Calum C. Imrie
ABSTRACT
The paradox of choice refers to the observation that numerous choices can have a detrimental effect on the quality of decision making. We study this effect in swarms in the context of a resource foraging task. We simulate the evolution of swarms gathering energy from a number of energy sources distributed in a two-dimensional environment. As the number of sources increases, the evolution of the swarm results in reduced levels of efficiency, despite the sources covering more space. Our results indicate that this effect arises because the simultaneous detection of multiple sources is incompatible with an evolutionary scheme that favours greedy energy consumption. In particular, the communication among the agents tends to reduce their efficiency by precluding the evolution of a clear preference for an increasing number of options. The overabundance of explicit information in the swarm about fitness-related options cannot be exploited by the agents lacking complex planning capabilities. If the sensor ranges evolve in addition to the behaviour of the agents, a preference for reduced choice results, and the average range approaches zero as the number of sources increases. Our study thus presents a minimal model for the paradox of choice, which implies several options of experimental verification.
- C. Adami. 1998. Introduction to Artificial Life. TELOS Springer-Verlag, Santa Clara.Google Scholar
- Per Bak and Kim Sneppen. 1993. Punctuated equilibrium and criticality in a simple model of evolution. Physical review letters 71, 24 (1993), 4083.Google Scholar
- M. Birattari, B. Delhaisse, G. Francesca, and Y. Kerdoncuff. 2016. Observing the Effects of Overdesign in the Automatic Design of Control Software for Robot Swarms. In International Conference on Swarm Intelligence. Springer, Cham, 149--160.Google Scholar
- E. Bonabeau and G. Dorigo, M. and Theraulaz. 1999. Swarm intelligence: from natural to artificial systems. Oxford University Press, Oxford.Google Scholar
- D. Bratton and J. Kennedy. 2007. Defining a standard for particle swarm optimization. In 2007 IEEE Swarm Intelligence Symposium. IEEE, Honolulu, HI, 120--127.Google Scholar
- Dave Cliff, Phil Husbands, and Inman Harvey. 1993. Explorations in evolutionary robotics. Adaptive behavior 2, 1 (1993), 73--110.Google Scholar
- I. D. Couzin, J. Krause, N. R. Franks, and S. A. Levin. 2005. Effective leadership and decision-making in animal groups on the move. Nature 433, 7025 (2005), 513--516.Google ScholarCross Ref
- E. Crosato, L. Jiang, V. Lecheval, J. T. Lizier, X. R. Wang, P. Tichit, G. Theraulaz, and M. Prokopenko. 2018. Informative and misinformative interactions in a school of fish. Swarm Intelligence 12, 4 (2018), 283--305.Google ScholarCross Ref
- Jeffrey L Elman. 1990. Finding structure in time. Cognitive science 14, 2 (1990), 179--211.Google Scholar
- E. Garcia-Gonzalo and J. L. Fernandez-Martinez. 2012. A brief historical review of particle swarm optimization (PSO). Journal of Bioinformatics and Intelligent Control 1, 1 (2012), 3--16.Google ScholarCross Ref
- M. Gauci, M. E. Ortiz, M. Rubenstein, and R. Nagpal. 2017. Error Cascades in Collective Behavior: A Case Study of the Gradient Algorithm on 1000 Physical Agents. In Proc. (AAMAS '17). Int. Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 1404--1412.Google Scholar
- M. F. Hale, M. Angus, E. Buchanan, W. Li, R. Woolley, L. K. Le Goff, M. De Carlo, J. Timmis, A. F. Winfield, E. Hart, A. E. Eiben, and A. M. Tyrrell. 2020. Hardware design for autonomous robot evolution. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, Canberra, Australia, 2140--2147.Google Scholar
- J. Michael. Herrmann. 2001. Dynamical systems for predictive control of autonomous robots. Theory in Biosciences 120, 3-4 (2001), 241--252.Google Scholar
- Sheena S. Iyengar and Mark R. Lepper. 2000. When choice is demotivating: Can one desire too much of a good thing? Journal of Personality and Social Psychology 79, 6 (2000), 995.Google ScholarCross Ref
- S. Kriegman, D. Blackiston, M. Levin, and J. Bongard. 2020. A scalable pipeline for designing reconfigurable organisms. Proceedings of the National Academy of Sciences of the U.S.A. 117, 4 (2020), 1853--1859.Google ScholarCross Ref
- R. Legenstein and W. Maass. 2007. Edge of chaos and prediction of computational performance for neural circuit models. Neural Networks 20, 3 (2007), 323--334.Google ScholarDigital Library
- Y. Liu, D. Gong, J. Sun, and Y. Jin. 2017. A many-objective evolutionary algorithm using a one-by-one selection strategy. IEEE Transactions on Cybernetics 47, 9 (2017), 2689--2702.Google ScholarCross Ref
- J. T. Lizier, M. Prokopenko, and A. Y. Zomaya. 2012. Local measures of information storage in complex distributed computation. Information Sciences 208 (2012), 39--54.Google ScholarDigital Library
- R. Martínez-García, J. M. Calabrese, T. Mueller, K. A. Olson, and C. López. 2013. Optimizing the search for resources by sharing information: Mongolian gazelles as a case study. Physical Review Letters 110, 24 (2013), 248106.Google ScholarCross Ref
- J. M. Miller, X. R. Wang, J. T. Lizier, M. Prokopenko, and L. F. Rossi. 2014. Measuring information dynamics in swarms. In Guided self-organization: Inception. Springer, Berlin, Heidelberg, 343--364.Google Scholar
- H. Oh, A. R. Shirazi, C. Sun, and Y. Jin. 2017. Bio-inspired self-organising multi-robot pattern formation: A review. Robotics and Autonomous Systems 91 (2017), 83--100.Google ScholarDigital Library
- L. Pitonakova, R. Crowder, and S. Bullock. 2016. Information flow principles for plasticity in foraging robot swarms. Swarm Intelligence 10, 1 (2016), 33--63.Google ScholarCross Ref
- H. Sayama. 2009. Swarm chemistry. Artificial Life 15, 1 (2009), 105--114.Google ScholarDigital Library
- Barry Schwartz. 2009. The paradox of choice: Why more is less (revised edition ed.). HarperCollins, New York.Google Scholar
- H.-P. Schwefel. 1984. Evolution strategies: A family of non-linear optimization techniques based on imitating some principles of organic evolution. Annals of Operations Research 1, 2 (1984), 165--167.Google ScholarCross Ref
- W. L. Shew and D. Plenz. 2013. The functional benefits of criticality in the cortex. The neuroscientist 19, 1 (2013), 88--100.Google Scholar
- H. Suganuma, Y. Kawai, J. Park, and M. Asada. 2019. Maximization of Transfer Entropy leads to Evolution of Functional Differentiation of Swarms. In The 2018 Conference on Artificial Life: A Hybrid of the European Conference on Artificial Life (ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALIFE). MIT Press, Cambridge, MA, 414--415.Google Scholar
- C. J. Torney, A. Berdahl, and I. D. Couzin. 2011. Signalling and the evolution of cooperative foraging in dynamic environments. PLoS Computational Biology 7, 9 (2011), e1002194.Google ScholarCross Ref
- H. Trautmann, T. Wagner, B. Naujoks, M. Preuss, and J. Mehnen. 2009. Statistical methods for convergence detection of multi-objective evolutionary algorithms. Evolutionary Computation 17, 4 (2009), 493--509.Google ScholarDigital Library
- O. Witkowski and T. Ikegami. 2016. Emergence of swarming behavior: foraging agents evolve collective motion based on signaling. PloS One 11, 4 (2016), e0152756.Google ScholarCross Ref
- H. Yanagisawa, O. Kawamata, and K. Ueda. 2019. Modeling Emotions Associated with Novelty Under Uncertainty: A Bayesian Approach. Frontiers in computational neuroscience 13 (2019), 2.Google Scholar
Index Terms
- The paradox of choice in evolving swarms: information overload leads to limited sensing
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