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  1.  25
    A Dynamical Systems Approach to Causation.Peter Fazekas, Balázs Gyenis, Gábor Hofer-Szabó & Gergely Kertész - forthcoming - Synthese:1-23.
    Our approach aims at accounting for causal claims in terms of how the physical states of the underlying dynamical system evolve with time. Causal claims assert connections between two sets of physicals states—their truth depends on whether the two sets in question are genuinely connected by time evolution such that physical states from one set evolve with time into the states of the other set. We demonstrate the virtues of our approach by showing how it is able to account for (...)
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  2.  95
    When Can Statistical Theories Be Causally Closed?Balazs Gyenis & Miklos Redei - 2002 - Foundations of Physics 34 (9):1285-1303.
    The notion of common cause closedness of a classical, Kolmogorovian probability space with respect to a causal independence relation between the random events is defined, and propositions are presented that characterize common cause closedness for specific probability spaces. It is proved in particular that no probability space with a finite number of random events can contain common causes of all the correlations it predicts; however, it is demonstrated that probability spaces even with a finite number of random events can be (...)
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  3.  13
    Is It the Principal Principle That Implies the Principle of Indifference?Balazs Gyenis & Leszek Wronski - 2017 - In Gábor Hofer-Szabó & Leszek Wroński (eds.), Making it Formally Explicit: Probability, Causality and Indeterminism. Springer International Publishing.
    Hawthorne, Landes, Wallmann and Williamson argue that the Principal Principle implies a version of the Principle of Indifference. We show that what the Authors take to be the Principle of Indifference can be obtained without invoking anything which would seem to be related to the Principal Principle. In the Appendix we also discuss several Conditions proposed in the same paper.
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  4.  15
    Maxwell and the Normal Distribution: A Colored Story of Probability, Independence, and Tendency Toward Equilibrium.Balázs Gyenis - 2017 - Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 57:53-65.
    We investigate Maxwell's attempt to justify the mathematical assumptions behind his 1860 Proposition IV according to which the velocity components of colliding particles follow the normal distribution. Contrary to the commonly held view we find that his molecular collision model plays a crucial role in reaching this conclusion, and that his model assumptions also permit inference to equalization of mean kinetic energies, which is what he intended to prove in his discredited and widely ignored Proposition VI. If we take a (...)
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  5.  6
    How Macrostates Come About?Marton Gomori, Balazs Gyenis & Gábor Hofer-Szabó - unknown
    This paper is a further consideration of Hemmo and Shenker’s ideas about the proper conceptual characterization of macrostates in statistical mechanics. We provide two formulations of how macrostates come about as elements of certain partitions of the system’s phase space imposed on by the interaction between the system and an observer, and we show that these two formulations are mathematically equivalent. We also reflect on conceptual issues regarding the relationship of macrostates to distinguishability, thermodynamic regularity, observer dependence, and the general (...)
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  6. Causal Completeness of Probability Theories-Results and Open Problems.Miklos Redei & Balazs Gyenis - 2011 - In Phyllis McKay Illari, Federica Russo & Jon Williamson (eds.), Causality in the Sciences. Oxford University Press.
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  7.  12
    Bayes Rules All: On the Equivalence of Various Forms of Learning in a Probabilistic Setting.Balazs Gyenis - unknown
    Jeffrey conditioning is said to provide a more general method of assimilating uncertain evidence than Bayesian conditioning. We show that Jeffrey learning is merely a particular type of Bayesian learning if we accept either of the following two observations: – Learning comprises both probability kinematics and proposition kinematics. – What can be updated is not the same as what can do the updating; the set of the latter is richer than the set of the former. We address the problem of (...)
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  8.  10
    Determinizmus És Interpretáció.Balázs Gyenis - unknown
    We argue that the truth of determinism is not an interpretation-free fact and we systematically overview relevant interpretational choices that are less known in the philosophical literature. After bypassing the well known interpretational problem that arises in quantum mechanics we identify three further questions about the representational role of the mathematical structures employed by physical theories. Finally we point out that even if we settle all representational issues the received view of physical possibility may also allow the truth of determinism (...)
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