Digest

This podcast features a conversation with Doan Farmer, a physicist and complexity economist, discussing his book "Making Sense of Chaos: A Better Economics for a Better World." The episode delves into the concept of complexity economics, which applies complex systems science and methods to economics, offering a more realistic and quantitative approach to modeling economic phenomena. Farmer contrasts complexity economics with mainstream economics, criticizing the latter's reliance on utility maximization and its limitations in dealing with real-world complexities. He argues that complexity economics, with its focus on agent-based modeling and simulations, can better capture the nuances of human behavior and provide more accurate predictions. The discussion explores the application of agent-based modeling to the housing market, highlighting how this approach can account for individual preferences and behaviors, including irrational ones, which are often ignored in aggregate demand models. Farmer emphasizes the importance of understanding how prices are set through aspiration level adaptation and how lending policies can significantly influence housing bubbles. The episode further examines the limitations of mainstream economic models in predicting events like the 2008 housing crisis, acknowledging that while economists may understand the factors involved, their models often lack the necessary detail and complexity to capture the dynamics of real-world situations. This leads to inaccurate predictions and a lack of understanding of the transition process between different economic states. The conversation explores the concept of disequilibrium in economic models, arguing that complexity economics, with its focus on dynamic simulations, can better capture the path of economic change, rather than simply predicting an eventual equilibrium. The importance of incorporating learning and constraints into models to account for how individuals adapt to the system's limitations is emphasized. The discussion highlights the importance of incorporating heterogeneity and detailed data into economic models, arguing that complexity economics allows for the inclusion of factors like age, income, race, and geography, which are often simplified or ignored in mainstream models. The need for models that can capture the nuances of individual behavior and their impact on aggregate outcomes is emphasized. Farmer clarifies that complexity economics is not a challenge to econometrics but rather an alternative to theoretical models that rely on utility maximization. He argues that complexity models are essential for counterfactual analysis, allowing economists to explore the potential consequences of policy changes in situations that may not have historical precedents. The conversation addresses the slow adoption of complexity economics within the mainstream profession, attributing this to the field's relative youth, limited number of models, and the inherent resistance to new ideas within a closed profession. The need for more real-world applications and evidence to demonstrate the effectiveness of complexity models is highlighted. Farmer outlines his strategy for promoting the adoption of complexity economics, emphasizing the importance of focusing on commercial applications and central banks, where the need for accurate predictions is paramount. He believes that success in these areas will eventually lead to greater acceptance within academia. The episode concludes with a discussion of the anthropological aspects of economics and the need for dialogue between different schools of thought. Farmer emphasizes the importance of understanding and engaging with mainstream economics, even while advocating for complexity economics. He acknowledges the challenges of gaining acceptance within a closed profession but remains optimistic about the future of complexity economics.

Outlines

00:00:00

Introduction to Complexity Economics

The episode introduces Doan Farmer, a physicist and complexity economist, and his book "Making Sense of Chaos: A Better Economics for a Better World." The conversation begins by exploring the concept of complexity economics, which involves applying complex systems science and methods to economics. Farmer argues that this approach offers a more realistic and quantitative way to model economic phenomena, particularly in complex situations.

00:01:11

Complexity Economics vs. Mainstream Economics

Farmer contrasts complexity economics with mainstream economics, highlighting the differences in their approaches to modeling human behavior. He criticizes the mainstream's reliance on utility maximization and its limitations in dealing with real-world complexities. He argues that complexity economics, with its focus on agent-based modeling and simulations, can better capture the nuances of human behavior and provide more accurate predictions.

00:13:30

Agent-Based Modeling and the Housing Market

The discussion delves into the application of agent-based modeling to the housing market. Farmer explains how this approach can account for individual preferences and behaviors, including irrational ones, which are often ignored in aggregate demand models. He emphasizes the importance of understanding how prices are set through aspiration level adaptation and how lending policies can significantly influence housing bubbles.

