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Chapter 13 - Sequential Decisions from Sampling:

Inductive Generation of Stopping Decisions Using Instance-Based Learning Theory

from Part IV - Truncation and Stopping Rules

Published online by Cambridge University Press:  01 June 2023

By
Cleotilde Gonzalez  and
Palvi Aggarwal
Edited by
Klaus Fiedler ,
Peter Juslin  and
Jerker Denrell
Klaus Fiedler
Affiliation:
Universität Heidelberg
Peter Juslin
Affiliation:
Uppsala Universitet, Sweden
Jerker Denrell
Affiliation:
University of Warwick
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Summary

Sequential decisions from sampling are common in daily life: we often explore alternatives sequentially, decide when to stop such exploration process, and use the experience acquired during sampling to make a choice for what is expected to be the best option. In decisions from experience, theories of sampling and experiential choice are unable to explain the decision of when to stop the sequential exploration of alternatives. In this chapter, we propose a mechanism to inductively generate stopping decisions, and we demonstrate its plausibility in a large and diverse human data set of the binary choice sampling paradigm. Our proposed stopping mechanism relies on the choice process of a theory of experiential choice, Instance-Based Learning Theory (IBLT). The new stopping mechanism tracks the relative prediction errors of the two options during sampling, and stops when such difference is close to zero. Our results from simulation are able to accurately predict human stopping decisions distributions in the dataset. This model provides an integrated theoretical account of decisions from experience, where the stopping decisions are generated inductively from the sampling process.

Keywords

decisions from experiencesamplinginstance-based learning theorysatisficingprediction error
Type
Chapter
Information
Sampling in Judgment and Decision Making , pp. 289 - 310
Publisher: Cambridge University Press
Print publication year: 2023

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