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Effect of network topology and node centrality on trading

Author

Listed:
  • Felipe Maciel Cardoso
  • Carlos Gracia-Lázaro
  • Frédéric Moisan

    (GATE Lyon Saint-Étienne - Groupe d'Analyse et de Théorie Economique Lyon - Saint-Etienne - ENS de Lyon - École normale supérieure de Lyon - Université de Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique)

  • Sanjeev Goyal
  • Ángel Sánchez

    (GAVAB - Grupo de Algorítmica para la Visión Artificial y la Biometría - URJC - Universidad Rey Juan Carlos = Rey Juan Carlos University)

  • Yamir Moreno

    (Universidad de Zaragoza = University of Zaragoza [Saragossa University] = Université de Saragosse)

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 the 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 experimental finding is that prices are larger and that trade is significantly less efficient in small-world networks as compared to random networks. Our second experimental 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.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Felipe Maciel Cardoso & Carlos Gracia-Lázaro & Frédéric Moisan & Sanjeev Goyal & Ángel Sánchez & Yamir Moreno, 2020. "Effect of network topology and node centrality on trading," Post-Print halshs-03052105, HAL.
  • Handle: RePEc:hal:journl:halshs-03052105
    DOI: 10.1038/s41598-020-68094-z
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    Cited by:

    1. Tamas David-Barrett, 2022. "Clustering Drives Cooperation on Reputation Networks, All Else Fixed," Papers 2203.00372, arXiv.org.
    2. Teodoro Criscione, 2024. "Topological Components in a Community Currency Network," Papers 2409.13674, arXiv.org, revised Apr 2025.
    3. Zhuoming, Ren & Wan, Wang & Yu, Lin & Li, Zhao, 2024. "Modeling complex network perturbations on resilience of the bilateral regional trade agreements," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 655(C).

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