Trading in Complex Networks

TL;DR

This study investigates how network topology influences pricing and efficiency in complex supply networks through human experiments and agent-based modeling, revealing that small-world networks lead to higher prices and lower efficiency.

Contribution

It introduces an experimental approach combined with an agent-based model to understand price formation in complex networks, highlighting the impact of network topology.

Findings

Prices are higher and trade less efficient in small-world networks.

Location within the network does not significantly affect pricing.

Active paths influence price dynamics, with traders raising or lowering prices accordingly.

Abstract

Global supply networks in agriculture, manufacturing, and services are a defining feature of the modern world. The efficiency and the distribution of surpluses across different parts of these networks depend on choices of intermediaries. This paper conducts price formation experiments with human subjects located in large complex networks to develop a better understanding of the principles governing behavior. Our first finding is that prices are larger and that trade is significantly less efficient in small-world networks as compared to random networks. Our second finding is that location within a network is not an important determinant of pricing. An examination of the price dynamics suggests that traders on cheapest -- and hence active -- paths raise prices while those off these paths lower them. We construct an agent-based model (ABM) that embodies this rule of thumb. Simulations of this ABM yield macroscopic patterns consistent with the experimental findings. Finally, we extrapolate the ABM on to significantly larger random and small world networks and find that network topology remains a key determinant of pricing and efficiency.