Confirmation of Scientific Hypotheses as Relations

Summary  In spite of several attempts to explicate the relationship between a scientific hypothesis and evidence, the issue still cries for a satisfactory solution. Logical approaches to confirmation, such as the hypothetico-deductive method and the positive instance account of confirmation, are problematic because of their neglect of the semantic dimension of hypothesis confirmation. Probabilistic accounts of confirmation are no better than logical approaches in this regard. An outstanding probabilistic account of confirmation, the Bayesian approach, for instance, is found to be defective in that it treats evidence as a formal entity and this creates the problem of relevance of evidence to the hypothesis at issue, in addition to the difficulties arising from the subjective interpretation of probabilities. This essay purports to satisfy the need for a successful account of hypothesis confirmation by offering an original formulation based on the notion of instantiation of the relation urged by an hypothesis.     

Selective influence through conditional independence

Let each of several (generally interdependent) random vectors, taken separately, be influenced by a particular set of external factors. Under what kind of the joint dependence of these vectors on the union of these factor sets can one say that each vector is selectively influenced by “its own” factor set? The answer proposed and elaborated in this paper is: One can say this if and only if one can find a factor-independent random vector given whose value the vectors in question are conditionally independent, with their conditional distributions selectively influenced by the corresponding factor sets. Equivalently, the random vectors should be representable as deterministic functions of “their” factor sets and of some mutually independent and factor-independent random variables, some of which may be shared by several of the functions.     

Reinventing the University Through the Teaching of Diversity

University reform encompasses an increased emphasis on the social and emotional development of students. In particular, the development of caring, empathy, and community are emphasized. This team of psychology professors approaches the task of university reform by presenting both its philosophy of diversity inclusion and specific inclusion strategies as means of promoting enhanced development of our students. Although the discipline of psychology is used as an example, the philosophical argument and the strategic approaches may be applied to other disciplines.     

Bayesian estimation and testing of structural equation models

The Gibbs sampler can be used to obtain samples of arbitrary size from the posterior distribution over the parameters of a structural equation model (SEM) given covariance data and a prior distribution over the parameters. Point estimates, standard deviations and interval estimates for the parameters can be computed from these samples. If the prior distribution over the parameters is uninformative, the posterior is proportional to the likelihood, and asymptotically the inferences based on the Gibbs sample are the same as those based on the maximum likelihood solution, for example, output from LISREL or EQS. In small samples, however, the likelihood surface is not Gaussian and in some cases contains local maxima. Nevertheless, the Gibbs sample comes from the correct posterior distribution over the parameters regardless of the sample size and the shape of the likelihood surface. With an informative prior distribution over the parameters, the posterior can be used to make inferences about the parameters underidentified models, as we illustrate on a simple errors-in-variables model.We thank David Spiegelhalter for suggesting applying the Gibbs sampler to structural equation models to the first author at a 1994 workshop in Wiesbaden. We thank Ulf Böckenholt, Chris Meek, Marijtje van Duijn, Clark Glymour, Ivo Molenaar, Steve Klepper, Thomas Richardson, Teddy Seidenfeld, and Tom Snijders for helpful discussions, mathematical advice, and critiques of earlier drafts of this paper.     

Likelihoodism,Bayesianism, and relational confirmation

Likelihoodists and Bayesians seem to have a fundamental disagreement about the proper probabilistic explication of relational (or contrastive) conceptions of evidential support (or confirmation). In this paper, I will survey some recent arguments and results in this area, with an eye toward pinpointing the nexus of the dispute. This will lead, first, to an important shift in the way the debate has been couched, and, second, to an alternative explication of relational support, which is in some sense a “middle way” between Likelihoodism and Bayesianism. In the process, I will propose some new work for an old probability puzzle: the “Monty Hall” problem. Thanks to the participants of the Philosophy, Probability, and Modeling (PPM) Seminar at the University of Konstanz (especially Stephan Hartmann, Franz Huber, Wolfgang Spohn, and Teddy Seidenfeld), for a very fruitful discussion of an early draft of this paper in July, 2004. Since then, discussions and correspondences with Prasanta Bandyopadhyay, Luc Bovens, Alan Hájek, Jim Hawthorne, Jim Joyce, Jon Kvanvig (and other participants of his “Certain Doubts” blog, which had a thread on a previous draft of this paper), Patrick Maher, Sherri Roush, Richard Royall, Elliott Sober, Dan Steel, and an anonymous referee of Synthese has been very valuable.     

