Discrete multi-criteria decision problems with numerous Pareto-efficient solution candidates place a significant cognitive burden on the decision maker. An interactive, aspiration-based search process that iteratively progresses toward the most preferred solution can alleviate this task. In this paper, we study three ways of representing such problems in a DSS, and compare them in a laboratory experiment using subjective and objective measures of the decision process as well as solution quality and problem understanding. In addition to an immediate user evaluation, we performed a re-evaluation several weeks later. Furthermore, we consider several levels of problem complexity and user characteristics. Results indicate that different problem representations have a considerable influence on search behavior, although long-term consistency appears to remain unaffected. We also found interesting discrepancies between subjective evaluations and objective measures. Conclusions from our experiments can help designers of DSS for large multi-criteria decision problems to fit problem representations to the goals of their system and the specific task at hand.

具有众多帕累托有效解决方案候选的离散多标准决策问题给决策者带来了重大的认知负担。一个交互式的、基于愿望的搜索过程，迭代地朝着最喜欢的解决方案前进，可以减轻这个任务。在本文中，我们研究了在 DSS 中表示此类问题的三种方法，并在实验室实验中使用决策过程的主观和客观度量以及解决方案质量和问题理解对它们进行了比较。除了立即进行用户评估外，我们在几周后还进行了重新评估。此外，我们考虑了问题复杂性和用户特征的几个级别。结果表明，不同的问题表示对搜索行为有相当大的影响，尽管长期一致性似乎不受影响。我们还发现主观评价和客观测量之间存在有趣的差异。我们的实验得出的结论可以帮助 DSS 设计人员解决大型多标准决策问题，使问题表示适合他们的系统目标和手头的特定任务。