Using Game-Theoretic Value of Information for Sequential Allocation of Human Resources in Human-in-the-Loop

Asset protection constitutes identifying threats and responding to them in time. The process of analyzing a situation that potentially presents a threat and deciding on how to respond to it typically engages both automated machine learning algorithms and human analysts. Human judgment is more accurate than that of a trained algorithm, but humans take more time to piece the available evidence together. The best assessment strategy is to let human analysts partially rely on machines, i.e., generally trust the machine’s judgment but occasionally take time to assess risks themselves, depending on the asset value at stake. This paper presents an optimal just-in-time policy to assess the incoming situations – threat cases – by a human-machine tandem where humans have a limited bandwidth for personally reviewing the cases. This presents the problem of balancing the human attention allocation, conditioned on a prior assessment of the risks the cases might present. The problem is modeled and solved as a stochastic sequential assignment problem informed by the concepts of decision game theory and the value of information.