October  2018, 5(4): 265-282. doi: 10.3934/jdg.2018017

On the stability of an adaptive learning dynamics in traffic games

1. 

San Diego State University, Computational Science Research Center, San Diego, CA 92182, USA

2. 

Universidad Adolfo Ibáñez, Facultad de Ingeniería y Ciencias, Santiago, Chile

* Corresponding author: mdumett@sdsu.edu

Computational Science Research Center, San Diego State University, California, USA. 
Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Santiago, Chile.

Received  August 2017 Revised  June 2018 Published  August 2018

This paper investigates the dynamic stability of an adaptive learning procedure in a traffic game. Using the Routh-Hurwitz criterion we study the stability of the rest points of the corresponding mean field dynamics. In the special case with two routes and two players we provide a full description of the number and nature of these rest points as well as the global asymptotic behavior of the dynamics. Depending on the parameters of the model, we find that there are either one, two or three equilibria and we show that in all cases the mean field trajectories converge towards a rest point for almost all initial conditions.

Citation: Miguel A. Dumett, Roberto Cominetti. On the stability of an adaptive learning dynamics in traffic games. Journal of Dynamics & Games, 2018, 5 (4) : 265-282. doi: 10.3934/jdg.2018017
References:
[1]

T. Ando, Totally positive matrices, Linear Algebra and Its Applications, 90 (1987), 165-219. doi: 10.1016/0024-3795(87)90313-2. Google Scholar

[2]

W. B. Arthur, On designing economic agents that behave like human agents, J. Evolutionary Econ., 3 (1933), 1-22. doi: 10.1007/BF01199986. Google Scholar

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P. AuerN. Cesa-BianchiY. Freund and R. E. Schapire, The non-stochastic multiarmed bandit problem, SIAM J. on Computing, 32 (2002), 48-77. doi: 10.1137/S0097539701398375. Google Scholar

[4]

E. Avinieri and J. Prashker, The impact of travel time information on travelers' learning under uncertainty, Transportation, 33 (2006), 393-408. doi: 10.1007/s11116-005-5710-y. Google Scholar

[5]

A. Beggs, On the convergence of reinforcement learning, Journal of Economic Theory, 122 (2005), 1-36. doi: 10.1016/j.jet.2004.03.008. Google Scholar

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A. Beggs, Learning in Bayesian games with binary actions, B. E. J. Theor. Econ., 9 (2009), Art. 33, 30 pp. doi: 10.2202/1935-1704.1452. Google Scholar

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M. Benaïm, Dynamics of stochastic approximation algorithms, in Séminaire de Probabilités, Lecture Notes in Math., Springer, Berlin, 1709 (1999), 1–68. doi: 10.1007/BFb0096509. Google Scholar

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M. Benaïm and M. Faure, Stochastic approximation, cooperative dynamics and supermodular games, Ann. Appl. Probab., 22 (2012), 2133-2164. doi: 10.1214/11-AAP816. Google Scholar

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M. Benaïm and M. W. Hirsch, Stochastic approximation algorithms with constant step size whose average is cooperative, Ann. Appl. Probab., 9 (1999), 216-241. doi: 10.1214/aoap/1029962603. Google Scholar

[10]

T. Börgers and R. Sarin, Learning through reinforcement and replicator dynamics, Journal of Economic Theory, 77 (1997), 1-14. doi: 10.1006/jeth.1997.2319. Google Scholar

[11]

V. Borkar, Cooperative dynamics and Wardrop equilibria, Systems and Control Letters, 58 (2009), 91-93. doi: 10.1016/j.sysconle.2008.08.006. Google Scholar

[12]

M. Bravo, An adjusted payoff-based procedure for normal form games, Mathematics of Operations Research, 41 (2016), 1469-1483. doi: 10.1287/moor.2016.0785. Google Scholar

[13]

G. Brown, Iterative solution of games by fictitious play, in Activity Analysis of Production and Allocation, Cowles Commission Monograph No. 13, John Wiley & Sons, Inc., New York, N. Y., (1951), 374–376. Google Scholar

[14]

R. CominettiE. Melo and S. Sorin, A payoff based learning procedure and its application to traffic games, Games and Economic Behavior, 70 (2010), 71-83. doi: 10.1016/j.geb.2008.11.012. Google Scholar

[15]

