论纽科姆难题的因果决策结构及其消解方案

纽科姆难题是关于决策者合理行为的一个选择困境问题,它的提出使得贝叶斯主义者在理性主体应该采取的行为方式这个问题上出现严重分歧,经典决策理论也因此分化为证据决策理论和因果决策理论两大理论流派,从而极大地促进了贝叶斯决策理论的发展。但随着研究的深入,一些学者对纽科姆难题本身的合理性提出了质疑,认为其存在构造缺陷或所设置的选择困境超出了人类的理性边界。事实上,纽科姆难题具有一种特殊的因果决策结构,借助赖欣巴哈原理、屏蔽原理和可证实性原则等,在考察决策者斟酌过程的基础上,我们可以构建一种新的方案来消解纽科姆难题中的选择困境。     

确证非必要性论题的实证研究

知识的JTB三元定义认为,确证、真和信念是构成知识的三个元素。确证必要性论题一直为主流知识论者所信奉。虽然有学者曾主张确证不是知识的必要条件,然而由于其理由的思辨性,并没有产生多大的影响。最新的实证数据发现:在缺乏好的证据或适当的证据时,普通人愿意归赋知识;在知识归赋低的情况下,却有较高的确证归赋;确证不是知识的最重要的三个特征之一。实证研究证明了确证非必要性论题。     

论信念的知识规范

信念规范问题是当代知识论的研究热点。知识是人们进行相信行为所要遵循的规范,即S应当相信p,当且仅当S知道p。我们之所以选择知识规范,乃是因为知识有着充分高的信念度。知识对信念的范导作用是通过确证完成的。在确证过程中,证据被加以收集和评估,并对信念度的改变起到了决定性作用。实用考量和情绪也会通过证据抉择间接地影响信念度。真之规范面临着应用难题,证据规范则派生于知识规范,也是有缺陷的。     

确证悖论的相关变量法解决方案探微

确证悖论是科学哲学和逻辑哲学中的难题,自亨普尔1945年将之发表以来,许多人致力于解决它.目前较为流行的解悖方案是相干型解悖方案.本文也是沿着相干型方案,从相关变量法(RVM)理论的角度,提出了两种解悖方案.进一步地,分析RVM方案总的来讲是属于情境迟钝方案,并指出RVM的解悖方案是现有知识背景下的最佳归纳确证方案.     

"葛梯尔问题"与知识的条件(下)

三、可信赖性 (Ｒｅｌｉａｂｉｌｉｔｙ)这种理论认为 ,使某一信念有资格成为知识或成为在认识上得到确证的 ,是它与真理的可信赖的联系 ,这种联系主要指论证的过程与方法 ,也就是说 ,如果一个合理的真信念来自可信赖的过程或方法 ,它就能够是知识。“可信赖”指的是所运用过程或方法的可靠性 ,遵循它就不会导致错误的信念对于古德曼来说 ,这种可信赖的过程特指心理过程。他认为 ,信念的可确证的状态依赖于产生或保持它的心理过程。合理的信念是由恰当的、可信赖的心理过程产生的 ,而不合理的信念则是由不恰当的、不可信赖的心理过程产生的…     

任务情境和证据信息对青少年贝叶斯推理的影响研究

采用生活情境测查任务和经典测查任务探讨了任务情境对青少年贝叶斯推理的影响,以及生活情境测查任务中不同证据信息对青少年贝叶斯判断的影响作用.结果表明:(1)在生活情境测查任务中,从小学六年级到大学二年级,被试的贝叶斯推理能力稳步缓慢提升(相邻的两个被试组之间差异不显著,而不相邻的两个被试组之间差异更容易达到显著水平),发展没有出现加速期,而经典测查任务情境下没有表现出年龄差异;(2)贝叶斯推理作为条件概率的判断不仅受任务情境的影响,而且同一任务情境中不同证据信息也会影响贝叶斯判断.当证据信息与先验信念一致时,被试可以充分利用线索进行推理.     

谈谈卡尔纳普的归纳逻辑

文章介绍了卡尔纳普的逻辑概率的概念,即认为逻辑概率是确证程度,并区分逻辑规则和方法论规则这样两个概念。归纳逻辑的方法论规则不仅要求证据要真实,而且要完全,即“全证据要求”。这样就把归纳逻辑的一个重要作用理解为替人们的实践决策提供合理的依据。为此,卡尔纳普提出了另一条重要的方法论规则,即“效用估计量取最大规则”。文章认为卡尔纳普后来也意识到当初把确证程度等同于逻辑概率的论证是不充分的,于是不得不回避科学假设的确证问题而专门研究有关打赌的决策理论。 文章通过分析认为归纳逻辑不仅是科学验证的辅助工具,也是科学发现的辅助工具,具有“助确证法”和“助发现法”的双重性质。     

与指称主义相冲突的满足贝叶斯确证的信念对象

大卫·查尔默斯对指称主义一直采取怀疑的态度,他在<概率与命题>这篇文章中,通过揭示指称主义和穆勒关于专名的论点与贝叶斯确证理论之间的冲突,提出了一种否定指称主义的新论证.查尔默斯论证的核心,在于表明贝叶斯确证需要一种指称主义不能满足的条件,即它需要非指称的信念对象.非指称的信念对象需要恰当的描述,可是,查尔默斯在他的语义学中所提出的"中心世界"等概念工具,在给出这些描述时,可能会遇到理论上和实践上的困难.     

科学理论检验的复杂性问题

科学理论检验的复杂性问题陈敏强,毛建儒科学理论的检验有两种方式：一种是直接检验;另一种是间接检验。直接检验是把科学理论直接付诸实践,根据实践结果的成败判定科学理论的真伪。间接检验不是直接检验某一科学理论,而是通过一定的中介,间接检验某一科学理论。中介可以是一个,也可以是多个。一般地说,随着科学的高度发展,中介越来越多,直接检验,特别是间接检验,存在着很多不确定因素,因而是一个复杂过程。在直接检验过程中,如果实践结论与实践的结果完全一致,用它来判断理论,那么,理论就得到证实或证伪。但证实不等于科学理论就是真理;证伪也不等于科学理论就是谬误,这是因为①实践本身经常出现问题;②实践对象的确定也会发生误差。③实践结论与科学理论的一致,有时只是一种表面上的吻合。如果实践结论与实践结果不一致,用它来判断科学理论,就会走人错误的“深渊”。这种不一致是由以下原因造成的：①反映的问题;②判断的失误;③解释的歧途。间接检验有很大的不确定性,这是因为①从科学理论的原理到结论,有一个逻辑推导的过程,而逻辑推导是经常出错的。②从科学理论的原理推导出预测性的结论,还必须借助背景知识,背景知识也可称为先行条件,可能出错;③科学理论原理的     

概率与命题

本文的主题是揭示信念对象。关于信念对象最流行的观点是指称主义的观点,指称主义者或把个体与性质组成的命题视为信念对象,或把可能世界集视为信念对象,但若从贝叶斯的确证理论出发来考察信念对象的话,就会发现指称主义与贝叶斯主义间的冲突,由于贝叶斯主义是一种相当成功的理论,因而指称主义是错误的。贝叶斯主义需要的是一种非指称的信念对象,对贝叶斯理论而言,首内涵可以扮演置信对象这一角色。不过首内涵缺乏结构,缺乏指称信息,前者可以用借助于结构化首内涵来解决,后者可以在前者的基础上借助于丰富化内涵进而借助于丰富化的命题来解决。     

Should physicians be Bayesian agents?

Because physicians use scientific inference for the generalizations of individual observations and the application of general knowledge to particular situations, the Bayesian probability solution to the problem of induction has been proposed and frequently utilized. Several problems with the Bayesian approach are introduced and discussed. These include: subjectivity, the favoring of a weak hypothesis, the problem of the false hypothesis, the old evidence/new theory problem and the observation that physicians are not currently Bayesians. To the complaint that the prior probability is subjective, Bayesians reply that there will be ultimate convergence, but the rebuttal to this is that there will not be uniform convergence. Secondly, since the Bayesian scheme favors a weak hypothesis, theories turn out to be a gratuitous risk. The problem with the false hypothesis comes out in the denominator of the theorem, revealing that a factor which is not a theory at all is being considered in the reasoning. On the old evidence/new theory problem old evidence cannot confirm a new theory so that the posterior probability will equal the prior probability. Finally, empiric studies have shown that current physicians are not Bayesians. But on consideration of Bayesian inference as a system of inference, it can be reasoned that physicians should be Bayesians. However, the problem of physicians' and patients' own subjectivity continue to plague this system of medical decision making.     

Goodman’s “New Riddle”

First, a brief historical trace of the developments in confirmation theory leading up to Goodman’s infamous “grue” paradox is presented. Then, Goodman’s argument is analyzed from both Hempelian and Bayesian perspectives. A guiding analogy is drawn between certain arguments against classical deductive logic, and Goodman’s “grue” argument against classical inductive logic. The upshot of this analogy is that the “New Riddle” is not as vexing as many commentators have claimed (especially, from a Bayesian inductive-logical point of view). Specifically, the analogy reveals an intimate connection between Goodman’s problem, and the “problem of old evidence”. Several other novel aspects of Goodman’s argument are also discussed (mainly, from a Bayesian perspective).     

Non-Bayesian Accounts of Evidence: Howson’s Counterexample Countered

There is a debate in Bayesian confirmation theory between subjective and non-subjective accounts of evidence. Colin Howson has provided a counterexample to our non-subjective account of evidence: the counterexample refers to a case in which there is strong evidence for a hypothesis, but the hypothesis is highly implausible. In this article, we contend that, by supposing that strong evidence for a hypothesis makes the hypothesis more believable, Howson conflates the distinction between confirmation and evidence. We demonstrate that Howson’s counterexample fails for a different pair of hypotheses.     

Assessing theories,Bayes style

The problem addressed in this paper is “the main epistemic problem concerning science”, viz. “the explication of how we compare and evaluate theories [...] in the light of the available evidence” (van Fraassen, BC, 1983, Theory comparison and relevant Evidence. In J. Earman (Ed.), Testing scientific theories (pp. 27–42). Minneapolis: University of Minnesota Press). Sections 1– 3 contain the general plausibility-informativeness theory of theory assessment. In a nutshell, the message is (1) that there are two values a theory should exhibit: truth and informativeness—measured respectively by a truth indicator and a strength indicator; (2) that these two values are conflicting in the sense that the former is a decreasing and the latter an increasing function of the logical strength of the theory to be assessed; and (3) that in assessing a given theory by the available data one should weigh between these two conflicting aspects in such a way that any surplus in informativeness succeeds, if the shortfall in plausibility is small enough. Particular accounts of this general theory arise by inserting particular strength indicators and truth indicators. In Section 4 the theory is spelt out for the Bayesian paradigm of subjective probabilities. It is then compared to incremental Bayesian confirmation theory. Section 4 closes by asking whether it is likely to be lovely. Section 5 discusses a few problems of confirmation theory in the light of the present approach. In particular, it is briefly indicated how the present account gives rise to a new analysis of Hempel’s conditions of adequacy for any relation of confirmation (Hempel, CG, 1945, Studies in the logic of comfirmation. Mind, 54, 1–26, 97–121.), differing from the one Carnap gave in § 87 of his Logical foundations of probability (1962, Chicago: University of Chicago Press). Section 6 adresses the question of justification any theory of theory assessment has to face: why should one stick to theories given high assessment values rather than to any other theories? The answer given by the Bayesian version of the account presented in section 4 is that one should accept theories given high assessment values, because, in the medium run, theory assessment almost surely takes one to the most informative among all true theories when presented separating data. The concluding section 7 continues the comparison between the present account and incremental Bayesian confirmation theory.     

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.     

The evidential relevance of self-locating information

Philosophical interest in the role of self-locating information in the confirmation of hypotheses has intensified in virtue of the Sleeping Beauty problem. If the correct solution to that problem is 1/3, various attractive views on confirmation and probabilistic reasoning appear to be undermined; and some writers have used the problem as a basis for rejecting some of those views. My interest here is in two such views. One of them is the thesis that self-locating information cannot be evidentially relevant to a non-self-locating hypothesis. The other, a basic tenet of Bayesian confirmation theory, is the thesis that an ideally rational agent updates her credence in a non-self-locating hypothesis in response to new information only by conditionalization. I argue that we can disprove these two theses by way of cases that are much less puzzling than Sleeping Beauty. I present two such cases in this paper.     

Inductive explanation and Garber–Style solutions to the problem of old evidence

Synthese - The Problem of Old Evidence is a perennial issue for Bayesian confirmation theory. Garber (Test Sci Theor 10:99–131, 1983) famously argues that the problem can be solved by...     

Old evidence and new theories

Conclusion The discussion above indicates that there is some reason for optimism with regard to the project of incorporating logical-mathematical learning within Bayesianism, though it is obvious that there are many difficulties to be overcome. The biggest obstacle in this regard is overcoming the tension that exists between considerations of formal tractability and the desire to avoid demanding too much of rational agents. Jeffrey's solution seems more flexible in this regard than Garber's, but that may very well be because the general mechanisms underlying Jeffrey's rule of reparation have not been specified. In any case, in my view the verdict we should draw with regard to the problem of old evidence is a bit more pessimistic. Jeffrey's and Garber's solutions only address certain aspects of the problem, and thus can only provide partial solutions at best. The basic strategy of using logical-mathematical learning to induce a confirmation event does not completely evade the problem, which reemerges when the required logical-mathematical knowledge becomes old news. Thus, despite the solutions that have been offered to date, the problem of old evidence remains a fundamental challenge to the Bayesian point of view.This paper has benefited greatly due to valuable input from Bas van Fraassen, Richard Jeffrey, Alan Hájek, Gil Harman, Mike Thau, and Ned Hall.     

The theory of random propositions

The theory of random propositions is a theory of confirmation that contains the Bayesian and Shafer—Dempster theories as special cases, while extending both in ways that resolve many of their outstanding problems. The theory resolves the Bayesian problem of the priors and provides an extension of Dempster's rule of combination for partially dependent evidence. The standard probability calculus can be generated from the calculus of frequencies among infinite sequences of outcomes. The theory of random propositions is generated analogously from the calculus of frequencies among pairs of infinite sequences of suitably generalized outcomes and in a way that precludes the inclusion of contrived orad hoc elements. The theory is also formulated as an uninterpreted calculus.I am grateful to Frank Arntzenius, John Earman, Allan Franklin, Teddy Seidenfeld, Brian Skyrms, Sandy Zabell and an anonymous referee for helpful discussion.     

Confirmation and the ordinal equivalence thesis

Synthese - According to a widespread but implicit thesis in Bayesian confirmation theory, two confirmation measures are considered equivalent if they are ordinally equivalent—call this the...