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A Study of Misinformation Games

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PRICAI 2021: Trends in Artificial Intelligence (PRICAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13031))

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Abstract

A common assumption in game theory is that players have a common and correct (albeit not always complete) knowledge with regards to the abstract formulation of the game. However, in many real-world situations it could be the case that (some of) the players are misinformed with regards to the game that they play, essentially having an incorrect understanding of the setting, without being aware of it. This would invalidate the common knowledge assumption. In this paper, we present a new game-theoretic framework, called misinformation games, that provides the formal machinery necessary to study this phenomenon, and present some basic results regarding its properties.

For this work the fourth author was supported by the Stavros Niarchos-FORTH postdoc fellowship for the project ARCHERS.

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References

  1. Acar, E., Meir, R.: Distance-based equilibria in normal-form games. In: The 34th AAAI Conference on Artificial Intelligence, AAAI 2020, The 32nd Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The 10th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2020, pp. 1750–1757. AAAI Press (2020)

    Google Scholar 

  2. Antos, D., Pfeffer, A.: Representing Bayesian games without a common prior. In: 9th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010, pp. 1457–1458. IFAAMAS (2010)

    Google Scholar 

  3. Bakker, C., Bhattacharya, A., Chatterjee, S., Vrabie, D.L.: Metagames and hypergames for deception-robust control. ACM Trans. Cyber-Phys. Syst. 5(3), 25 (2021)

    Article  Google Scholar 

  4. Banks, D., Gallego, V., Naveiro, R., Ríos Insua, D.: Adversarial risk analysis: an overview. WIREs Comput. Stat., e1530 (2020). https://doi.org/10.1002/wics.1530

  5. Bennett, P.G.: Hypergames: developing a model of conflict. Futures 12(6), 489–507 (1980)

    Article  Google Scholar 

  6. Chaib-Draa, B.: Hypergame analysis in multiagent environments. In: Proceedings of the AAAI Spring Symposium, pp. 147–149 (2001)

    Google Scholar 

  7. Cho, J.-H., Zhu, M., Singh, M.: Modeling and analysis of deception games based on hypergame theory. In: Al-Shaer, E., Wei, J., Hamlen, K.W., Wang, C. (eds.) Autonomous Cyber Deception, pp. 49–74. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-02110-8_4

    Chapter  Google Scholar 

  8. Copic, J., Galeotti, A.: Awareness as an equilibrium notion: normal-form games (2006)

    Google Scholar 

  9. Daskalakis, C., Goldberg, P.W., Papadimitriou, C.H.: The complexity of computing a Nash equilibrium. SIAM J. Comput. 39(1), 195–259 (2009)

    Article  MathSciNet  Google Scholar 

  10. Esponda, I., Pouzo, D.: Berk-Nash equilibrium: a framework for modeling agents with misspecified models. Econometrica 84(3), 1093–1130 (2016)

    Article  MathSciNet  Google Scholar 

  11. Feinberg, Y.: Games with unawareness. B.E. J. Theor. Econ. 21, 433–488 (2020)

    Article  MathSciNet  Google Scholar 

  12. Gairing, M., Monien, B., Tiemann, K.: Selfish routing with incomplete information. Theor. Comput. Syst. 42(1), 91–130 (2008)

    Article  MathSciNet  Google Scholar 

  13. Gao, J.: Uncertain bimatrix game with applications. Fuzzy Optim. Decis. Making 12(1), 65–78 (2013). https://doi.org/10.1007/s10700-012-9145-6

    Article  MathSciNet  MATH  Google Scholar 

  14. Gharesifard, B., Cortés, J.: Stealthy strategies for deception in hypergames with asymmetric information. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference, pp. 5762–5767 (2011)

    Google Scholar 

  15. Halpern, J.Y., Rêgo, L.C.: Extensive games with possibly unaware players. Math. Soc. Sci. 70, 42–58 (2014)

    Article  MathSciNet  Google Scholar 

  16. Harsanyi, J.C.: Games with incomplete information played by “Bayesian” players, I–III part I. The basic model. In: Management Science, vol. 14, no. 3, pp. 159–182 (1967)

    Google Scholar 

  17. Koutsoupias, E., Papadimitriou, C.: Worst-case equilibria. In: Proceedings of the 16th Annual Conference on Theoretical Aspects of Computer Science (STACS), pp. 404–413 (1999)

    Google Scholar 

  18. Kovach, N.S., Gibson, A.S., Lamont, G.B.: Hypergame theory: a model for conflict, misperception, and deception. Game Theor. 2015, 20 (2015)

    Article  MathSciNet  Google Scholar 

  19. Luce, R.D., Raiffa, H.: Games and Decisions: Introduction and Critical Survey. Wiley, New York (1957)

    MATH  Google Scholar 

  20. Meir, R., Parkes, D.C.: Playing the wrong game: smoothness bounds for congestion games with behavioral biases. SIGMETRICS Perf. Eval. Rev. 43(3), 67–70 (2015)

