Abstract
We consider a class of interdependent security games where the security risk experienced by a player depends on her own investment in security as well as the investments of other players. In contrast to much of the existing work that considers risk neutral players in such games, we investigate the impacts of behavioral probability weighting by players while making security investment decisions. This weighting captures the transformation of objective probabilities into perceived probabilities, which influence the decisions of individuals in uncertain environments. We show that the Nash equilibria that arise after incorporating probability weightings have much richer structural properties and equilibrium risk profiles than in risk neutral environments. We provide comprehensive discussions of these effects on the properties of equilibria and the social optimum when the players have homogeneous weighting parameters, including comparative statics results. We further characterize the existence and uniqueness of pure Nash equilibria in Total Effort games with heterogeneous players.
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Notes
- 1.
In a related class of security games known as Stackelberg security games with two players, one attacker and one defender, there have been recent studies [4, 13, 17, 31, 32] that incorporate behavioral decision theoretic models, including prospect theory and quantal response equilibrium. However, this class of games is very different from interdependent security games [20], which is the focus of the current work.
- 2.
Both positive and negative externalities have been studied in the literature. Negative externalities capture settings where more investment by others makes a player more vulnerable, and this is usually the case where the attack is targeted towards individuals who have invested less in security. Most of the literature in security games has focused on positive externalities [20].
- 3.
- 4.
While we focus on the Prelec weighting function here, many of our results will also hold under a broader class of weighting functions with similar qualitative properties as the Prelec weighting function.
References
Baddeley, M.: Information security: lessons from behavioural economics. In: Security and Human Behavior (2011)
Barberis, N.C.: Thirty years of prospect theory in economics: a review and assessment. J. Econ. Perspect. 27(1), 173–196 (2013)
Böhme, R., Schwartz, G.: Modeling cyber-insurance: towards a unifying framework. In: Workshop on the Economics of Information Security (WEIS) (2010)
Brown, M., Haskell, W.B., Tambe, M.: Addressing scalability and robustness in security games with multiple boundedly rational adversaries. In: Saad, W., Poovendran, R. (eds.) GameSec 2014. LNCS, vol. 8840, pp. 23–42. Springer, Heidelberg (2014)
Camerer, C.F., Loewenstein, G., Rabin, M.: Advances in Behavioral Economics. Princeton University Press, Princeton (2011)
Christin, N.: Network security games: combining game theory, behavioral economics, and network measurements. In: Katz, J., Baras, J.S., Altman, E. (eds.) GameSec 2011. LNCS, vol. 7037, pp. 4–6. Springer, Heidelberg (2011)
Christin, N., Egelman, S., Vidas, T., Grossklags, J.: It’s all about the benjamins: an empirical study on incentivizing users to ignore security advice. In: Danezis, G. (ed.) FC 2011. LNCS, vol. 7035, pp. 16–30. Springer, Heidelberg (2012)
Gonzalez, R., Wu, G.: On the shape of the probability weighting function. Cogn. Psychol. 38(1), 129–166 (1999)
Grossklags, J., Christin, N., Chuang, J.: Secure or insure?: a game-theoretic analysis of information security games. In: Proceedings of the 17th International Conference on World Wide Web, pp. 209–218. ACM (2008)
Grossklags, J., Christin, N., Chuang, J.: Security and insurance management in networks with heterogeneous agents. In: Proceedings of the 9th ACM Conference on Electronic Commerce, pp. 160–169. ACM (2008)
