Simulating an agent’s decision-making process in black-box managerial environment: An estimation-and-optimisation approach

Abstract With the growing need to guide decision-making in today’s complex managerial environment, researchers of the Operations Research/Management Science community have shown a considerable interest in modelling complex managerial systems using the agent-based modelling and simulation technique. This paper presents an estimation-and-optimisation (ESTOPT) architecture to simulate an agent’s decision-making process in black-box managerial environment. An ESTOPT agent’s behaviour is considered as a two-stage process of solving its optimisation problem, some parameters of which are uncertain and need to be estimated. In the first stage, the agent collects and records information for estimation; in the next stage, it attempts to solve the optimisation problem. The solution guides the agent’s actions on the environment which, in turn, provides the agent with new information and payoff as feedback. In this paper, two agent-based models are introduced to demonstrate the implementation of the ESTOPT approach. The simulation outcomes compare favourably with both empirical and theoretical results, suggesting that the ESTOPT approach can be used to simulate an agent’s decision-making process in black-box managerial environment.