“Unrealistic Expectations and Misguided Learning,” Philipp Strack, UC Berkeley

Event time: 
Tuesday, October 3, 2017 - 12:00pm to 1:15pm
Institution for Social and Policy Studies (PROS077 ), A002 See map
77 Prospect Street
New Haven, CT 06511
Event description: 


We explore the learning process and behavior of an individual with unrealistically high expectations (“overconfidence”) when outcomes also depend on an external fundamental that affects the optimal action. Moving beyond existing results in the literature, we show that the agent’s beliefs regarding the fundamental converge under weak conditions. Furthermore, we identify a broad class of situations in which “learning” about the fundamental is self-defeating: it leads the individual systematically away from the correct belief and toward lower performance. Due to his overconfidence, the agent—even if initially correct—becomes too pessimistic about the fundamental. As he adjusts his behavior in response, he lowers outcomes and hence becomes even more pessimistic about the fundamental, perpetuating the misdirected learning. The greater is the loss from choosing a suboptimal action, the further the agent’s action ends up from optimal. We partially characterize environments in which self-defeating learning occurs, and show that the decisionmaker learns to take the optimal action if and only if a specific non-identifiability condition is satisfied. In contrast to an overconfident agent, an underconfident agent’s misdirected learning is self-limiting and therefore not very harmful. We argue that the decision situations in question are common in economic settings, including delegation, organizational, effort, and public-policy choices.

Phillip Strack is a microeconomic theorist who studies peoples’ behavior in dynamic situations. He is especially interested in the role of stochastically evolving private information. Strack studied the role of private learning in dynamic contests, strategic experimentation games, and dynamic mechanism design settings. The techniques used are drawn from game theory, optimization and probability theory. If necessary, he develops new techniques and mathematical results. Applications of his work include revenue maximizing sale of airplane tickets, dynamic ad-word auctions, optimal unemployment benefits, model-free option pricing and competition between mutuals fund managers.

The workshop is held jointly between the Yale departments of Economics, Political Science, Psychology, and the School of Management (SOM). The workshop is cosponsored by the Center for the Study of American Politics (CSAP) and the School of Management’s International Center for Finance and Whitebox Advisors fund. Lunch will be served.

Open to: