Abstract
The Iterated Prisoner’s Dilemma (IPD) game is a one of the most popular subjects of study in game theory. Numerous experiments have investigated many properties of this game over the last decades. However, topics related to the simulation scale did not always play a significant role in such experimental work. The main contribution of this paper is the optimization of IPD strategies performed in a distributed actor-based computing and simulation environment. Besides showing the scalability and robustness of the framework, we also dive into details of some key simulations, analyzing the most successful strategies obtained.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Classic values for the payoff matrix are \(Temptation=5\), \(Reward=3\), \(Punishment=1\) and \(Sucker=0\).
- 2.
- 3.
References
Axelrod, R.: The Evolution of Cooperation. Basic Books, New York (2006)
Rapoport, A., Chammah, A.M.: Prisoner’s Dilemma: A Study in Conflict and Cooperation. University of Michigan Press (1965)
Roth, A., Murnighan, J.: Equilibrium behavior and repeated play of the prisoner’s dilemma. J. Math. Psychol. 17(2), 189–198 (1978)
Fogel, D.: Evolving behaviors in the iterated prisoner’s dilemma. Evol. Comput. 1, 77–97 (1993)
Kendall, G., Yao, X., Chong, S.: The Iterated Prisoners’ Dilemma: 20 Years on. World Scientific, Singapore (2006)
Van Veelen, M., Garcia, J., Rand, D., Nowak, M.: Direct reciprocity in structured populations. Proc. Natl. Acad. Sci. 109(25), 9929–9934 (2012)
Peleteiro, A., Burguillo, J.C., Chong, S.Y.: Exploring indirect reciprocity in complex networks using coalitions and rewiring. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems, AAMAS 2014, Richland, SC, pp. 669–676. International Foundation for Autonomous Agents and Multiagent Systems (2014)
Wellman, M.: Putting the agent in agent-based modeling. Auton. Agent. Multi-Agent Syst. 30, 1175–1189 (2016)
Wiedenbeck, B., Wellman, M.: Scaling simulation-based game analysis through deviation- preserving reduction. In: Proceedings of 11th International Conference on Autonomous Agents and Multi-Agent Systems. ACM (2012)
Faber, L., Pietak, K., Byrski, A., Kisiel-Dorohinicki, M.: Agent-based simulation in AgE framework. In: Byrski, A., Oplatková, Z., Carvalho, M., Kisiel-Dorohinicki, M. (eds.) Advances in Intelligent Modelling and Simulation: Simulation Tools and Applications, vol. 416, pp. 55–83. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28888-3_3
Kisiel-Dorohinicki, M.: Agent-based models and platforms for parallel evolutionary algorithms. In: Bubak, M., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2004. LNCS, vol. 3038, pp. 646–653. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24688-6_84
Hewitt, C., Bishop, P., Steiger, R.: A universal modular actor formalism for artificial intelligence. In: Proceedings of the 3rd International Joint Conference on Artificial Intelligence. IJCAI 1973, San Francisco, CA, USA, pp. 235–245. Morgan Kaufmann Publishers Inc. (1973)
Agha, G.: Actors: A Model of Concurrent Computation in Distributed Systems. MIT Press, Cambridge (1986)
Haller, P., Odersky, M.: Scala actors: unifying thread-based and event-based programming. Theoret. Comput. Sci. 410(2), 202–220 (2009)
Snijders, T.A., van de Bunt, G.G., Steglich, C.E.: Introduction to stochastic actor-based models for network dynamics. Soc. Netw. 32(1), 44–60 (2010). Dynamics of Social Networks
Esposito, A., Loia, V.: Integrating concurrency control and distributed data into workflow frameworks: an actor model perspective. In: 2000 IEEE International Conference on Systems, Man, and Cybernetics, vol. 3, pp. 2110–2114 (2000)
Skiba, G., et al.: Flexible asynchronous simulation of iterated prisoner’s dilemma based on actor model. Simul. Model. Pract. Theory 83, 75–92 (2018)
Peleteiro, A., Burguillo, J.C., Luck, M., Arcos, J.L., Rodígruez-Aguilar, J.A.: Using reputation and adaptive coalitions to support collaboration in competitive environments. Eng. Appl. Artif. Intell. 45, 325–338 (2015)
Peleteiro, A., Burguillo, J.C., Bazzan, A.L.C.: How coalitions enhance cooperation in the IPD over complex networks. In: 2012 Third Brazilian Workshop on Social Simulation, pp. 68–74, October 2012
Peleteiro, A., Burguillo, J.C., Arcos, J.L., Rodriguez-Aguilar, J.A.: Fostering cooperation through dynamic coalition formation and partner switching. ACM Trans. Auton. Adapt. Syst. 9(1), 1:1–1:31 (2014)
Huberman, B., Glance, N.: Evolutionary games and computer simulations. Proc. Natl. Acad. Sci. USA 90, 7716–7718 (1993)
Grilo, C., Correia, L.: What makes spatial prisoner’s dilemma game sensitive to asynchronism? In: Proceedings of 11th International Conference on the Simulation and Synthesis of Living Systems, Alife XI. MIT (2008)
Grilo, C., Correia, L.: The influence of asynchronous dynamics in the spatial prisoner’s dilemma game. In: Asada, M., Hallam, J.C.T., Meyer, J.-A., Tani, J. (eds.) SAB 2008. LNCS (LNAI), vol. 5040, pp. 362–371. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69134-1_36
Newth, D.: Asynchronous iterated prisoner’s dilemma. Adapt. Behav. 17(2), 175–183 (2009)
Newth, D., Cornforth, D.: Asynchronous spatial evolutionary games. Biosystems 95(2), 120–129 (2009)
Abar, S., Theodoropoulos, G.K., Lemarinier, P., O’Hare, G.M.: Agent based modelling and simulation tools: a review of the state-of-art software. Comput. Sci. Rev. 24, 13–33 (2017)
Collier, N., North, M.: Parallel agent-based simulation with repast for high performance computing. Simulation 89(10), 1215–1235 (2013)
Coakley, S., Gheorghe, M., Holcombe, M., Chin, S., Worth, D., Greenough, C.: Exploitation of high performance computing in the flame agent-based simulation framework. In: 2012 IEEE 14th International Conference on High Performance Computing and Communication and 2012 IEEE 9th International Conference on Embedded Software and Systems, pp. 538–545, June 2012
Suryanarayanan, V., Theodoropoulos, G., Lees, M.: PDES-MAS: distributed simulation of multi-agent systems. Procedia Comput. Sci. 18, 671–681 (2013)
Wittek, P., Rubio-Campillo, X.: Scalable agent-based modelling with cloud HPC resources for social simulations. In: 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp. 355–362, December 2012
Allen, J.: Effective Akka. O’Reilly Media, Sebastopol (2013)
Piccolo, E., Squillero, G.: Adaptive opponent modelling for the iterated prisoner’s dilemma. In: 2011 IEEE Congress of Evolutionary Computation (CEC), pp. 836–841, June 2011
Hein, O., Schwind, M., König, W.: Scale-free networks. Wirtschaftsinformatik 48(4), 267–275 (2006)
Axelrod, R., Axelrod, R.M.: The Evolution of Cooperation, vol. 5145. Basic Books, New York (1984)
Boyd, R., Lorberbaum, J.P.: No pure strategy is evolutionarily stable in the repeated prisoner’s dilemma game. Nature 327(6117), 58–59 (1987)
Friedman, J.W.: A non-cooperative equilibrium for supergames. Rev. Econ. Stud. 38(1), 1–12 (1971)
Acknowledgment
This research was supported by AGH University of Science and Technology Statutory Project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer-Verlag GmbH Germany, part of Springer Nature
About this chapter
Cite this chapter
Starzec, G., Starzec, M., Byrski, A., Kisiel-Dorohinicki, M., Burguillo, J.C., Lenaerts, T. (2019). Towards Large-Scale Optimization of Iterated Prisoner Dilemma Strategies. In: Nguyen, N., Kowalczyk, R., Hernes, M. (eds) Transactions on Computational Collective Intelligence XXXII. Lecture Notes in Computer Science(), vol 11370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58611-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-662-58611-2_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-58610-5
Online ISBN: 978-3-662-58611-2
eBook Packages: Computer ScienceComputer Science (R0)