Litcius/Paper detail

Optimality Justifications

Gerhard Schurz

202422 citationsDOI

Abstract

Abstract The leading idea of epistemology in the Enlightenment tradition was foundation-theoretic: to reach knowledge we must not legitimize our beliefs by external authorities, but justify them by rational arguments. Presently the foundation-theoretic ideal of justification has come under attack, the chief criticism being that been that its universal standards of justification are illusionary because the justificational regress problem is unsolvable. Alternatives to foundation-theoretic epistemology such as coherentism, externalism, or dogmatism have been developed that give up central claims of enlightenment epistemology such as empirical support, cognitive accessibility, or rational justifiability. In this book a new account of foundation-theoretic epistemology is developed based on the method of optimality justifications. Optimality justifications offer a solution to the regress problem. Rather than striving for a priori demonstrations of reliability, which are impossible, they show that certain epistemic methods are optimal in regard to all accessible alternatives, which is more modestly but provably possible. In particular, optimality justifications can achieve a non-circular justification of deductive, inductive, and abductive reasoning. All in all this book pursues two goals: a general renewal of foundation-theoretic epistemology based on the account of optimality justifications, and the advancement of methods of optimality justification in important domains of epistemology and philosophy of science, logic, and cognition. Connected with these two goals is the aspiration of this book to develop new ideas for mainstream epistemology as well as for formal epistemology, philosophy of science, and cognitive science, intended to attract researchers, students, and all other readers interested in these fields.

Topics & Concepts

Computer scienceMathematical economicsEconomicsComputability, Logic, AI AlgorithmsLogic, Reasoning, and KnowledgeBayesian Modeling and Causal Inference