A two-step fusion process for multi-criteria decision applied to natural hazards in mountains

Mountain river torrents and snow avalanches generate human and material damages with dramatic consequences. Knowledge about natural phenomenona is often lacking and expertise is required for decision and risk management purposes using multi-disciplinary quantitative or qualitative approaches. Expertise is considered as a decision process based on imperfect information coming from more or less reliable and conflicting sources. A methodology mixing the Analytic Hierarchy Process (AHP), a multi-criteria aid-decision method, and information fusion using Belief Function Theory is described. Fuzzy Sets and Possibilities theories allow to transform quantitative and qualitative criteria into a common frame of discernment for decision in Dempster-Shafer Theory (DST ) and Dezert-Smarandache Theory (DSmT) contexts. Main issues consist in basic belief assignments elicitation, conflict identification and management, fusion rule choices, results validation but also in specific needs to make a difference between importance and reliability and uncertainty in the fusion process

Decision making under incompleteness based on soft set theory

[EN]Decision making with complete and accurate information is ideal but infrequent. Unfortunately, in most cases the available infor- mation is vague, imprecise, uncertain or unknown. The theory of soft sets provides an appropriate framework for decision making that may be used to deal with uncertain decisions. The aim of this paper is to propose and analyze an effective algorithm for multiple attribute decision-making based on soft set theory in an incomplete information environment, when the distribution of incomplete data is unknown. This procedure provides an accurate solution through a combinatorial study of possible cases in the unknown data. Our theoretical development is complemented by practical examples that show the feasibility and implementability of this algorithm. Moreover, we review recent research on decision making from the standpoint of the theory of soft sets under incomplete information

Opinion Pooling under Asymmetric Information

If each member of a group assigns a certain probability to a hypothesis, what probability should the collective as a whole assign? More generally, how should individual probability functions be merged into a single collective one? I investigate this question in case that the individual probability functions are based on different information sets. Under suitable assumptions, I present a simple solution to this aggregation problem, and a more complex solution that can cope with any overlaps between different persons' information sets. The solutions are derived from an axiomatic system that models the individuals as well as the collective as Bayesian rational agents. Two notable features are that the solutions may be parameter-free, and that they incorporate each individual's information although the individuals need not communicate their (perhaps very complex) information, but rather reveal only the resulting probabilities.opinion pooling, probability aggregation, decision theory, social choice theory, Bayesian rationality, Bayesian aggregation, information

Fuzzy systems and applications in innovation and sustainability

One of the main characteristics of humankind is the ability to interpret via natural language incomplete, imprecise, vague, subjective, fragmentary, or scarce information i.e. information in uncertainty and transform it to actions, reason and decision-making [9]. Fuzzy sets theory firstly introduced the treatment of such concepts in 1965 with the foremost influential paper "Fuzzy Sets" [29]. The groundbreaking standpoint of fuzzy systems allows the treatment of uncertain information with the utilization of a strict mathematical framework [8]. Ever since the publication of the pivotal paper from Zadeh, a plethora of contributions have shaped the fuzzy sets theory scope and applications, from developments in engineering, mathematics, computer, decision, life, physical, health, social sciences and humanities [17 (...

Order-of-Magnitude Influence Diagrams

In this paper, we develop a qualitative theory of influence diagrams that can be used to model and solve sequential decision making tasks when only qualitative (or imprecise) information is available. Our approach is based on an order-of-magnitude approximation of both probabilities and utilities and allows for specifying partially ordered preferences via sets of utility values. We also propose a dedicated variable elimination algorithm that can be applied for solving order-of-magnitude influence diagrams

A Multi-Attribute Group Decision Approach Based on Rough Set Theory and Application in Supply Chain Partner Selection

In multi-attribute group decision, decision makers (DMs) are willing or able to provide only incomplete information because of time pressure, lack of knowledge or data, and their limited expertise related with problem domain, so the alternative sets judged by different decision makers are inconsistent in allusion to a certain decision problem, how to form consistent alternative sets becomes a very important problem. There have been a few studies considering incomplete information in group settings, but few papers consider the adjustment of inconsistent alternative sets. We suggest a method, utilizing individual decision results to form consistent alternative sets based on Rough Set theory. The method can be depicted as follows: (1) decision matrix of every decision maker is transformed to decision table through an new discretization algorithm of condition attributes ; (2) we analyze the harmony of decision table of every DM in order to filter some extra alternatives with the result that new alternative sets are formed; (3) if the new alternative sets of different DMs are inconsistent all the same, learning quality of DMs for any inconsistent alternative is a standard of accepting the alternative

Experiments on individual decision-making and information

This thesis is composed by three experiments that explore the role of information in individual decision-making. In Chapter 1 it is presented an experimental test of the contextual inference theory (Kamenica,2008). The experiment shows that the dimension of choice sets conveys payoff-relevant information in decision-making: even when options are not directly observable, the likelihood of finding an option that fits individual tastes can be inferred from the set size. Information on the lenght of a product line is then shown to be relevant in individual decision making. In Chapter 2 the decision-maker is presented with payoff irrelevant information: group-membership and others' behavior. The experiment test if and how these information affect individual decision of behaving ethically. The results provide evidence of the effectiveness of these information in shaping moral behavior. Chapter 3 aims at going into the black box of information processing under uncertainty with an eye-tracking experiment. The aim of this last chapter is to contribute to the understanding of the choice process under different dimensions of the choice sets (small and large), and its relation with the response time