Routing Games in the Wild: Efficiency, Equilibration and Regret

Large-Scale Field Experiments in Singapore
  • Barnabé Monnot
  • Francisco Benita
  • Georgios Piliouras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10660)

Abstract

Routing games are amongst the most well studied domains of game theory. How relevant are these theoretical models and results to capturing the reality of everyday traffic? We focus on a semantically rich dataset that captures detailed information about the daily behavior of thousands of Singaporean commuters and examine the following basic questions:
  • Does the traffic equilibrate?

  • Is the system behavior consistent with latency minimizing agents?

  • Is the resulting system efficient?

The answers to all three questions are shown to be largely positive. Finally, in order to capture the efficiency of the traffic network in a way that agrees with our everyday intuition we introduce a new metric, the stress of catastrophe, which reflects the combined inefficiencies of both tragedy of the commons as well as price of anarchy effects.

Notes

Acknowledgements

The authors would like to thank the National Science Experiment team at SUTD for their help: Garvit Bansal, Sarah Nadiawati, Hugh Tay Keng Liang, Nils Ole Tippenhauer, Bige Tunçer, Darshan Virupashka, Erik Wilhelm and Yuren Zhou. The National Science Experiment is supported by the Singapore National Research Foundation (NRF), Grant RGNRF1402.

Barnabé Monnot acknowledges the SUTD Presidential Graduate Fellowship. Francisco Benita acknowledges CONACyT CVU 369933 (Mexico). Georgios Piliouras acknowledges SUTD grant SRG ESD 2015 097, MOE AcRF Tier 2 Grant 2016-T2-1-170 and a NRF fellowship. Part of the work was completed while Barnabé Monnot and Georgios Piliouras were visiting scientists at the Simons Institute for the Theory of Computing.