M. Coste, An Introduction to O-minimal Geometry, Institut de Recherche Mathématique de Rennes, 1999.Google Scholar

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R. Cressman, The Stability Concept of Evolutionary Game Theory: A Dynamic Approach, Springer-Verlag, Berlin, Heidelberg, 1992. doi: 10.1007/978-3-642-49981-4. Google Scholar

[17]

C. Daganzo and Y. Sheffi, On stochastic models of traffic assignment, Transportation Science, 11 (1977), 253-274. doi: 10.1287/trsc.11.3.253. Google Scholar

[18]

D. K. Dimitrov, D. K. and J. M. Peña, Almost strict total positivity and a class of Hurwitz polynomials, Journal of Approximation Theory, 132 (2005), 212–223. doi: 10.1016/j.jat.2004.10.010. Google Scholar

[19]

I. Erev and A. E. Roth, Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria, American Economic Review, 88 (1998), 848-881. Google Scholar

[20]

C. Fisk, Some developments in equilibrium traffic assignment, Transportation Research, 14 (1980), 243-255. doi: 10.1016/0191-2615(80)90004-1. Google Scholar

[21]

D. Foster and R. V. Vohra, Calibrated learning and correlated equilibria, Games and Economic Behavior, 21 (1997), 40-55. doi: 10.1006/game.1997.0595. Google Scholar

[22]

D. Foster and R. V. Vohra, Asymptotic calibration, Biometrika, 85 (1998), 379-390. doi: 10.1093/biomet/85.2.379. Google Scholar

[23]

Y. Freund and R. E. Schapire, Adaptive game playing using multiplicative weights, Games and Economic Behavior, 29 (1999), 79-103. doi: 10.1006/game.1999.0738. Google Scholar

[24]

D. Fudenberg and D. K. Levine, The Theory of Learning in Games, MIT Press, Cambridge, MA, 1998. Google Scholar

[25]

F. R. Gantmacher, Applications of the Theory of Matrices, Interscience, New York, 1959. Google Scholar

[26]

J. Guckenheimer and P. Holmes, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields, 5th edition, Springer-Verlag, New York, 1997.Google Scholar

[27]

J. Hannan, Approximation to Bayes risk in repeated plays, in Contributions to the Theory of Games (eds. M. Dresher, A. W. Tucker, and P. Wolfe), Princeton University Press, 3 (1957), 97–139. Google Scholar

[28]

S. Hart, Adaptive heuristics, Econometrica, 73 (2005), 1401-1430. doi: 10.1111/j.1468-0262.2005.00625.x. Google Scholar

[29]

S. Hart and A. Mas-Colell, A reinforcement procedure leading to correlated equilibrium, in Economics Essays: A Festschrift for Werner Hildenbrand (eds G. Debreu, W. Neuefeing and W. Trockel, Springer, (2001), 181–200. Google Scholar

[30]

J. Hofbauer and W. Sandholm, On the global convergence of stochastic fictitious play, Econometrica, 70 (2002), 2265-2294. doi: 10.1111/1468-0262.00376. Google Scholar

[31]

J. Hofbauer and K. Sigmund, Evolutionary Games and Population Dynamics, Cambridge University Press, UK, 1998. doi: 10.1017/CBO9781139173179. Google Scholar

[32]

J. Horowitz, The stability of stochastic equilibrium in a two-link transportation network, Transportation Research Part B, 18 (1984), 13-28. doi: 10.1016/0191-2615(84)90003-1. Google Scholar

[33]

H. J. Kushner and G. G. Yin, Stochastic Approximations Algorithms and Applications, Applications of Mathematics, 35, Springer-Verlag, New York, 1997. doi: 10.1007/978-1-4899-2696-8. Google Scholar

[34]

Y. A. Kuznetsov, Elements of Applied Bifurcation Theory, 2nd edition, Springer-Verlag, New York, 1995. doi: 10.1007/978-1-4757-2421-9. Google Scholar

[35]

J.-F. LaslierR. Topol and B. Walliser, A behavioral learning process in games, Games and Economic Behavior, 37 (2001), 340-366. doi: 10.1006/game.2000.0841. Google Scholar

[36]

R. McKelvey and T. Palfrey, Quantal response equilibria for normal form games, Games and Economic Behavior, 10 (1995), 6-38. doi: 10.1006/game.1995.1023. Google Scholar

