Strategic Group Formation in the El Farol Bar Problem

Part of the Understanding Complex Systems book series (UCS)


The El Farol bar problem sprung from one patron’s wish to avoid the bar’s busy nights in the 1990s. The problem became of interest to economists because of its potential application to other consumer choice behavior problems, e.g., route selection on a congested roadway. The El Farol bar problem involves multiple decision-making agents trying to outwit each other and only attend the bar when it is not overcrowded. Each agent makes their decision based on historical data and draws their attendance strategy from a limited pool. We adapt the model of the problem developed, by Rand and Wilensky, to include group decision-making behavior and strategic group formation. In our version, agents can use the best strategy from the whole group, not just their own set. However, the larger the group, the more it adds to the overcrowding issue. Thus, an agent must balance access to a large attendance strategy pool with group size. We had hypothesized that including strategic group formation will increase overall social welfare, but our analysis shows that allowing agent groups results in a undesirable scenario for all agents; this is due to the limited rationality of the agents.


Agent-based modeling and simulation Group formation El Farol bar problem 


  1. 1.
    Arthur, W.B.: Bounded rationality and inductive behavior (the El Farol problem). Am. Econ. Rev. 84, 406–411 (1994)Google Scholar
  2. 2.
    Chakrabarti, A.S., Chakrabarti, B.K., Chatterjee, A., Mitra, M.: The Kolkata Paise Restaurant problem and resource utilization. Phys. A: Stat. Mech. Its Appl. 388, 2420–2426 (2009)CrossRefGoogle Scholar
  3. 3.
    Challet, D., Zhang, Y.-C.: Emergence of cooperation and organization in an evolutionary game. Phys. A: Stat. Mech. Its Appl. 246, 407–418 (1997)CrossRefGoogle Scholar
  4. 4.
    Challet, D., Marsili, M., Zhang, Y.-C.: Minority games: interacting agents in financial markets. OUP Catalogue (2013)Google Scholar
  5. 5.
    Collins, A.J., Frydenlund, E.: Agent-based modeling and strategic group formation: a refugee case study. In: Proceedings of the 2016 Winter Simulation Conference, Washington, DC, pp. 1–12 (2016)Google Scholar
  6. 6.
    Collins, A.J., Frydenlund, E.: Strategic group formation in agent-based simulation. In: 2016 Spring Simulation Multi-conference, Pasadena, CA, pp. 1–8 (2016)Google Scholar
  7. 7.
    Wilensky, U.: Netlogo (1999).
  8. 8.
    Axelrod, R.: The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press, Princeton (1997)Google Scholar
  9. 9.
    Fudenberg, D., Levine, D.K.: The Theory of Learning in Games. MIT Press, London (1998)zbMATHGoogle Scholar
  10. 10.
    Fogel, D.B., Chellapilla, K., Angeline, P.J.: Inductive reasoning and bounded rationality reconsidered. IEEE Trans. Evol. Comput. 3, 142–146 (1999)CrossRefGoogle Scholar
  11. 11.
    Chen, S.-H., Gostoli, U.: On the complexity of the El Farol Bar game: a sensitivity analysis. Evol. Intel. 9, 113–123 (2016)CrossRefGoogle Scholar
  12. 12.
    Chen, S.-H., Gostoli, U.: Coordination in the El Farol bar problem: the role of social preferences and social networks. J. Econ. Interac. Coord. 12, 59–93 (2017)CrossRefGoogle Scholar
  13. 13.
    Corgnet, B.: Team formation and self-serving biases. J. Econ. Manag. Strat. 19, 117–135 (2010)CrossRefGoogle Scholar
  14. 14.
    Wi, H., Oh, S., Mun, J., Jung, M.: A team formation model based on knowledge and collaboration. Expert Syst. Appl. 36, 9121–9134 (2009)CrossRefGoogle Scholar
  15. 15.
    Watts, D.J.: The “new” science of networks. Annu. Rev. Sociol. 30, 243–270 (2004)CrossRefGoogle Scholar
  16. 16.
    Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. In: presented at the 14th Annual Conference on Computer Graphics and Interactive Techniques (1987)Google Scholar
  17. 17.
    Dash, R.K., Jennings, N.R., Parkes, D.C.: Computational-mechanism design: a call to arms. IEEE Intell. Syst. 18, 40–47 (2003)CrossRefGoogle Scholar
  18. 18.
    Cao, Y., Yu, W., Ren, W., Chen, G.: An overview of recent progress in the study of distributed multi-agent coordination. IEEE Trans. Industr. Inf. 9, 427–438 (2013)CrossRefGoogle Scholar
  19. 19.
    Martínez-Miranda, J., Pavón, J.: Modeling the influence of trust on work team performance. Simulation 88, 408–436 (2011)CrossRefGoogle Scholar
  20. 20.
    Celik, N., Xi, H., Xu, D., Son, Y.-J., Lemaire, R., Provan, K.: Simulation-based workforce assignment considering position in a social network. Simulation 88, 72–96 (2011)CrossRefGoogle Scholar
  21. 21.
    Jiang, G., Hu, B., Wang, Y.: Agent-based simulation approach to understanding the interaction between employee behavior and dynamic tasks. Simulation 87, 407–422 (2010)CrossRefGoogle Scholar
  22. 22.
    Rahwan, T., Michalak, T.P., Wooldridge, M., Jennings, N.R.: Coalition structure generation: a survey. Artif. Intell. 229, 139–174 (2015)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Chakravarty, S.R., Mitra, M., Sarkar, P.: A Course on Cooperative Game Theory. Cambridge University Press (2015)Google Scholar
  24. 24.
    Shapley, L.: A value of n-person games. In: Kuhn, H.W., Tucker, A.W. (eds.) Contributions to the Theory of Games, vol. II, pp. 307–317. Princeton University Press, Princeton (1953)Google Scholar
  25. 25.
    Thomas, L.C.: Games, Theory and Applications. Dover Publications, Mineola, NY (2003)zbMATHGoogle Scholar
  26. 26.
    Gillies, D.B.: Solutions to general non-zero-sum games. Contrib. Theory Games 4, 47–85 (1959)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Shapley, L.S.: On balanced sets and cores. Nav. Res. Logist. Q. 14, 453–460 (1967)CrossRefGoogle Scholar
  28. 28.
    Elzie, T., Frydenlund, E., Collins, A.J., Robinson, R.M.: Conceptualizing intragroup and intergroup dynamics within a controlled crowd evacuation. J. Emerg. Manag. 13, 109–120 (2014)CrossRefGoogle Scholar
  29. 29.
    Collins, A.J., Elzie, T., Frydenlund, E., Robinson, R.M.: Do groups matter? an agent-based modeling approach to pedestrian egress. Transp. Res. Procedia 2, 430–435 (22–24 Oct 2014)CrossRefGoogle Scholar
  30. 30.
    Frydenlund, E., Collins, A.J., Elzie, T., Robinson, R.M.: Group dynamics and exit-blocking behaviors: a look at pedestrian modeling evacuations. In: 94th Transportation Research Board Annual Meeting, Washington, DC (2015)Google Scholar
  31. 31.
    Guimera, R., Uzzi, B., Spiro, J., Amaral, L.A.N.: Team assembly mechanisms determine collaboration network structure and team performance. Science 308, 697–702 (2005)CrossRefGoogle Scholar
  32. 32.
    Wilensky, U., Rand, W.: An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. MIT Press (2015)Google Scholar
  33. 33.
    Gladwell, M.: The Tipping Point: How Little Things Can Make a Big Difference. Back Bay Books (2002)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Old Dominion UniversityNorfolkUSA

Personalised recommendations