Cílem této práce je rozebrat možnosti argumentu holandské sázky ve prospěch probabilismu a stanovit jeho meze. Existuje mnoho podob argumentu, proto se nejprve budu věnovat popisu argumentu v jeho klasické podobě. Následně je podroben kritice ve třech oblastech: (1) problémy behaviorismu, (2) užitku peněz, (3) vztah koherence, jisté ztráty a racionality. Zásadní je dle mého především třetí oblast týkající se neschopnosti jednoduše propojit probabilistickou nekoherenci stupňů přesvědčení s jistou ztrátou a pragmatickou racionalitou. Věnuji se třem současným reinterpretacím argumentu ve prospěch (...) probabilistických norem (Armendt, Howson, Christensen), přičemž v textu docházím k závěru, že ani jedna z nich není uspokojivá. Selhávají v řešení problémů (Armendt, Christensen), nebo neříkají nic zajímavého ve prospěch probabilismu (Howson). Proto se domnívám, že rozbor argumentu především ukazuje, že pragmatická obhajoba epistemických norem racionality, jako je ta v argumentu holandské sázky, není pro probabilismus schůdnou cestou. Závěr se snaží nastínit, proč tomu tak je. (shrink)

We examine in detail three classic reasoning fallacies, that is, supposedly ``incorrect'' forms of argument. These are the so-called argumentam ad ignorantiam, the circular argument or petitio principii, and the slippery slope argument. In each case, the argument type is shown to match structurally arguments which are widely accepted. This suggests that it is not the form of the arguments as such that is problematic but rather something about the content of those examples with which they are typically justified. This (...) leads to a Bayesian reanalysis of these classic argument forms and a reformulation of the conditions under which they do or do not constitute legitimate forms of argumentation. (shrink)

Additionally, the text shows how to develop computationally feasible methods to mesh with this framework.

If there are two dependent positive real variables x1 and x2, and only x1 is known, what is the probability that x2 is larger versus smaller than x1? There is no uniquely correct answer according to “frequentist” and “subjective Bayesian” definitions of probability. Here we derive the answer given the “objective Bayesian” definition developed by Jeffreys, Cox, and Jaynes. We declare the standard distance metric in one dimension, d(A,B)≡|A−B|, and the uniform prior distribution, as axioms. If neither variable is known, (...) P(x2x1)=P(x2>x1). This appears obvious, since the state spaces x2x1 and x2>x1 have equal size. However, if x1 is known and x2 unknown, there are infinitely more numbers in the space x2>x1 than x2x1. Despite this asymmetry, we prove P(x2x1∣x1)=P(x2>x1∣x1), so that x1 is the median of p(x2|x1), and x1 is statistically independent of ratio x2/x1. We present three proofs that apply to all members of a set of distributions. Each member is distinguished by the form of dependence between variables implicit within a statistical model (gamma, Gaussian, etc.), but all exhibit two symmetries in the joint distribution p(x1,x2) that are required in the absence of prior information: exchangeability of variables, and non-informative priors over the marginal distributions p(x1) and p(x2). We relate our conclusion to physical models of prediction and intelligence, where the known ’sample’ could be the present internal energy within a sensor, and the unknown the energy in its external sensory cause or future motor effect. (shrink)

An inductive logic is a system of inference that describes the relation between propositions on data, and propositions that extend beyond the data, such as predictions over future data, and general conclusions on all possible data. Statistics, on the other hand, is a mathematical discipline that describes procedures for deriving results about a population from sample data. These results include predictions on future samples, decisions on rejecting or accepting a hypothesis about the population, the determination of probability assignments over such (...) hypotheses, the selection of a statistical model for studying the population, and so on. Both inductive logic and statistics are calculi for getting from the given data to propositions or results that transcend the data. (shrink)

Epistemic scoring rules are the en vogue tool for justifications of the probability norm and further norms of rational belief formation. They are different in kind and application from statistical scoring rules from which they arose. In the first part of the paper I argue that statistical scoring rules, properly understood, are in principle better suited to justify the probability norm than their epistemic brethren. Furthermore, I give a justification of the probability norm applying statistical scoring rules. In the second (...) part of the paper I give a variety of justifications of norms for rational belief formation employing statistical scoring rules. Furthermore, general properties of statistical scoring rules are investigated. Epistemic scoring rules feature as a useful technical tool for constructing statistical scoring rules. (shrink)