Information design for multiple independent and self-interested defenders: Work less, pay off more

Chenghan Zhou, Andrew Spivey, Haifeng Xu, Thanh Hong Nguyen
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PMLR 180:2404-2413, 2022.

Abstract

This paper studies the problem of information design in a general security game setting in which multiple independent self-interested defenders attempt to provide protection simultaneously on the same set of important targets against an unknown attacker. A principal, who can be one of the defenders, has access to certain private information (i.e., attacker type) whereas other defenders do not. We investigate the question of how that principal, with additional private information, can influence the decisions of the defenders by partially and strategically revealing her information. We focus on the algorithmic study of information design for private signaling in this game setting. In particular, we develop a polynomial-time ellipsoid algorithm to compute an optimal private signaling scheme. Our key finding is that the separation oracle in the ellipsoid approach can be carefully reduced to bipartite matching. Furthermore, we introduce a compact representation of any ex-ante persuasive signaling schemes by exploiting intrinsic security resource allocation structures, enabling us to compute an optimal scheme significantly faster. Our experiment results show that by strategically revealing private information, the principal can significantly enhance the protection effectiveness on the targets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v180-zhou22c, title = {Information design for multiple independent and self-interested defenders: Work less, pay off more}, author = {Zhou, Chenghan and Spivey, Andrew and Xu, Haifeng and Hong Nguyen, Thanh}, booktitle = {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence}, pages = {2404--2413}, year = {2022}, editor = {Cussens, James and Zhang, Kun}, volume = {180}, series = {Proceedings of Machine Learning Research}, month = {01--05 Aug}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v180/zhou22c/zhou22c.pdf}, url = {https://proceedings.mlr.press/v180/zhou22c.html}, abstract = {This paper studies the problem of information design in a general security game setting in which multiple independent self-interested defenders attempt to provide protection simultaneously on the same set of important targets against an unknown attacker. A principal, who can be one of the defenders, has access to certain private information (i.e., attacker type) whereas other defenders do not. We investigate the question of how that principal, with additional private information, can influence the decisions of the defenders by partially and strategically revealing her information. We focus on the algorithmic study of information design for private signaling in this game setting. In particular, we develop a polynomial-time ellipsoid algorithm to compute an optimal private signaling scheme. Our key finding is that the separation oracle in the ellipsoid approach can be carefully reduced to bipartite matching. Furthermore, we introduce a compact representation of any ex-ante persuasive signaling schemes by exploiting intrinsic security resource allocation structures, enabling us to compute an optimal scheme significantly faster. Our experiment results show that by strategically revealing private information, the principal can significantly enhance the protection effectiveness on the targets.} }
Endnote
%0 Conference Paper %T Information design for multiple independent and self-interested defenders: Work less, pay off more %A Chenghan Zhou %A Andrew Spivey %A Haifeng Xu %A Thanh Hong Nguyen %B Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2022 %E James Cussens %E Kun Zhang %F pmlr-v180-zhou22c %I PMLR %P 2404--2413 %U https://proceedings.mlr.press/v180/zhou22c.html %V 180 %X This paper studies the problem of information design in a general security game setting in which multiple independent self-interested defenders attempt to provide protection simultaneously on the same set of important targets against an unknown attacker. A principal, who can be one of the defenders, has access to certain private information (i.e., attacker type) whereas other defenders do not. We investigate the question of how that principal, with additional private information, can influence the decisions of the defenders by partially and strategically revealing her information. We focus on the algorithmic study of information design for private signaling in this game setting. In particular, we develop a polynomial-time ellipsoid algorithm to compute an optimal private signaling scheme. Our key finding is that the separation oracle in the ellipsoid approach can be carefully reduced to bipartite matching. Furthermore, we introduce a compact representation of any ex-ante persuasive signaling schemes by exploiting intrinsic security resource allocation structures, enabling us to compute an optimal scheme significantly faster. Our experiment results show that by strategically revealing private information, the principal can significantly enhance the protection effectiveness on the targets.
APA
Zhou, C., Spivey, A., Xu, H. & Hong Nguyen, T.. (2022). Information design for multiple independent and self-interested defenders: Work less, pay off more. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 180:2404-2413 Available from https://proceedings.mlr.press/v180/zhou22c.html.

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