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.
Dynamic Agent-Based Modeling
Complexity economics utilizes dynamic agent-based models, where agents with heterogeneous characteristics make decisions based on various behavioral rules. These models can incorporate millions of agents, allowing for a richer representation of the economy compared to traditional methods that are limited by mathematical equations. For example, agents might follow simple heuristics or adapt their strategies based on feedback, creating complex outcomes that reflect real market behaviors. This approach contrasts with standard models that predominantly use a small number of assumptions and struggle to capture the diverse nature of economic participants.
Modeling and Uncertainty in Economic Predictions
Competing models in complexity economics aim to enhance the predictability of economic systems by accommodating various shocks and disruptions. The discussion highlights the limitations of traditional models in adapting to sudden changes, such as those illustrated during the COVID-19 pandemic. A case study looked at how a model was able to simulate the impacts of demand and supply shocks on different industries and estimate unemployment rates accurately. This ability to account for intricate interactions and generate specific predictions establishes complexity economics as a promising tool for economic analysis and policy-making.
Future Aspirations in Complexity Economics
The ultimate vision for complexity economics is to develop comprehensive models akin to advanced simulation tools, allowing for deeper insights into economic planning. This includes creating interconnected global economic models to analyze the effects of policy changes and corporate strategies in real-time. Such robust frameworks would enable companies and governments to simulate various scenarios, leading to informed decision-making that balances economic outcomes and sustainability goals. As these models evolve, they aspire to provide significant advancements in understanding how complex economic systems operate and respond over time.
Welcome to The Orthogonal Bet, an ongoing mini-series that explores the unconventional ideas and delightful patterns that shape our world. Hosted by Samuel Arbesman.
In this episode, Sam speaks with J. Doyne Farmer, a physicist, complexity scientist, and economist. Doyne is currently the Director of the Complexity Economics program at the Institute for New Economic Thinking at the Oxford Martin School and the Baillie Gifford Professor of Complex Systems Science at the Smith School of Enterprise and the Environment at the University of Oxford.
Sam wanted to explore Doyne’s intriguing history in complexity science, his new book, and the broader field of complexity economics. Together, they discuss the nature of simulation, complex systems, the world of finance and prediction, and even the differences between biological complexity and economic complexity. They also touch on Doyne’s experience building a small wearable computer in the 1970s that fit inside a shoe and was designed to beat the game of roulette.