Simulated results from an agent-based model examining inequality and innovation in social networks

Theories of innovation often balance contrasting views that either smart people create smart things or smartly constructed institutions create smart things. While population models have shown factors including population size, connectivity, and agent behavior as crucial for innovation, few have taken the individual-central approach seriously by examining the role individuals play within their groups. To explore how network structures influence not only population-level innovation but also performance among individuals, we studied an agent-based model of the Potions Task, a paradigm developed to test how structure affects a group's ability to solve a difficult exploration task. We explore how size, connectivity, and rates of information sharing in a network influence innovation and how these have an impact on the emergence of inequality in terms of agent contributions. We find, in line with prior work, population size has a positive effect on innovation, but that large and small populati..., The data presented here were generated from an agent-based model of cultural innovation.
Each model is comprised of agents assembled as nodes on a network. The principle model dynamic is elaborated through pairs of agents (dyads) combining sets of items beginning from an initial inventory of six that each agent starts with. Each ideal network is unweighted, but several of the real-world networks (chimpanzee, baboon, and Agta hunter-gatherer) are weighted networks.
Items in each agent's inventory are initialized in an array containing two values: the name of the item and the item's score. In order to craft new items, three specific items must be combined between two agents. With the initial set of six items, there are two valid combinations which can be made: a combination of items a1, a2, and a3 or a combination of items b1, b2, and b3. These will form items 1a and 1b, respectively, which can be combined with items from the initial set in order to make further items. Agents select each ..., , # Innovation-Facilitating Networks Create Inequality

[https://doi.org/10.5061/dryad.hhmgqnknz](https://doi.org/10.5061/dryad.hhmgqnknz)

This is the repository for Moser & Smaldino (2023) \"Innovation-Facilitating Networks Create Inequality\". The manuscript is publicly available at [https://osf.io/preprints/socarxiv/n3hc6](https://osf.io/preprints/socarxiv/n3hc6).

This repository contains all data analyzed in the study. The Python code for generating the data with an agent-based model, the R script for analysis, and CSVs containing real-world edge lists are in a separate GitHub repository, described below.

**For assistance, please contact the corresponding author: Cody Moser ([[e-mail]](mailto:[e-mail]))**
Recommended citation: Moser, C., & Smaldino, P.E (In press). Innovation-Facilitating Networks Create Inequality. *Proceedings of the Royal Society B: Biological Sciences.*

## Description of the Data

The data are described in terms of what the datase...