I argue that there are non-trivial objective chances (that is, objective chances other than 0 and 1) even in deterministic worlds. The argument is straightforward. I observe that there are probabilistic special scientific laws even in deterministic worlds. These laws project non-trivial probabilities for the events that they concern. And these probabilities play the chance role and so should be regarded as chances as opposed, for example, to epistemic probabilities or credences. The supposition of non-trivial deterministic chances might seem to (...) land us in contradiction. The fundamental laws of deterministic worlds project trivial probabilities for the very same events that are assigned non-trivial probabilities by the special scientific laws. I argue that any appearance of tension is dissolved by recognition of the level-relativity of chances. There is therefore no obstacle to accepting non-trivial chance-role-playing deterministic probabilities as genuine chances. (shrink)

Thirty years after Lewis's paper, this book brings together some of the most important recent work connecting—or, in some cases, disputing the connection ...

Evolutionary theory is awash with probabilities. For example, natural selection is said to occur when there is variation in fitness, and fitness is standardly decomposed into two components, viability and fertility, each of which is understood probabilistically. With respect to viability, a fertilized egg is said to have a certain chance of surviving to reproductive age; with respect to fertility, an adult is said to have an expected number of offspring.1 There is more to evolutionary theory than the theory of (...) natural selection, and here too one finds probabilistic concepts aplenty. When there is no selection, the theory of neutral evolution says that a gene’s chance of eventually reaching fixation is 1/(2N), where N is the number of organisms in the generation of the diploid population to which the gene belongs. The evolutionary consequences of mutation are likewise conceptualized in terms of the probability per unit time a gene has of changing from one state to another. The examples just mentioned are all “forwarddirected” probabilities; they describe the probability of later events, conditional on earlier events. However, evolutionary theory also uses “backwards probabilities” that describe the probability of a cause conditional on its effects; for example, coalescence theory allows one to calculate the expected number of generations in the past that the genes in the present generation find their most recent common ancestor. (shrink)

Kolmogorov''s axiomatization of probability includes the familiarratio formula for conditional probability: 0).$$ " align="middle" border="0">.

Counter factual theories of Causation have had problems with cases of probabilistic causation and preemption. I put forward a counterfactual theory that seems to deal with these problematic cases and also has the virtue of providing an account of the alleged asymmetry between hasteners and delayers: the former usually being counted as causes, the latter not. I go on to consider a new type of problem case that has not received so much attention in the literature, those I dub catalysts (...) and anti-catalysts, and show how my account needs to be adjusted to deliver the right verdicts in these cases. The net result is a particular conception of a cause that I try to spell out in the closing section of the paper. In that section, I also briefly discuss causal asymmetry and the purpose behind providing a counterfactual theory of causation. (shrink)

This paper examines a promising probabilistic theory of singular causation developed by David Lewis. I argue that Lewis' theory must be made more sophisticated to deal with certain counterexamples involving pre-emption. These counterexamples appear to show that in the usual case singular causation requires an unbroken causal process to link cause with effect. I propose a new probabilistic account of singular causation, within the framework developed by Lewis, which captures this intuition.

First para: Before the 17th century, there was not much discussion, and little uniformity in conception, of natural laws. The rise of science in 17th century, Newton’s mathematization of physics, and the provision of strict, deterministic laws that applied equally to the heavens and to the terrestrial realm had a profound impact in transforming the philosophical imagination. A philosophical conception of physical law built on the example of Newtonian Mechanics became quickly entrenched. Between the 17th and 20th centuries, there was (...) a great deal of philosophical interest in probabilities, but probabilities were mostly regarded as having something to do with the management of opinion, not as having a fundamental role in science. Probabilities made their first appearance in an evidently ineliminable way in the laws of a fundamental theory with the advent of quantum mechanics. Quantum probabilities have come to be called ‘chances’ in the philosophical literature, and their interpretation has been one of the central problems in philosophy of science now for almost a century. There continue to be hold-outs that insist that there must be an underlying probability-free replacement for quantum mechanics and Bohmians have had some success in formulating a deterministic alternative to quantum mechanics, but most physicists accept that the probabilistic character of the quantum mechanical laws is likely to be retained in any successor theory. While physics has adjusted itself comfortably to the existence of ineliminably probabilistic laws, philosophy has not managed arrive at a stable interpretation of quantum probability. The difficulty is that there are a number of constraints that an interpretation of chance must satisfy, constraints that appear to be partially definitive of the concept and it proves to be extraordinarily difficult to meet them simultaneously. (shrink)

In this important book, Ellery Eells explores and refines philosophical conceptions of probabilistic causality. In a probabilistic theory of causation, causes increase the probability of their effects rather than necessitate their effects in the ways traditional deterministic theories have specified. Philosophical interest in this subject arises from attempts to understand population sciences as well as indeterminism in physics. Taking into account issues involving spurious correlation, probabilistic causal interaction, disjunctive causal factors, and temporal ideas, Professor Eells advances the analysis of what (...) it is for one factor to be a positive causal factor for another. A salient feature of the book is a theory of token level probabilistic causation in which the evolution of the probability of a later event from an earlier event is central. This will be a book of crucial significance to philosophers of science and metaphysicians; it will also prove stimulating to many economists, psychologists, and physicists. (shrink)

A definition of causation as probability-raising is threatened by two kinds of counterexample: first, when a cause lowers the probability of its effect; and second, when the probability of an effect is raised by a non-cause. In this paper, I present an account that deals successfully with problem cases of both these kinds. In doing so, I also explore some novel implications of incorporating into the metaphysical investigation considerations of causal psychology.

The starting point in the development of probabilistic analyses of token causation has usually been the naïve intuition that, in some relevant sense, a cause raises the probability of its effect. But there are well-known examples both of non-probability-raising causation and of probability-raising non-causation. Sophisticated extant probabilistic analyses treat many such cases correctly, but only at the cost of excluding the possibilities of direct non-probability-raising causation, failures of causal transitivity, action-at-a-distance, prevention, and causation by absence and omission. I show that (...) an examination of the structure of these problem cases suggests a different treatment, one which avoids the costs of extant probabilistic analyses. (shrink)

Germund Hesslow has argued recently [2] that a probabilistic theory of causality as advocated by Patrick Suppes [4] has two problems that a deterministic theory avoids. In this paper, I argue that Suppes' probabilistic causal calculus is free of each of these problems and, moreover, that several broader issues raised by Hesslow's discussion tend to support a probabilistic conception of causes.