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"Opponent's Foresight and Optimal Choices" by Dr. Jeevant Rampal
Friday, April 05, 2019, 02:00am - 03:00pm
Lecture / Reading / Talk
Lecture / Reading / Talk
The Centre for Experimental Social Sciences (CESS) Nuffield - FLAME University is pleased to host Dr. Jeevant Rampal, Assistant Professor of Economics at the Indian Institute of Management Ahmedabad. Dr. Rampal will be giving a talk titled "Opponent's Foresight and Optimal Choices".
Abstract:
Using two experiments, we demonstrate that in a “winner-take-all” extensive-form game, where players’ payoffs are directly opposed, optimal behavior can deviate from the unique “sure-win” backward-induction strategy due to beliefs about the opponent’s expertise. In the game we use, a particular deviation from the “sure-win” strategy yields a higher payoff only if one’s opponent makes a mistake; otherwise, the deviation leads to a loss. We find that experienced subjects were more likely to deviate when they were informed that their opponent was inexperienced and, in a different experiment, when they inferred their opponent’s inexperience by only observing their opponent’s preceding moves in the game. Maximum likelihood estimation indicates that Rampal’s (2018) model of limited foresight and uncertainty about the opponent’s foresight fits the data better than the Dynamic Level-k (Ho and Su (2013)) and Agent Quantal Response Equilibrium (McKelvey and Palfrey (1998)) models.
Bio:
Jeevant Rampal is an Assistant Professor of Economics at the Indian Institute of Management Ahmedabad (IIMA). His research interests are Behavioural and Experimental Economics (methods and application), Game Theory and Mechanism Design. Dr. Rampal finished is PhD from Ohio State University.
Abstract:
Using two experiments, we demonstrate that in a “winner-take-all” extensive-form game, where players’ payoffs are directly opposed, optimal behavior can deviate from the unique “sure-win” backward-induction strategy due to beliefs about the opponent’s expertise. In the game we use, a particular deviation from the “sure-win” strategy yields a higher payoff only if one’s opponent makes a mistake; otherwise, the deviation leads to a loss. We find that experienced subjects were more likely to deviate when they were informed that their opponent was inexperienced and, in a different experiment, when they inferred their opponent’s inexperience by only observing their opponent’s preceding moves in the game. Maximum likelihood estimation indicates that Rampal’s (2018) model of limited foresight and uncertainty about the opponent’s foresight fits the data better than the Dynamic Level-k (Ho and Su (2013)) and Agent Quantal Response Equilibrium (McKelvey and Palfrey (1998)) models.
Bio:
Jeevant Rampal is an Assistant Professor of Economics at the Indian Institute of Management Ahmedabad (IIMA). His research interests are Behavioural and Experimental Economics (methods and application), Game Theory and Mechanism Design. Dr. Rampal finished is PhD from Ohio State University.
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