Risk Assessment of Deliberate Contamination of Food Production Facilities

The deliberate contamination of food is well recognized as a major public health threat. A food supply chain offers several possible targets for the intentional insertion of a biological or chemical toxin by a perpetrator, which can result in significant morbidity and mortality. We assume that both manager (defender) of the food production facility and perpetrator (attacker) select actions at each of a possibly countable number of decision epochs, based on possibly inaccurate real-time observations of the other agent. The defender's objectives are to maximize long-run expected total discounted system productivity and to minimize the long-run expected total discounted consequence of an attack. The attacker's objective is to maximize its reward, which combines the long-run expected total discounted consequence of an attack with a penalty if the attack is unsuccessful. We model this problem as a leader-follower, two-agent partially observed Markov game. We show that system risk is dynamic, determine the impact of observation accuracy on facility productivity and risk, thus providing a measure of the value of information, and perform a sensitivity analysis on key parameters. We present an illustrative example involving a liquid egg production system.