This paper examines the use of qualitative representations in modeling the similarities and differences in causal reasoning for biological kinds between Menominee Native Americans and US majority culture. Qualitative Concept Maps are used for modeling and analyzing transcripts of interviews conducted with these groups. The individual models are used to construct generalizations for the groups, which are tested both by inspection and by creating a classifier to distinguish models from these two cultures.

We outline a new computational account of learning in children using the causal Bayes net formalism. We also present evidence that children as young as two years old use something like causal Bayes net learning mechanisms to infer the causal structure of the world around them. This kind of learning may play an important role in the development of intuitive theories. Finally we contrast causal Bayes net and neural net learning mechanisms.

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 recognitionbased 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,