Probability theory is important because of its relevance for decision making, which also means: its relevance for the single case. The propensity theory of objective probability, which addresses the single case, is subject to two problems: Humphreys’ problem of inverse probabilities and the problem of the reference class. The paper solves both problems by restating the propensity theory using (an objectivist version of) Pearl’s approach to causality and probability, and by applying a decision-theoretic perspective. Contrary to a widely held view, (...) decision making on the basis of given propensities can proceed without a subjective-probability supplement to propensities. (shrink)

In order to comprehend the world around us and construct explaining theories for this purpose, we need a conception of physical probability, since we come across many (apparently) probabilistic phenomena in our world. But how should we understand objective probability claims? Since pure frequency approaches of probability are not appropriate, we have to use a single case propensity interpretation. Unfortunately, many philosophers believe that this understanding of probability is burdened with significant difficulties. My main aim is to show that we (...) can treat propensity as a theoretical concept that exhibits many similarities to other theoretical concepts, and its difficulties are not insuperable if we make explicit some general presuppositions of scientific practice and apply them to propensities. At least this is true if we formulate the right bridge principle for propensity and rely on further methodological rules in dealing with propensity assertions to make them empirically testable. (shrink)

Popper’s introduction of ‘‘propensity’’ was intended to provide a solid conceptual foundation for objective single-case probabilities. By considering the partly opposed contributions of Humphreys and Miller and Salmon, it is argued that when properly understood, propensities can in fact be understood as objective single-case causal probabilities of transitions between concrete events. The chief claim is that propensities are well-explicated by describing how they fit into the existing formal theory of branching space-times, which is simultaneously indeterministic and causal. Several problematic examples, (...) some commonsense and some quantum-mechanical, are used to make clear the advantages of invoking branching space-times theory in coming to understand propensities. r 2007 Elsevier Ltd. All rights reserved. (shrink)

CHAPTER ONE Causality and Chance in Natural Law. INTRODUCTION IN nature nothing remains constant. Everything is in a perpetual state of transformation, ...

I argue that any broadly dispositional analysis of probability will either fail to give an adequate explication of probability, or else will fail to provide an explication that can be gainfully employed elsewhere (for instance, in empirical science or in the regulation of credence). The diversity and number of arguments suggests that there is little prospect of any successful analysis along these lines.

An approach to inference to the best explanation integrating a Popperianconception of natural laws together with a modified Hempelian account of explanation, one the one hand, and Hacking's law of likelihood (in its nomicguise), on the other, which provides a robust abductivist model of sciencethat appears to overcome the obstacles that confront its inductivist,deductivist, and hypothetico-deductivist alternatives.This philosophy of scienceclarifies and illuminates some fundamental aspects of ontology and epistemology, especially concerning the relations between frequencies and propensities. Among the most important (...) elements of this conception is thecentral role of degrees of nomic expectability in explanation, prediction,and inference, for which this investigation provides a theoretical defense. (shrink)

This paper investigates the relations between causality and propensity. Aparticular version of the propensity theory of probability is introduced, and it is argued that propensities in this sense are not causes. Some conclusions regarding propensities can, however, be inferred from causal statements, but these hold only under restrictive conditions which prevent cause being defined in terms of propensity. The notion of a Bayesian propensity network is introduced, and the relations between such networks and causal networks is investigated. It is argued (...) that causal networks cannot be identified with Bayesian propensity networks, but that causal networks can be a valuable heuristic guide for the construction of Bayesian propensity networks. (shrink)

The propensity interpretation of probability was introduced by Popper ([1957]), but has subsequently been developed in different ways by quite a number of philosophers of science. This paper does not attempt a complete survey, but discusses a number of different versions of the theory, thereby giving some idea of the varieties of propensity. Propensity theories are classified into (i) long-run and (ii) single-case. The paper argues for a long-run version of the propensity theory, but this is contrasted with two single-case (...) propensity theories, one due to Miller and the later Popper, and the other to Fetzer. The three approaches are compared by examining how they deal with a key problem for the propensity approach, namely the relationship between propensity and causality and Humphreys' paradox. (shrink)

Propensities are presented as a generalization of classical determinism. They describe a physical reality intermediary between Laplacian determinism and pure randomness, such as in quantum mechanics. They are characterized by the fact that their values are determined by the collection of all actual properties. It is argued that they do not satisfy Kolmogorov axioms; other axioms are proposed.

