No abstract

This paper studies the problem of information design in a general security game setting in which multiple self-interested defenders attempt to provide protection simultaneously for 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. 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 for the targets.

Most algorithmic studies on multi-agent information design so far have focused on the restricted situation with no inter-agent externalities; a few exceptions investigated special game classes such as zero-sum games and second-price auctions but have all focused only on optimal public signaling and exhibit sweepingly negative results. This paper initiates the algorithmic information design of both public and private signaling in a fundamental class of games with negative externalities, i.e., atomic singleton congestion games, with wide application in today's digital economy, machine scheduling, routing, etc.For both public and private signaling, we show that the optimal information design can be efficiently computed when the number of resources is a constant. To our knowledge, this is the first set of computationally efficient algorithms for information design in succinctly representable many-player games. Our results hinge on novel techniques such as developing "reduced forms" to compactly represent players' marginal beliefs. When there are many resources, we show computational intractability results. To overcome the challenge of multiple equilibria, here we introduce a new notion of equilibrium-oblivious NP-hardness, which rules out any possibility of computing a good signaling scheme, irrespective of the equilibrium selection rule.