Optimizing multi-criteria decision-making: empirical analyses and quantitative insights for reducing the effort of decision-makers in a decision support system

Making decisions in crucial situations is complicated by various challenges, e.g., the inability to define relevant objectives (Bond et al. 2008) and alternatives (Siebert and Keeney 2015), cognitive biases (Montibeller and von Winterfeldt 2015), or information overload. These factors often lead to impulsive or low-quality decisions, especially under time constraints that limit thorough evaluation of options (Kocher and Sutter 2006). Structured methods like multi-criteria decision analysis (MCDA) have been developed to tackle decision-making challenges by creating quantitative or qualitative models that represent the decision situation. These models help organize information, facilitate trade-offs, and select alternatives, although developing them can be complex and challenging to understand. Decision support systems (DSSs) are designed to assist decision-makers in managing MCDA approaches enhancing decision quality and efficiency (Tripathi 2011). Nevertheless, these tools also often reach their limits when implementing scientifically sound approaches in a user-friendly and simple way. This dissertation aims to optimize an MCDA-based DSS called ENTSCHEIDUNGSNAVI, designed for decision-makers seeking to enhance their understanding while minimizing the effort required for a scientifically sound approach. The open-source web tool supports reflective decision-making (von Nitzsch and Methling 2021) through a five-step process based on multi-attribute utility theory (Keeney and Raiffa 1976) and value-focused thinking (Keeney 1992). The study provides insights into the theoretical and practical foundations of the ENTSCHEIDUNGSNAVI and examines the extent to which the reflective decision-making process can be simplified for the decision-maker. Therefore, it examines whether value-nudging leads to more value-focused and, thus, better decisions. Moreover, the use of an imprecise information approach is critically analyzed in terms of helpfulness and impact on the decision. In addition, the study investigates how a linear transformation of one-dimensional utility functions affects decision quality by examining its effect on the final ranking of alternatives.