Bayesian Graphs of Intelligent Causation
Preetha Ramiah, Jim Q. Smith, Silvia Liverani, F. O. Bunnin, Jamie Addison, Annabel Whipp · Bayesian Analysis · 2025
Probabilistic Graphical Bayesian models of causation have continued to impact on strategic analyses designed to help evaluate the efficacy of different interventions on systems. The standard causal algebras upon which these inferences are based typically assume that the intervened population does not react intelligently to frustrate an intervention. In an adversarial setting this is rarely an appropriate assumption. In this paper, we extend an established Bayesian methodology called Adversarial Risk Analysis to apply it to settings that can legitimately be designated as causal in this graphical sense. To embed this technology we first need to generalise the concept of a causal graph. We then import recent developments in ARA into the generalised causal graph. These are used to demonstrate how the intelligent reactions of an adversary to defensive interventions can be modelled and thus predicted by the defender. The new methodologies and supporting protocols are illustrated through applications associated with an adversary attempting to infiltrate a friendly state. Disclaimer The views and opinions expressed are those of the authors and not those of any entities they are attached to.