Consensus Measure with Multi-stage Fluctuation Utility Based on China’s Urban Demolition Negotiation

Abstract

Utility functions are often used to reflect decision makers' (DMs') preferences. They have the following two merits: one refers to the representation of the DM's utility (satisfaction) level, the other one to the measuring of the consensus level in a negotiation process. Taking the background of China's urban house demolition, a new kind of consensus model is established by using different types of multi-stage fluctuation utility functions, such as concave, convex, S-shaped, reversed S-shaped, reversed U-shaped as well as their combinations, to reveal negotiators' dynamic physiological preferences and consensus level. Moreover, the effects of the decision-making budget and the individual compensation tolerance on the consensus level are also discussed in this paper. Compared with previous research, the proposed model takes both the negotiation cost and DM's preference structure into consideration, and most importantly, it is computational less complex.

Minimum cost consensus models based on random opinions

“…Compared with the optimal minimum negotiation budget of 14.7 obtained in (Gong et al, 2017), it is clear that our iteration results produce an optimal result close to the above one after 200 iterations. Apart from interval values, our algorithm can also deal with fuzzy numbers obeyed random distributions.…”

A Selection Model on Risk Response Scheme for Complex Equipment Research and Manufacturing

“…Therefore, the decision-making behavior of the suppliers can be described by the utility of the preference degree of the compensated scheme. In view of this, in order to realize the stability of the consensus scheme of the research and manufacturing system, we exploit the utility function of [2,[34][35][36][37][38] for reference to measure the decision utility of the suppliers and then construct the model to maximize the suppliers' utility.…”

“…Let represent the utility of the supplier group (this utility value must not be less than the minimum utility value of all suppliers), which is a function of the risk response scheme preference value of the supplier , and 0 ≤ ≤ 1, ∈ [ 0 , + ]. The supplier's risk attitude and preference are generally nonlinear; according to the characteristic of supplier's utility preference and referring to the nonlinear utility function [2,[34][35][36][37][38], three kinds of parabola utility functions can be constructed: left skew type, interval type, and right skew type. As shown in Figures 1-3, the corresponding formulas (4)-(6) are shown, respectively:…”

Data envelopment analysis based on team reasoning

“…Decision making based on such collective preferences is called team reasoning (Colman, ), which is very different from individual utility maximization. Theories of team reasoning generally assume that decision makers are motivated to maximize either collective or individual utilities depending on the circumstances (Gold and Sugden, ; Gong et al., ).…”