In the process of reaching consensus, it is necessary to coordinate different views to form a general group opinion. However, there are many uncertain factors in this process, which has brought different degrees of influence in group decision-making. Besides, these uncertain elements bring the risk of loss to the whole process of consensus building. Currently available models not account for these two aspects. To deal with these issues, three different modeling methods for constructing the two-stage mean-risk stochastic minimum cost consensus models (MCCMs) with asymmetric adjustment cost are investigated. Due to the complexity of the resulting models, the L-shaped algorithm is applied to achieve an optimal solution. In addition, a numerical example of a peer-to-peer online lending platform demonstrated the utility of the proposed modeling approach. To verify the result obtained by the L-shaped algorithm, it is compared with the CPLEX solver. Moreover, the comparison results show the accuracy and efficiency of the given method. Sensitivity analyses are undertaken to assess the impact of risk on results. And in the presence of asymmetric cost, the comparisons between the new proposed risk-averse MCCMs and the two-stage stochastic MCCMs and robust consensus models are also given.

在达成共识的过程中，需要协调不同的意见，形成一个普遍的群体意见。然而，这一过程中存在许多不确定因素，给群体决策带来了不同程度的影响。此外，这些不确定因素给整个共识建立过程带来了损失风险。目前可用的模型没有考虑到这两个方面。为了解决这些问题，研究了构建具有不对称调整成本的两阶段平均风险随机最小成本共识模型（MCCM）的三种不同建模方法。由于所得模型的复杂性，应用 L 形算法来实现最优解。此外，一个点对点在线借贷平台的数值示例展示了所提出的建模方法的实用性。为了验证 L 形算法得到的结果，将其与 CPLEX 求解器进行比较。此外，比较结果表明了给定方法的准确性和效率。进行敏感性分析以评估风险对结果的影响。并且在成本不对称的情况下，还给出了新提出的风险规避 MCCM 与两阶段随机 MCCM 和稳健共识模型之间的比较。