Stochastic Choice and Optimal Sequential Sampling
نویسندگان
چکیده
We model the joint distribution of choice probabilities and decision times in binary choice tasks as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. In the resulting optimal policy, the agent’s choices are more likely to be correct when the agent chooses to decide quickly, provided that the agent’s prior beliefs are correct. For this reason it better matches the observed correlation between decision time and choice probability than does the classical drift-diffusion model, where the agent is uncertain which of two actions is best but knows the utility difference between them. ∗We thank Stefano DellaVigna and Antonio Rangel for very stimulating conversations, Ian Krajbich, Carriel Armel and Antonio Rangel for sharing their experimental data with us, In Young Cho and Jonathan Libgober for expert research assistance, the Sloan Foundation and NSF grants SES-0954162, SES-1123729, and CAREER grant SES-1255062 for support, and seminar audiences at the ASSA annual meetings, Behavioral Game Theory conference at UCL; economics seminars at Chicago, Harvard/MIT, Northwestern, Queen’s, SITE, Stanford GSB, and Toronto; and a seminar at the Computational Cognitive Science Group at MIT for useful comments. †Department of Economics, Harvard University ‡Department of Economics, University of California, Berkeley §Department of Economics, Harvard University 1 ar X iv :1 50 5. 03 34 2v 1 [ qbi o. N C ] 1 3 M ay 2 01 5
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