Two-Stage Distributionally Robust Minimum Cost Consensus Modeling With Loss Aversion

In group decision making (GDM), decision makers (DMs) may exhibit loss averse behavior. In addition, due to the complexity of GDM in the real world, there are various uncertainties. However, traditional studies ignore DMs’ loss aversion behavior under uncertain environments. To address this gap, this article develops a two-stage distributionally robust minimum cost consensus model under uncertainty with loss aversion (TSDR-MCCM-LA). In the first stage, group opinion is obtained based on a predefined consensus level function. The second stage considers loss aversion under uncertainty. Further, considering DMs’ acceptance degrees of recommendations, we develop $\varepsilon$-TSDR-MCCM-LA and RB-TSDR-MCCM-LA to address DMs’ compromise limit and rejection behaviors, respectively. The proposed models are equivalently transformed into tractable mixed-integer programming models. Finally, the effectiveness of the proposed models is proved by the case of urban demolition compensation negotiation. Sensitivity and comparison analysis further demonstrate the validity of the proposed model in coping with uncertainty and improving consensus outcomes.