefs. However, if the agents’ models have enough in common, then interacting will lead agents to similar beliefs, even if their models also exhibit some bizarre idiosyncrasies and their information is widely dispersed.