Evidence map›Paper›PMID 38412126›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2024

Confusion cannot explain cooperative behavior in public goods games.

Guangrong Wang, Jianbiao Li, Wenhua Wang, Xiaofei Niu, Yue Wang

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. The private solution trap in collective action problems across 34 nations.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Apparent learning biases emerge from optimal inference: Insights from master equation analysis.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Article
  4. Beyond a binary theorizing of prosociality.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  5. Increasing benefits in one-time public goods does not promote cooperation.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  6. Reply to Fosgaard and Wengström: Confusion should not be used to explain cooperative behavior in public goods game experiments.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  7. Insufficient evidence to conclude that confusion cannot explain cooperative behavior.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  8. Confusion remains an important issue in public goods game experiments.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  9. Article
  10. Confusion cannot explain cooperative behavior in public goods games.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Guangrong Wang *Neural Decision Science Laboratory, School of Economics and Management, Weifang University, Weifang 261061, China.ORCID 0009-0003-4690-3301
Jianbiao Li *Institute for Study of Brain-like Economics, School of Economics, Shandong University, Jinan 250100, China.ORCID 0000-0002-8097-8150
Wenhua Wang *Institute for Study of Brain-like Economics, School of Economics, Shandong University, Jinan 250100, China.ORCID 0009-0004-7636-0835
Xiaofei Niu *Institute for Study of Brain-like Economics, School of Economics, Shandong University, Jinan 250100, China.ORCID 0000-0002-3430-2774
Yue Wang *Institute for Study of Brain-like Economics, School of Economics, Shandong University, Jinan 250100, China.ORCID 0009-0005-2717-4945

Funding

| Natural Science Foundation of Shandong Province () ZR2022MG068| Natural Science Foundation of Shandong Province () ZR2022QG048NSSFC | National Social Science Fund of China (NSSFC) 20AZD044NSSFC | National Social Science Fund of China (NSSFC) 22&ZD150NSSFC | National Social Science Fund of China (NSSFC) 72203132Project of Humanities and Social Sciences of Shandong 21RWZD15Taishan Scholar Program of Shandong Province tsqn201909013
6 · The paper itself

Abstract

Some scholars find that behavioral variation in the public goods game is explained by variations in participants' understanding of how to maximize payoff and that confusion leads to cooperation. Their findings lead them to question the common assumption in behavioral economics experiments that choices reflect motivations. We conduct two experiments, in which we minimize confusion by providing participants with increased training. We also introduce a question that specifically assesses participants' understanding of payoff maximization choices. Our experimental results show that the distribution of behavior types is significantly different when participants play with computers versus humans. A significant increase in contributions is also observed when participants play with humans compared to when they play with computers. Moreover, social norms may be the main motive for contributions when playing with computers. Our findings suggest that social preferences, rather than confusion, play a crucial role in determining contributions in public goods games when playing with humans. We therefore argue that the assumption in behavioral economics experiments that choices reveal motivations is indeed valid.

Indexed as

Cooperative BehaviorEconomics, BehavioralGame TheoryHumansconfusioncooperative behaviorpublic goods gamesocial preferences

Identifiers

PMID38412126
PMCPMC10927562

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.