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Pennsylvania Convention Center, 106-B
Bounded Rationality, Level-k Reasoning, and Cognitive Hierarchies
Friday, Jan. 5, 2018 8:00 AM - 10:00 AM
- Chair: Colin F. Camerer, California Institute of Technology
A Diagnosis on the Relationship Between Equilibrium Play, Stated Beliefs, and Best Responses
AbstractStrategic games have two dimensions of difficulty for subjects in the laboratory. One is understanding the rules of the game and forming a best response to whatever beliefs they hold. The other is forming these beliefs correctly. Typically, these two dimensions cannot be disentangled as belief formation crucially depends on the understanding of the game. We present a variation of the Two Player Guessing Game (Grosskopf and Nagel (2008)) which turns an otherwise strategic game into an individual decision-making task. This allows us to perform a within subject analysis of the decisions made for the same ``game'' with and without strategic uncertainty. The results show that subjects with a better score at the individual decision making task form more accurate beliefs of other player's choices, and better-respond to these beliefs. Additionally, we show that those who score higher at our new task modify their beliefs based on the population they play against. This suggests that out of equilibrium play is mostly driven by a limited understanding of the game mechanics.
What Can Be Learned From Behavior? Predictive Ability in Discrete Choice Environments
AbstractRevealed preference restrictions provide testable implications for many theories of consumption behavior. Often, empirical evidence finds violations to the model which raises the question of how severe these are. The severity of the violations does not only depend on the extent of the observed deviations, but also on the sensitivity of the test to detect them if any. This paper provides a joint treatment for the severity of the violations and the sensitivity of the test in discrete choice environments by assessing the amount of information about underlying preference that can be inferred from data while allowing for errors. The proposed approach allows to compare across (limited) data sets and different models of behavior, addressing the concern raised by DeClippel and Rozen (2014) about extensibility of bounded rationality models.
Bounded Rationality And Learning: A Framework and A Robustness Result
AbstractWe explore model misspecification in an observational learning framework. Individuals learn from private and public signals and the actions of others. An agent's type specifies her model of the world. Misspecified types have incorrect beliefs about the signal distribution, how other agents draw inference and/or others' payoffs. We establish that the correctly specified model is robust in that agents with approximately correct models almost surely learn the true state asymptotically. We develop a simple criterion to identify the asymptotic learning outcomes that arise when misspecification is more severe. Depending on the nature of the misspecification, learning may be correct, incorrect or beliefs may not converge. Different types may asymptotically disagree, despite observing the same sequence of information. This framework captures behavioral biases such as confirmation bias, false consensus effect, partisan bias and correlation neglect, as well as models of inference, such as level-k and cognitive hierarchy.
Stephanie W. Wang,
University of Pittsburgh
California Institute of Technology
University of Pennsylvania
Massachusetts Institute of Technology
- D12 - Consumer Economics: Empirical Analysis
- C91 - Laboratory, Individual Behavior