Parameter estimation of a two-colored urn model class

TL;DR

This paper develops a statistical framework for a two-colored urn model, enabling parameter estimation from experimental data, with applications to ant colony path selection, and assesses estimator accuracy through simulations and bootstrap methods.

Contribution

It introduces maximum likelihood and weighted least squares estimators for a two-colored urn model, addressing inhomogeneous Markov chain challenges with i.i.d. experiments.

Findings

Estimators accurately recover parameters in simulations

Bootstrap confidence regions are effective

Results align with biological literature

Abstract

Though widely used in applications, reinforced random walk on graphs have never been the subject of a valid statistical inference. We develop in this paper a statistical framework for a general two-colored urn model. The probability to draw a ball at each step depends on the number of balls of each color and on a multidimensional parameter $\theta$ through a function $f$, called a choice function. We introduce two estimators of $\theta$: the maximum likelihood estimator and a weighted least squares estimator which is less efficient, but is closer to the calibration techniques used in the applied literature. In general, the model is an inhomogeneous Markov chain and this property makes the estimation of the parameter impossible on a single path, even if it were infinite. Therefore we assume that we observe i.i.d. experiments, each of a predetermined finite length. This is coherent with the usual experimental set-ups. We apply the statistical framework to a real life experiment: the selection of a path among pre-existing channels by an ant colony. We performed experiments, which consisted of letting ants pass through the branches of a fork. We consider the particular urn model proposed by J.-L. Deneubourg in 1990 to describe this phenomenon. We simulate this model for several parameter values in order to assess the accuracy of the MLE and the WLSE. Then we estimate the parameter from the experimental data and evaluate confident regions with Bootstrap algorithms. The findings of this paper do not contradict the biological literature, but give statistical significance to the values of the parameter found therein.