Research On Cost Consensus Theory And Its Application Based On Decision-Maker’s Behavioral Constraints

Rapid industrialization and economic growth have led to a continuous high growth of carbon dioxide and other greenhouse gas emissions,which in turn has led to an increase in the frequency of environmental pollution and extreme weather events year by year,thus severely limiting the development of the economy,human-beings and the society,and for this reason,China has proposed a dual carbon policy(i.e.,carbon peaking and carbon neutral)to deal with the above development dilemma,in which how to efficiently and rationally allocate carbon resources has become a common concern and a big challenge in the academic community.In fact,the rational allocation of carbon resources can be regarded as a group decision-making(GDM)process,which mainly addresses the unstructured decision-making problems involving various experts’ subjective participation.In general,when solving GDM problems,decision-makers(DMs)eventually form a clear support or objection(i.e.,consensus)via multiple rounds of negotiations with an established consensus cost.Actually,factors affecting the consensus reaching process(CRP)normally include DMs’ preference structures,decision environment,the influence of particular individuals,DMs’ interpersonal relationship types,and etc.,making the GDM full of uncertainty and thus unable to accurately predict the outcome in advance.Therefore,a moderator,on behalf of the collective interest,is often introduced to increase the speed and efficiency of GDM.Inspired by the concept of minimum cost consensus(MCC)in the literature,this dissertation aims to construct a series of new consensus optimization models to address real-life GDM problems from two perspectives of either minimizing the moderator’s total cost or maximizing the individual DM’s total revenue.In building these new models,diverse behavioral constraints,such as non-cooperation,trade-off between interest and equity,unbalanced adjustment willingness or various mutual relationship types,are incorporated to figure out the mechanisms of GDM,thereby reaching the consensus quickly and effectively.Meanwhile,this dissertation adopts specific GDM phenomenon such as carbon reduction negotiation or carbon trading to conduct case studies and numerical analysis,so as to provide scientific decision aid and managerial implications for solving real-life problems.Specifically,we conduct fourfold discussions as follows:(1)Introduce uncertainty theory into the optimal consensus modeling to address unreliable results yielded when the reliability of decisions is determined only by experts due to the absence of sufficient historical data.To do that,we use uncertainty distributions and belief degree as a whole to fit individual preference,and further discuss five scenarios of uncertain chanceconstrained MCC models(MCCMs)from the angles of the moderator,individual DMs and noncooperators.Besides,we provide consensus reaching conditions and the analytic formulae of the minimum total cost through deductions.Finally,the new models are verified as an extension of the traditional crisp number or interval preference-based MCCMs with an application of carbon quota negotiation between the government and four heavily polluting enterprises.(2)Extend uncertain MCCMs into a CRP framework by incorporating DM’s unbalanced willingness of modifying preferences and designing a feedback mechanism over both preferences and weights due to the concept of democratic consensus.To do that,we build two consensus optimization models: one is to obtain the MCC on account of asymmetric costs,aggregation function and consensus measure;while the other provides more flexible decision aids without pre-setting a consensus level threshold.Moreover,binary variables are utilized to reduce the calculation complexity of piecewise functions transformed from uncertain distance measures in the multi-coefficient goal programming models.Finally,the feasibility of the new proposal is verified by case studies and comparative analyses.(3)The MCCMs can be transformed into the maximum compensation consensus models using the linear primal-dual optimization theory,and based on the latter type,we build new consensus optimization models to maximize the total revenue of either the whole group or individuals,thus acquiring flexible(e.g.,optimal or fair)carbon quota allocation schemes within a closed-loop trading system.Later,a relaxation method based on the PSO algorithm is proposed to address these new models.Moreover,considering that the inability to perform real-life GDM usually stems from the conflicts of interest,we further suggest two strategies to deal with their unfair issues.Finally,case studies show that sufficient interactions among DMs help achieve the balanced development of the closed-loop trading systems.(4)Interpersonal interaction is of importance for GDM,thus novel consensus optimization models are proposed based on fuzzy measures to handle the peer-to-peer(P2P)carbon trading problems.Concretely,we use the 2-additive fuzzy measures to exhibit various relationship types between DMs,and develop a series of mathematical models from the perspectives of economic rationality,fairness concerns or single/multiple periods,where two indicators and their associated models are separately used to solve fairness concerns;and a multi-period trading model is built based on adjustment rules to improve conflicting relationships,thus being more consistent with real GDM situations.After solving these models,we can complete the missing coefficients of DMs’ relationships,but also obtain trading details such as the transaction routes.Finally,a case study of carbon trading among four leading enterprises located in Shanghai demonstrates the feasibility and validity of the new method.