[37]

F. A. Maldonado, Estudio de una Dinámica Adaptativa Para Juegos Repetidos y su Aplicación a un Juego de Congestión, Memoria, Universidad de Chile, Facultad de Ciencias Físicas y Matemáticas, Departamento de Ingeniería Matemática, Santiago, Chile, 2012.Google Scholar

[38]

C. D. Meyer, Matrix Analysis and Applied Linear Algebra, SIAM, Philadelphia, 2000. doi: 10.1137/1.9780898719512. Google Scholar

[39]

K. W. Morton and D. Mayers, Numerical Solution of Partial Differential Equations, Cambridge University Press, 2nd Edition, 2005. doi: 10.1017/CBO9780511812248. Google Scholar

[40]

J. D. Murray, Mathematical Biology, 2nd edition, Springer, Berlin, Germany, 1993. doi: 10.1007/b98869. Google Scholar

[41]

M. Posch, Cycling in a stochastic learning algorithm for normal form games, J. Evol. Econ., 7 (1997), 193-207. doi: 10.1007/s001910050041. Google Scholar

[42]

J. Robinson, An iterative method of solving a game, Ann. Math., 54 (1951), 296-301. doi: 10.2307/1969530. Google Scholar

[43]

R. W. Rosenthal, A class of games possessing pure-strategy Nash equilibria, International Journal of Game Theory, 2 (1973), 65-67. doi: 10.1007/BF01737559. Google Scholar

[44]

H. L. Smith, Systems of ordinary differential equations which generate an order preserving flow. A survey of results, SIAM Review, 30 (1988), 87-113. doi: 10.1137/1030003. Google Scholar

[45]

R. SeltenT. ChmuraT. PitzS. Kube and M. Schreckenberg, Commuters route choice behaviour, Games and Economic Behavior, 58 (2007), 394-406. doi: 10.1016/j.geb.2006.03.012. Google Scholar

[46]

L. van den Dries, O-minimal structures and real analytic geometry, in Current Development in Mathematics, International Press, Cambridge, MA, (1999), 105–152. Google Scholar

[47]

J. Wardrop, Some theoretical aspects of road traffic research, Proceedings of the Institution of Civil Engineers, 1 (1952), 767-768. doi: 10.1680/ipeds.1952.11362. Google Scholar

[48]

H. P. Young, Strategic Learning and its Limits, Oxford University Press, 2004. doi: 10.1093/acprof:oso/9780199269181.001.0001. Google Scholar

show all references

References:
[1]

T. Ando, Totally positive matrices, Linear Algebra and Its Applications, 90 (1987), 165-219. doi: 10.1016/0024-3795(87)90313-2. Google Scholar

[2]

W. B. Arthur, On designing economic agents that behave like human agents, J. Evolutionary Econ., 3 (1933), 1-22. doi: 10.1007/BF01199986. Google Scholar

[3]

P. AuerN. Cesa-BianchiY. Freund and R. E. Schapire, The non-stochastic multiarmed bandit problem, SIAM J. on Computing, 32 (2002), 48-77. doi: 10.1137/S0097539701398375. Google Scholar

[4]

E. Avinieri and J. Prashker, The impact of travel time information on travelers' learning under uncertainty, Transportation, 33 (2006), 393-408. doi: 10.1007/s11116-005-5710-y. Google Scholar

[5]

A. Beggs, On the convergence of reinforcement learning, Journal of Economic Theory, 122 (2005), 1-36. doi: 10.1016/j.jet.2004.03.008. Google Scholar

[6]

A. Beggs, Learning in Bayesian games with binary actions, B. E. J. Theor. Econ., 9 (2009), Art. 33, 30 pp. doi: 10.2202/1935-1704.1452. Google Scholar

[7]

M. Benaïm, Dynamics of stochastic approximation algorithms, in Séminaire de Probabilités, Lecture Notes in Math., Springer, Berlin, 1709 (1999), 1–68. doi: 10.1007/BFb0096509. Google Scholar

[8]

M. Benaïm and M. Faure, Stochastic approximation, cooperative dynamics and supermodular games, Ann. Appl. Probab., 22 (2012), 2133-2164. doi: 10.1214/11-AAP816. Google Scholar

[9]

