The Distribution of Power in Exchange Networks: Theory and Experimental Results

TLDR

The study contrasts two traditions for assessing power in exchange networks: point centrality, which is scalable, and power‑dependence theory, which is more complex for large networks. The paper aims to analyze how network structure determines power, integrating theoretical insights with experimental and simulation evidence. The authors use a theoretical framework, laboratory experiments with five‑person networks, and computer simulations of bargaining to examine power distribution and propose a hybrid model of centrality and power‑dependence. Power‑dependence theory accurately predicts power distributions in both experimental and simulated networks, whereas centrality measures fail to do so and have limited generality.

Abstract

This paper presents a theoretical analysis of the structural determinants of power in exchange networks, along with research findings from laboratory experiments and a computer simulation of bargaining in network structures. Two theoretical traditions are dealth with: (1) point centrality in graph-theoretic representations of structure, as an approach to power distributions; and (2) power dependence principles applied to exchange networks. Measures of centrality available in the literature have the advantage of being easily applied to large and complex networks. In contrast, power dependence concepts were conceived for use in microsociology and are found to be cumbersome in the analysis of complex networks. But despite the relative difficulty of applying power-dependence theory to network structures, that approach generates hypotheses about power distributions which are confirmed at nearly every point in a laboratory experiment with five-person networks and at every point in a computer simulation of networks too large for laboratory study. In contrast, centrality measures applied to the type of networks studied fail to predict power distributions. Although centrality measures might predict power in some networks, their generality is limited. Toward resolution of the issues raised, this study offers two theoretical points: (1) a distinction between two different principles of "connection" in social networks suggests that current measures of centrality might predict power in one type of network but not in the other; and (2) it offers a first step toward a fusion of power-dependence theory and structural centrality in a way which might be general across networks of both types.