Skip to main content

Routing Games in the Wild: Efficiency, Equilibration and Regret

Large-Scale Field Experiments in Singapore

  • Conference paper
  • First Online:
Web and Internet Economics (WINE 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10660))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Indeed, if we keep increasing the total flow in e.g. Pigou’s example, eventually both in the optimal and equilibrium flows almost all flow will be routed through the slow link.

  2. 2.

    Statistics were compiled from data.gov.sg.

  3. 3.

    Household Interview Travel Survey 2012: Public Transport Mode Share Rises To 63%, LTA News Release.

References

  1. Ackermann, H., Berenbrink, P., Fischer, S., Hoefer, M.: Concurrent imitation dynamics in congestion games. Distrib. Comput. 29(2), 105–125 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  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

    Chapter  Google Scholar 

  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. Bar-Gera, H.: Transportation network test problems (2011). https://github.com/bstabler/TransportationNetworks. Accessed 10 Nov 2017

  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. 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. 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. 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

    Chapter  Google Scholar 

  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. 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

    Chapter  Google Scholar 

  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. 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. Jalaly, P., Nekipelov, D., Tardos, É.: Learning and trust in auction markets. arXiv:1703.10672 (2017)

  14. Kleinberg, R., Piliouras, G., Tardos, É.: Multiplicative updates outperform generic no-regret learning in congestion games. In: STOC (2009)

    Google Scholar 

  15. Koutsoupias, E., Papadimitriou, C.H.: Worst-case equilibria. In: STACS, pp. 404–413 (1999)

    Google Scholar 

  16. Kurokawa, D., Procaccia, A.D., Shah, N.: Leximin allocations in the real world. In: EC, pp. 345–362. ACM (2015)

    Google Scholar 

  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. 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. Monderer, D., Shapley, L.S.: Potential games. Games Econ. Behav. 4(1), 124–143 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  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. Monnot, B., Piliouras, G.: Limits and limitations of no-regret learning in games. Knowl. Eng. Rev. 32 (2017)

    Google Scholar 

  22. Nekipelov, D., Syrgkanis, V., Tardos, E.: Econometrics for learning agents. In: EC, pp. 1–18. ACM (2015)

    Google Scholar 

  23. Nekipelov, D., Wang, T.: Inference and auction design in online advertising. Commun. ACM 60(7), 70–79 (2017)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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)

    Article  MathSciNet  Google Scholar 

  27. Rosenthal, R.: A class of games possessing pure-strategy Nash equilibria. Int. J. Game Theor. 2(1), 65–67 (1973)

    Article  MathSciNet  MATH  Google Scholar 

  28. Roughgarden, T.: Intrinsic robustness of the price of anarchy. In: STOC, pp. 513–522. ACM (2009)

    Google Scholar 

  29. Roughgarden, T.: Twenty Lectures on Algorithmic Game Theory. Cambridge University Press, Cambridge (2016)

    Book  MATH  Google Scholar 

  30. Roughgarden, T., Tardos, É.: How bad is selfish routing? J. ACM (JACM) 49(2), 236–259 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  31. Roughgarden, T., Tardos, É.: Bounding the inefficiency of equilibria in nonatomic congestion games. Games Econ. Behav. 47(2), 389–403 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  32. Sheffi, Y.: Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods. Prentice-Hall, New Jersey, US (1985)

    Google Scholar 

  33. Syrgkanis, V.: Algorithmic game theory and econometrics. ACM SIGecom Exch. 14(1), 105–108 (2015)

    Article  Google Scholar 

  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)

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Barnabé Monnot .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Monnot, B., Benita, F., Piliouras, G. (2017). Routing Games in the Wild: Efficiency, Equilibration and Regret. In: R. Devanur, N., Lu, P. (eds) Web and Internet Economics. WINE 2017. Lecture Notes in Computer Science(), vol 10660. Springer, Cham. https://doi.org/10.1007/978-3-319-71924-5_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-71924-5_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71923-8

  • Online ISBN: 978-3-319-71924-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics