Social learning in a simple task allocation game

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.

[1]  Iain D. Couzin,et al.  Collective Learning and Optimal Consensus Decisions in Social Animal Groups , 2014, PLoS Comput. Biol..

[2]  Guy Theraulaz,et al.  Dynamic Scheduling and Division of Labor in Social Insects , 2000, Adapt. Behav..

[3]  Åke Brännström,et al.  The Hitchhiker's Guide to Adaptive Dynamics , 2013, Games.

[4]  M. Nowak,et al.  Evolutionary game dynamics in a Wright-Fisher process , 2006, Journal of mathematical biology.

[5]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[6]  E. Bonabeau,et al.  Quantitative study of the fixed threshold model for the regulation of division of labour in insect societies , 1996, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[7]  Dario Floreano,et al.  Neural Networks as Mechanisms to Regulate Division of Labor , 2012, The American Naturalist.

[8]  Laurent Keller,et al.  Evolution of self-organized division of labor in a response threshold model , 2012, Behavioral Ecology and Sociobiology.

[9]  Raphaël Jeanson,et al.  Interindividual variability in social insects – proximate causes and ultimate consequences , 2014, Biological reviews of the Cambridge Philosophical Society.

[10]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[11]  R. Dukas Evolutionary biology of insect learning. , 2008, Annual review of entomology.

[12]  A. Dornhaus,et al.  When doing nothing is something. How task allocation strategies compromise between flexibility, efficiency, and inactive agents , 2015 .

[13]  Falko Dressler,et al.  Self-organization in sensor and actor networks , 2007, Wiley series in communications networking and distributed systems.

[14]  C. Hauert,et al.  The Evolutionary Origin of Cooperators and Defectors , 2004, Science.

[15]  Thomas Bck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[16]  S. Gavrilets,et al.  20 Questions on Adaptive Dynamics , 2005, Journal of evolutionary biology.