Our mission is to help computational modelers at all levels engage in the establishment and adoption of community standards and good practices for developing and sharing computational models. Model authors can freely publish their model source code in the Computational Model Library alongside narrative documentation, open science metadata, and other emerging open science norms that facilitate software citation, reproducibility, interoperability, and reuse. Model authors can also request peer review of their computational models to receive a DOI.
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This is an agent-based model of a population of scientists alternatively authoring or reviewing manuscripts submitted to a scholarly journal for peer review. Peer-review evaluation can be either ‘confidential’, i.e. the identity of authors and reviewers is not disclosed, or ‘open’, i.e. authors’ identity is disclosed to reviewers. The quality of the submitted manuscripts vary according to their authors’ resources, which vary according to the number of publications. Reviewers can assess the assigned manuscript’s quality either reliably of unreliably according to varying behavioural assumptions, i.e. direct/indirect reciprocation of past outcome as authors, or deference towards higher-status authors.
This model takes into consideration Peer Reviewing under the influence of Impact Factor (PRIF) and it has the purpose to explore whether the infamous metric affects assessment of papers under review. The idea is to consider to types of reviewers, those who are agnostic towards IF (IU1) and those that believe that it is a measure of journal (and article) quality (IU2). This perception is somehow reflected in the evaluation, because the perceived scientific value of a paper becomes a function of the journal in which an article has been submitted. Various mechanisms to update reviewer preferences are also implemented.
NetLogo software for the Peer Review Game model. It represents a population of scientists endowed with a proportion of a fixed pool of resources. At each step scientists decide how to allocate their resources between submitting manuscripts and reviewing others’ submissions. Quality of submissions and reviews depend on the amount of allocated resources and biased perception of submissions’ quality. Scientists can behave according to different allocation strategies by simply reacting to the outcome of their previous submission process or comparing their outcome with published papers’ quality. Overall bias of selected submissions and quality of published papers are computed at each step.
This is an agent-based model of peer review built on the following three entities: papers, scientists and conferences. The model has been implemented on a BDI platform (Jason) that allows to perform both parameter and mechanism exploration.
This model looks at implications of author/referee interaction for quality and efficiency of peer review. It allows to investigate the importance of various reciprocity motives to ensure cooperation. Peer review is modelled as a process based on knowledge asymmetries and subject to evaluation bias. The model includes various simulation scenarios to test different interaction conditions and author and referee behaviour and various indexes that measure quality and efficiency of evaluation […]
This ABM looks at the effect of multiple reviewers and their behavior on the quality and efficiency of peer review. It models a community of scientists who alternatively act as “author” or “reviewer” at each turn.