Grossklags, J., Johnson, B., Christin, N.: The price of uncertainty in security games. In: Moore, T., Pym, D., Ioannidis, C. (eds.) Economics of Information Security and Privacy, pp. 9–32. Springer, Heidelberg (2010)
Hota, A.R., Garg, S., Sundaram, S.: Fragility of the commons under prospect-theoretic risk attitudes. (2014, arXiv preprint). arXiv:1408.5951
Jiang, A.X., Nguyen, T.H., Tambe, M., Procaccia, A.D.: Monotonic maximin: a robust stackelberg solution against boundedly rational followers. In: Das, S.K., Nita-Rotaru, C., Kantarcioglu, M. (eds.) GameSec 2013. LNCS, vol. 8252, pp. 119–139. Springer, Heidelberg (2013)
Jiang, L., Anantharam, V., Walrand, J.: How bad are selfish investments in network security? IEEE/ACM Trans. Netw. 19(2), 549–560 (2011)
Johnson, B., Böhme, R., Grossklags, J.: Security games with market insurance. In: Altman, E., Baras, J.S., Katz, J. (eds.) GameSec 2011. LNCS, vol. 7037, pp. 117–130. Springer, Heidelberg (2011)
Kahneman, D., Tversky, A.: Prospect theory: an analysis of decision under risk. Econom. J. Econom. Soc. 47(2), 263–291 (1979)
Kar, D., Fang, F., Delle Fave, F., Sintov, N., Tambe, M.: A game of thrones: when human behavior models compete in repeated Stackelberg security games. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, pp. 1381–1390 (2015)
Kunreuther, H., Heal, G.: Interdependent security. J. Risk Uncertain. 26(2–3), 231–249 (2003)
La, R.J.: Interdependent security with strategic agents and cascades of infection. IEEE Trans. Netw. (2015, To appear)
Laszka, A., Felegyhazi, M., Buttyan, L.: A survey of interdependent information security games. ACM Comput. Surveys (CSUR) 47(2), 23:1–23:38 (2014)
Lelarge, M., Bolot, J.: A local mean field analysis of security investments in networks. In: Proceedings of the 3rd International Workshop on Economics of Networked Systems, pp. 25–30. ACM (2008)
Lelarge, M., Bolot, J.: Network externalities and the deployment of security features and protocols in the Internet. ACM SIGMETRICS Perform. Eval. Rev. 36(1), 37–48 (2008)
Naghizadeh, P., Liu, M.: Voluntary participation in cyber-insurance markets. In: Workshop on the Economics of Information Security (WEIS) (2014)
Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V.: Algorithmic Game Theory. Cambridge University Press, Cambridge (2007)
Pal, R., Golubchik, L., Psounis, K., Hui, P.: Will cyber-insurance improve network security? A market analysis. In: 2014 Proceedings IEEE INFOCOM, pp. 235–243. IEEE (2014)
Prelec, D.: The probability weighting function. Econometrica 66(3), 497–527 (1998)
Rosoff, H., Cui, J., John, R.S.: Heuristics and biases in cyber security dilemmas. Environ. Syst. Decis. 33(4), 517–529 (2013)
Schwartz, G.A., Sastry, S.S.: Cyber-insurance framework for large scale interdependent networks. In: Proceedings of the 3rd International Conference on High Confidence Networked Systems, pp. 145–154. ACM (2014)
Tversky, A., Kahneman, D.: Advances in prospect theory: cumulative representation of uncertainty. J. Risk Uncertain. 5(4), 297–323 (1992)
Varian, H.: System reliability and free riding. In: Camp, L.J., Lewis, S. (eds.) Economics of Information Security. AIS, pp. 1–15. Springer, Heidelberg (2004)
Yang, R., Kiekintveld, C., Ordóñez, F., Tambe, M., John, R.: Improving resource allocation strategies against human adversaries in security games: an extended study. Artif. Intell. 195, 440–469 (2013)
Zhuang, J.: Modeling attacker-defender games with risk preference. Current Research Project Synopses. Paper 69 (2014). http://research.create.usc.edu/current_synopses/69
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Hota, A.R., Sundaram, S. (2015). Interdependent Security Games Under Behavioral Probability Weighting. In: Khouzani, M., Panaousis, E., Theodorakopoulos, G. (eds) Decision and Game Theory for Security. GameSec 2015. Lecture Notes in Computer Science(), vol 9406. Springer, Cham. https://doi.org/10.1007/978-3-319-25594-1_9
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