A comparison of representations for discrete multi-criteria decision problems

Area of Science:

• Decision Analysis
• Human-Computer Interaction
• Cognitive Psychology

Background:

• Complex multi-criteria decision problems with many Pareto-efficient solutions overwhelm decision-makers.
• Interactive, aspiration-based search processes can simplify decision-making.
• The way problems are represented in decision support systems (DSS) can influence user behavior and outcomes.

Purpose of the Study:

• To compare three distinct problem representations within a DSS.
• To evaluate the impact of these representations on decision-making processes and outcomes.
• To assess user understanding and solution quality across different representation methods.

Main Methods:

• A laboratory experiment was conducted to compare three DSS problem representations.
• Subjective and objective measures assessed decision process, solution quality, and problem understanding.
• Evaluations included immediate feedback and a delayed re-evaluation several weeks later, considering varying complexity and user characteristics.

Main Results:

• Different problem representations significantly influenced user search behavior during decision-making.
• Long-term consistency of user preferences was not affected by the representation method.
• Discrepancies were observed between users' subjective evaluations and objective performance measures.

Conclusions:

• Problem representation is a critical factor in designing effective DSS for complex decisions.
• Designers should align DSS problem representations with system goals and specific task requirements.
• Understanding the gap between subjective user perception and objective outcomes is crucial for DSS development.