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Cited by 388

The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.

Groups Make Better Self-Interested Decisions

Charness, Gary, Sutter, Matthias·2012·Journal of Economic Perspectives

Abstract

In this paper, we describe what economists have learned about differences between group and individual decision-making. This literature is still young, and in this paper, we will mostly draw on experimental work (mainly in the laboratory) that has compared individual decision-making to group decision-making, and to individual decision-making in situations with salient group membership. The bottom line emerging from economic research on group decision-making is that groups are more likely to make choices that follow standard game-theoretic predictions, while individuals are more likely to be influenced by biases, cognitive limitations, and social considerations. In this sense, groups are generally less “behavioral” than individuals. An immediate implication of this result is that individual decisions in isolation cannot necessarily be assumed to be good predictors of the decisions made by groups. More broadly, the evidence casts doubts on traditional approaches that model economic behavior as if individuals were making decisions in isolation.

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Metadata

Authors
Charness, G., & Sutter, M.
Venue
Journal of Economic Perspectives
Published
2012-08-01
Volume
26
Issue
3
Pages
157-176
ISSN
0895-3309 (ISSN-L)
OpenAlex
W2039940601
Cited by
388
References
57
FWCI
71.20
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Groups Make Better Self-Interested Decisions. https://app.cochat.ai/papers/10.1257/jep.26.3.157
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Charness, G., & Sutter, M. (2012). Groups Make Better Self-Interested Decisions. Journal of Economic Perspectives. https://doi.org/10.1257/jep.26.3.157

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.