#4 – Doyne Farmer: Chaos Theory & Complexity Economics
May 6, 2024
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J. Doyne Farmer, expert in Complexity Economics, discusses chaos theory's role in scaling theory, predicting the distant future, conflicts with Moore's Law, agent-based modeling, dominating with theories, and casino strategies. Explore the interplay of chaos theory, scaling laws, innovation impact on economic growth, and advancements in agent-based modeling for understanding human behavior and economic predictions.
Chaos theory can enhance scaling theory by explaining long-term predictions better than short-term ones.
Utilizing chaos theory's principles can lead to successful real-world applications like predicting roulette outcomes accurately.
Deep dives
The Journey to Complexity Economics
The podcast episode delves into Dohin Farmer's journey to becoming a renowned Complexity economist. Starting from his undergraduate days, inspired by science fiction stories, he developed an interest in chaos theory. This interest led him and his fellow graduate students to explore chaos and subsequently delve into self-organization and cybernetics. Farmer's path involved predicting chaotic time series, studying ice ages and sunspot cycles, and eventually venturing into applying complex systems ideas to economics.
Entrepreneurial Spirit and Casino Endeavor
Farmer shares an intriguing tale of utilizing chaos theory in beating the stock market with a wearable computer. He recounts a daring casino endeavor where they leveraged knowledge of roulette ball physics and computing power to predict outcomes. This venture involved intricate calculations and hardware challenges, yet yielded successful outcomes in outsmarting roulette wheels, demonstrating the power of understanding chaotic systems in real-world applications.
Predictability: Short-term vs Long-term
The discussion explores the limitations of chaos theory in short-term predictions due to sensitive initial conditions. While chaos may render precise prediction challenging, statistical predictions remain viable due to system attractors preserving some order. Farmer illustrates the predictability variance across different time horizons, showcasing how chaos impacts accurate forecasting. Emphasizing that even in chaotic systems like weather, statistical predictions maintain relevance despite specific day-to-day uncertainties.
Driving Innovation and Economic Growth
Highlighting the significance of technology in fueling economic progress, Farmer delves into Moore's law and Wright's law. He elucidates the exponential cost reduction in technologies like transistors and solar cells, contrasting with the relative stagnation in traditional sectors like mining. The interaction between production growth and cost reductions unfolds a dynamic interplay shaping technological advancement. The narrative underscores how advancements in technology, driven by intrinsic laws, propel innovation, economic growth, and the evolving landscape of industries.
J. Doyne Farmer is the Director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, a Professor at the Mathematical Institute of Oxford University, and an External Professor at the Santa Fe Institute.
In this episode, we explore Doyne’s latest book, “Making Sense of Chaos.” We focus on the relationship between chaos and scaling theory, and more specifically, how chaos can be factored into scaling theory. By the end of this conversation, you will learn why it might be easier to predict the long distant future than predicting tomorrow, how Moore’s Law conflicts with other scaling laws that underpin technological progress, how agent-based modeling can help all scientists and policymakers, how to dominate the world with your theories (...), and even how to trick casinos. I hope you enjoy the conversation.
Find me on X at @ProfSchrepel. Also, be sure to subscribe to the Scaling Theory podcast; it helps its growth.
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