Research: Analysis of Transport Model that Approximates Decision Taker's Preferences

TL;DR

This paper introduces a method to infer decision-taker preferences by solving an inverse transport problem, enabling adaptive modeling based on accumulated decision data for improved decision support.

Contribution

It presents a novel approach to model decision-taker preferences through inverse transport problem solutions, allowing adaptive and experience-based preference estimation.

Findings

Method effectively captures decision-taker preferences

Model can be updated regularly for relevance

Approach facilitates decision support in new situations

Abstract

Paper provides a method for solving the reverse Monge-Kantorovich transport problem (TP). It allows to accumulate positive decision-taking experience made by decision-taker in situations that can be presented in the form of TP. The initial data for the solution of the inverse TP is the information on orders, inventories and effective decisions take by decision-taker. The result of solving the inverse TP contains evaluations of the TPs payoff matrix elements. It can be used in new situations to select the solution corresponding to the preferences of the decision-taker. The method allows to gain decision-taker experience, so it can be used by others. The method allows to build the model of decision-taker preferences in a specific application area. The model can be updated regularly to ensure its relevance and adequacy to the decision-taker system of preferences. This model is adaptive to the current preferences of the decision taker.