capra_05

Emory University
Department of Economics
Working Papers

Title: Introspection in one-shot traveler’s dilemma games

Number: 05-04

Author: Susana Cabrera, C. Mónica Capra and Rosario Gómez

Issue Date: January 2005

Abstract: We report results of one-shot traveler’s dilemma game experiments to test the predictions of a model of introspection. The model describes a noisy out-of-equilibrium process by which players reach a decision of what to do in one-shot strategic interactions. To test the robustness of the model and to compare it to other models of introspection
without noise, we introduce non-binding advice. Advice has the effect of coordinating all players’ beliefs onto a common strategy. Experimentally, advice is implemented by asking subjects who participated in a repeated traveler’s dilemma game to recommend an action to subjects playing one-shot games with identical parameters. In contrast to observations, models based on best-response dynamics would predict lower claims than the advised. We show that our model’s predictions with and without advice are consistent with the data.

Title:	Introspection in one-shot traveler’s dilemma games
Number:	05-04
Author:	Susana Cabrera, C. Mónica Capra and Rosario Gómez
Issue Date:	January 2005
Abstract:	We report results of one-shot traveler’s dilemma game experiments to test the predictions of a model of introspection. The model describes a noisy out-of-equilibrium process by which players reach a decision of what to do in one-shot strategic interactions. To test the robustness of the model and to compare it to other models of introspection without noise, we introduce non-binding advice. Advice has the effect of coordinating all players’ beliefs onto a common strategy. Experimentally, advice is implemented by asking subjects who participated in a repeated traveler’s dilemma game to recommend an action to subjects playing one-shot games with identical parameters. In contrast to observations, models based on best-response dynamics would predict lower claims than the advised. We show that our model’s predictions with and without advice are consistent with the data.

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