A Satisfying Game Theoretic Framework for Retrieving Relevant Objects from a Database

In this paper we consider the problem of retrieving objects from a database that respond to a pursued goal (relevancy) by the extractors or decision makers to form a short list from which the final selection will be made because it is well known that human beings are good discriminators when facing few objects but perform very poorly in face of a great number of objects. These problems arise in domains such as finance and e-business (selecting a group of firms from a stock exchange database in which to invest, extracting desired goods to buy from a website, etc.), logistics (selecting a group of potential suppliers for a given product from a group of suppliers), administration (selecting a set of potential sites where facilities such as hospital, school, airport ... can be located). Most of the time the objects to be extracted are described by many attributes that can be measured, observed or supplied by experts. The purpose of this paper is to establish a method that permit to extract, from a database, a short list of objects relevant to the pursued goal using information about attributes and decision makers’ preferences. We argue that, for a given goal, there are, almost always, attributes that work towards this goal and those that work against the goal; from this observation we propose to use the satisficing game theory approach that is based on the notion of "being good enough" as the underlying mathematical tool to establish the extraction method. The main idea of the method that will be established in this paper is to exploit the positive (working towards the pursued goal)/negative (working against the pursued goal) properties of attributes along with decision makers’ preferences expressed by weighting these attributes to construct two measures known as selectability (related to positive attributes) and rejectability (related to negative attributes) in the framework of satisficing game theory. The objects arguable of "being good enough" are those for which the selectability measure exceeds the rejectability measure in some sense.