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cover of episode Can We Predict The Unpredictable? with J. Doyne Farmer

Can We Predict The Unpredictable? with J. Doyne Farmer

2024/11/14
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Doyne Farmer
旁白
知名游戏《文明VII》的开场动画预告片旁白。
Topics
旁白: 本节目探讨了利用数据预测经济的可能性,以及复杂性经济学在经济预测和政策制定中的应用。 传统经济学方法存在局限性,难以预测像股市波动这样复杂的事件。复杂性经济学则提供了一种新的方法,利用基于主体建模等技术,模拟经济体的运行,并预测其未来走势。 复杂性经济学考虑了现实世界中个体行为的局限性和不确定性,更接近于现实。 该理论可以应用于预测经济政策的影响,例如税收变化对贫富差距的影响,以及如何逆转通货膨胀等。 J. Doyne Farmer: 我在轮盘赌中利用物理学原理成功预测了小球的落点,这启发了我将复杂系统科学应用于经济预测。 我们成立了Prediction Company,利用复杂系统模型在股票市场进行交易,获得了比标准投资更高的风险回报率。 复杂性经济学与传统经济学的主要区别在于其建模方法。传统经济学假设个体行为理性且信息完全,而复杂性经济学则考虑个体行为的局限性和现实世界中的不确定性。 基于主体建模模拟了人们在复杂情境中使用的“足够好”的策略,其结果更接近现实。 我们建立的模型成功预测了英国疫情期间GDP的下降幅度,以及住房市场的运行情况。 我们正在开发一个因果关系的经济模型,并将其开源,以促进其应用于公共利益。 我们希望为经济决策提供类似于谷歌地图的工具,帮助企业做出更明智的决策,并促进能源转型。 我们需要考虑模型的伦理影响,例如可能加剧经济不平等,并通过政策干预来缓解。 复杂性经济学将经历类似于混沌理论和复杂系统理论的转变过程,并产生巨大的实际影响。

Deep Dive

Key Insights

What is complexity economics and how does it differ from traditional economics?

Complexity economics uses advanced computers and simulations to model the economy with millions of agents, each making decisions based on real-world constraints. Unlike traditional economics, which assumes rational agents and equilibrium, complexity economics incorporates bounded rationality and the messiness of real-world decision-making.

Why did J. Doyne Farmer start studying complex systems?

Farmer began studying complex systems as a physicist, initially focusing on predicting the seemingly random motion of a roulette ball. This project, which involved building wearable computers to predict outcomes, planted the seed for his later work in complexity economics.

How did Farmer's Prediction Company perform in the stock market?

Prediction Company, which used complex systems models to trade, had a return-to-risk ratio six times better than standard market bets. The company made significant profits for UBS and lasted 28 years, with profits in 27 of those years.

What is agent-based modeling in complexity economics?

Agent-based modeling involves creating simulations where individual agents (e.g., households, businesses) make decisions based on heuristics and real-world constraints. These models capture the heterogeneity and interactivity of real-world agents better than traditional economic models.

What are some real-world applications of complexity economics?

Complexity economics has been used to model housing markets, predict the economic impact of the COVID-19 pandemic, and guide national banks in decision-making. For example, an agent-based model of housing markets is now used by several central banks.

What is Farmer's vision for the future of complexity economics?

Farmer envisions complexity economics becoming as influential as Google Maps in guiding economic decision-making. He aims to create tools that businesses and governments can use to predict outcomes and make more informed, sustainable decisions.

How does complexity economics address inequality and economic stability?

Complexity economics models show that inequality can naturally arise in a laissez-faire economy due to random luck. The field aims to guide policies that reduce inequality and smooth out business cycles, potentially leading to smaller recessions and more stable economies.

What role does technology play in complexity economics?

Technological innovation drives economic growth, and complexity economics helps predict the rate of improvement in technologies like solar panels and electronic circuits. It also models how industries, such as energy, evolve over time based on investment and policy changes.

What does Farmer predict for the energy transition due to climate change?

Farmer predicts the energy transition will happen faster than expected, driven by exponential improvements in solar, wind, and storage technologies. He believes the transition will be mostly complete within 20 years, with solar and wind dominating and fossil fuels becoming less competitive.

Why is Farmer optimistic about the adoption of complexity economics?

Farmer believes complexity economics will gain widespread adoption as success stories emerge and models prove their effectiveness. He compares its potential impact to previous scientific revolutions like chaos theory, which went from being unconventional to widely accepted.

Chapters
This chapter explores the concept of predicting the future using data, particularly in the context of economics. It questions whether chaotic systems, like the stock market, can be predicted and introduces J. Doyne Farmer, a leading expert in complex systems.
  • The possibility of using data to predict the future, even chaotic events like human actions is explored.
  • The application of this idea to economics is discussed, specifically in relation to forecasting the stock market and the potential impact on policymaking.

Shownotes Transcript

What if we could predict the economy the way we predict the weather? What if governments could run simulations to forecast the effects of new policies—before they happen? And what if the key to all of this lies in the same chaotic systems that explain spinning roulette wheels and rolling dice?

J. Doyne Farmer is a University of Oxford professor, complexity scientist, and former physicist who once beat Las Vegas casinos using his scientific-based methods. In his recent book “Making Sense of Chaos: A Better Economics for a Better World” Farmer is using those same principles to build a new branch of economics called complexity economics—one that uses big data to help forecast market crashes, design better policies and find ways to confront climate change.

But can we really predict the unpredictable? And how will using chaos theory shake up well-established economic approaches?