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Sensitivity of Electrophysiological Patterns in Level-K States as Function of Individual Coordination Ability

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 542))

Abstract

Tacit coordination games are games in which players get rewarded by choosing the same alternatives as an unknown player when communication between the players is not allowed or not possible. Classical game theory fails to correctly predict the probability of successful coordination due to its inability to prioritize salient Nash equilibrium points (in this setting, also known as “focal points”). To bridge this gap a variety of theories have been proposed. A prominent theory is the level-k theory which assumes that players’ reasoning depth relies on their subjective level of reasoning. Previous studies have shown that there is an inherent difference in the coordination ability of individual players, and that there is a correlation between the individual coordination ability and electrophysiological measurements. The goal of this study is to measure and quantify the electrophysiological sensitivity patterns in level-k states in relation to the individual coordination abilities of players. We showed that the combined model capabilities (precision and recall) improve linearly depending on the player’s individual coordination ability. That is, player with higher coordination abilities exhibit a more pronounced and prominent electrophysiological patterns. This result enables the detection of capable coordinators based solely on their brain patterns. These results were obtained by constructing a machine-learning classification model which predicting one of the two cognitive states, picking (level-k = 0) or coordination (level-k > 0), based on electrophysiological recordings. The results of the classification process were analyzed for each participant individually in order to assess the sensitivity of the electrophysiological patterns according to his individual coordination ability.

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References

  1. Schelling, T.C.: The Strategy of Conflict. Cambridge (1960)

    Google Scholar 

  2. Nash, J.: Equilibrium points in n-person games. Proc. Natl. Acad. Sci. USA 36, 48–49 (1950)

    Article  MathSciNet  Google Scholar 

  3. Mailath, G.J.: Do people play Nash equilibrium? Lessons from evolutionary game theory. J. Econ. Lit. 36, 1347–1374 (1998)

    Google Scholar 

  4. Mehta, J., Starmer, C., Sugden, R.: Focal points in pure coordination games: an experimental investigation. Theory Decis. 36, 163–185 (1994)

    Article  Google Scholar 

  5. Bardsley, N., Mehta, J., Starmer, C., Sugden, R.: Explaining focal points : cognitive hierarchy theory versus team reasoning. Econ. J. 120, 40–79 (2009)

    Article  Google Scholar 

  6. Sitzia, S., Zheng, J.: Group behaviour in tacit coordination games with focal points – an experimental investigation. Games Econ. Behav. 117, 461–478 (2019)

    Article  MathSciNet  Google Scholar 

  7. Zuckerman, I., Kraus, S., Rosenschein, J.S.: Using focal point learning to improve human-machine tacit coordination. Auton. Agent. Multi. Agent. Syst. 22, 289–316 (2011)

    Article  Google Scholar 

  8. Strzalecki, T.: Depth of reasoning and higher order beliefs. J. Econ. Behav. Organ. 108, 108–122 (2014)

    Article  Google Scholar 

  9. Costa-Gomes, M.A., Crawford, V.P., Iriberri, N.: Comparing models of strategic thinking in Van Huyck, Battalio, and Beil’s coordination games. J. Eur. Econ. Assoc. 7, 365–376 (2009)

    Article  Google Scholar 

  10. Faillo, M., Smerilli, A., Sugden, R.: The Roles of Level-k and Team Reasoning in Solving Coordination Games (2013)

    Google Scholar 

  11. Mizrahi, D., Laufer, I., Zuckerman, I.: Level-K classification from eeg signals using transfer learning. Sensors 21, 7908 (2021)

    Article  Google Scholar 

  12. Zuckerman, I., Mizrahi, D., Laufer, I.: EEG pattern classification of picking and coordination using anonymous random walks. Algorithms 15, 114 (2022)

    Article  Google Scholar 

  13. Kneeland, T.: Coordination under limited depth of reasoning. Games Econ. Behav. 96, 49–64 (2016)

    Article  MathSciNet  Google Scholar 

  14. Georganas, S., Healy, P.J., Weber, R.A.: On the persistence of strategic sophistication. J. Econ. Theory 159, 369–400 (2015)

    Article  MathSciNet  Google Scholar 

  15. Colman, A.M., Pulford, B.D., Lawrence, C.L.: Explaining strategic coordination: cognitive hierarchy theory, strong Stackelberg reasoning, and team reasoning. Decision 1, 35–58 (2014)

    Article  Google Scholar 

  16. Mizrahi, D., Laufer, I., Zuckerman, I.: Collectivism-individualism: strategic behavior in tacit coordination games. PLoS One 15(2), e0226929 (2020)

