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Affinity-based extension of non-extensive entropy and statistical mechanics

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  • Okamura, Keisuke

Abstract

Tsallis’ non-extensive entropy is extended to incorporate the dependence on affinities between the microstates of a system. At the core of our construction of the extended entropy (H) is the concept of the effective number of dissimilar states, termed the ‘effective diversity’ (Δ). It is a unique integrated measure derived from the probability distribution among states and the affinities between states. The effective diversity is related to the extended entropy through the Boltzmann’s-equation-like relation, H=lnqΔ, in terms of the Tsallis’ q-logarithm. A new principle called the Nesting Principle is established, stating that the effective diversity remains invariant under arbitrary grouping of the constituent states. It is shown that this invariance property holds only for q=2; however, the invariance is recovered for general q in the zero-affinity limit (i.e. the Tsallis and Boltzmann–Gibbs case). Using the affinity-based extended Tsallis entropy, the microcanonical and the canonical ensembles are constructed in the presence of general between-state affinities. It is shown that the classic postulate of equal a priori probabilities no longer holds but is modified by affinity-dependent terms. As an illustration, a two-level system is investigated by the extended canonical method, which manifests that the thermal behaviour of the thermodynamic quantities at equilibrium are affected by the between-state affinity. Furthermore, some applications and implications of the affinity-based extended diversity/entropy for information theory and biodiversity theory are addressed in appendices.

Suggested Citation

  • Okamura, Keisuke, 2020. "Affinity-based extension of non-extensive entropy and statistical mechanics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
  • Handle: RePEc:eee:phsmap:v:557:y:2020:i:c:s0378437120304404
    DOI: 10.1016/j.physa.2020.124849
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    1. Solow Andrew & Polasky Stephen & Broadus James, 1993. "On the Measurement of Biological Diversity," Journal of Environmental Economics and Management, Elsevier, vol. 24(1), pages 60-68, January.
    2. Steven Polasky & Andrew R. Solow, 1993. "Option Value, Gallot's Inequality, And The Measurement Of Biological Diversity," Boston College Working Papers in Economics 241, Boston College Department of Economics.
    3. T. Wada & A. M. Scarfone, 2005. "A non self-referential expression of Tsallis' probability distribution function," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 47(4), pages 557-561, October.
    4. Tsallis, Constantino & Mendes, RenioS. & Plastino, A.R., 1998. "The role of constraints within generalized nonextensive statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 261(3), pages 534-554.
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