Understanding Economic Relationships Through Simulation

Agent-Based Modeling

Agent-based modeling (ABM) is a core technique in computational economics. It represents economic actors – individuals, firms, or even entire industries – as autonomous ‘agents’ with their own rules and behaviors. These agents interact within a defined environment, creating emergent patterns at the system level.

Each agent's actions are governed by a set of predefined rules, which can be simple (e.g., maximizing profit) or complex (incorporating factors like risk aversion, social influence, or learning). The simulation then tracks the interactions and outcomes of these agents over time.

Game Theory & Dynamic Simulations

Game theory provides a mathematical framework for analyzing strategic interactions between economic agents. Computational models can implement game-theoretic scenarios, allowing researchers to explore how rational decision-making leads to outcomes like market equilibrium or cooperation.

Dynamic simulations extend this by incorporating time and feedback loops. Agents react to changes in the environment and adjust their behavior accordingly, creating a more realistic representation of evolving systems.

Iterated Prisoner's Dilemma:  P = (1 - δ) * q + δ * P_q

Network Analysis

Many economic phenomena, such as information diffusion or financial contagion, are best understood through network analysis. Computational models can represent economic actors and their relationships as a network – nodes representing agents and edges representing connections.

Analyzing the structure of these networks (e.g., degree centrality, clustering coefficient) reveals insights into how information flows, how shocks propagate, and how different parts of the system are interconnected.

Calibration & Validation

A crucial aspect of computational economic models is calibration – adjusting model parameters to match observed data. This often involves using statistical techniques like regression analysis to estimate the values that best fit the simulation results.

Validation then assesses how well the model replicates real-world outcomes, ensuring its reliability and usefulness for forecasting or policy evaluation.

R² = 1 - (SSR / SST)

Frequently asked questions

Try it live

Everything above runs in your browser — open Solow Growth Model and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.