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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, wesimulate an agent-based model with a high enough population tractably. To dothis, we characterize an entity called \textit{society}, which helps us reducethe complexity of each step from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$. Wepropose a very realistic setting, where we design a joint alternatemaximization step algorithm to maximize a certain \textit{fitness} function,which we believe simulates the way societies develop. Our key contributionsinclude (i) proposing a novel protocol for simulating the evolution of asociety with cheap, non-optimal joint alternate maximization steps (ii)providing a framework for carrying out experiments that adhere to thisjoint-optimization simulation framework (iii) carrying out experiments to showthat it makes sense empirically (iv) providing an alternate justification forthe use of \textit{society} in the simulations.

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