Social learning in a simple task allocation game

2017/02/19 by Chen Rui, Chen, Rui, Garcia Julian +3
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · Social Sciences · #Evolution and Genetic Dynamics #Evolutionary Game Theory and Cooperation #FOS: Biological sciences #FOS: Computer and information sciences #Multiagent Systems (cs.MA) #Opinion Dynamics and Social Influence #Populations and Evolution (q-bio.PE)

paper · pdf · doi:10.48550/arxiv.1702.05739

openalex publication_date 2017/02/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We investigate the effects of social interactions in task al- location using Evolutionary Game Theory (EGT). We propose a simple task-allocation game and study how different learning mechanisms can give rise to specialised and non- specialised colonies under different ecological conditions. By combining agent-based simulations and adaptive dynamics we show that social learning can result in colonies of generalists or specialists, depending on ecological parameters. Agent-based simulations further show that learning dynamics play a crucial role in task allocation. In particular, introspective individual learning readily favours the emergence of specialists, while a process resembling task recruitment favours the emergence of generalists.

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