# Accelerating Hybrid Agent-Based Models and Fuzzy Cognitive Maps: How to Combine Agents who Think Alike?

arXiv: https://arxiv.org/abs/2409.00824 · HTML: https://mlsanity.com/paper/2409.00824/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Metrics for Graph Comparison: A Practitioner's Guide](https://mlsanity.com/paper/1904.07414/) (2019)
- [A General Optimization Technique for High Quality Community Detection in Complex Networks](https://mlsanity.com/paper/1308.3508/) (2013)
- [Hierarchical Graph Clustering using Node Pair Sampling](https://mlsanity.com/paper/1806.01664/) (2018)
- [A Survey on Agent-based Simulation using Hardware Accelerators](https://mlsanity.com/paper/1807.01014/) (2018)
- [Adaptive parallelization of multi-agent simulations with localized dynamics](https://mlsanity.com/paper/2304.01724/) (2023)

## Abstract (arXiv)

While Agent-Based Models can create detailed artificial societies based on individual differences and local context, they can be computationally intensive. Modelers may offset these costs through a parsimonious use of the model, for example by using smaller population sizes (which limits analyses in sub-populations), running fewer what-if scenarios, or accepting more uncertainty by performing fewer simulations. Alternatively, researchers may accelerate simulations via hardware solutions (e.g., GPU parallelism) or approximation approaches that operate a tradeoff between accuracy and compute time. In this paper, we present an approximation that combines agents who `think alike', thus reducing the population size and the compute time. Our innovation relies on representing agent behaviors as networks of rules (Fuzzy Cognitive Maps) and empirically evaluating different measures of distance between these networks. Then, we form groups of think-alike agents via community detection and simplify them to a representative agent. Case studies show that our simplifications remain accuracy.