The Origin of Wealth

1. The economy is a complex adaptive system, not an equilibrium system

The economy is not a closed equilibrium system; it is an open disequilibrium system and, more specifically, a complex adaptive system.

Complex adaptive systems are characterized by:

• Many interacting agents
• Non-linear relationships
• Feedback loops
• Emergence of macro patterns from micro behaviors
• Adaptation and evolution over time

The economy exhibits all these characteristics. It is constantly changing, with businesses and consumers adapting their behaviors based on new information and interactions. This dynamic nature makes the economy inherently unpredictable and resistant to equilibrium. Understanding the economy as a complex adaptive system helps explain phenomena like business cycles, market crashes, and technological revolutions that are difficult to account for in traditional equilibrium models.

2. Traditional economics relies on unrealistic assumptions and fails empirical tests

Traditional Economics has what computer programmers call a "garbage in, garbage out" problem.

Key flaws in traditional economics:

• Assumes perfect rationality of economic agents
• Ignores real-world frictions and delays
• Treats important factors as exogenous
• Focuses on equilibrium states

These assumptions lead to models that fail to match reality. For example:

• The "law of one price" is routinely violated in real markets
• Stock prices do not follow the predicted random walk
• Business cycles exhibit more volatility than models predict

Empirical evidence consistently contradicts core predictions of traditional economic theory. This suggests the need for new approaches that can better capture the complexities of real-world economic systems and human behavior.

3. Human decision-making is based on inductive reasoning, not perfect rationality

Humans are intelligent in their decision making, but intelligent in ways very different from the picture presented by Traditional Economics.

Key aspects of human decision-making:

• Pattern recognition and completion
• Use of heuristics and rules of thumb
• Learning and adaptation over time
• Bounded rationality and satisficing

People make economic decisions using inductive reasoning based on past experiences and observed patterns. They use mental shortcuts and approximations rather than complex calculations. This approach is well-suited for navigating the complex, ambiguous environments of the real world, but it can also lead to systematic biases and errors.

Behavioral economics research has revealed many ways in which actual human behavior deviates from the assumptions of perfect rationality, including:

• Loss aversion
• Framing effects
• Availability bias
• Overconfidence

Understanding these cognitive tendencies is crucial for developing more accurate models of economic behavior and designing effective policies and institutions.

4. Networks play a crucial role in economic systems and outcomes

Networks are an essential ingredient in any complex adaptive system. Without interactions between agents, there can be no complexity.

Key network effects in economics:

• Information and resource flows
• Contagion of ideas and behaviors
• Power law distributions
• Small world phenomena

The structure of economic networks influences outcomes in profound ways. For example:

• Innovation spreads through networks of researchers and businesses
• Financial contagion can propagate shocks through interconnected banks
• A few highly connected hubs often dominate industries (e.g. Amazon, Google)

Network theory provides tools for analyzing these interconnections and their consequences. Understanding network structures can help explain phenomena like industry concentration, the diffusion of technologies, and the resilience or fragility of economic systems.

5. Emergent patterns arise from micro-level behaviors in the economy

Macroeconomic patterns as emergent phenomena, that is, characteristics of the system as a whole that arise endogenously out of interactions of agents and their environment.

Key emergent phenomena in economics:

• Business cycles
• Price dynamics
• Income distributions
• Industrial organization

These macro-level patterns cannot be reduced to or predicted from the behavior of individual economic agents. Instead, they emerge from the complex interactions between many agents over time. For example:

• Boom-bust cycles can emerge from the collective behavior of businesses and investors, even without external shocks
• Power law distributions of firm sizes and wealth arise from competitive dynamics
• Market structures evolve as businesses adapt their strategies in response to each other

Studying these emergent phenomena requires new methodological approaches, such as agent-based modeling and network analysis, that can capture the dynamic, non-linear nature of economic systems.

6. Evolution is the algorithm that creates economic growth and innovation

Evolution creates designs, or more appropriately, discovers designs, through a process of trial and error.

Key aspects of economic evolution:

• Variation: New ideas and strategies constantly emerge
• Selection: Market forces determine which innovations succeed
• Replication: Successful strategies are copied and spread
• Adaptation: Businesses and consumers learn and improve over time

This evolutionary process drives economic growth and innovation. It explains how complex economic structures and technologies can arise without central planning or perfect foresight. The process is inherently unpredictable and open-ended, constantly generating novelty and increasing complexity.

Examples of economic evolution in action:

• Businesses experimenting with new products and services
• Investors trying different investment strategies
• Policymakers testing various regulatory approaches
• Consumers adopting (or rejecting) new technologies

Understanding the economy as an evolutionary system helps explain phenomena like technological progress, changing industry structures, and long-term economic growth.

7. Physical and social technologies coevolve to drive economic progress

The agricultural, industrial, and information revolutions can each be viewed as coevolutionary merry-go-rounds of advances in PTs leading to new forms of STs, which in turn were crucial for further advances in PTs, and so on.

Key types of technologies:

• Physical technologies: Methods for transforming matter, energy, and information
• Social technologies: Methods for organizing people and institutions

These technologies evolve together, with advances in one enabling progress in the other. For example:

• The invention of agriculture (PT) enabled larger settled communities (ST)
• The development of banking systems (ST) facilitated industrial investment (PT)
• Computer networks (PT) enabled new organizational forms like virtual teams (ST)

This coevolution explains the accelerating pace of economic and technological change over human history. Each breakthrough opens up new possibilities in both the physical and social realms, creating a positive feedback loop of innovation and progress.

8. Businesses evolve through variation, selection, and replication of strategies

Just as the churning of the evolutionary algorithm through the Library of All Prisoner's Dilemma Strategies created patterns of innovation, growth, and creative destruction, so too does the churning of evolution through the Library of Smith lead to these patterns in the real economy.

Key aspects of business evolution:

• Businesses constantly experiment with new strategies
• Market forces select successful approaches
• Winning strategies are copied and spread
• Industries undergo cycles of growth, maturation, and disruption

This evolutionary process explains patterns observed in real economies:

• The rise and fall of dominant firms and industries
• Waves of innovation and creative destruction
• The gradual improvement of business practices over time
• The emergence of new organizational forms and business models

Understanding business as an evolutionary process highlights the importance of experimentation, adaptation, and learning in economic success. It also suggests limits to how much the future of industries and economies can be predicted or controlled.

Last updated: January 25, 2025