Physical probabilities

A conception of probability as an irreducible feature of the physical world is outlined. Propensity analyses of probability are examined and rejected as both formally and conceptually inadequate. It is argued that probability is a non-dispositional property of trial-types; probabilities are attributed to outcomes as event-types. Brier's Rule in an objectivist guise is used to forge a connection between physical and subjective probabilities. In the light of this connection there are grounds for supposing physical probability to obey some standard set of axioms. However, there is no a priori reason why this should be the case.     

Prior probabilities

The theoretical construction and practical use of prior probabilities, in particular for systems having many degrees of freedom, are investigated. It becomes clear that it is operationally unsound to use mutually consistent priors if one wishes to draw sensible conclusions from practical experiments. The prior cannot usefully be identified with a state of knowledge, and indeed it is not so identified in common scientific practice. Rather, it can be identified with the question one asks. Accordingly, priors are free constructions. Their informal, ill-defined and subjective characteristics must carry over into the conclusions one chooses to draw from experiments or observations.     

Unknown probabilities

From a point of view like de Finetti's, what is the judgmental reality underlying the objectivistic claim that a physical magnitude X determines the objective probability that a hypothesis H is true? When you have definite conditional judgmental probabilities for H given the various unknown values of X, a plausible answer is sufficiency, i.e., invariance of those conditional probabilities as your probability distribution over the values of X varies. A different answer, in terms of conditional exchangeability, is offered for use when such definite conditional probabilities are absent.     

Interpersonal comparison of subjective probabilities: Toward translating linguistic probabilities

Interpersonal variability in understanding linguistic probabilities can adversely affect decision making. Using the fact that everyone judges canonical probability events similarly in a manner consistent with axiom systems that yield a probability measure, we developed and tested a method for comparing the meanings of probability phrases across individuals. An experiment demonstrated that despite extreme heterogeneity in participants' linguistic probability lexicons, interpersonal similarity in phrase meaning is well predicted by phrase rank order within the lexicons. Thus, equally ranked phrases have similar meanings, and individual differences in linguistic probabilities may simply be explained by the phrases people use at each rank.     

Archimedean qualitative probabilities

Necessary and sufficient conditions for the existence of a probability measure agreeing with a weak order on an algebra of events are given. In the case of a countable algebra they consist of an extension of Kraft, Pratt, and Seidenberg's (1959. Annals of Mathematical Statistics, 38, 780–786) additivity condition through the requirement of an Archimedean property. In the case of a σ-algebra and a σ-additive agreeing probability, Villegas' (1964. Annals of Mathematical Statistics, 35, 1787–1796) monotone continuity condition, which becomes necessary, is merely added to them.     

Understanding conditional probabilities

In two experiments, subjects were asked to judge whether the probability of A given B was greater than, equal to, or less than the probability of B given A for various events A and B. In addition, in Experiment 2, subjects were asked to estimate the conditional probabilities and also to calculate conditional probabilities from contingency data. For problems in which one conditional probability was objectively larger than the other, performance ranged from about 25–80% correct, depending on the nature of A and B. Changes in the wording of problems also affected performance, although less dramatically. Patterns of responses consistent with the existence of a causal bias in judging probabilities were observed with one of the wordings used but not with the other. Several features of the data suggest that a major source of error was the confusion between conditional and joint probabilities.     

Underdetermination of infinitesimal probabilities

Synthese - A number of philosophers have attempted to solve the problem of null-probability possible events in Bayesian epistemology by proposing that there are infinitesimal probabilities....     

Causes and mixed probabilities

In Section 1 I examine the use of probabilistic data to establish causal conclusions in non‐experimental research. In Section 2 I show that the probabilities involved in such research are inhomogeneous ‘mixed’ probabilities. Section 3 then argues that such mixed probabilities are responsible for the way common causes screen off correlations between their joint effects. Section 4 concludes that mixed probabilities are therefore crucial for the nature of the causal relation itself.     

Objectively reliable subjective probabilities

Subjective Bayesians typically find the following objection difficult to answer: some joint probability measures lead to intuitively irrational inductive behavior, even in the long run. Yet well-motivated ways to restrict the set of reasonable prior joint measures have not been forthcoming. In this paper I propose a way to restrict the set of prior joint probability measures in particular inductive settings. My proposal is the following: where there exists some successful inductive method for getting to the truth in some situation, we ought to employ a (joint) probability measure that is inductively successful in that situation, if such a measure exists. In order to do show that the restriction is possible to meet in a broad class of cases, I prove a Bayesian Completeness Theorem, which says that for any solvable inductive problem of a certain broad type, there exist probability measures that a Bayesian could use to solve the problem. I then briefly compare the merits of my proposal with two other well-known proposals for constraining the class of admissible subjective probability measures, the leave the door ajar condition and the maximize entropy condition.The author owes special thanks to Kevin Kelly, for a number of helpful ideas for the proof of the Bayesian Completeness Theorem, as well as other aspects of the paper. Thanks also to Clark Glymour for some helpful suggestions for improvement of an earlier draft. Part of the work leading to this paper was funded by a Summer Research Grant from the University Research Institute of the University of Texas at Austin.     

