Research: Analysis of Transport Model that Approximates Decision Taker's Preferences
This provides a method for modeling decision-taker preferences in specific applications, allowing others to leverage accumulated experience, though it appears incremental as it applies an existing transport problem framework to a new context.
The paper tackles the problem of inferring a decision-taker's preferences from past decisions by solving the reverse Monge-Kantorovich transport problem, resulting in a model that can be used to select solutions aligned with those preferences in new situations.
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.