Knowledge reorganization and reasoning style

To study the learning of expertise, two closely related stages of expertise in economics reasoning are analyzed and modelled, and a mechanism for going from the first to the second is proposed. Both stages share the same basic concepts and generate plausible economie scenarios, but reasoning in the first stage oversimplifies by focussing on how the goals of a few actors are affected. Reasoning in the second stage produces better arguments by taking into account how all the relevant parts of the economy might be affected. The first stage is modelled by highly interconnected goal forests and very selective, story understanding search heuristics. The second stage is modelled with more explicit links between economie quantities and a more appropriate set of search heuristics. The learning mechanism is a failure-driven process that not only records better arguments as they are seen, but also records the failure of existing inference rules to find these arguments on their own. The collected failures are used to determine which search heuristics work best in which situations.