Most Games Violate the Common Priors Doctrine

The type of a player in a game describes the beliefs of that player about the types of others.

On the Informational Content of Advice: A Theoretical and Experimental Study

This paper examines the market for advice and the underlying perception that advice is useful and information…

Comparing Learning Models with Ideal Micro & Experimental Data Sets

This paper compares three learning models. The models compared are the Reinforcement Learning Model of Erev and Roth, the EWA model of Camerer and Ho and a Belief Learning model in which beliefs are elicited from subjects using a proper scoring rule…

The Truth is in the Eye of the Beholder: or Equilibrium in Beliefs and Rational Learning in Games

Games with incomplete information or randomness in the moves of others typically have many decision-theoretically equivalent formulations of the type space…

Learning, Quantal Response Equilibrium and Equilibrium in Beliefs

This paper makes two points. First, the modeling used in the rational (Bayesian) learning literature can be generalized to handle the repeated shocks to preferences inherent and implicit in models of quantal response equilibria (QRE).

Bayesian Learning and Convergence to Nash Equilibria without Common Priors

Consider an infinitely repeated game where each player is characterized by a ‘type’ which may be unknown to the other plays in the game. Suppose further that each player’s belief about others is independent of that player’s type…

Convergence in Economic Models with Bayesian Hierarchies of Beliefs

I study a model where hierarchies of beliefs (the beliefs about the beliefs of other agents, etc.) are important. I provide conditions under which optimal actions of agents will converge to the Nash equilibrium of the model characterized by the true, previously unknown ``fundamentals.''

Optimal Growth with Unobservable Resources and Learning

This paper examines the problem of choosing optimal resource consumption from an imperfectly observable aggregate capital, wealth, or resource stock with the decision-maker learns about over time.

The Bayesian Foundations of Learning by Doing

This paper explores a one-agent Bayesian model of learning by doing and technological choice. To produce output, the agent can choose among various technologies…

A Bayesian Learning Model Fitted to a Variety of Empirical Learning Curves

Where does technological progress come from and what determines its rate of advance? In answering these questions, it is useful to decompose technological progress into the invention of new techniques and products and the improvement of existing ones.

Savage-Bayesian Models of Economics

The “state of the art” in learning models in economics is highly unsettled. On the one hand, we have the optimizing models in which learning occurs as a byproduct…

On the Convexity of the Value Function in Bayesian Optimal Control Problems

I study the question on the convexity of the value function and Blackwell (1951)’s Theorem and relate this to the uniqueness of optimal policies…

Bounded Rationality and Learning

Many have objected to the use of the Nash equilibrium (or more generally, Bayesian Nash equilibrium) concept in game theory, and similarly to the use of the rational expectations concept in the theory of competitive markets…

Bayesian Learning Leads to Correlated Equilibria in Normal Form Games

Consider an infinitely repeated normal form game where each player is characterized by a “type” which may be unknown to the other players of the game…

On the Convergence of Bayesian Posterior Processes in Linear Economic Models: Counting Equations and Unknowns

I propose a technique, counting ‘equations’ and ‘unknowns’, for determining when the posterior distributions of the parameters of a linear regression process converge to their true values.

Learning in Mis-Specified Models and the Possibility of Cycles

I study the problem of a monopolist maximizing a sum of discounted profits facing a linear demand curve whose slope and intercept are unknown. I show that if the monopolist has a mis-specified model…

Optimal Bayesian Control of a Nonlinear Regression Process with Unknown Parameters

Economic Agents operating in uncertain, stochastic environments can face a tradeoff between current period expected reward and accumulation of information of uncertain value.

Optimal Control of an Unknown Linear Process with Learning

Optimal control of a linear process with unknown parameters is considered when the horizon is infinite and rewards are discounted. Active learning strategies are considered…

Control of a Linear Regression Process with Unknown Parameters

Applications of forms of control theory to economic policy making have been studied by Theil (1958), Chow (1975, 1981), and Prescott (1972). Many of the applications are approximations to the optimal policy…