Epistatic Signaling and Minority Games, the Adversarial Dynamics in Social Technological Systems

Abstract

We present a game theoretic framework that models strategic interactions among humans and things that are assumed to be interconnected by a social-technological network, as in an internet of humans and things (IOHT). Often a pair of agents in the network interacts in order for an informed sender-agent to signal an uninformed receiver-agent to take an action that benefits each of the players; the benefits to the pair of agents are modeled by two separate utility functions, both depending on the sender's private information, the signal exchanged, and the receiver's revealed (and also possibly unrevealed) action. In general, the two agents' utilities may not be aligned and may encourage deceptive behavior. For example, a sender, aware of his/her own private "state of ignorance", may seek useful information from a receiver who owns powerful computational resources to search a large corpus of webpages; the sender does so by sending a signal to the receiver in the form of a keyword. Obvious examples of deceptiveness here range from attempts to hide one's intentions to auctioning the keywords on an ad exchange through real-time bidding. A rather troublesome situation occurs when deceptions are employed to breach the security of the system, thus making the entire social-technological network unreliable. Earlier, we proposed a signaling-game-theoretic framework to alleviate this problem. This paper further enhances that framework by reconfiguring signals to possess more complex structures (epistatic signals to represent attack and defense options over a given set of vulnerabilities). We explore two augmentations to the original evolutionary signaling game by first enhancing mutation bias toward strategies performing well in previous populations and second allowing the parameters of the utility functions to depend on population preferences giving rise to a minority game with epistatic signaling. The resulting game systems are empirically studied through extensive computer simulation.