When does evidence suffice for conviction? Martin Smith (Glasgow) There is something puzzling about statistical evidence. One place this manifests is in the law, where courts are reluctant to use evidence of this kind, in spite of the fact that it is quite capable of meeting the standards of proof enshrined in legal doctrine. After surveying some proposed solutions to this problem, I shall outline a somewhat different approach ‚Ai one that makes use of a notion of normalcy that is distinct from the idea of statistical frequency. The problem is not, however, merely a legal one. Our unwillingness to base beliefs on statistical evidence is by no means limited to the courtroom, and is at odds with almost every general principle that epistemologists have ever proposed as to how we ought to manage our beliefs. Dogmas, Dominance, and Disintegrations Paul Pedersen (Berlin) Foundations for boundedly rational learning Simon Huttegger (UC Irvine) The core idea of rational Bayesian learning is that learning from experience should be consistent with one's inductive assumptions. I will use this idea to develop foundations for so-called bounded rationality learning rules, which are based on much less informational inputs than Bayesian conditioning. This results in foundations for bounded rationality learning very similar to those of a more standard Bayesian type.

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什么时候证据足以定罪？

什么时候证据足以定罪？Martin Smith（格拉斯哥）统计证据有些令人费解。这体现在法律中，法院不愿使用此类证据，尽管它完全能够满足法律学说中规定的证明标准。在调查了这个问题的一些建议解决方案之后，我将概述一种稍微不同的方法，即利用与统计频率概念不同的常态概念的方法。然而，问题不仅仅是一个法律问题。我们不愿意将信念建立在统计证据上，这绝不仅限于法庭，而且几乎与认识论者提出的关于我们应该如何管理信念的所有一般原则都不一致。教条，统治，和解体 Paul Pedersen（柏林） 有限理性学习的基础 Simon Huttegger（加州大学欧文分校） 理性贝叶斯学习的核心思想是从经验中学习应该与一个人的归纳假设一致。我将利用这个想法为所谓的有限理性学习规则奠定基础，这些规则基于比贝叶斯条件少得多的信息输入。这导致有限理性学习的基础与更标准的贝叶斯类型的基础非常相似。它们基于比贝叶斯条件少得多的信息输入。这导致有限理性学习的基础与更标准的贝叶斯类型的基础非常相似。它们基于比贝叶斯条件少得多的信息输入。这导致有限理性学习的基础与更标准的贝叶斯类型的基础非常相似。