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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.
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1 Universidad de Zaragoza, Institute for Biocomputation and Physics of Complex Systems, Zaragoza, Spain (GRID:grid.11205.37) (ISNI:0000 0001 2152 8769); Unidad Mixta Interdisciplinar de Comportamiento y Complejidad Social (UMICCS), UC3M-UV-UZ, Madrid, Spain (GRID:grid.11205.37)
2 Universidad de Zaragoza, Institute for Biocomputation and Physics of Complex Systems, Zaragoza, Spain (GRID:grid.11205.37) (ISNI:0000 0001 2152 8769); Unidad Mixta Interdisciplinar de Comportamiento y Complejidad Social (UMICCS), UC3M-UV-UZ, Madrid, Spain (GRID:grid.11205.37); Universidad de Zaragoza, Department of Theoretical Physics, Faculty of Sciences, Zaragoza, Spain (GRID:grid.11205.37) (ISNI:0000 0001 2152 8769)
3 Cambridge University, Faculty of Economics, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934)
4 Cambridge University, Faculty of Economics and Christ’s College, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934)
5 Universidad de Zaragoza, Institute for Biocomputation and Physics of Complex Systems, Zaragoza, Spain (GRID:grid.11205.37) (ISNI:0000 0001 2152 8769); Unidad Mixta Interdisciplinar de Comportamiento y Complejidad Social (UMICCS), UC3M-UV-UZ, Madrid, Spain (GRID:grid.11205.37); Universidad Carlos III de Madrid, Grupo Interdisciplinar de Sistemas Complejos, Departamento de Matemáticas, Leganés, Spain (GRID:grid.7840.b) (ISNI:0000 0001 2168 9183); Institute UC3M-BS for Financial Big Data (IBiDat), Universidad Carlos III de Madrid, Getafe, Spain (GRID:grid.7840.b) (ISNI:0000 0001 2168 9183)
6 Universidad de Zaragoza, Institute for Biocomputation and Physics of Complex Systems, Zaragoza, Spain (GRID:grid.11205.37) (ISNI:0000 0001 2152 8769); Unidad Mixta Interdisciplinar de Comportamiento y Complejidad Social (UMICCS), UC3M-UV-UZ, Madrid, Spain (GRID:grid.11205.37); Universidad de Zaragoza, Department of Theoretical Physics, Faculty of Sciences, Zaragoza, Spain (GRID:grid.11205.37) (ISNI:0000 0001 2152 8769); ISI Foundation, Turin, Italy (GRID:grid.418750.f) (ISNI:0000 0004 1759 3658)