Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions

For computer simulation models to usefully inform climate risk management, uncertainties in model projections must be explored and characterized. Because doing so requires running the model many ti..

Accounts and Reality

This book proposes a way to view the results that are obtained in academic disciplines. It is to see them as disclosing not how the world is, but what may and what must be said about the world. The aim is to avoid the question of realism, while keeping the evident epistemic value of disciplines explicable. The approach is intended to apply across a full range of disciplines, from physics to history, even though the question of realism has traditionally been thought of as one kind of problem in the philosophy of the natural sciences and a different kind in the philosophy of the social sciences and the humanities

Ockham Efficiency Theorem for Stochastic Empirical Methods

Ockham’s razor is the principle that, all other things being equal, scientists ought to prefer simpler theories. In recent years, philosophers have argued that simpler theories make better predictions, possess theoretical virtues like explanatory power, and have other pragmatic virtues like computational tractability. However, such arguments fail to explain how and why a preference for simplicity can help one find true theories in scientific inquiry, unless one already assumes that the truth is simple. One new solution to that problem is the Ockham efficiency theorem (Kelly 2002, 2004, 2007a-d, Kelly and Glymour 2004), which states that scientists who heed Ockham’s razor retract their opinions less often and sooner than do their non-Ockham competitors. The theorem neglects, however, to consider competitors following random (“mixed”) strategies and in many applications random strategies are known to achieve better worst-case loss than deterministic strategies. In this paper, we describe two ways to extend the result to a very general class of random, empirical strategies. The first extension concerns expected retractions, retraction times, and errors and the second extension concerns retractions in chance, times of retractions in chance, and chances of errors.</p