    Article  Google Scholar 

  17. Mizrahi, D., Laufer, I., Zuckerman, I.: Modeling individual tacit coordination abilities. In: International Conference on Brain Informatics, pp. 29–38. Springer, Cham, Haikou, China (2019)

    Google Scholar 

  18. Mizrahi, D., Laufer, I., Zuckerman, I.: Individual strategic profiles in tacit coordination games. J. Exp. Theor. Artif. Intell. 33, 1–16 (2020)

    Google Scholar 

  19. Mizrahi, D., Laufer, I., Zuckerman, I.: Modeling and predicting individual tacit coordination ability. Brain Inf. 9, 4 (2022)

    Article  Google Scholar 

  20. Mizrahi, D., Laufer, I., Zuckerman, I.: Predicting focal point solution in divergent interest tacit coordination games. J. Exp. Theor. Artif. Intell. 1–21 (2021)

    Google Scholar 

  21. Mizrahi, D., Zuckerman, I., Laufer, I.: Using a stochastic agent model to optimize performance in divergent interest tacit coordination games. Sensors 20, 7026 (2020)

    Article  Google Scholar 

  22. Rosenfeld, A., Zuckerman, I., Azaria, A., Kraus, S.: Combining psychological models with machine learning to better predict people’s decisions. Synthese 189, 81–93 (2012)

    Article  Google Scholar 

  23. Mizrahi, D., Laufer, I., Zuckerman, I.: The effect of individual coordination ability on cognitive-load in tacit coordination games. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, P.-M., Randolph, A.B., Fischer, T. (eds.) NeuroIS 2020. LNISO, vol. 43, pp. 244–252. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60073-0_28

    Chapter  Google Scholar 

  24. Mizrahi, D., Laufer, I., Zuckerman, I.: Topographic analysis of cognitive load in tacit coordination games based on electrophysiological measurements. In: Davis, F.D., Riedl, R., vom Brocke, J., Léger, P.-M., Randolph, A.B., Müller-Putz, G. (eds.) NeuroIS 2021. LNISO, vol. 52, pp. 162–171. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-88900-5_18

    Chapter  Google Scholar 

  25. Laufer, I., Mizrahi, D., Zuckerman, I.: An electrophysiological model for assessing cognitive load in tacit coordination games. Sensors 22, 477 (2022)

    Article  Google Scholar 

  26. Lin, Y.-P., Jung, T.-P.: Improving EEG-based emotion classification using conditional transfer learning. Front. Hum. Neurosci. 11, 334 (2017)

    Article  Google Scholar 

  27. Zarjam, P., Epps, J., Chen, F.: Spectral EEG features for evaluating cognitive load. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3841–3844. EMBS (2011)

    Google Scholar 

  28. Renard, Y., et al.: Openvibe: an open-source software platform to design, test, and use brain–computer interfaces in real and virtual environments. Presence Teleoperators Virtual Environ. 19, 35–53 (2010)

    Article  Google Scholar 

  29. Gartner, M., Grimm, S., Bajbouj, M.: Frontal midline theta oscillations during mental arithmetic: effects of stress. Front. Behav. Neurosci. 9, 1–8 (2015)

    Article  Google Scholar 

  30. De Vico Fallani, F., et al.: Defecting or not defecting: how to “read” human behavior during cooperative games by EEG measurements. PLoS One 5(12), e14187 (2010)

    Article  Google Scholar 

  31. Boudewyn, M., Roberts, B.M., Mizrak, E., Ranganath, C., Carter, C.S.: Prefrontal transcranial direct current stimulation (tDCS) enhances behavioral and EEG markers of proactive control. Cogn. Neurosci. 10, 57–65 (2019)

    Article  Google Scholar 

  32. Moliadze, V., et al.: After-effects of 10 Hz tACS over the prefrontal cortex on phonological word decisions. Brain Stimul. 12, 1464–1474 (2019)

    Article  Google Scholar 

  33. Mallat, S.: Wavelet zoom. In: A Wavelet Tour of Signal Processing, pp. 163–219. Elsevier (1999). https://doi.org/10.1016/B978-012466606-1/50008-8

    Chapter  MATH  Google Scholar 

  34. Rioul, O., Duhamel, P.: Fast algorithms for discrete and continuous wavelet transforms. IEEE Trans. Inf. theory. 38, 569–586 (1992)

    Article  MathSciNet  Google Scholar 

  35. Hazarika, N., Chen, J.Z., Tsoi, A.C., Sergejew, A.: Classification of EEG signals using the wavelet transform. Signal Process. 59, 61–72 (1997)

