# Decentralized core-periphery structure in social networks accelerates cultural innovation in agent-based model

## TL;DR

This study demonstrates that decentralized core-periphery social network structures significantly speed up cultural innovation in agent-based models by facilitating efficient problem-solving and discovery of high-value innovations.

## Contribution

The paper introduces a novel analysis of decentralized core-periphery networks' impact on innovation speed and quality, supported by spectral embedding methods and stochastic block models.

## Key findings

- Decentralized core-periphery networks outperform others in innovation speed.
- Spectral embedding (ASE) effectively captures core-periphery structure.
- Decentralized structures shield peripheral nodes from local optima.

## Abstract

Previous investigations into creative and innovation networks have suggested that innovations often occurs at the boundary between the network's core and periphery. In this work, we investigate the effect of global core-periphery network structure on the speed and quality of cultural innovation. Drawing on differing notions of core-periphery structure from [arXiv:1808.07801] and [doi:10.1016/S0378-8733(99)00019-2], we distinguish decentralized core-periphery, centralized core-periphery, and affinity network structure. We generate networks of these three classes from stochastic block models (SBMs), and use them to run an agent-based model (ABM) of collective cultural innovation, in which agents can only directly interact with their network neighbors. In order to discover the highest-scoring innovation, agents must discover and combine the highest innovations from two completely parallel technology trees. We find that decentralized core-periphery networks outperform the others by finding the final crossover innovation more quickly on average. We hypothesize that decentralized core-periphery network structure accelerates collective problem-solving by shielding peripheral nodes from the local optima known by the core community at any given time. We then build upon the "Two Truths" hypothesis regarding community structure in spectral graph embeddings, first articulated in [arXiv:1808.07801], which suggests that the adjacency spectral embedding (ASE) captures core-periphery structure, while the Laplacian spectral embedding (LSE) captures affinity. We find that, for core-periphery networks, ASE-based resampling best recreates networks with similar performance on the innovation SBM, compared to LSE-based resampling. Since the Two Truths hypothesis suggests that ASE captures core-periphery structure, this result further supports our hypothesis.