Compliance signaling games: toward modeling the deterrence of insider threats

Authored by William Casey, Jose Andre Morales, Evan Wright, Quanyan Zhu, Bud Mishra

Date Published: 2016

DOI: 10.1007/s10588-016-9221-5

Sponsors: Department of Defense Software Engineering Institute

Platforms: No platforms listed

Model Documentation: Other Narrative Mathematical description

Model Code URLs: Model code not found

Abstract

In a typical workplace, organizational policies and their compliance requirements set the stage upon which the behavioral patterns of individual agents evolve. The agents' personal utilities, access to information, and strategic deceptions shape the signaling systems of an intricate information-asymmetric game, thus mystifying assessment and management of organizational risks, which are primarily due to unintentional insider threats. Compliance games, as discussed here, model a rudimentary version of this signaling game between a sender (employee) and a receiver (organization). The analysis of these games' equilibria as well as their dynamics in repeated game settings illuminate the effectiveness or risks of an organizational policy. These questions are explored via a repeated and agent-based simulation of compliance signaling games, leading to the following: (1) a simple but broadly applicable model for interactions between sender agents (employees) and receiver agents (principals in the organization), (2) an investigation of how the game theoretic approach yields the plausible dynamics of compliance, and (3) design of experiments to estimate parameters of the systems: evolutionary learning rates of agents, the efficacy of auditing using a trembling hand strategy, effects of non-stationary and multiple principal agents, and ultimately, the robustness of the system under perturbation of various related parameters (costs, penalties, benefits, etc.). The paper concludes with a number of empirical studies, illustrating a battery of compliance games under varying environments designed to investigate agent based learning, system control, and optimization. The studies indicate how agents through limited interactions described by behavior traces may learn and optimize responses to a stationary defense, expose sensitive parameters and emergent properties and indicate the possibility of controlling interventions which actuate game parameters. We believe that the work is of practical importance-for example, in constraining the vulnerability surfaces arising from compliance games.

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Evolution Cooperation Populations Chromodynamics