Lara Dal Molin
ABSTRACT
There is evidence of a relationship between the dynamics of resource availability and the evolution of cooperative behaviour in complex networks. Previous studies have used mathematical models, agent-based models, and studies of hunter-gatherer societies to investigate the causal mechanisms behind this relationship. Here, we present a novel, agent-based software system, built using Unity 3D, which we employ to investigate the adaptation of food sharing networks to fluctuating resource availability. As a benefit of using Unity, our system possesses an easily customisable, visually compelling interface where evolution can be observed in real-time. Across four types of populations, under three environmental conditions, we performed a quantitative analysis of the evolving structure of social interactions. A biologically-inspired gene-sequencing function translates an arbitrarily extendable genome into phenotypic behaviour. Our results contribute to the understanding of how resource availability affects the evolutionary path taken by artificial societies. It emerges that environmental conditions have a greater impact on social evolution compared to the initial genetic configurations of each society. In particular, we find that scenarios of periodically fluctuating resources lead to the evolution of stable, tightly organised societies, which form small, local, mutualistic food-sharing networks.
- Chris Adami, C. Titus Brown, and W.K. Kellogg. 1994. Evolutionary Learning in the 2D Artificial Life System "Avida". In Artificial Life IV. MIT Press, 377--381.Google Scholar
- Steven S Branda, José Eduardo González-Pastor, Sigal Ben-Yehuda, Richard Losick, and Roberto Kolter. 2001. Fruiting body formation by Bacillus subtilis. Proceedings of the National Academy of Sciences 98, 20 (2001), 11621--11626.Google ScholarCross Ref
- Carlos García-Martínez, Francisco J Rodríguez, and Manuel Lozano. 2018. Genetic Algorithms. (2018).Google Scholar
- Andy Gardner, Ashleigh S Griffin, and Stuart A West. 2009. Theory of cooperation. eLS (2009).Google Scholar
- Herbert Gintis, Eric Alden Smith, and Samuel Bowles. 2001. Costly signaling and cooperation. Journal of theoretical biology 213, 1 (2001), 103--119.Google ScholarCross Ref
- Rachel Green and Harry F Noller. 1997. Ribosomes and translation. Annual review of biochemistry 66, 1 (1997), 679--716.Google Scholar
- Michael Gurven, Kim Hill, and Hillard Kaplan. 2002. From forest to reservation: Transitions in food-sharing behavior among the Ache of Paraguay. Journal of anthropological research 58, 1 (2002), 93--120.Google ScholarCross Ref
- Marcus J Hamilton, Oskar Burger, John P DeLong, Robert S Walker, Melanie E Moses, and James H Brown. 2009. Population stability, cooperation, and the invasibility of the human species. Proceedings of the National Academy of Sciences 106, 30 (2009), 12255--12260.Google ScholarCross Ref
- W.D. Hamilton. 1964. The genetical evolution of social behaviour. I. Journal of Theoretical Biology 7, 1 (1964), 1 -- 16. http://www.sciencedirect.com/science/article/pii/0022519364900384Google ScholarCross Ref
- Gregory A Johnson. 1982. Organizational structure and scalar stress. Theory and explanation in archaeology (1982), 389--421.Google Scholar
- Hillard S Kaplan, Eric Schniter, Vernon L Smith, and Bart J Wilson. 2012. Risk and the evolution of human exchange. Proceedings of the Royal Society B: Biological Sciences 279, 1740 (2012), 2930--2935.Google ScholarCross Ref
- Lawrence H Keeley. 1988. Hunter-gatherer economic complexity and "population pressure": A cross-cultural analysis. Journal of anthropological archaeology 7, 4 (1988), 373--411.Google ScholarCross Ref
- William M Kenkel, Allison M Perkeybile, and C Sue Carter. 2017. The neurobiological causes and effects of alloparenting. Developmental neurobiology 77, 2 (2017), 214--232.Google Scholar