Gillies introduced a propensity interpretation of probability which is linked to experience by a falsifying rule for probability statements. The present paper argues that general statistical tests should qualify as falsification rules. The ‘goodness-of-fit paradox’ is introduced: the confirmation of a probability model by a test refutes the model's validity. An example is given in which an independence test introduces dependence. Several possibilities to interpret the paradox and to deal with it are discussed. It is concluded that the propensity interpretation (...) properly reflects statistical practice, but it is not as objective as some adherents claim. (shrink)

The aim of this paper is to distinguish between, and examine, three issues surrounding Humphreys's paradox and interpretation of conditional propensities. The first issue involves the controversy over the interpretation of inverse conditional propensities — conditional propensities in which the conditioned event occurs before the conditioning event. The second issue is the consistency of the dispositional nature of the propensity interpretation and the inversion theorems of the probability calculus, where an inversion theorem is any theorem of probability that makes explicit (...) (or implicit) appeal to a conditional probability and its corresponding inverse conditional probability. The third issue concerns the relationship between the notion of stochastic independence which is supported by the propensity interpretation, and various notions of causal independence. In examining each of these issues, it is argued that the dispositional character of the propensity interpretation provides a consistent and useful interpretation of the probability calculus. (shrink)

Evolutionary theory (ET) is teeming with probabilities. Probabilities exist at all levels: the level of mutation, the level of microevolution, and the level of macroevolution. This uncontroversial claim raises a number of contentious issues. For example, is the evolutionary process (as opposed to the theory) indeterministic, or is it deterministic? Philosophers of biology have taken different sides on this issue. Millstein (1997) has argued that we are not currently able answer this question, and that even scientific realists ought to remain (...) agnostic concerning the determinism or indeterminism of evolutionary processes. If this argument is correct, it suggests that, whatever we take probabilities in ET to be, they must be consistent with either determinism or indeterminism. This raises some interesting philosophical questions: How should we understand the probabilities used in ET? In other words, what is meant by saying that a certain evolutionary change is more or less probable? Which interpretation of probability is the most appropriate for ET? I argue that the probabilities used in ET are objective in a realist sense, if not in an indeterministic sense. Furthermore, there are a number of interpretations of probability that are objective and would be consistent with ET under determinism or indeterminism. However, I argue that evolutionary probabilities are best understood as propensities of population-level kinds. (shrink)

Susan Mills and John Beatty's propensity interpretation of fitness encountered very different philosophical criticisms by Alexander Rosenberg and Kenneth Waters. These criticisms and the rejoinders to them are both predictable and important. They are predictable as raisingkinds of issues typically associated with disposition concepts (this is established through a systematic review of the problems generated by Carnap's dispositional interpretation of all scientific terms). They are important as referring the resolution of these issues to the development of evolutionary biology. This historical (...) approach to the propensity interpretation of fitness draws attention to the precarious relation between philosophical clarification of scientific concepts and any given state of the empirical arts. (shrink)

This study responds to Bay and Greenberg's (Bay, D.D. and Greenberg, R.R. (2001). The relationship of the DIT and behavior: A replication. Issues in Accounting Education 10(3): 367–380) call to investigate alternative psychometric instruments to measure ethical behavior other than the heavily relied upon Defining Issues Test. The Mach IV scale (Christie, 1970) has been cited in more than 500 published psychological studies; however, it has not been used extensively in the accounting ethics research. This study provides some preliminary evidence (...) on the use of the Mach IV scale in an accounting ethics context. Similar to ethics studies in other academic disciplines, results across two dependent measures indicate accounting students high in Machiavellianism are more likely to view questionable ethical behavior as acceptable. The research findings also indicate that the Machiavellian construct appears to be a better predictor of ethical propensities in comparison to the commonly used Defining Issues Test. The paper concludes with a discussion on how these reported research findings impact the accounting profession and accounting education. (shrink)

This paper offers a metaphysics of physical probability in (or if you prefer, truth conditions for probabilistic claims about) deterministic systems based on an approach to the explanation of probabilistic patterns in deterministic systems called the method of arbitrary functions. Much of the appeal of the method is its promise to provide an account of physical probability on which probability assignments have the ability to support counterfactuals about frequencies. It is argued that the eponymous arbitrary functions are of little philosophical (...) use, but that they can be substituted for facts about frequencies without losing the ability to provide counterfactual support. The result is an account of probability in deterministic systems that has a “propensity-like” look and feel, yet which requires no supplement to the standard modern empiricist tool kit of particular matters of fact and principles of physical dynamics. (shrink)

These are the introduction chapters to the forthcoming collection of essays published by Springer (Synthese Library) and entitled Probabilities, Causes and Propensities in Physics.