    Article  Google Scholar 

  36. Simonyan, K., Andrew, Z.: Very Deep Convolutional Networks for Large-scale Image Recognition. arXiv Prepr. 1409:1556 (2014)

    Google Scholar 

  37. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp. 248–255 (2009)

    Google Scholar 

  38. Tsung, F., Zhang, K., Cheng, L., Song, Z.: Statistical transfer learning: a review and some extensions to statistical process control. Qual. Eng. 30, 115–128 (2018)

    Article  Google Scholar 

  39. Mitchell, M.: An Introduction to Genetic Algorithms. MIT press (1998)

    Google Scholar 

  40. Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE Trans. Evol. Comput. 3, 287–297 (1999)

    Article  Google Scholar 

  41. Mizrahi, D., Laufer, I., Zuckerman, I., Zhang, T.: The effect of culture and social orientation on Player’s performances in tacit coordination games. In: Wang, S., Yamamoto, V., Jianzhong, S., Yang, Y., Jones, E., Iasemidis, L., Mitchell, T. (eds.) BI 2018. LNCS (LNAI), vol. 11309, pp. 437–447. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-05587-5_41

    Chapter  Google Scholar 

  42. Raghavan, V., Bollmann, P., Jung, G.S.: A critical investigation of recall and precision as measures of retrieval system performance. ACM Trans. Inf. Syst. 7, 205–229 (1989)

    Article  Google Scholar 

  43. Buckland, M., Gey, F.: The relationship between recall and precision. J. Am. Soc. Inf. Sci. 45, 12–19 (1994)

    Article  Google Scholar 

  44. Cleverdon, C.W.: On the inverse relationship of recall and precision. J. Doc. (1972)

    Google Scholar 

  45. Michel, C.M., Murray, M.M., Lantz, G., Gonzalez, S., Spinelli, L., de Peralta, R.G.: EEG source imaging. Neurophysiology 115, 2195–2222 (2004)

    Article  Google Scholar 

  46. Pascual-Marqui, R.D., Christoph, M.M., Lehmann, D.: Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int. J. Psychophysiol. 18, 49–65 (1994)

    Article  Google Scholar 

  47. Cox, T.H., Lobel, S.A., Mcleod, P.L.: Effects of ethnic group cultural differences on cooperative and competitive behavior on a group task. Acad. Manage. J. 34, 827–847 (1991)

    Google Scholar 

  48. Mizrahi, D., Laufer, I., Zuckerman, I.: The effect of expected revenue proportion and social value orientation index on players’ behavior in divergent interest tacit coordination games. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds.) BI 2021. LNCS (LNAI), vol. 12960, pp. 25–34. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86993-9_3

    Chapter  Google Scholar 

  49. Mizrahi, D., Laufer, I., Zuckerman, I.: The effect of loss-aversion on strategic behaviour of players in divergent interest tacit coordination games. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds.) BI 2020. LNCS (LNAI), vol. 12241, pp. 41–49. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59277-6_4

    Chapter  Google Scholar 

  50. Liu, W., Song, S., Wu, C.: Impact of loss aversion on the newsvendor game with product substitution. Int. J. Prod. Econ. 141, 352–359 (2013)

    Article  Google Scholar 

  51. Kraus, S.: Predicting human decision-making: from prediction to action. In: Proceedings of the 6th International Conference on Human-Agent Interaction, p. 1 (2018)

    Google Scholar 

  52. Fenster, M., Kraus, S., Rosenschein, J.S.: Coordination without communication: experimental validation of focal point techniques. In: Proceedings of the First International Conference on Multiagent Systems, pp. 102–108. San Francisco, California, USA (1995)

    Google Scholar 

  53. Zuckerman, I., Kraus, S., Rosenschein, J.S.: The adversarial activity model for bounded rational agents. Auton. Agent. Multi. Agent. Syst. 24, 374–409 (2012). https://doi.org/10.1007/s10458-010-9153-2

    Article  Google Scholar 

  54. Bacharach, M.: Interactive team reasoning: a contribution to the theory of cooperation. Res. Econ. 53, 117–147 (1999)

    Article  Google Scholar 

  55. Colman, A.M., Gold, N.: Team reasoning: Solving the puzzle of coordination. Psychon. Bull. Rev. 25(5), 1770–1783 (2017). https://doi.org/10.3758/s13423-017-1399-0

    Article  Google Scholar 

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Mizrahi, D., Zuckerman, I., Laufer, I. (2023). Sensitivity of Electrophysiological Patterns in Level-K States as Function of Individual Coordination Ability. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2022. Lecture Notes in Networks and Systems, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-031-16072-1_25

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