Dynamic patterns in similarity-based cooperation: an agent-based investigation

Authored by Paolo Pellizzari, Caterina Cruciani, Anna Moretti

Date Published: 2017

DOI: 10.1007/s11403-015-0155-7

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

Understanding what motivates and fosters collective actions has major implications in the governance and management of organizations, in the regulation and design of public policies, and has long attracted the interests of scholars and practitioners in business and economics. This paper deals with how groups of agents emerge in a dynamic contest characterized by lack of formal structure and uncertainty regarding the possible individual outcomes, focusing on the features of the cooperators and on the dynamics emerging among them. Through the development of a stylized agent-based model we start by showing how similarity in values can be a successful driver for cooperation but are also able to highlight the limits of such process, by looking at how and how much agents cooperate with similar others. A second-version of the model, where memory of past interactions has a role, introduces further dynamics and is able to create successful and relatively stable groups.

Tags

Evolution Cooperation Reciprocity Trust identity networks Groups Similarity Game Prisoners-dilemma Social trust