My thesis will cover two of the most fundamental points of science: 1) How we should and do develop structural causal models of the world (existential abduction; Haig, 2005) and 2) how we should and do test these models and evaluate the uncertainty associated with them (induction). Search algorithms (also Teddy’s mention of when a model should have an additional parameter from Jevons?) can help elucidate the first question, and Bayesian meta-analysis, the second.
Exploratory factor analysis is one of the only tools for existential abduction (Haig, 2005). I want to propose new tools and procedures. As we abduct and enumerate possible causal explanations for an effect (phenomenon), we assign probabilities to each one; these are our priors. How we create a well-defined, mutually exclusive and exhaustive set of abductive inferences (hypotheses) about a phenomenon is currently unknown, and this dissertation hopes to illuminate. Each element—well-definedness, exclusivity, and exhaustiveness—are extremely difficult to achieve on their own. Thus existential abduction is difficult.