P. George Benson

Belief, knowledge, and uncertainty: A cognitive perspective on subjective probability

Abstract This paper presents a cognitive analysis of subjective probability judgments and proposes that these are assessments of belief-processing activities. The analysis is motivated by an investigation of the concepts of belief, knowledge, and uncertainty. Judgment and reasoning are differentiated, Toulmins (1958) theory of argument being used to explicate the latter. The paper discusses a belief-processing model in which reasoning is used to translate data into conclusions, while judgmental processes qualify those conclusions with degrees of belief. The model sheds light on traditional interpretations of probability and suggests that different characteristics of belief—likelihood and support—are addressed by different representational systems. In concluding, the paper identifies new lines of research implied by its analysis.

Timing Decisions and the Behavior of Mutual Fund Systematic Risk

The investment performance of professionally managed portfolios, in general, and mutual funds, in particular, has been the subject of considerable attention in finance. Fama [9] has suggested that overall portfolio performance be broken down in such a manner that the individual sources of performance can be identified. Two basic sources are: (1) the ability of the portfolio manager to forecast price movements of individual common stocks relative to stocks in general (selectivity or microforecasting); and (2) the ability to forecast the direction of the stock market relative to fixed income securities (timing or macroforecasting).

Evaluating second-order probability judgments with strictly proper scoring rules

Empirical studies have demonstrated that uncertainty about event probabilities, also known as ambiguity or second-order uncertainty, can affect decision makers’ choice preferences. Despite the importance of second-order uncertainty in decision making, almost no effort has been directed towards the development of methods that evaluate the accuracy of second-order probabilities. In this paper, we describe conditions under which strictly proper scoring rules can be used to assess the accuracy of second-order probability judgments. We investigate the effectiveness of using a particular strictly proper scoring rule the ranked probability score - to discourage biased assessments of second-order uncertainty.