Background
Agent-based modelling (ABM) refers to the use of computational and mathematical techniques to simulate the complex interactions and decisions of individual agents within an economic environment. Agents typically represent entities such as consumers and firms, who aim to maximize utility and profit respectively.
Historical Context
The origins of agent-based modelling can be traced back to the early developments in complex systems theory and computer science. Pioneering work in the 1980s and 1990s, particularly within evolutionary biology and computer programming, laid the groundwork. Its adoption in economics gained momentum through interdisciplinary contributions from behavioral economics and theoretical computer science.
Definitions and Concepts
- Agent: An entity in the model, such as a consumer or firm, which makes independent decisions.
- Utility: A measure of satisfaction or benefit derived by consumers from consuming goods or services.
- Profit: The financial gain realized by firms from their business activities after accounting for costs.
In ABM, each agent’s decisions impact the system, which iteratively evolves over time based on these interactions.
Major Analytical Frameworks
Classical Economics
Classical economics revolves around the ideas of free markets, the invisible hand, and natural tendencies towards equilibrium. While ABM itself does not directly stem from classical economics, its principles may be applied within individual-agent frameworks to explore market behaviors.
Neoclassical Economics
Neoclassical economics, focused on optimization and equilibrium, utilizes ABM to simulate how agents maximize utility and profit while balancing supply and demand. ABM helps extend neoclassical models by accommodating agent heterogeneity and interaction dynamics, challenging the notion of static equilibrium.
Keynesian Economics
Keynesian economics stresses aggregate demand and government intervention. ABM allows the modeling of complex agent interactions that can disrupt the straight paths to market equilibrium often assumed in Keynesian models. Simulations may demonstrate how individual stress or confidence impacts macroeconomic variables.
Marxian Economics
Marxian goals, driven by class struggle, capital accumulation, and social revolt, can be analyzed through ABM by representing different social classes as agents. This allows for examining capital-labor relationships in evolving economic systems.
Institutional Economics
Focusing on the pivotal role of institutions, rules, and norms, ABM can be used to simulate environments where institutional change and agent behavior significantly impact economic outcomes. ABM showcases how these institutions evolve and affect agent interaction.
Behavioral Economics
Incorporating ideas from psychology, ABM is particularly effective in behavioral economics for modeling realistic, boundedly rational behavior and cognitive biases. It provides a platform to explore how irrational decision-making aggregates into macro patterns.
Post-Keynesian Economics
ABM supports Post-Keynesian ideas highlighting non-equilibrium states and path dependency in economic systems. It excels in modeling economic dynamics where inherent discontinuities and historical dependencies dominate agent behavior.
Austrian Economics
Austrian perspectives valorize individualism, market processes, and dissipation of centralized information. ABM agrees with these views by providing decentralized agent-driven dynamics instead of top-down optimization strategies.
Development Economics
ABM assists in the examination of complex interactions in developing economies, from grassroots-level agent activities to systemic policy impacts. Agents may represent varied societal stakeholders whose interactions foster insights into development processes.
Monetarism
Monetarist theories, emphasizing the control of money supply to manage economies, can benefit from ABM to simulate the small-scale interactions of monetary policies and manage the feedback loops influencing inflation and demand.
Comparative Analysis
Agent-based models provide meaningful dichotomies contrasting deterministic, equilibrium-focused traditional economic models by adding nuanced features of dynamism, heterogeneity, and realism. They offer a multifaceted matt to test theoretical predictions against non-linear practicalities shaping economies.
Case Studies
Numerous case studies in market behavior, urban planning, financial networks, and epidemiological impacts underscore ABM’s utility in empirical research. Simulating microcredit schemes in rural economies, analyzing stock market trends through trader behavior, and public good provision scenarios are illustrative instances.
Suggested Books for Further Studies
- “Growing Artificial Societies: Social Science from the Bottom Up” by Joshua M. Epstein and Robert Axtell.
- “Agent-Based and Individual-Based Modeling: A Practical Introduction” by Steven F. Railsback and Volker Grimm.
- “Simulation for the Social Scientist” by Nigel Gilbert and Klaus Troitzsch.
- “An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo” by Uri Wilensky and William Rand.
Related Terms with Definitions
- Game Theory: The study of mathematical models of strategic interaction among rational decision-makers.
- Computational Economics: The application of computational methods to economic problems.
- System Dynamics Modelling: A method to
