Dynamics of price formation on complex networks
Andrea Civilini1,2, Vito Latora1,2,3
1School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom.
This study introduces a network-based economic model for price formation. It reveals how market structure and buyer information influence price dynamics and competition strength.
Area of Science:
- Economics
- Network Science
- Computational Social Science
Background:
- Economic models traditionally ignore market network structures.
- Real-world markets involve complex spatial and abstract networks.
Purpose of the Study:
- To develop a dynamical model of price formation on complex networks.
- To analyze how network topology and buyer information affect market dynamics.
- To quantify competition strength and identify optimal seller locations.
Main Methods:
- A dynamical model with buyers as random walkers and sellers adjusting prices.
- Simulation of price formation on networks with varying topologies and buyer information levels.
- Development of a novel node centrality measure based on seller payoff.
Main Results:
- Model exhibits convergence to fixed prices or price cycles based on network structure and buyer information.
- Demonstrates how market structure dynamically influences competition.
- Identifies profitable seller locations using urban street network data.
Conclusions:
- Network structure is crucial for understanding price formation and market competition.
- The model provides a framework for quantifying competition and optimizing seller placement.
- Novel centrality measure offers insights into economic node importance.
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