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

TL;DR
This paper introduces an affinity-based extension of Tsallis' non-extensive entropy, incorporating microstate affinities, leading to a new invariant diversity measure and modified statistical ensembles with potential applications in information and biodiversity theories.
Contribution
The paper develops an affinity-dependent extension of Tsallis entropy, establishes the Nesting Principle, and modifies statistical ensembles to include affinities, advancing non-extensive statistical mechanics.
Findings
Effective diversity remains invariant under state grouping only for q=2.
Affinity influences the thermodynamic behaviour at equilibrium.
Modified ensembles alter the classic equal a priori probabilities.
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 () is the concept of the effective number of dissimilar states, termed the effective diversity (). 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, , in terms of the Tsallis' -logarithm. A new principle called the Nesting Principle is established, stating that the effective diversity remains invariant under an arbitrary grouping of the constituent states. It is shown that this invariance property holds only for ; however, the invariance is recovered for…
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