CAT选题策略分类概述

选题是计算机化自适应测验（CAT）测试过程的关键环节,选题策略的目标是要达到较高的测量精度,同时也实现试题曝光率控制及其他测验目标的实现.本文根据选题策略的基本原理和衍生发展,将众多CAT选题策略分为五大选题策略系列：Fisher函数系列、K-LI函数系列、α分层系列、贝叶斯系列、b匹配系列;并根据测验目标（测验精度、试题曝光率控制、内容平衡、多条件约束）对这些选题策略进行了细分,并对CAT选题策略的选择思路进行归纳.     

Distortion of probability and outcome information in risky decisions

Substantial evidence indicates that information is distorted during decision making, but very few studies have assessed the distortion of probability and outcome information in risky decisions. In two studies involving six binary decisions (e.g., banning blood donations from people who have visited England, because of “mad cow disease”), student and nonstudent participants distorted their evaluations of probability and outcome information in the direction of their preferred decision alternative and used these biased evaluations to update their preferences. Participants also evaluated the utilities of possible outcomes more positively when the outcomes could follow only from the preferred alternative and more negatively when they could follow only from the competing alternative. Such circular reasoning is antithetical to the normative consequentialist principles underlying decision analysis. Presenting numerical information as precise values or as ranges of values did not significantly affect information distortion, apparently because the manipulation did not affect perceived ambiguity as intended.     

Global Model Analysis of Cognitive Variability

Residual fluctuations produced in typical experimental methodologies are examined as correlated noises. The effective range of the correlations was assessed by determining whether the decay over look-back time is better described as a power law or exponential. Both of these decay laws contain free parameters and it is argued that it is not possible to distinguish their models on the basis of simple measures of goodness-of-fit. Global analyses that evaluate models on the basis of how well they generalize are conducted. The models are examined in terms of three constructs that all bear on generalization: cross-validity, flexibility, and representativeness. Quantitative assessment of a large ensemble of data suggests that the correlations decay over time as a power law. The conclusion is that human residual fluctuation is a correlated fractal.     

A causal model theory of the meaning of cause, enable, and prevent

The verbs cause , enable , and prevent express beliefs about the way the world works. We offer a theory of their meaning in terms of the structure of those beliefs expressed using qualitative properties of causal models, a graphical framework for representing causal structure. We propose that these verbs refer to a causal model relevant to a discourse and that "A causes B" expresses the belief that the causal model includes a link from A to B. "A enables/allows B" entails that the model includes a link from A to B, that A represents a category of events necessary for B, and that an alternative cause of B exists. "A prevents B" entails that the model includes a link from A to B and that A reduces the likelihood of B. This theory is able to account for the results of four experiments as well as a variety of existing data on human reasoning.     

不同概率表征的贝叶斯推理试题形式结构的同质性研究

研究从先验概率、概率表征、推理任务等方面探讨了经典贝叶斯推理研究中存在的不足,试图在＂知识和试题双重模型＂框架下,探索现实和标准贝叶斯试题的形式结构的同质性,结果表明：1）自然频次表征比百分比表征的贝叶斯推算题正确率高,这是因为试题的形式结构不同,与概率表征无关;2）贝叶斯判断题与贝叶斯推算题的试题形式结构存在显著差异;3）贝叶斯推算题中,概率词表征与其它两种表征的试题形式结构存在显著差异,其实质是贝叶斯判断。