Information temperature as a measure of complexity of random symbolic sequences

Oleg V. Usatenko and Galyna M. Pritula

Chaos, Solitons & Fractals, 2026, vol. 202, issue P1

Abstract: To advance the characterization of random symbolic sequences using macroscopic parameters, we propose the concept of specific information capacity, defined as the sensitivity of entropy to variations in information temperature for binary, stationary, ergodic sequences. We show that the complexity of a random sequence reaches its maximum when this specific information capacity approaches its maximum. Additionally, we discuss the potential of information temperature as an indicator of the cognitive or informational activity of a text-generating agent—whether human or artificial.

Keywords: Binary Markov chain; Complexity; Information entropy and temperature; Information capacity; Artificial intelligence (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:202:y:2026:i:p1:s0960077925014651

DOI: 10.1016/j.chaos.2025.117452

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