Probability out of Determinism

Strevens, Michael

doi:10.1093/acprof:oso/9780199577439.003.0013

Cited by 48 publications

(35 citation statements)

References 317 publications

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“…Poincaré describes them as "conventions," which, it must be admitted, is less than helpful. Strevens (2003) is noncommittal on the interpretation of the input probabilities, whereas Strevens (2011) and Abrams (2012) opt for distributions based on actual frequencies. Savage (1973) suggested that the input probabilities be given a subjectivist interpretation.…”

Section: Almost-objective Probabilitiesmentioning

confidence: 99%

Probabilities in Statistical Mechanics

Myrvold

2017

Oxford Handbooks Online

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This chapter reviews selected aspects of the terrain of discussion of the role of probabilities in statistical mechanics. Among the topics addressed are the reasons for introduction of probabilities into statistical mechanics, the status of the standard equilibrium distribution, and the question of interpretation of statistical mechanical probabilities. The chapter starts with a brief history of probabilities in physics and the evolution of statistical mechanics therefrom. The approaches of Boltzmann and Gibbs are presented, and then some approaches to justifying choice of probability measures. The chapter closes with a presentation of possible resolutions to some puzzling aspects of the use of standard probability measures.

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Section: Almost-objective Probabilitiesmentioning

confidence: 99%

Probabilities in Statistical Mechanics

Myrvold

2017

Oxford Handbooks Online

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“…I will compare the respective definitions of ontic probability with their weaknesses and strengths and then scrutinize whether the approaches can be combined to overcome some of the respective difficulties. This will lead to an account that has been proposed by Strevens (2011) and Abrams (2012).…”

mentioning

confidence: 78%

“…Thus, the function defined by binning the initial conditions varies slowly within each test region, and in this sense, the distribution is wellbehaved. Following Strevens (2011), p. 352, we call an empirical distribution that obeys this condition macroperiodic. Note that macroperiodicity presupposes a breakdown of microconstancy at some small scale.…”

Section: Strengthening Spielraum Probabilitiesmentioning

confidence: 99%

“…2 Recent years have seen a number of works that attempt to provide an account of ontic probability by drawing on von Kries and Poincaré (Strevens 2003, Rosenthal 2010, Strevens 2011, Abrams 2012, Myrvold 2012and Rosenthal 2012. In this paper, I'll discuss a specific proposal of this family.…”

Section: Introductionmentioning

confidence: 99%

“…Myrvold (2012) combines Spielraum probabilities with ideas by D. Lewis, but confines himself to Lewis's Principal Principle (Lewis 1980) and does not consider best system probabilities. The most important references for my paper are Frigg & Hoefer (2010), Strevens (2011), Abrams (2012) and Rosenthal (2012).…”

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confidence: 99%

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A Humean Guide to Spielraum Probabilities

Beisbart

2016

J Gen Philos Sci

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The most promising accounts of ontic probability include the Spielraum conception of probabilities, which can be traced back to J. v. Kries and H. Poincaré, and the best system account by D. Lewis. This paper aims at comparing both accounts and at combining them to obtain the best of both worlds. The extensions of both Spielraum and best system probabilities do not coincide because the former only apply to systems with a special dynamics. Conversely, Spielraum probabilities may not be part of the best system, e.g. if a broad class of random devices is not often used. Spielraum probabilities have the potential to provide illuminating explanations of frequencies with which outcomes of trials on gambling devices arise. They ultimately fail to account for such frequencies though because they are compatible with frequencies that grossly differ from the values of the respective probabilities. I thus follow recent proposals by M. Strevens and M. Abrams and strengthen the definition of Spielraum probabilities by adding a further condition that restricts some actual frequencies. The resulting account remains limited in scope, but stands objections raised against it in the recent literature: It is neither circular nor based upon arbitrary choices of a measure or of physical variables. Nor does it lack any explanatory power.

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Taming fitness: Organism‐environment interdependencies preclude long‐term fitness forecasting

2020

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Fitness is a central but notoriously vexing concept in evolutionary biology. The propensity interpretation of fitness is often regarded as the least problematic account for fitness. It ties an individual's fitness to a probabilistic capacity to produce offspring. Fitness has a clear causal role in evolutionary dynamics under this account. Nevertheless, the propensity interpretation faces its share of problems. We discuss three of these. We first show that a single scalar value is an incomplete summary of a propensity. Second, we argue that the widespread method of "abstracting away" environmental idiosyncrasies by averaging over reproductive output in different environments is not a valid approach when environmental changes are irreversible. Third, we point out that expanding the range of applicability for fitness measures by averaging over more environments or longer time scales (so as to ensure environmental reversibility) reduces one's ability to distinguish selectively relevant differences among individuals because of mutation and eco-evolutionary feedbacks. This series of problems leads us to conclude that a general value of fitness that is both explanatory and predictive cannot be attained. We advocate for the use of propensity-compatible methods, such as adaptive dynamics, which can accommodate these difficulties.

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Probability out of Determinism

Cited by 48 publications

References 317 publications

Probabilities in Statistical Mechanics

Probabilities in Statistical Mechanics

A Humean Guide to Spielraum Probabilities

Taming fitness: Organism‐environment interdependencies preclude long‐term fitness forecasting

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