Less is more: information needs, information wants, and what makes causal models useful

Area of Science:

• Decision Science
• Cognitive Psychology
• Information Visualization

Background:

• Individuals frequently make complex decisions in areas like health and finance.
• Causal models can aid decision-making, but their complexity often impedes usability.
• The effectiveness of simplified causal models for decision support is not well understood.

Purpose of the Study:

• To investigate the impact of simplicity versus complexity in causal models on decision-making.
• To determine how tailoring information presentation affects decision quality.
• To explore individual perceptions of information needs in decision tasks.

Main Methods:

• Five experiments were conducted to test the effects of model simplicity and information presentation.
• Diagrams were tailored to specific choices and highlighted relevant causal pathways.
• Participant performance was evaluated based on the decisions made.

Main Results:

• Simpler causal models and highlighted causal paths led to improved decision-making.
• Including extraneous information, even if minimal, negatively impacted decision quality.
• Participants who underestimated their information needs or sought complexity performed worse.

Conclusions:

• Simplified and targeted causal information enhances decision-making effectiveness.
• Overly complex or irrelevant information is detrimental to making sound choices.
• Effective decision support requires presenting information that is both simple and relevant to the specific decision.