Who you know is what you know: Modeling boundedly rational social sampling.

Christin Schulze, Ralph Hertwig, Thorsten Pachur

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The social environment provides a sampling space for making informed inferences about features of the world at large, such as the distribution of preferences, risks, behaviors, or other events. How do people search this sampling space and make inferences based on the instances sampled? Inspired by existing models of bounded rationality and in accord with research on the structure of social memory, we develop and test the social-circle model, a parameterized, probabilistic process account of how people make inferences about relative event frequencies. The model extends to social sampling the idea that cognitive search is both structured and limited; moreover, it captures individual differences in the order in which sections of the sampling space are probed, in difference thresholds, and in response error. Using a hierarchical Bayesian latent-mixture approach, we submit the model to a rigorous model comparison. In Study 1, a reanalysis of published data, the social-circle model outperformed both a model assuming exhaustive search and a simple heuristic assuming no individual differences in search or difference thresholds. Study 2 establishes the robustness of these findings in a different domain and across age groups (adults and children). We find that children also consult their social memories for inferential purposes and rely on sequential and limited search. Finally, model and parameter recovery analyses (Study 3) demonstrate the ability of the social-circle model to recover the characteristics of the cognitive processes assumed to underlie social sampling. Our analyses establish that social sampling in both children and adults follows key principles of bounded rationality.

Original languageEnglish
Pages (from-to)221-241
Number of pages21
JournalJournal of Experimental Psychology: General
Volume150
Issue number2
DOIs
StatePublished - Feb 2021
Externally publishedYes

Keywords

  • Bayesian cognitive modeling
  • availability
  • child development
  • individual differences
  • probabilistic inference

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