We report on experimental evidence rationalizing the use of heterogeneous agent models. We provide compelling evidence that subjects in laboratory experiments often behave in ways that depart from the rational choice ideal. Further, these subjects' heuristic approaches often differ from one...

A representative consumer uses Bayes' law to learn about parameters of several models and to construct probabilities with which to perform ongoing model averaging. The arrival of signals induces the consumer to alter his posterior distribution over models and parameters. The consumer's...

Overconfidence is a well-established behavioral phenomenon that involves an overestimation of own capabilities. We introduce a model, in which managers and agents exert effort in a joint production, after the manager decides on the allocation of the tasks. A rational manager tends to delegate...

Models of choice where agents see others as less sophisticated than themselves have significantly different, sometimes more accurate, predictions in games than does Nash equilibrium. When it comes to mechanism design, however, they turn out to have surprisingly similar implications. This paper...

Predictions under common knowledge of payoffs may differ from those under arbitrarily, but finitely, many orders of mutual knowledge; Rubinstein's (1989)Email game is a seminal example. Weinstein and Yildiz (2007) showed that the discontinuity in the example generalizes: for all types with...

Mechanism design theory strongly relies on the concept of Nash equilibrium. However, studies of experimental games show that Nash equilibria are rarely played and that subjects may be thinking only a finite number of iterations. We study one of the most influential benchmarks of mechanism design...

We consider mechanism design in contexts in which agents exhibit bounded depth of reasoning (level k) instead of rational expectations. We use simple direct mechanisms, in which agents report only first-order beliefs. While level 0 agents are assumed to be truth tellers, level k agents...