Wise or mad crowds? The cognitive mechanisms underlying information cascades

Area of Science:

• Social Psychology
• Computational Social Science
• Decision Science

Background:

• Human decisions are rarely made in isolation, often occurring sequentially within social systems.
• Sequential decision-making can lead to information cascades, where early choices influence later ones.
• Understanding the dynamics of social decision-making is crucial for analyzing information cascades.

Purpose of the Study:

• To introduce and validate a novel computational model for understanding social decision-making dynamics.
• To capture how individuals integrate personal and social information over time during sequential choices.
• To investigate the interplay between accuracy, confidence, and response time in information cascades.

Main Methods:

• Development of the social drift-diffusion model to simulate sequential decision-making.
• Empirical testing of the model using a controlled sequential choice task.
• Analysis of how the model recovers decision dynamics and information integration.

Main Results:

• The social drift-diffusion model accurately captured the dynamics of social decision-making.
• The model successfully illustrated how individuals integrate personal and social information over time.
• Key interrelationships between accuracy, confidence, and response time were identified as critical for cascade quality.

Conclusions:

• Capturing the dynamic nature of decision processes is essential for understanding social information cascades.
• The social drift-diffusion model provides a valuable tool for studying information flow in social systems.
• Findings have implications for various social systems where sequential decision-making occurs.