    Article  Google Scholar 

  21. Nash, J.F.: Non-cooperative games. Ann. Math. 54(2), 286–295 (1951)

    Article  MathSciNet  Google Scholar 

  22. Suzuki, M., Vetta, A.: How many freemasons are there? The consensus voting mechanism in metric spaces. In: Harks, T., Klimm, M. (eds.) SAGT 2020. LNCS, vol. 12283, pp. 322–336. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-57980-7_21

    Chapter  Google Scholar 

  23. Ozbay, E.Y.: Unawareness and strategic announcements in games with uncertainty. In: Proceedings of the 11th Conference on Theoretical Aspects of Rationality and Knowledge, pp. 231–238 (2007)

    Google Scholar 

  24. Roponen, J., Ríos Insua, D., Salo, A.: Adversarial risk analysis under partial information. Eur. J. Oper. Res. 287(1), 306–316 (2020)

    Article  MathSciNet  Google Scholar 

  25. Rosenthal, R.W.: A class of games possessing pure-strategy Nash equilibria. Int. J. Game Theor. 2, 65–67 (1973). https://doi.org/10.1007/BF01737559

    Article  MathSciNet  MATH  Google Scholar 

  26. Sasaki, Y.: Generalized Nash equilibrium with stable belief hierarchies in static games with unawareness. Ann. Oper. Res. 256(2), 271–284 (2017)

    Article  MathSciNet  Google Scholar 

  27. Sasaki, Y., Kijima, K.: Hypergames and Bayesian games: a theoretical comparison of the models of games with incomplete information. J. Syst. Sci. Complexity 25(4), 720–735 (2012)

    Article  MathSciNet  Google Scholar 

  28. Schipper, B.C.: Unawareness - a gentle introduction to both the literature and the special issue. Math. Soc. Sci. 70, 1–9 (2014)

    Article  MathSciNet  Google Scholar 

  29. Schipper, B.C.: Self-confirming games: Unawareness, discovery, and equilibrium. In: TARK (2017)

    Google Scholar 

  30. Schipper, B.C.: Discovery and Equilibrium in Games with Unawareness. University Library of Munich, Germany, MPRA Paper (2018)

    Google Scholar 

  31. Székely, G.J., Rizzo, M.L.: The uncertainty principle of game theory. Am. Math. Mon. 114(8), 688–702 (2007)

    Article  MathSciNet  Google Scholar 

  32. Teneketzis, D., Castañón, D.: Informational aspects of a class of subjective games of incomplete information: static case. J. Optim. Theor. Appl. 54, 413–422 (1983)

    Article  MathSciNet  Google Scholar 

  33. von Thadden, E.-L., Zhao, X.: Multi-task agency with unawareness. Theor. Decis. 77(2), 197–222 (2013). https://doi.org/10.1007/s11238-013-9397-9

    Article  MathSciNet  MATH  Google Scholar 

  34. Vane, R., Lehner, P.: Using hypergames to increase planned payoff and reduce risk. Auton. Agent. Multi-Agent Syst. 5(3), 365–380 (2002)

    Article  MathSciNet  Google Scholar 

  35. Zamir, S.: Bayesian games: games with incomplete information. In: Meyers, R. (eds.) Encyclopedia of Complexity and Systems Science. Springer, New York (2009). https://doi.org/10.1007/978-0-387-30440-3_29

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Authors and Affiliations

  1. Institute of Computer Science, Foundation for Research and Technology, Vasilika Vouton 100, 70013, Heraklion, Greece

    Constantinos Varsos, Giorgos Flouris, Marina Bitsaki & Michail Fasoulakis

  2. Computer Science Department, University of Crete, Voutes University Campus, 70013, Heraklion, Greece

    Constantinos Varsos

Authors
  1. Constantinos Varsos
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  2. Giorgos Flouris
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  3. Marina Bitsaki
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  4. Michail Fasoulakis
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Corresponding author

Correspondence to Constantinos Varsos .

Editor information

Editors and Affiliations

  1. MIMOS Berhad, Kuala Lumpur, Malaysia

    Duc Nghia Pham

  2. Sirindhorn International Institute of Science and Technology, Thammasat University, Mueang Pathum Thani, Thailand

    Thanaruk Theeramunkong

  3. Data61, CSIRO, Brisbane, QLD, Australia

    Guido Governatori

  4. Department of Philosophy, Tsinghua University, Beijing, China

    Fenrong Liu

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Varsos, C., Flouris, G., Bitsaki, M., Fasoulakis, M. (2021). A Study of Misinformation Games. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13031. Springer, Cham. https://doi.org/10.1007/978-3-030-89188-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-89188-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89187-9

  • Online ISBN: 978-3-030-89188-6

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