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

TL;DR

This paper introduces a method to accelerate hybrid agent-based models by grouping similar agents using Fuzzy Cognitive Maps and community detection, reducing computational costs while maintaining accuracy.

Contribution

It presents a novel approximation technique that combines agents with similar behaviors to simplify models and improve computational efficiency.

Findings

Significant reduction in simulation time with maintained accuracy.

Effective grouping of agents based on network similarity measures.

Validation through case studies demonstrating practical applicability.

Abstract

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.