Explaining the evolution of gossip

Contributed by Michele J. Gelfand; received August 17, 2022; accepted December 31, 2023; reviewed by Christian Hilbe and Michael Muthukrishna
February 20, 2024
121 (9) e2214160121

Significance

From Mesopotamian cities to industrialized nations, gossip has been at the center of bonding human groups. Yet the evolution of gossip remains a puzzle. The current article argues that gossip evolves because its dissemination of individuals’ reputations induces individuals to cooperate with those who gossip. As a result, gossipers proliferate as well as sustain the reputation system and cooperation.

Abstract

Gossip, the exchange of personal information about absent third parties, is ubiquitous in human societies. However, the evolution of gossip remains a puzzle. The current article proposes an evolutionary cycle of gossip and uses an agent-based evolutionary game-theoretic model to assess it. We argue that the evolution of gossip is the joint consequence of its reputation dissemination and selfishness deterrence functions. Specifically, the dissemination of information about individuals’ reputations leads more individuals to condition their behavior on others’ reputations. This induces individuals to behave more cooperatively toward gossipers in order to improve their reputations. As a result, gossiping has an evolutionary advantage that leads to its proliferation. The evolution of gossip further facilitates these two functions of gossip and sustains the evolutionary cycle.

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Data, Materials, and Software Availability

Code and data for these results are available at the Open Science Framework (https://osf.io/w3kjq/?view_only=7b1fa51a66874a0fbcaf92f3e036c4c7) (48). All other data are included in the manuscript and/or SI Appendix.

Acknowledgments

We thank Paul J. Hanges, James A. Grand, and Joshua C. Jackson for valuable suggestions on the manuscript. This research was funded in part by Air Force Office of Scientific Research Grant 1010GWA357. The views expressed are those of the authors and do not reflect the official policy or position of the funders. We acknowledge the University of Maryland supercomputing resources (http://hpcc.umd.edu/) made available for conducting the research reported in this article. Portions of this article originally appeared as part of the first author’s master’s thesis.

Author contributions

X.P., V.H., D.S.N., and M.J.G. designed research; X.P. performed research; X.P. contributed new reagents/analytic tools; X.P. analyzed data; and X.P., D.S.N., and M.J.G. wrote the paper.

Competing interests

The authors declare no competing interest.

Supporting Information

Appendix 01 (PDF)

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Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 121 | No. 9
February 27, 2024
PubMed: 38377206

Classifications

Data, Materials, and Software Availability

Code and data for these results are available at the Open Science Framework (https://osf.io/w3kjq/?view_only=7b1fa51a66874a0fbcaf92f3e036c4c7) (48). All other data are included in the manuscript and/or SI Appendix.

Submission history

Received: August 17, 2022
Accepted: December 31, 2023
Published online: February 20, 2024
Published in issue: February 27, 2024

Keywords

  1. gossip
  2. cooperation
  3. indirect reciprocity
  4. evolutionary game theory
  5. agent-based model

Acknowledgments

We thank Paul J. Hanges, James A. Grand, and Joshua C. Jackson for valuable suggestions on the manuscript. This research was funded in part by Air Force Office of Scientific Research Grant 1010GWA357. The views expressed are those of the authors and do not reflect the official policy or position of the funders. We acknowledge the University of Maryland supercomputing resources (http://hpcc.umd.edu/) made available for conducting the research reported in this article. Portions of this article originally appeared as part of the first author’s master’s thesis.
Author Contributions
X.P., V.H., D.S.N., and M.J.G. designed research; X.P. performed research; X.P. contributed new reagents/analytic tools; X.P. analyzed data; and X.P., D.S.N., and M.J.G. wrote the paper.
Competing Interests
The authors declare no competing interest.

Notes

Reviewers: C.H., Max-Planck-Institut fur Evolutionsbiologie; and M.M., London School of Economics and Political Science.
*
Our rationale to make an exploitive agent cooperate with another reputation-sensitive agent is based on Kantian reasoning: When an exploitive agent meets a reputation-sensitive agent, they both assume that their interaction partner will act as they do. Under this new constraint, they should both cooperate to maximize their utility (49).
Our rationale of making GCs be perceived as CCs is that among all the strategies, CCs and CDs are mostly likely to be treated cooperatively by others. Moreover, compared with CDs, CCs also represent a more virtuous strategy. Thus, if the GC strategy resembles a person who wants to manage their reputation positively, they should present that they are a CC. On the contrary, our rationale of making GDs be perceived as ADs is that among all the strategies, ADs are mostly likely to be defected by others. We also tried another reputation management option for GD—by making GDs be perceived as CCs by nongossipers but perceived as GDs by gossipers. That way, GC and GD strategies are completely symmetric. The results remain robust. See SI Appendix, section 2.3.2 for details.
Agents are still tagged as “gossipers” or “nongossipers,” but gossipers do not gossip.
§
Though these agents can no longer dissemble their real strategies to gossipers, we still let them behave discriminatively toward gossipers vs. nongossipers. This is to rule out the alternative explanation that the results are purely driven by pro- vs. antigossiper behavior instead of reputation management.
The total gossip cost is subtracted after calculating the average payoff from cooperation interactions.
#
We only calculate their information accuracy about the immediate neighbors in the social network because these neighbors are the only agents that an individual interacts with.

Authors

Affiliations

School of Management and Economics and Shenzhen Finance Institute, The Chinese University of Hong Kong, Shenzhen, China
Vincent Hsiao
Department of Computer Science, University of Maryland, College Park, MD 20742
Dana S. Nau
Department of Computer Science, University of Maryland, College Park, MD 20742
Institute for Systems Research, University of Maryland, College Park, MD 20742
Graduate School of Business, Stanford University, Stanford, CA 94305
Department of Psychology, Stanford University, Stanford, CA 94305

Notes

1
To whom correspondence may be addressed. Email: [email protected] or [email protected].

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Explaining the evolution of gossip
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