Elephant random walk with attributed steps and extractions of random sizes

TL;DR

This paper models a market with customers choosing between two products based on sampled past customer choices and satisfaction, analyzing the resulting stochastic process as a variant of the elephant random walk.

Contribution

It introduces a novel market model linked to elephant random walk dynamics, analyzing convergence properties of the sales performance ratio.

Findings

Almost sure convergence of sales ratio S_n/n.

Distributional convergence depends on the regime.

The model captures complex customer choice dynamics.

Abstract

We study a model of market economics wherein the $(n+1)$-st customer, for each $n\geqslant N$, with $N$ being a prespecified positive integer, draws a sample of (random) size $K_{n}$, either with replacement or without, from the customers of the past. Each sampled customer is queried as to which of the two products, A and B, available in the oligopolistic market, they chose, and whether they are satisfied or not with their choice. The $(n+1)$-st customer now employs a stochastic rule, based on the information collected from the sampled customers, to decide which of the two products to buy. The probability that a customer is satisfied with the product they have purchased equals $q_{1}$ when the product is A, and $q_{2}$ when it is B, independent of all else. The resulting stochastic process may be represented as a variant of the celebrated elephant random walk, with the relative performance (in terms of sale) of A with respect to B, up to and including the $n$-th sale, captured by the position $S_{n}$ of the walker at time $n$. We study the almost sure convergence of $S_{n}/n$, as well as the convergence in distribution of suitably scaled versions of $S_{n}$ (where the scaling depends on the regime we are in).