This tutorial presents applications of coherence-based probability logic (CPL) to selected problems of philosophy and psychology as well as in the field of nonmonotonic reasoning. In particular, it illustrates how CPL can be used as a rationality framework for artificial and human reasoning under uncertainty. Normatively CPL prescribes how ideal cognition works under uncertainty. Descriptively, CPL allows for deriving testable psychological hypotheses.

After a brief characterisation of CPL, I illustrate why CPL provides a unified rationality framework for investigating problems in philosophy and psychology. Specifically, I show that coherence allows for a better understanding of many philosophical problems in logic, like the understanding and proper treatment of conditionals (if-then-constructions) and nonmonotonic reasoning (i.e., reasoning systems which allow for retracting conclusions in the light of new evidence). Moreover, I argue that CPL makes many interesting psychological predictions, some of which have been validated experimentally in recent years. For example, most people interpret beliefs in conditionals as conditional probabilities, draw coherent conclusions, reason nonmonotonic, and connexively.

The aim of my tutorial is to provide an overview on how CPL can serve as a unified rationality framework for studying diverse problems in different disciplines including the philosophy of logic, formal epistemology, and the psychology of reasoning.