nach oben

Progress in Artificial Intelligence

Erschienen in:

04.08.2021 | Regular Paper

Predicting human behavior in size-variant repeated games through deep convolutional neural networks

verfasst von: Afrooz Vazifedan, Mohammad Izadi

Erschienen in: Progress in Artificial Intelligence | Ausgabe 1/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

We present a novel deep convolutional neural network (DCNN) model for predicting human behavior in repeated games. The model is the first deep neural network presented on repeated games that is able to be trained on games with arbitrary size of payoff matrices. Our neural network takes the players’ payoff matrices and the history of the play as input, and outputs the predicted action picked by the first player in the next round. To evaluate the model’s performance, we apply it to some experimental games played by humans and measure the rate of correctly predicted actions. The results show that our model obtains an average prediction accuracy of about 63% across all the studied games, which is about 6% higher than the best average accuracy obtained by the baseline models in the literature.

Vorheriger Artikel Automatic scoring of arabic essays over three linguistic levels

Nächster Artikel A green fuzzy multi-objective approach to the RNP problem for LTE networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Camerer, C.F., Ho, T.H.: Behavioral game theory experiments and modeling (chapter 10). In: Handbook of Game Theory with Economic Applications (Vol. 4, pp. 517–573). Elsevier (2015).

Chen, W., Chen, Y., Levine, D.K.: A unifying learning framework for building artificial game-playing agents. Ann. Math. Artif. Intell. 73(3–4), 335–358 (2015)MathSciNetCrossRef

Fudenberg, D., Levine, D.K.: Whither game theory? Towards a theory of learning in games. VOPROSY ECONOMIK I, 5 (2017)

Fudenberg, D., Liang, A.: Predicting and understanding initial play. Am. Econ. Rev. 109(12), 4112–4141 (2019)CrossRef

Hyndman, K., Ozbay, E.Y., Schotter, A., Ehrblatt, W.Z.E.: Convergence: an experimental study of teaching and learning in repeated games. J. Eur. Econ. Assoc. 10(3), 573–604 (2012)CrossRef

Izquierdo, L.R., Izquierdo, S.S., & Vega-Redondo, F.: Learning and evolutionary game theory. In: Encyclopedia of the Sciences of Learning. Springer, Boston (2012)

Mengel, F.: Learning by (limited) forward looking players. J. Econ. Behav. Organ. 108, 59–77 (2014)CrossRef

Kolumbus, Y., Noti, G.: Neural networks for predicting human interactions in repeated games. arXiv preprint arXiv:1911.03233 (2019).

Mathevet, L., & Romero, J. (2012). Predictive repeated game theory: Measures and experiments.

10.

Cason, T.N., Lau, S.H.P., Mui, V.L.: Learning, teaching, and turn taking in the repeated assignment game. Econ. Theor. 54(2), 335–357 (2013)MathSciNetCrossRef

11.

Camerer, C.F., Ho, T.H.: Experience-weighted attraction learning in normal form games. Econometrica 67(4), 827–874 (1999)CrossRef

12.

Camerer, C., Ho, T., Chong, K.: Models of thinking, learning, and teaching in games. Am. Econ. Rev. 93(2), 192–195 (2003)CrossRef

13.

Bhatia, S., Golman, R.: A recurrent neural network for game theoretic decision making. In: Proceedings of the Annual Meeting of the Cognitive Science Society (Vol. 36, No. 36) (2014).

14.

Hartford, J.S., Wright, J.R., Leyton-Brown, K.: Deep learning for predicting human strategic behavior. In: Advances in Neural Information Processing Systems (2016).

15.

Erev, I., Roth, A.E.: Predicting how people play games: reinforcement learning in experimental games with unique, mixed strategy equilibria. Am. Econ. Rev. 848–881 (1998).

16.

Brown, G.W.: Iterative solution of games by fictitious play. Act. Anal. Prod. Allocat. 13(1), 374–376 (1951)MathSciNetMATH

17.

