Simulating market dynamics: interactions between consumer psychology and social networks

Area of Science:

• Agent-based modeling
• Market dynamics
• Social network analysis

Background:

• Previous work explored market dynamics using multi-agent simulations, highlighting psychological factors like social networks and identity.
• Market dynamics vary from stable to highly dynamic, influenced by product penetration and consumer behavior.

Purpose of the Study:

• To systematically investigate the effects of different network structures on market dynamics.
• To extend previous agent-based models by incorporating scale-free network properties.

Main Methods:

• Utilized a multi-agent simulation model to explore market dynamics.
• Extended network analysis from small-world models to scale-free networks with power-law distributions.
• Analyzed the interplay between agent decision-making heuristics, product characteristics, and network structures.

Main Results:

• Market dynamics are a self-organized property.
• Scale-free networks introduce influential 'hubs' (early adopters) impacting consumer behavior.
• Consumer decision-making, product attributes (satisfaction, visibility), and network topology (size, hubs) collectively shape market outcomes.

Conclusions:

• Consumer behavior and market evolution are significantly influenced by social network structures, particularly the presence of hubs.
• Agent-based models incorporating scale-free networks provide deeper insights into complex market dynamics.
• Understanding these interconnected factors is crucial for predicting and managing market behavior.