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A new measure of group decision-making efficiency
Cheng-Ju Hsieh1, Mario Fifić2, Cheng-Ta Yang3,4
1Department of Chemical Engineering, National Cheng Kung University, Tainan, Taiwan.
Cognitive Research: Principles and Implications
|September 17, 2020
Summary
Group decisions show superior accuracy and faster information processing than individuals. The assessment function of capacity (A_AND(t)) effectively measures collective benefits by combining accuracy and response time.
Area of Science:
- Cognitive Psychology
- Social Psychology
- Decision Science
Background:
- Group decision-making is often considered superior to individual decision-making in terms of accuracy.
- However, the efficiency of group decisions, specifically their information-processing speed, remains under-researched.
Purpose of the Study:
- To investigate the relationship between accuracy and response time (RT) in group decision-making.
- To explore whether group decisions are more efficient than individual decisions.
- To apply systems' factorial technology to study information-processing properties in groups.
Main Methods:
- Systems' factorial technology was employed to analyze information processing.
- Workload capacity measures, C_AND(t) (correct responses) and A_AND(t) (speed-accuracy trade-off), were calculated.
- Two experiments were conducted: a two-interval, forced-choice oddball detection task and a yes/no Gabor detection task.
Main Results:
- Group detection sensitivity surpassed that of the best individual, particularly when individuals had similar sensitivities.
- Both C_AND(t) and A_AND(t) indicated supercapacity processing, suggesting a collective benefit.
- A specific relationship between accuracy-based and RT-based collective benefit was observed in A_AND(t) for correct and fast responses.
Conclusions:
- Group decisions demonstrate a collective benefit in both accuracy and information-processing speed.
- The assessment function of capacity, A_AND(t), is a valuable tool for studying group decision-making by integrating accuracy and response time.
- Findings highlight A_AND(t) as a novel method for uncovering processing mechanisms behind collective benefits.
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