References

  1. 1.
    Ackermann, H., Berenbrink, P., Fischer, S., Hoefer, M.: Concurrent imitation dynamics in congestion games. Distrib. Comput. 29(2), 105–125 (2016)MathSciNetCrossRefMATHGoogle Scholar
  2. 2.
    Angelidakis, H., Fotakis, D., Lianeas, T.: Stochastic congestion games with risk-averse players. In: Vöcking, B. (ed.) SAGT 2013. LNCS, vol. 8146, pp. 86–97. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-41392-6_8 CrossRefGoogle Scholar
  3. 3.
    Bajari, P., Hong, H., Nekipelov, D.: Game theory and econometrics: a survey of some recent research. In: Advances in Economics and Econometrics, 10th World Congress, vol. 3, pp. 3–52 (2013)Google Scholar
  4. 4.
    Bar-Gera, H.: Transportation network test problems (2011). https://github.com/bstabler/TransportationNetworks. Accessed 10 Nov 2017
  5. 5.
    Blum, A., Hajiaghayi, M., Ligett, K., Roth, A.: Regret minimization and the price of total anarchy. In: STOC, pp. 373–382 (2008)Google Scholar
  6. 6.
    Buriol, L., Ritt, M., Rodrigues, F., Schäfer, G.: On the smoothed price of anarchy of the traffic assignment problem. In: ATMOS, pp. 122–133. ATMOS (2011)Google Scholar
  7. 7.
    Colini-Baldeschi, R., Cominetti, R., Mertikopoulos, P., Scarsini, M.: On the asymptotic behavior of the price of anarchy: is selfish routing bad in highly congested networks? ArXiv e-prints (2017)Google Scholar
  8. 8.
    Colini-Baldeschi, R., Cominetti, R., Scarsini, M.: On the price of anarchy of highly congested nonatomic network games. In: Gairing, M., Savani, R. (eds.) SAGT 2016. LNCS, vol. 9928, pp. 117–128. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-662-53354-3_10 CrossRefGoogle Scholar
  9. 9.
    Feldman, M., Immorlica, N., Lucier, B., Roughgarden, T., Syrgkanis, V.: The price of anarchy in large games. In: Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, pp. 963–976. ACM (2016)Google Scholar
  10. 10.
    Fotakis, D., Kaporis, A.C., Spirakis, P.G.: Atomic congestion games: fast, myopic and concurrent. In: Monien, B., Schroeder, U.-P. (eds.) SAGT 2008. LNCS, vol. 4997, pp. 121–132. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-79309-0_12 CrossRefGoogle Scholar
  11. 11.
    Gal, Y.K., Mash, M., Procaccia, A.D., Zick, Y.: Which is the fairest (rent division) of them all? In: EC, pp. 67–84. ACM (2016)Google Scholar
  12. 12.
    Hoy, D., Nekipelov, D., Syrgkanis, V.: Robust data-driven guarantees in auctions. In: Preliminary version at 1st Workshop on Algorithmic Game Theory and Data Science (2015)Google Scholar
  13. 13.
    Jalaly, P., Nekipelov, D., Tardos, É.: Learning and trust in auction markets. arXiv:1703.10672 (2017)
  14. 14.
    Kleinberg, R., Piliouras, G., Tardos, É.: Multiplicative updates outperform generic no-regret learning in congestion games. In: STOC (2009)Google Scholar
  15. 15.
    Koutsoupias, E., Papadimitriou, C.H.: Worst-case equilibria. In: STACS, pp. 404–413 (1999)Google Scholar
  16. 16.
    Kurokawa, D., Procaccia, A.D., Shah, N.: Leximin allocations in the real world. In: EC, pp. 345–362. ACM (2015)Google Scholar
  17. 17.
    Lykouris, T., Syrgkanis, V., Tardos, É.: Learning and efficiency in games with dynamic population. In: SODA, pp. 120–129. SIAM (2016)Google Scholar
  18. 18.
    Mehta, R., Panageas, I., Piliouras, G.: Natural selection as an inhibitor of genetic diversity: multiplicative weights updates algorithm and a conjecture of haploid genetics. In: ITCS (2015)Google Scholar
  19. 19.
    Monderer, D., Shapley, L.S.: Potential games. Games Econ. Behav. 4(1), 124–143 (1996)MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Monnot, B., Benita, F., Piliouras, G.: Routing games in the wild: efficiency, equilibration and regret (Large-Scale Field Experiments in Singapore). arXiv preprint arXiv:1708.04081 (2017)
  21. 21.
    Monnot, B., Piliouras, G.: Limits and limitations of no-regret learning in games. Knowl. Eng. Rev. 32 (2017)Google Scholar
  22. 22.
    Nekipelov, D., Syrgkanis, V., Tardos, E.: Econometrics for learning agents. In: EC, pp. 1–18. ACM (2015)Google Scholar
  23. 23.
    Nekipelov, D., Wang, T.: Inference and auction design in online advertising. Commun. ACM 60(7), 70–79 (2017)CrossRefGoogle Scholar
  24. 24.
    Nikolova, E., Stier-Moses, N.E.: A mean-risk model for the traffic assignment problem with stochastic travel times. Oper. Res. 62(2), 366–382 (2014)MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Panageas, I., Piliouras, G.: Average case performance of replicator dynamics in potential games via computing regions of attraction. In: EC, pp. 703–720. ACM (2016)Google Scholar
  26. 26.
    Piliouras, G., Nikolova, E., Shamma, J.S.: Risk sensitivity of price of anarchy under uncertainty. ACM Trans. Econ. Comput. 5(1), 5:1–5:27 (2016)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Rosenthal, R.: A class of games possessing pure-strategy Nash equilibria. Int. J. Game Theor. 2(1), 65–67 (1973)MathSciNetCrossRefMATHGoogle Scholar
  28. 28.
    Roughgarden, T.: Intrinsic robustness of the price of anarchy. In: STOC, pp. 513–522. ACM (2009)Google Scholar
  29. 29.
    Roughgarden, T.: Twenty Lectures on Algorithmic Game Theory. Cambridge University Press, Cambridge (2016)CrossRefMATHGoogle Scholar
  30. 30.
    Roughgarden, T., Tardos, É.: How bad is selfish routing? J. ACM (JACM) 49(2), 236–259 (2002)MathSciNetCrossRefMATHGoogle Scholar
  31. 31.
    Roughgarden, T., Tardos, É.: Bounding the inefficiency of equilibria in nonatomic congestion games. Games Econ. Behav. 47(2), 389–403 (2004)MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    Sheffi, Y.: Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. Prentice-Hall, New Jersey, US (1985)Google Scholar
  33. 33.
    Syrgkanis, V.: Algorithmic game theory and econometrics. ACM SIGecom Exch. 14(1), 105–108 (2015)CrossRefGoogle Scholar
  34. 34.
    Zhang, J., Pourazarm, S., Cassandras, C.G., Paschalidis, I.C.: Data-driven estimation of origin-destination demand and user cost functions for the optimization of transportation networks. arXiv preprint arXiv:1610.09580 (2016)

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Barnabé Monnot
    • 1
  • Francisco Benita
    • 1
  • Georgios Piliouras
    • 1
  1. 1.Engineering Systems and DesignSingapore University of Technology and DesignSingaporeSingapore

Personalised recommendations