Elie, R., Pérolat, J., Laurière, M., Geist, M., Pietquin, O.: Approximate fictitious play for mean field games. arXiv preprint arXiv:1907.02633 (2019).

18.

Kamra, N., Gupta, U., Wang, K., Fang, F., Liu, Y., Tambe, M.: Deep fictitious play for games with continuous action spaces. In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (pp. 2042–2044). International Foundation for Autonomous Agents and Multiagent Systems (2019).

19.

Lanctot, M., Lockhart, E., Lespiau, J.B., Zambaldi, V., Upadhyay, S., Pérolat, J., et al.: Openspiel: a framework for reinforcement learning in games. arXiv preprint arXiv:1908.09453.

20.

Ma, W.C., Huang, D.A., Lee, N., Kitani, K.M.: Forecasting interactive dynamics of pedestrians with fictitious play. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 774–782) (2017).

21.

Wang, H., Yu, C., Wu, L.: A neighborhood correlated empirical weighted algorithm for fictitious play. In: Life System Modeling and Intelligent Computing (pp. 305–311). Springer, Berlin (2010)

22.

Ansari, A., Montoya, R., Netzer, O.: Dynamic learning in behavioral games: a hidden Markov mixture-of-experts approach. Quant. Mark. Econ. 10(4), 475–503 (2012)CrossRef

23.

Ho, T.H., Camerer, C.F., Chong, J.K.: Self-tuning experience weighted attraction learning in games. J. Econ. Theory 133(1), 177–198 (2007)MathSciNetCrossRef

24.

Hart, S., Mas-Colell, A.: A simple adaptive procedure leading to correlated equilibrium. Econometrica 68(5), 1127–1150 (2000)MathSciNetCrossRef

25.

McKelvey, R.D., Palfrey, T.R.: Quantal response equilibria for normal form games. Games Econom. Behav. 10(1), 6–38 (1995)MathSciNetCrossRef

26.

Camerer, C.F., Ho, T.H., Chong, J.K.: A cognitive hierarchy model of games. Q. J. Econ. 119(3), 861–898 (2004)CrossRef

27.

Selten, R., Chmura, T.: Stationary concepts for experimental 2x2-games. Am. Econ. Rev. 98(3), 938–966 (2008)CrossRef

28.

Mookherjee, D., Sopher, B.: Learning and decision costs in experimental constant sum games. Games Econom. Behav. 19(1), 97–132 (1997)MathSciNetCrossRef

29.

Rapoport, A., Amaldoss, W.: Mixed strategies and iterative elimination of strongly dominated strategies: an experimental investigation of states of knowledge. J. Econ. Behav. Organ. 42(4), 483–521 (2000)CrossRef

30.

Van Huyck, J.B., Battalio, R.C., Beil, R.O.: Tacit coordination games, strategic uncertainty, and coordination failure. Am. Econ. Rev. 80(1), 234–248 (1990)

31.

Andreoni, J., Miller, J.H.: Rational cooperation in the finitely repeated prisoner’s dilemma: experimental evidence. Econ. J. 103(418), 570–585 (1993)CrossRef

32.

Marchiori, D., Warglien, M.: Predicting human interactive learning by regret-driven neural networks. Science 319(5866), 1111–1113 (2008)CrossRef

Titel: Predicting human behavior in size-variant repeated games through deep convolutional neural networks
verfasst von: Afrooz Vazifedan
Mohammad Izadi
Publikationsdatum: 04.08.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Progress in Artificial Intelligence / Ausgabe 1/2022
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-021-00258-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1/2022

Knowledge-based sentence semantic similarity: algebraical properties

Semi-causal decision trees

Improving link prediction in social networks using local and global features: a clustering-based approach

Interpretable entity meta-alignment in knowledge graphs using penalized regression: a case study in the biomedical domain

SVGPM: evolving SVM decision function by using genetic programming to solve imbalanced classification problem

Automatic scoring of arabic essays over three linguistic levels