Markovian Memory Embedded in Two-State Natural Processes
Fotini Pallikari, Nikitas Papasimakis

TL;DR
This paper explores how Markovian memory influences binary processes, affecting state clustering, dispersion, and long-term behavior, with applications to animal behavior and quantum state dynamics.
Contribution
It introduces a mathematical framework for analyzing Markovian memory effects in natural binary processes, demonstrating its applicability to diverse systems.
Findings
Markovian memory causes clustering or dispersion of states.
Balanced transition probabilities can mimic random process evolution.
Model successfully estimates transition probabilities in quantum Zeno effect.
Abstract
Markovian memory embedded in a binary system is shaping its evolution on the basis of its current state and introduces either clustering or dispersion of binary states. The consequence is directly observed in the lengthening or shortening of the runs of the same binary state and also in the way the proportion of a state within a sequence of state measurements scatters about its true average, which is quantifiable through the Markovian self-transition probabilities. It is shown that the Markovian memory can even imitate the evolution of a random process, regarding the long-term behavior of the frequencies of its binary states. This situation occurs when the associated binary state self-transition probabilities are balanced. To exemplify the behavior of Markovian memory, two natural processes are selected. The first example is studying the preferences of nonhuman troglodytes regarding…
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Taxonomy
TopicsNeural Networks and Applications
