Research Evidence Supporting Aggregation Methods

Key Research and Findings

1. James Surowiecki's "The Wisdom of Crowds" (2004)
   • Demonstrated how aggregated judgments consistently outperform individual expert opinions
   • Identified four key conditions for effective crowd wisdom: diversity of opinion, independence, decentralization, and aggregation
   • Provided case studies across diverse domains including economic markets, sports betting, and political forecasting
   • Showed that crowds outperform experts particularly in complex scenarios with multiple variables
2. Kahneman and Tversky, "Judgment Under Uncertainty" (1974)
   • Identified systematic biases in individual decision-making
   • Demonstrated how aggregation methods help mitigate cognitive biases like anchoring, availability heuristic, and confirmation bias
   • Showed that aggregated judgments tend to cancel out individual errors
   • Found that structured aggregation methods perform better than simple averaging in many cases
3. Edward Tufte, "The Visual Display of Quantitative Information"
   • Established principles for effective visual representation of aggregated data
   • Demonstrated how well-designed data visualizations reveal patterns otherwise hidden in raw data
   • Introduced concepts like "data-ink ratio" that maximize information transfer in aggregated displays
   • Showed how visual aggregations enable both macro-pattern recognition and micro-detail examination

Application to Consensus and Relationship Analysis

These research findings support the effectiveness of consensus priority lists and relationship graphs by demonstrating:

• The superiority of aggregated judgments over individual opinions
• How structured aggregation methods reduce individual cognitive biases
• The importance of effective visualization in revealing patterns from aggregated data

The research suggests that properly implemented aggregation methods like those used in SimScore provide more reliable guidance than traditional approaches to group decision-making.