Seminar series, Michaelmas/Lent Term 2004. Time: Wednesdays, 5-7pm Venue: CPNSS, LSE; Lakatos Bld., T206 (second floor)

When the claim is made that something (eg. a fact, a ‘good’ reason, etc.) is evidence that a hypothesis is true, what exactly is being claimed? Are there different concepts of evidence; or just one concept, and different uses? Does evidence depend on time, circumstances and knowledge? Are philosophical theories of evidence of some use to practicing scientists? How does/should evidence relate to probability? Is evidence an ‘objective’ concept, and if so in what sense?

These are some of the questions we intend to address in the seminar series, by exploring how evidence is used in different sciences, and more generally in different knowledge-oriented practices which require well- founded and empirically/rationally justified results.

The seminars intend to be truly interdisciplinary, and aim at promoting philosophical and methodological discussion across the wide spectrum of social and natural inquiry.

First Meeting : 27 October 2004, 5-7pm; CPNSS (LSE), Lakatos Bld., T206 (second floor)

Luc Bovens (LSE) Beta Functions and Decision-Making on the Basis of Uncertain Evidence

Abstract: In risk analysis, the precautionary principle is held up as a counter weight to expected utility maximization. But clearly, a strict application of the adage that it is better to be safe than sorry is stifling. We do not make policy by focusing strictly on the worst outcomes and choosing the policy that yields the best worst outcome. Although the probability of worst outcomes is not known with precision, we do make estimates of the risks and decide to accept certain risky prospects and not others.

For Ellsberg, expected-utility maximization for decision-making under risk and the maximin solution for decision-making under uncertainty are two poles of a continuum. Between these poles we have varying degrees of confidence in our probability assessment. Ellsberg modeled this continuum by introducing a measure of our degree of confidence in our probability assessment. He maximizes the sum of the expected utility, weighted by rho, and the utility of the worst outcome, weighted by 1 – rho.

I argue that the same results can be obtained by means of expected utility maximization within a strictly Bayesian framework. We represent our degree of confidence in our probability assessment by means of a Beta density function. By letting our probability assessment be the value of p for which the density function reaches its maximum and by calculating the expected utility by means of the upper bound of a confidence interval, expected utility maximization enjoins us to be the more cautious, the lower our degree of confidence in our probability assessment is. My approach has the following advantages: (i) it respects the intuition that we are more inclined to take account of the worst-case scenario when our degree of confidence concerning our probability assessment is low; (ii) it avoids the dogmatic stand of the precautionary principle; (iii) It does not bring in any machinery outside of the Bayesian framework of expected-utility maximization.

Calendar of other meetings, Michaelmas Term 2004

10 November Mike Redmayne (Law, LSE) The Law of Evidence

24 November Phil Dawid (Statistics, UCL) Statistics and the Law

8 December. John Worrall (Philosophy, LSE) Evidence in Medicine

For more details on the seminar, please contact Vincent Guillin: [email protected]