Localization transition induced by learning in random searches

TL;DR

This paper studies how learning through reinforcement in random search models causes a phase transition leading to localized states around a trap, linking to Anderson localization and highlighting adaptive search strategies.

Contribution

It introduces a model showing a phase transition in adaptive search due to reinforcement, connecting non-Markovian walks to localization phenomena.

Findings

A phase transition occurs at a critical resetting rate.

Localized stationary states emerge around traps.

The transition class matches Anderson localization theory.

Abstract

We solve an adaptive search model where a random walker or L\'evy flight stochastically resets to previously visited sites on a $d$-dimensional lattice containing one trapping site. Due to reinforcement, a phase transition occurs when the resetting rate crosses a threshold above which non-diffusive stationary states emerge, localized around the inhomogeneity. The threshold depends on the trapping strength and on the walker's return probability in the memoryless case. The transition belongs to the same class as the self-consistent theory of Anderson localization. These results show that similarly to many living organisms and unlike the well-studied Markovian walks, non-Markov movement processes can allow agents to learn about their environment and promise to bring adaptive solutions in search tasks.