Random walkers with extreme value memory: modelling the peak-end rule

TL;DR

This paper models the peak-end rule from psychology using a random walk with extreme value memory, revealing how decision stability depends on reflection scale and noise, with implications for questionnaire design.

Contribution

It introduces a novel random walk model incorporating extreme value memory to analyze the peak-end rule in decision-making.

Findings

Long-term behavior depends on the reflection scale used.

Increased noise does not always lead to more decision switching.

Different classes of dynamics emerge based on extreme value theory.

Abstract

Motivated by the psychological literature on the "peak-end rule" for remembered experience, we perform an analysis within a random walk framework of a discrete choice model where agents' future choices depend on the peak memory of their past experiences. In particular, we use this approach to investigate whether increased noise/disruption always leads to more switching between decisions. Here extreme value theory illuminates different classes of dynamics indicating that the long-time behaviour is dependent on the scale used for reflection; this could have implications, for example, in questionnaire design.