Trust and reputation in dynamic scientific communities

Abstract

The formation of collaboration networks (or communities) is an important latent effect in many computational science undertakings. Generally, collaboration networks bring together participants who wish to achieve some common goal or outcome (often over short time frames). Increasingly, scientific collaborations are becoming interdisciplinary, and scientists are working in informal collaborations to solve complex problems that require multiple types of skills. Such networks generally might consist of participants with complementary or similar skills, who might decide to collaborate to solve more efficiently a single large problem. We argue that, given the diverse skills that such collaborations involve, deciding which partners to cooperate with is both critical and difficult. Two particularly important factors in this process are trust and reputation.