- Michael Kirley. 2005. Competition, cooperation and collective behaviour: resource utilization in non-stationary environments. In IEEE/WIC/ACM International Conference on Intelligent Agent Technology. IEEE, 572--578.Google ScholarDigital Library
- Charles M Macal. 2020. Agent-based modeling and artificial life. Complex Social and Behavioral Systems: Game Theory and Agent-Based Models (2020), 725--745.Google Scholar
- Michael O'Neill and Conor Ryan. 2001. Grammatical evolution. IEEE Transactions on Evolutionary Computation 5, 4 (2001), 349--358.Google ScholarDigital Library
- María Pereda, Débora Zurro, José I Santos, Ivan Briz i Godino, Myrian Álvarez, Jorge Caro, and José M Galán. 2017. Emergence and evolution of cooperation under resource pressure. Scientific reports 7 (2017), 45574.Google Scholar
- Alison E Rautman. 1993. Resource variability, risk, and the structure of social networks: An example from the prehistoric Southwest. American antiquity (1993), 403--424.Google Scholar
- Thomas S Ray and Joseph Hart. 1999. Evolution of differentiated multi-threaded digital organisms. In Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No. 99CH36289), Vol. 1. IEEE, 1--10.Google ScholarCross Ref
- António MM Rodrigues and Tiffany B Taylor. 2018. Ecological and demographic correlates of cooperation from individual to budding dispersal. Journal of evolutionary biology 31, 7 (2018), 1058--1070.Google ScholarCross Ref
- Ovi Chris Rouly. 2018. A computer simulation to investigate the association between gene-based gifting and pair-bonding in early hominins. Journal of human evolution 116 (2018), 43--56.Google ScholarCross Ref
- Seth Tisue and Uri Wilensky. 2004. Netlogo: A simple environment for modeling complexity. In International conference on complex systems, Vol. 21. Boston, MA, 16--21.Google Scholar
- Colin J Torney, Andrew Berdahl, and Iain D Couzin. 2011. Signalling and the evolution of cooperative foraging in dynamic environments. PLoS Comput Biol 7, 9 (2011), e1002194.Google ScholarCross Ref
- Unity Technologies. 2020. Unity (version 2020.1.17f). (2020). https://www.unity.comGoogle Scholar
- Stuart A West, Ashleigh S Griffin, Andy Gardner, and Stephen P Diggle. 2006. Social evolution theory for microorganisms. Nature reviews microbiology 4, 8 (2006), 597--607.Google Scholar
- Edward O Wilson and Bert Hölldobler. 2005. Eusociality: origin and consequences. Proceedings of the National Academy of Sciences 102, 38 (2005), 13367--13371.Google ScholarCross Ref
- Larry Yaeger. 1994. Computational genetics, physiology, metabolism, neural systems, learning, vision, and behavior or Poly World: Life in a new context. In SANTA FE INSTITUTE STUDIES IN THE SCIENCES OF COMPLEXITY-PROCEEDINGS VOLUME-, Vol. 17. Citeseer, 263--263.Google Scholar
Index Terms
- Resource availability and the evolution of cooperation in a 3D agent-based simulation
Recommendations
The evolution of cooperation in self-interested agent societies: a critical study
AAMAS '11: The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2We study the phenomenon of evolution of cooperation in a society of self-interested agents using repeated games in graphs. A repeated game in a graph is a multiple round game, where, in each round, an agent gains payoff by playing a game with its ...
Social Manifestation of Guilt Leads to Stable Cooperation in Multi-Agent Systems
AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent SystemsInspired by psychological and evolutionary studies, we present here theoretical models wherein agents have the potential to express guilt with the ambition to study the role of this emotion in the promotion of pro-social behaviour. To achieve this goal, ...
Effects of excessive elitism on the evolution of artificial creatures with NEAT
AbstractThis paper proposes a simple method based on a novel use of elitism to increase the population size of artificial creatures while minimizing evaluation cost. This can contribute to preventing premature convergence of the population. We propose the ...
Comments