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The Orthogonal Bet: Complex economics is applying complex systems methods
Oct 9, 2024
J. Doyne Farmer, a physicist and complexity scientist, dives into the intriguing world of complexity economics. He discusses how traditional and complexity economics differ, emphasizing emergent behaviors and simulation methods. Remarkably, Doyne shares a story about building a wearable computer in the 70s to predict roulette outcomes, bridging the gap between gambling and finance. The conversation also touches on predicting economic shocks, generational shifts in economic thought, and the vital role of interdisciplinary collaboration in understanding complex systems.
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Quick takeaways
- Complexity economics recognizes that economies behave like complex systems, where the interactions between agents lead to unpredictable emergent properties.
- Agent-based models in complexity economics provide a richer representation of economic behaviors, enhancing predictability and informing policy-making during disruptions.
Deep dives
Understanding Complexity Economics
Complexity economics is defined as the application of complex systems methods to economic analysis. This field acknowledges that economies, like complex systems in nature, have emergent properties that cannot be predicted solely by evaluating individual components. For instance, just as a human brain emerges from the interactions of billions of neurons, economic phenomena arise from the intricate interactions among agents such as consumers and firms. Unlike traditional economics, which often relies on rigid models assuming rational expectations and equilibrium, complexity economics embraces dynamic decision-making processes that reflect the unpredictable nature of real-world economies.
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