This paper reviews four attempts throughout the history of quantum mechanics to explicitly employ dispositional notions in order to solve the quantum paradoxes, namely: Margenau’s latencies, Heisenberg’s potentialities, Maxwell’s propensitons, and the recent selective propensities interpretation of quantum mechanics. Difficulties and challenges are raised for all of them, and it is concluded that the selective propensities approach nicely encompasses the virtues of its predecessors. Finally, some strategies are discussed for reading dispositional notions into two other well-known interpretations of quantum mechanics, (...) namely the GRW interpretation and Bohmian mechanics. (shrink)

This paper expands on, and provides a qualified defence of, Arthur Fine's selective interactions solution to the measurement problem. Fine's approach must be understood against the background of the insolubility proof of the quantum measurement. I first defend the proof as an appropriate formal representation of the quantum measurement problem. The nature of selective interactions, and more generally selections, is then clarified, and three arguments in their favour are offered. First, selections provide the only known solution to the measurement problem (...) that does not relinquish any of the explicit premises of the insolubility proofs. Second, unlike some no-collapse interpretations of quantum mechanics, selections suffer no difficulties with non-ideal measurements. Third, unlike most collapse interpretations, selections can be independently motivated by an appeal to quantum propensities. Introduction The problem of quantum measurement 2.1 The ignorance interpretation of mixtures 2.2 The eigenstate–eigenvalue link 2.3 The quantum theory of measurement The insolubility proof of the quantum measurement 3.1 Some notation 3.2 The transfer of probability condition (TPC) 3.3 The occurrence of outcomes condition (OOC) A defence of the insolubility proof 4.1 Stein's critique 4.2 Ignorance is not required 4.3 The problem of quantum measurement is an idealisation Selections 5.1 Representing dispositional properties 5.2 Selections solve the measurement problem 5.3 Selections and ignorance Non-ideal selections 6.1 No-collapse interpretations and non-ideal measurements 6.2 Exact and approximate measurements 6.3 Selections for non-ideal interactions 6.4 Approximate selections 6.5 Implications for ignorance Selective interactions test quantum propensities 7.1 Equivalence classes as physical ‘aspects’: a critique 7.2 Quantum dispositions 7.3 Selections as a propensity modal interpretation 7.4 A comparison with Popper's propensity interpretation. (shrink)

In an influential article published in 1982, Bas Van Fraassen developed an argument against causal realism on the basis of an analysis of the Einstein-Podolsky-Rosen correlations of quantum mechanics. Several philosophers of science and experts in causal inference -including some causal realists like Wesley Salmon- have accepted Van Fraassen’s argument, interpreting it as a proof that the quantum correlations cannot be given any causal model. In this paper I argue that Van Fraassen’s article can also be interpreted as a good (...) guide to the different causal models available for the EPR correlations, and their relative virtues. These models in turn give us insight into some of the unusual features that quantum propensicies might have. (shrink)

Peter Milne and Neal Grossman have argued against Popper's propensity interpretation of quantum mechanics, by appeal to the two-slit experiment and to the distinction between mixtures and superpositions, respectively. In this paper I show that a different propensity interpretation successfully meets their objections. According to this interpretation, the possession of a quantum propensity by a quantum system is independent of the experimental set-ups designed to test it, even though its manifestations are not.

Zusammenfassung Eine Besonderheit der propensity-Theorie von Popper liegt darin, daß in ihr — im Gegensatz zu anderen Wahrscheinlichkeitstheorien — die Versuchsbedingungen explizit als Argument in der Wahrscheinlichkeitsfunktion erscheinen. Diese Besonderheit teilt sie mit der Ludwigschen Wahrscheinlichkeitstheorie.