M. Benaïm and M. W. Hirsch, Stochastic approximation algorithms with constant step size whose average is cooperative, Ann. Appl. Probab., 9 (1999), 216-241. doi: 10.1214/aoap/1029962603. Google Scholar

[10]

T. Börgers and R. Sarin, Learning through reinforcement and replicator dynamics, Journal of Economic Theory, 77 (1997), 1-14. doi: 10.1006/jeth.1997.2319. Google Scholar

[11]

V. Borkar, Cooperative dynamics and Wardrop equilibria, Systems and Control Letters, 58 (2009), 91-93. doi: 10.1016/j.sysconle.2008.08.006. Google Scholar

[12]

M. Bravo, An adjusted payoff-based procedure for normal form games, Mathematics of Operations Research, 41 (2016), 1469-1483. doi: 10.1287/moor.2016.0785. Google Scholar

[13]

G. Brown, Iterative solution of games by fictitious play, in Activity Analysis of Production and Allocation, Cowles Commission Monograph No. 13, John Wiley & Sons, Inc., New York, N. Y., (1951), 374–376. Google Scholar

[14]

R. CominettiE. Melo and S. Sorin, A payoff based learning procedure and its application to traffic games, Games and Economic Behavior, 70 (2010), 71-83. doi: 10.1016/j.geb.2008.11.012. Google Scholar

[15]

M. Coste, An Introduction to O-minimal Geometry, Institut de Recherche Mathématique de Rennes, 1999.Google Scholar

[16]

R. Cressman, The Stability Concept of Evolutionary Game Theory: A Dynamic Approach, Springer-Verlag, Berlin, Heidelberg, 1992. doi: 10.1007/978-3-642-49981-4. Google Scholar

[17]

C. Daganzo and Y. Sheffi, On stochastic models of traffic assignment, Transportation Science, 11 (1977), 253-274. doi: 10.1287/trsc.11.3.253. Google Scholar

[18]

D. K. Dimitrov, D. K. and J. M. Peña, Almost strict total positivity and a class of Hurwitz polynomials, Journal of Approximation Theory, 132 (2005), 212–223. doi: 10.1016/j.jat.2004.10.010. Google Scholar

[19]

I. Erev and A. E. Roth, Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria, American Economic Review, 88 (1998), 848-881. Google Scholar

[20]

C. Fisk, Some developments in equilibrium traffic assignment, Transportation Research, 14 (1980), 243-255. doi: 10.1016/0191-2615(80)90004-1. Google Scholar

[21]

D. Foster and R. V. Vohra, Calibrated learning and correlated equilibria, Games and Economic Behavior, 21 (1997), 40-55. doi: 10.1006/game.1997.0595. Google Scholar

[22]

D. Foster and R. V. Vohra, Asymptotic calibration, Biometrika, 85 (1998), 379-390. doi: 10.1093/biomet/85.2.379. Google Scholar

[23]

Y. Freund and R. E. Schapire, Adaptive game playing using multiplicative weights, Games and Economic Behavior, 29 (1999), 79-103. doi: 10.1006/game.1999.0738. Google Scholar

[24]

D. Fudenberg and D. K. Levine, The Theory of Learning in Games, MIT Press, Cambridge, MA, 1998. Google Scholar

[25]

F. R. Gantmacher, Applications of the Theory of Matrices, Interscience, New York, 1959. Google Scholar

[26]

J. Guckenheimer and P. Holmes, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields, 5th edition, Springer-Verlag, New York, 1997.Google Scholar

[27]

J. Hannan, Approximation to Bayes risk in repeated plays, in Contributions to the Theory of Games (eds. M. Dresher, A. W. Tucker, and P. Wolfe), Princeton University Press, 3 (1957), 97–139. Google Scholar

[28]

S. Hart, Adaptive heuristics, Econometrica, 73 (2005), 1401-1430. doi: 10.1111/j.1468-0262.2005.00625.x. Google Scholar

[29]

S. Hart and A. Mas-Colell, A reinforcement procedure leading to correlated equilibrium, in Economics Essays: A Festschrift for Werner Hildenbrand (eds G. Debreu, W. Neuefeing and W. Trockel, Springer, (2001), 181–200. Google Scholar

[30]