Rationality and indeterminate probabilities

We argue that indeterminate probabilities are not only rationally permissible for a Bayesian agent, but they may even be rationally required. Our first argument begins by assuming a version of interpretivism: your mental state is the set of probability and utility functions that rationalize your behavioral dispositions as well as possible. This set may consist of multiple probability functions. Then according to interpretivism, this makes it the case that your credal state is indeterminate. Our second argument begins with our describing a world that plausibly has indeterminate chances. Rationality requires a certain alignment of your credences with corresponding hypotheses about the chances. Thus, if you hypothesize the chances to be indeterminate, your will inherit their indeterminacy in your corresponding credences. Our third argument is motivated by a dilemma. Epistemic rationality requires you to stay open-minded about contingent matters about which your evidence has not definitively legislated. Practical rationality requires you to be able to act decisively at least sometimes. These requirements can conflict with each other-for thanks to your open-mindedness, some of your options may have undefined expected utility, and if you are choosing among them, decision theory has no advice to give you. Such an option is playing Nover and Hájek??s Pasadena Game, and indeed any option for which there is a positive probability of playing the Pasadena Game. You can serve both masters, epistemic rationality and practical rationality, with an indeterminate credence to the prospect of playing the Pasadena game. You serve epistemic rationality by making your upper probability positive-it ensures that you are open-minded. You serve practical rationality by making your lower probability 0-it provides guidance to your decision-making. No sharp credence could do both.     

Reasoning defeasibly about probabilities

In concrete applications of probability, statistical investigation gives us knowledge of some probabilities, but we generally want to know many others that are not directly revealed by our data. For instance, we may know prob(P/Q) (the probability of P given Q) and prob(P/R), but what we really want is prob(P/Q&;R), and we may not have the data required to assess that directly. The probability calculus is of no help here. Given prob(P/Q) and prob(P/R), it is consistent with the probability calculus for prob(P/Q&;R) to have any value between 0 and 1. Is there any way to make a reasonable estimate of the value of prob(P/Q&;R)? A related problem occurs when probability practitioners adopt undefended assumptions of statistical independence simply on the basis of not seeing any connection between two propositions. This is common practice, but its justification has eluded probability theorists, and researchers are typically apologetic about making such assumptions. Is there any way to defend the practice? This paper shows that on a certain conception of probability—nomic probability—there are principles of “probable probabilities” that license inferences of the above sort. These are principles telling us that although certain inferences from probabilities to probabilities are not deductively valid, nevertheless the second-order probability of their yielding correct results is 1. This makes it defeasibly reasonable to make the inferences. Thus I argue that it is defeasibly reasonable to assume statistical independence when we have no information to the contrary. And I show that there is a function Y(r, s, a) such that if prob(P/Q) = r, prob(P/R) = s, and prob(P/U) = a (where U is our background knowledge) then it is defeasibly reasonable to expect that prob(P/Q&;R) = Y(r, s, a). Numerous other defeasible inferences are licensed by similar principles of probable probabilities. This has the potential to greatly enhance the usefulness of probabilities in practical application.     

Note on guessing probabilities

A hypothesis of stationary response probabilities (SRP), or that subjects equate the unconditional probability of each response to its frequency of occurrence in the task, is shown to be equivalent to the proposal of Atkinson et al. (1965) that guessing may occur only among unlearned responses. This hypothesis is contrasted with the usual assumption of stationary guessing probabilities (SGP). It is shown that assuming SGP when SRP is true results in a biased estimate of learning which first increases and then decreases with the actual degree of learning, and is a decreasing function of list length. This can lead to improper inferences, including erroneous rejection of an all-or-none model and spurious evidence of increasing difficulty in long lists.     

Overestimation of subjective probabilities

Abstract.— When asked to estimate the probability of outcomes of draws from a binomial population, student subjects tend to report p values that clearly exceed the objective ones. The probability of specific binomial sequences was found to be even more overestimated, while the answers became much more conservative when the outcomes were grouped into a few categories. These findings were replicated in a second experiment, where the probability of heights in a male and a female student population was estimated. When the task was to estimate frequency of occurrence, instead of probability, the answers became more realistic. The conclusion is drawn that the direct p estimates are relatively independent of frequency judgments, the chief determinant being the properties of the particular sample to be evaluated, irrespective of the number and probabilities of other possible samples.