Evidence map›Paper›PMID 30147203›Full record

ArticleScientometrics2018

The peer review game: an agent-based model of scientists facing resource constraints and institutional pressures.

Federico Bianchi, Francisco Grimaldo, Giangiacomo Bravo, Flaminio Squazzoni

Abstract read
In one paragraph

Article in Scientometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Paying peer reviewers: benefits, risks, and challenges.Naunyn-Schmiedeberg's archives of pharmacology · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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

4 authors.

Federico Bianchi1Department of Economics and Management, University of Brescia, Via San Faustino, 74/B, 25122 Brescia, Italy.
Francisco Grimaldo2Department of Computer Science, University of Valencia, Avinguda de la Universitat s/n, 46100 Burjassot, Spain.
Giangiacomo Bravo3Department of Social Studies and Center for Data Intensive Sciences and Applications, Linnaeus University, Universitetsplatsen, 1, 35195 Växjo, Sweden.
Flaminio Squazzoni1Department of Economics and Management, University of Brescia, Via San Faustino, 74/B, 25122 Brescia, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper looks at peer review as a cooperation dilemma through a game-theory framework. We built an agent-based model to estimate how much the quality of peer review is influenced by different resource allocation strategies followed by scientists dealing with multiple tasks, i.e., publishing and reviewing. We assumed that scientists were sensitive to acceptance or rejection of their manuscripts and the fairness of peer review to which they were exposed before reviewing. We also assumed that they could be realistic or excessively over-confident about the quality of their manuscripts when reviewing. Furthermore, we assumed they could be sensitive to competitive pressures provided by the institutional context in which they were embedded. Results showed that the bias and quality of publications greatly depend on reviewer motivations but also that context pressures can have a negative effect. However, while excessive competition can be detrimental to minimising publication bias, a certain level of competition is instrumental to ensure the high quality of publication especially when scientists accept reviewing for reciprocity motives.

Indexed as

Agent-based modelCooperationGame theoryPeer reviewScientist strategies

Identifiers

PMID30147203
PMCPMC6096663

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.