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Estimating depth of reasoning in a repeated guessing game with no feedback

Published online by Cambridge University Press:  14 March 2025

Mariano Runco*
Affiliation:
Department of Economics, Auburn University at Montgomery, 7071 Senators Drive, 36117 Montgomery, AL, USA

Abstract

This paper estimates depth of reasoning in an Iterative Best Response model using data from Weber (2003) ten-period repeated guessing game with no feedback. Different mixture models are estimated and the type (Level-0, Level-1, etc) of each player is determined in every round using the Expectation Maximization algorithm. The matrices showing the number of individuals transitioning among levels is computed in each case. It is found that most players either remain in the same level or advance to the next two levels they were in the previous period. The lowest levels (Level-0 and Level-1) have a higher probability of transitioning to a higher level than Level-2 or Level-3. Thus, we can conclude that subjects, through repetition of the task, quickly become more sophisticated strategic thinkers as defined by higher levels. However, in some specifications the highest levels have a relatively large probability of switching to a lower level in the next period. In general, depth of reasoning increases monotonically in small steps as individuals are subjected to the same task repeatedly.

Type
Original Paper
Copyright
Copyright © 2012 Economic Science Association

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