Enhancing Agent-Based Models with Artificial Intelligence for Complex Decision Making

Complexity in human behaviour can play a crucial role in socio-environmental processes like disease diffusion. An example of such complex behaviour is risk perception, and behavioural change due to perceived risk. Computational models, and in particular Agent-based models (ABMs), have evolved as tools for simulating complex real-world processes.
ABMs for describing and simulating a system composed of behavioural entities, ABMs provide the most natural environment. ABMs often use naive deterministic algorithms, which are rule-based, to simulate behavioural change in agents. While agents in ABMs are sometimes endowed with memory, the actual learning in machine learning style is rarely implemented. The endogenous switching of expectations formation strategies using learning algorithm is underdeveloped in ABMs.
The goal of my PhD research is to systematically test the effects of implementing social and environmental intelligence on the dynamics and emergent outcomes of spatial ABM. Spatial ABMs often use spatial data (GIS data) to construct real geographic environments in which agents are situated. Agents need to take changes in the spatial environment into account and adjust their behaviour accordingly. In this PhD research, intelligence, rational, and risk perception are playing an important role in the decision making of agents. Understanding the learning processes of agents in the spatial ABM can assist developing better strategies in problem-solving and coordination mechanisms.