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

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 society, which helps us reduce the complexity of each step from O(n2) to O(n). We propose a very realistic setting, where we design a joint alternate maximization step algorithm to maximize a certain 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 society in the simulations.