Entropy, Deterministic Chaos, and New Forms of Intelligibility: A Shared Frame of Reference for Physics and Psychology

Yıl 2024, Cilt: 1 Sayı: 1, 46 - 82, 26.04.2024

William Fisher

Öz

Entropy, Deterministic Chaos, and New Forms of Intelligibility:
A Shared Frame of Reference for Physics and Psychology

Keywords: entropy; nonequilibrium processes; measurement; probabilistic models; stochastic invariance.

Abstract. Prigogine’s theory of dissipative structures provides a general account of entropy-driven self-organized transitions through hierarchies of structures separated by discontinuities. The theory encompasses a wide range of evolving systems throughout nature and culture. Possibilities for operationalizing a new collective rationality spanning physics and psychology emerge from Prigogine’s emphases on two distinct senses of probability, on the concept of the sufficient statistic, and on the role and limitations of the Poisson distribution in formulating a “nonlinear master equation.” Unnoted by Prigogine are correspondences of all three of these issues in the mathematical foundations of statistics and measurement established in the works of Ronald Fisher and his student, Georg Rasch. The three areas of correspondence inform models enabling specifically metrological approaches to quality-assured quantification across the sciences. Prigogine’s sense of “deterministic chaos” is re-expressed in measurement terms as stochastic invariance and the need for “a supplementary parameter” augmenting the Poisson distribution is related to a rating scale model of measurement. In light of these connections, this paper proposes that what Prigogine anticipates as a “new intelligibility” and a new science of “collective rationality” could be pragmatically operationalized in a new metrological infrastructure made coherent by the generality of entropy-driven nonequilibrium processes.

Anahtar Kelimeler

measurement, modeling, probability, entropy, metrology, nonequilibrium thermodynamics, physics, psychology, semiotics, Rasch models, Poisson distribution, sufficient statistics

Kaynakça

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