Methodology for eliciting, encoding and simulating human decision making behaviour

Abstract

Agent-based models (ABM) are an increasingly important research tool for describing and predicting interactions among humans and their environment. A key challenge for such models is the ability to faithfully represent human decision making with respect to observed behaviour. This thesis aims to address this challenge by developing a methodology for empirical measurement and simulation of decision making in humanenvironment systems. The methodology employs the Beliefs-Desires-Intentions (BDI) model of human reasoning to directly translate empirically measured decision data into artificial agents, based on sound theoretical principles. A common simulated decision environment is used for both eliciting human decision making behaviour, and validating artificial agents. Using this approach facilitates the collection of decision making narratives by way of participatory simulation, and promotes a fair comparison of real and modelled decision making. The methodology is applied in two case studies: One to carry out a trial involving human subjects solving an abstract land-use problem, and another to examine the feasibility of up-scaling the methodology to a real agricultural scenario—dairy farming. Results from the experiments indicate that the BDI-based methodology achieved reasonably direct encoding of decision making behaviour from elicited human narratives. The main limitations found with the technique are: (1) the significant use of subjects’ time required to elicit their decision making behaviour; (2) the significant programming effort required; and (3) the challenge of aggregating behaviour from multiple subjects into a generalised decision making model. In spite of its limitations, BDI has shown its strengths as a tool for empirical analysis and simulation of decision making in research of human-environment systems.