Alternatives to Hypothesis Testing: Demonstrative Induction in Cognitive Science

There is a general view that scientific inferences from data to theory are rarely, if ever, deductive inferences. Against this view is a rising tide of work showing the importance of deductive inference in science. Such inferences have been called demonstrative inductions (DI). However, critics have argued against the epistemic advantages of DI, claiming that it merely passes the buck of inductive risk to the premises. In this paper, I defend the advantages of demonstrative induction. I argue that the virtues of structuring inferences deductively derive mainly from the ability to ``isolate'' inductive risk to specific areas of theory, as well as from the ability to ``export'' this risk to more certain domains than the one under investigation. I support this argument with examples from cognitive science, where I argue that the current context of inquiry makes non-deductive, hypothesis testing particularly ineffective. Properly characterized bounded rationality assumptions can, however, help facilitate DI, allowing cognitive scientists to derive process models from environmental structure, export inductive risk outside the head, and isolate the remaining inductive risk to claims about cognitive limitations. I show how this strategy ameliorates the underdetermination problems that plague hypothesis testing, especially in early investigations of complicated black-box systems.