We extend Akerlof's (1970) "Market for Lemons" by assuming that some buyers are overconfident. Buyers in our model receive a noisy signal about the quality of the good that is at display for sale. Overconfident buyers do not update according to Bayes' rule but take the noisy signal at face...

We extend Akerlof (1970)'s 'Market for Lemons' by assuming that some buyers are overconfident. Buyers in our model receive a noisy signal about the quality of the good that is on display for sale. Overconfident buyers do not update according to Bayes' rule but take the noisy signal at face...

This paper suggests a potential rationale for the recent empirical finding that overconfident agents tend to self-select into more competitive environments (e.g. Dohmen and Falk, 2006). In particular, it shows that moderate overconfidence in a contest can improve the agent's performance relative...

This paper suggests a potential rationale for the recent empirical finding that overconfident agents tend to self-select into more competitive environments (e.g. Dohmen and Falk, forthcoming). In particular, it shows that moderate overconfidence in a contest can improve the agent's performance...

This paper suggests a potential rationale for the recent empirical finding that overconfident agents tend to self-select into more competitive environments (e.g. Dohmen and Falk, forthcoming). In particular, it shows that moderate overconfidence in a contest can improve the agent's performance...

In this paper, we study the individual payoff effects of overconfident self-perception in teams. In particular, we demonstrate that the welfare of an overconfident agent in a team of one rational and one overconfident agent or a team of two overconfident agents can be higher than that of the...

This paper suggests a potential rationale for the recent empirical finding that overconfident agents tend to self-select into more competitive environments (e.g. Dohmen and Falk, forthcoming). In particular, it shows that moderate overconfidence in a contest can improve the agent's performance...