Learning models on rooted regular trees with majority update policy: convergence and phase transition
Moumanti Podder, Anish Sarkar

TL;DR
This paper analyzes a learning process on rooted regular trees with majority update rules, revealing phase transitions and convergence properties depending on parameters like branching factor and success probabilities.
Contribution
It introduces a detailed model of technology adoption on trees, characterizes fixed points of the process, and identifies phase transitions based on parameters.
Findings
Unique fixed point at 1/2 for certain parameters
Multiple fixed points indicating phase transitions
Explicit conditions for fixed point stability
Abstract
We study a learning model in which an agent is stationed at each vertex of , the rooted tree in which each vertex has children. At any time-step , they are allowed to select one of two available technologies: and . Let the technology chosen by the agent at vertex , at time-step , be . Let be i.i.d., where with probability . During epoch , the agent at performs an experiment that results in success with probability if , and with probability if . If the children of are , the agent at updates their technology to if the number of successes among all with exceeds, strictly, the number of successes among all with . If…
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Taxonomy
TopicsNeural Networks and Applications · Evolutionary Algorithms and Applications · Fuzzy Logic and Control Systems
