Agent based simulation of the evolution of society as an alternate maximization problem

TL;DR

This paper presents an agent-based simulation model for societal evolution using a joint alternate maximization algorithm, reducing computational complexity and providing empirical validation for the societal development framework.

Contribution

It introduces a novel simulation protocol for societal evolution with efficient optimization steps and offers a framework for empirical experiments supporting its validity.

Findings

Simulation protocol is computationally efficient.

Empirical results support the societal evolution model.

Framework enables scalable experiments on societal dynamics.

Abstract

Understanding the evolution of human society, as a complex adaptive system, is a task that has been looked upon from various angles. In this paper, we simulate an agent-based model with a high enough population tractably. To do this, we characterize an entity called \textit{society}, which helps us reduce the complexity of each step from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$. We propose a very realistic setting, where we design a joint alternate maximization step algorithm to maximize a certain \textit{fitness} function, which we believe simulates the way societies develop. Our key contributions include (i) proposing a novel protocol for simulating the evolution of a society with cheap, non-optimal joint alternate maximization steps (ii) providing a framework for carrying out experiments that adhere to this joint-optimization simulation framework (iii) carrying out experiments to show that it makes sense empirically (iv) providing an alternate justification for the use of \textit{society} in the simulations.