Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Progress in Artificial Intelligence
Published in: Progress in Artificial Intelligence 1/2022

04-08-2021 | Regular Paper

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

Authors: Afrooz Vazifedan, Mohammad Izadi

Published in: Progress in Artificial Intelligence | Issue 1/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article Automatic scoring of arabic essays over three linguistic levels
next article A green fuzzy multi-objective approach to the RNP problem for LTE networks

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
Appendix
Available only for authorised users
Literature
1.
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).
2.
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
3.
Fudenberg, D., Levine, D.K.: Whither game theory? Towards a theory of learning in games. VOPROSY ECONOMIK I, 5 (2017)
4.
Fudenberg, D., Liang, A.: Predicting and understanding initial play. Am. Econ. Rev. 109(12), 4112–4141 (2019)CrossRef
5.
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
6.
Izquierdo, L.R., Izquierdo, S.S., & Vega-Redondo, F.: Learning and evolutionary game theory. In: Encyclopedia of the Sciences of Learning. Springer, Boston (2012)
7.
Mengel, F.: Learning by (limited) forward looking players. J. Econ. Behav. Organ. 108, 59–77 (2014)CrossRef
8.
Kolumbus, Y., Noti, G.: Neural networks for predicting human interactions in repeated games. arXiv preprint arXiv:1911.03233 (2019).
9.
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
Metadata
Title
Predicting human behavior in size-variant repeated games through deep convolutional neural networks
Authors
Afrooz Vazifedan
Mohammad Izadi
Publication date
04-08-2021
Publisher
Springer Berlin Heidelberg
Published in
Progress in Artificial Intelligence / Issue 1/2022
Print ISSN: 2192-6352
Electronic ISSN: 2192-6360
DOI
https://doi.org/10.1007/s13748-021-00258-y

Other articles of this Issue 1/2022

Progress in Artificial Intelligence 1/2022 Go to the issue

Regular Paper

Knowledge-based sentence semantic similarity: algebraical properties

Regular Paper

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

Regular Paper

Automatic scoring of arabic essays over three linguistic levels

Regular Paper

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

Regular Paper

Prediction of national agricultural products wholesale price index in China using deep learning

Regular Paper

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

Premium Partner

    Image Credits