00:21:15

Limitations of Mainstream Economic Models

Farmer and Roberts discuss the limitations of mainstream economic models in predicting events like the 2008 housing crisis. They acknowledge that while economists may understand the factors involved, their models often lack the necessary detail and complexity to capture the dynamics of real-world situations. This leads to inaccurate predictions and a lack of understanding of the transition process between different economic states.

00:23:50

Disequilibrium and Economic Change

The conversation explores the concept of disequilibrium in economic models. Farmer argues that complexity economics, with its focus on dynamic simulations, can better capture the path of economic change, rather than simply predicting an eventual equilibrium. He emphasizes the importance of incorporating learning and constraints into models to account for how individuals adapt to the system's limitations.

00:28:02

The Role of Data and Heterogeneity

The discussion highlights the importance of incorporating heterogeneity and detailed data into economic models. Farmer argues that complexity economics allows for the inclusion of factors like age, income, race, and geography, which are often simplified or ignored in mainstream models. He emphasizes the need for models that can capture the nuances of individual behavior and their impact on aggregate outcomes.

00:30:38

Complexity Economics and Counterfactual Analysis

Farmer clarifies that complexity economics is not a challenge to econometrics but rather an alternative to theoretical models that rely on utility maximization. He argues that complexity models are essential for counterfactual analysis, allowing economists to explore the potential consequences of policy changes in situations that may not have historical precedents.

Keywords

Q&A

• What is complexity economics and how does it differ from mainstream economics?

  Complexity economics applies complex systems science to economics, focusing on agent-based modeling and simulations to capture the nuances of human behavior and predict economic outcomes, particularly in complex situations. It contrasts with mainstream economics, which often relies on utility maximization and simplified models that may not accurately reflect real-world complexities.

• How does agent-based modeling improve our understanding of the housing market?

  Agent-based modeling allows economists to simulate the behavior of individual buyers and sellers, incorporating factors like aspiration level adaptation and lending policies, which are often ignored in aggregate demand models. This approach provides a more realistic and detailed understanding of how housing prices are set and how bubbles can form.

• Why are mainstream economic models often criticized for their inability to predict economic crises?

  Mainstream models often lack the necessary detail and complexity to capture the dynamics of real-world situations, leading to inaccurate predictions. They often abstract from individual behaviors and fail to account for the transition process between different economic states, making it difficult to anticipate and understand crises.

• What are the potential benefits of incorporating heterogeneity and detailed data into economic models?

  Incorporating heterogeneity and detailed data allows economists to capture the nuances of individual behavior and their impact on aggregate outcomes. This can lead to more accurate predictions and a better understanding of how factors like age, income, race, and geography influence economic trends.

• How can complexity economics contribute to counterfactual analysis and policy evaluation?

  Complexity models, with their focus on causal relationships and simulations, are essential for counterfactual analysis. They allow economists to explore the potential consequences of policy changes in situations that may not have historical precedents, providing valuable insights for policymaking.

• What are the challenges and opportunities for the adoption of complexity economics within the mainstream profession?

  Complexity economics faces challenges due to its relative youth, limited number of models, and the inherent resistance to new ideas within a closed profession. However, the field has the potential to gain wider acceptance through successful real-world applications and evidence demonstrating its effectiveness in predicting economic outcomes.

• How does the evolution of weather forecasting illustrate the potential for advancements in economic forecasting?

  The success of weather forecasting, which has significantly improved since the adoption of physics-based models, demonstrates the potential for similar advancements in economic forecasting. By utilizing more sophisticated models and data, economists can potentially achieve greater accuracy in predicting economic events.

• What are the limits of predictability in economics, and how can we overcome them?

  There are inherent limits to economic predictability due to the complexity of the world and the difficulty of measuring all relevant factors. However, focusing on salient features and understanding the universality classes of economic systems can help economists make more accurate predictions, even without complete knowledge of all variables.

• What are the anthropological challenges of bridging the gap between complexity economics and mainstream economics?

  The different jargon, perspectives, and entrenched beliefs within the economics profession can create barriers to communication and collaboration between complexity economists and mainstream economists. However, fostering dialogue and encouraging open-mindedness can help bridge these divides and lead to a more comprehensive understanding of economic phenomena.

Show Notes