What are adaptive agents?

Adaptive agents are autonomous decision-making entities—individuals, organizations, or algorithms—that modify their behavior based on interactions with their environment and other agents. Unlike rational actors in classical economics, adaptive agents learn through trial and error, operate with limited information, and continuously adjust their strategies based on feedback.

How do adaptive agents relate to complexity economics?

Adaptive agents are the foundation of complexity economics. When many adaptive agents interact, they generate emergent behaviors—market bubbles, crashes, and trends—that cannot be predicted from individual actions alone. This is why complexity economics rejects equilibrium models in favor of understanding economies as living, evolving systems.

What is agent-based modeling?

Agent-based modeling is a computational technique that simulates the behavior of adaptive agents to understand how macro-level patterns emerge from micro-level interactions. By programming individual agents with simple rules and letting them interact, researchers can observe emergent phenomena like market dynamics, traffic patterns, or disease spread that are impossible to predict analytically.

How do adaptive agents differ from rational agents in traditional economics?

Traditional economics assumes rational agents with perfect information who optimize their decisions. Adaptive agents, by contrast, have bounded rationality—they make decisions with incomplete information, learn from mistakes, follow heuristics, and are influenced by what others do. This more realistic view explains phenomena like herding behavior, market bubbles, and the unpredictable nature of economic systems.

INFORMS Analytics Magazine Series

Complexity Economics

A five-part exploration of how complexity economics provides a new framework for understanding economic systems and recovery after major disruptions.

5 Articles

•

2020–2021

•

INFORMS

Series Overview

Written during and after the COVID-19 pandemic, this series examines why traditional equilibrium-based economic models fail to predict or explain real-world economic behavior during periods of significant disruption. Drawing on complexity science, it presents an alternative framework that treats economies as adaptive systems characterized by feedback loops, emergent behavior, and nonlinear dynamics.

Key Themes

Equilibrium vs. nonequilibrium economics, adaptive systems, feedback mechanisms, emergent behavior, network effects, path dependence, and practical applications for economic recovery planning.

Intended Audience

Business leaders, economists, policymakers, analytics professionals, and anyone interested in understanding why economies behave unpredictably and how to navigate uncertainty.

Publication

Published in INFORMS Analytics Magazine from April 2020 through February 2021, spanning the initial pandemic period through early recovery discussions.

Articles in This Series

The Situation Following a Pandemic

Introduces the fundamental question: why do traditional economic models fail during crises? Examines how pandemics and other major disruptions expose the limitations of equilibrium-based economic thinking.

April 2020

Equilibrium vs. Nonequilibrium Views of Recovery

Contrasts the assumptions of traditional equilibrium economics with the nonequilibrium perspective of complexity economics. Explains why the economy may not return to its pre-crisis state and what this means for recovery planning.

May 2020

4 Primary Concepts of Complexity Economics

Introduces the foundational concepts that distinguish complexity economics: adaptive agents, emergent behavior, feedback loops, and networks. Provides concrete examples of how these concepts manifest in real economic systems.

June 2020

8 More Concepts of Complexity Economics

Expands the conceptual framework with additional principles: path dependence, tipping points, power laws, self-organization, nonlinearity, bounded rationality, heterogeneous agents, and computation. These concepts complete the theoretical foundation.

January 2021

Applying Complexity Economics Lessons To Recovery

Synthesizes the series by applying complexity economics concepts to practical recovery scenarios. Offers actionable frameworks for business leaders and policymakers navigating post-crisis economic landscapes.

February 2021

Core Concepts Explored

Adaptive Agents

Economic actors who learn, adapt, and change their strategies based on experience and changing conditions.

Emergent Behavior

Complex patterns and phenomena that arise from simple interactions between individual agents at the system level.

Feedback Loops

Self-reinforcing or self-correcting mechanisms that amplify or dampen economic trends over time.

Path Dependence

How historical events and decisions constrain future possibilities, making economic trajectories hard to reverse.

Network Effects

How interconnections between economic actors create cascading effects and systemic vulnerabilities.

Nonlinearity

Why proportional thinking fails: small causes can have large effects, and large causes can have small effects.

Self-Organization

How order and structure emerge spontaneously from decentralized interactions without central coordination.

Tipping Points

Critical thresholds in complex systems where small changes trigger large, often irreversible shifts in system behavior.

Published in INFORMS Analytics Magazine

INFORMS (Institute for Operations Research and the Management Sciences) is the leading international association for professionals in operations research, analytics, and management science. Analytics Magazine reaches practitioners and academics working at the intersection of data science and decision-making.

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About Joseph Byrum

Joseph Byrum is an accomplished executive leader, innovator, and cross-domain strategist with a proven track record of success across multiple industries. With a diverse background spanning biotech, finance, and data science, he has earned over 50 patents that have collectively generated more than US $1 billion in revenue.