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Cited by 441

The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.

Which Side Are You On? Bias, Credibility, and Mediation

Kydd, Andrew·2003·American Journal of Political Science

Abstract

Mediators are often thought to be more effective if they are unbiased or have no preferences over the issue in dispute. This article presents a game theoretic model of mediation drawing on the theory of “cheap talk” which highlights a contrary logic. Conflict arises in bargaining games because of uncertainty about the resolve of the parties. A mediator can reduce the likelihood of conflict by providing information on this score. For a mediator to be effective, however, the parties must believe that the mediator is telling the truth, especially if the mediator counsels one side to make a concession because their opponent has high resolve and will fight. An unbiased mediator who is simply interested in minimizing the probability of conflict will have a strong incentive to make such statements even if they are not true, hence the parties will not find the mediator credible. Only mediators who are effectively “on your side” will be believed if they counsel restraint.

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Metadata

Authors
Kydd, A.
Venue
American Journal of Political Science
Publisher
Wiley
Published
2003-09-09
Volume
47
Issue
4
Pages
597-611
ISSN
  • 0092-5853 (ISSN-L)
  • 1540-5907
OpenAlex
W2142822385
Cited by
441
References
32
FWCI
10.13
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Which Side Are You On? Bias, Credibility, and Mediation. https://app.cochat.ai/papers/10.1111/1540-5907.00042
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Kydd, A. (2003). Which Side Are You On? Bias, Credibility, and Mediation. American Journal of Political Science. https://doi.org/10.1111/1540-5907.00042

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

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.