NORTHWESTERN UNIVERSITY A Cognitive Model of Recognition-Based Moral Decision Making A DISSERTATION SUBMITTED TO THE GRADUATE SCHOOL IN PARTIAL FULFILLMENT OF THE REQUIREMENTS for the degree DOCTOR OF PHILOSOPHY Field of Computer Science By

A Cognitive Model of Recognition-Based Moral Decision Making Morteza Dehghani The study of decision making has been dominated by economic perspectives, which model people as rational agents who carefully weigh costs and benefits and try to maximize the utility of every choice, without consideration of issues such as cultural norms, religious beliefs and moral rules. However, psychological findings indicate that in many situations people are not rational decision makers as defined by the economic theories. One of the domains in which traditional cost-benefit models fail to predict human behavior is the domain of moral reasoning. This work presents the first computational model of recognition-based moral decision making, MoralDM, which integrates several AI techniques in order to model recent psychological findings on moral decision making. MoralDM uses a natural language system to produce formal representations from psychological stimuli, reducing tailorability. The impacts of secular versus sacred values are modeled via qualitative reasoning, using an order of magnitude representation. MoralDM uses a combination of first-principles reasoning and analogical reasoning to model the recognition-based mode of decision making. The results of MoralDM experiments provided the impetus to further examine the role of cultural narratives and analogical reasoning on moral decision making. This work examines whether the processes by which core cultural narratives are applied in people’s lives follow the principles of analogical retrieval and mapping. In particular, it examines how analogical accessibility and alignability influence the use of canonical moral narratives. The results of a