We believe that humans learn about causal structure in a different way than our computer algorithms do, but we don't know. Research is needed into how humans learn about causation, and how they might be trained to do so more effectively. Many powerful programs exist for finding proofs in formal logical systems or for teaching logic. The community would benefit enormously from a repository of documented and downloadable software. Extending the Intercalation Calculus to Elementary Set Theory would be mathematically interesting and be useful pedagogically.
Many powerful algorithms exist for finding proofs in formal logical systems.
Some are explicitly built to use strategies that mimic human experts, and some not, even though little is known about how novice and experts actually search for proofs. Research needs to be done on how human expertise in proof search can be used to help computers become more efficient. Such models often exhibit properties that are strikingly similar to the actual social world, e.g., cooperation, social norms, and social stratification into different classes, and provide a unique window into understanding such phenomena. Using the methods outlined above, previously inaccessible, yet fundamental, questions are now becoming amenable to analysis. Discovering Causal Structure .