Agent‐Based Simulation Overview

Agent-based simulation (ABS) is a computational modeling paradigm in which a system is represented as a collection of autonomous, interacting entities known as agents. Rather than describing system-level behavior through aggregate equations, ABS models the local rules governing each individual agent and allows macro-level phenomena to emerge organically from their interactions. Each agent possesses a set of attributes — such as state variables, resource levels, or behavioral parameters — and a set of decision rules that govern how it perceives its environment and responds to stimuli from neighboring agents or the broader system. Agents are typically heterogeneous, meaning they may differ in their attributes and rules, and they act autonomously, meaning no central authority dictates their behavior. The simulation advances in discrete time steps, during which every agent evaluates its current state, applies its rules, and updates accordingly. A key strength of an ABS is its ability to capture emergent behavior: system-wide patterns that arise from local interactions and cannot be predicted from the properties of any single agent in isolation. Classic examples include the formation of traffic jams from individual driving decisions, the segregation of neighborhoods from individual preferences, and the collapse of supply chains from localized inventory decisions. Agent-based simulation contrasts with two dominant alternatives in computational social science. Equation-based models like system dynamics or differential equation models, describe aggregate stocks and flows but obscure individual heterogeneity and discrete interactions. Game-theoretic models assume rational, optimizing agents with perfect information, which limits their realism in settings where agents have bounded rationality and incomplete knowledge. ABS relaxes both assumptions, allowing agents to follow simple heuristics, learn from experience, and operate under genuine uncertainty. In the context of this thesis, ABS provides the natural framework for modeling a decentralized labor-time economy. The economy is composed of heterogeneous agents: workers, firms, and distributors, who coordinate through local feedback signals rather than a central planner. The macro-level properties of interest, including price stability, purchasing power equity, and the distribution of productivity gains, are emergent outcomes of these micro-level interactions. ABS therefore allows us to study whether a viable, equitable labor-time economy can arise from decentralized organization, without assuming away the coordination challenges that such a system must overcome in practice.