J. Hofbauer and W. Sandholm, On the global convergence of stochastic fictitious play, Econometrica, 70 (2002), 2265-2294. doi: 10.1111/1468-0262.00376. Google Scholar

[31]

J. Hofbauer and K. Sigmund, Evolutionary Games and Population Dynamics, Cambridge University Press, UK, 1998. doi: 10.1017/CBO9781139173179. Google Scholar

[32]

J. Horowitz, The stability of stochastic equilibrium in a two-link transportation network, Transportation Research Part B, 18 (1984), 13-28. doi: 10.1016/0191-2615(84)90003-1. Google Scholar

[33]

H. J. Kushner and G. G. Yin, Stochastic Approximations Algorithms and Applications, Applications of Mathematics, 35, Springer-Verlag, New York, 1997. doi: 10.1007/978-1-4899-2696-8. Google Scholar

[34]

Y. A. Kuznetsov, Elements of Applied Bifurcation Theory, 2nd edition, Springer-Verlag, New York, 1995. doi: 10.1007/978-1-4757-2421-9. Google Scholar

[35]

J.-F. LaslierR. Topol and B. Walliser, A behavioral learning process in games, Games and Economic Behavior, 37 (2001), 340-366. doi: 10.1006/game.2000.0841. Google Scholar

[36]

R. McKelvey and T. Palfrey, Quantal response equilibria for normal form games, Games and Economic Behavior, 10 (1995), 6-38. doi: 10.1006/game.1995.1023. Google Scholar

[37]

F. A. Maldonado, Estudio de una Dinámica Adaptativa Para Juegos Repetidos y su Aplicación a un Juego de Congestión, Memoria, Universidad de Chile, Facultad de Ciencias Físicas y Matemáticas, Departamento de Ingeniería Matemática, Santiago, Chile, 2012.Google Scholar

[38]

C. D. Meyer, Matrix Analysis and Applied Linear Algebra, SIAM, Philadelphia, 2000. doi: 10.1137/1.9780898719512. Google Scholar

[39]

K. W. Morton and D. Mayers, Numerical Solution of Partial Differential Equations, Cambridge University Press, 2nd Edition, 2005. doi: 10.1017/CBO9780511812248. Google Scholar

[40]

J. D. Murray, Mathematical Biology, 2nd edition, Springer, Berlin, Germany, 1993. doi: 10.1007/b98869. Google Scholar

[41]

M. Posch, Cycling in a stochastic learning algorithm for normal form games, J. Evol. Econ., 7 (1997), 193-207. doi: 10.1007/s001910050041. Google Scholar

[42]

J. Robinson, An iterative method of solving a game, Ann. Math., 54 (1951), 296-301. doi: 10.2307/1969530. Google Scholar

[43]

R. W. Rosenthal, A class of games possessing pure-strategy Nash equilibria, International Journal of Game Theory, 2 (1973), 65-67. doi: 10.1007/BF01737559. Google Scholar

[44]

H. L. Smith, Systems of ordinary differential equations which generate an order preserving flow. A survey of results, SIAM Review, 30 (1988), 87-113. doi: 10.1137/1030003. Google Scholar

[45]

R. SeltenT. ChmuraT. PitzS. Kube and M. Schreckenberg, Commuters route choice behaviour, Games and Economic Behavior, 58 (2007), 394-406. doi: 10.1016/j.geb.2006.03.012. Google Scholar

[46]

L. van den Dries, O-minimal structures and real analytic geometry, in Current Development in Mathematics, International Press, Cambridge, MA, (1999), 105–152. Google Scholar

[47]

J. Wardrop, Some theoretical aspects of road traffic research, Proceedings of the Institution of Civil Engineers, 1 (1952), 767-768. doi: 10.1680/ipeds.1952.11362. Google Scholar

[48]

H. P. Young, Strategic Learning and its Limits, Oxford University Press, 2004. doi: 10.1093/acprof:oso/9780199269181.001.0001. Google Scholar

Figure 1.  Stability region in the $2 \times 2$ traffic game
Figure 2.  Three fixed points with $\psi'(\bar w)>1$
Figure 3.  Fixed points of $\psi$: $\psi'(\bar w)>1$ (left) vs $\psi'(\bar w)<1$ (right)
Figure 4.  Stability region for a $2 \times 2$ symmetric game in the $\mu$-$q$ parameters
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