Podcast Summary

Podcast: New Books Network

Episode: J. Doyne Farmer, "Making Sense of Chaos" (Yale UP, 2024)
Host: Gregory McNiff
Guest: J. Doyne Farmer
Date: September 25, 2025

Episode Overview

This episode features economist and complexity scientist J. Doyne Farmer, who discusses his new book, Making Sense of Chaos: A Better Economics for a Better World (Yale UP, 2024). Farmer takes listeners on a tour of the emerging field of complexity economics, explaining how it challenges traditional economic models with interdisciplinary approaches drawn from physics, biology, and computation. He argues for modeling “the economy as it is, not as if,” and describes how new tools—especially agent-based modeling, network theory, and AI—can help us understand unpredictable economic realities, from pandemics to financial crises to climate change.

Key Discussion Points & Insights

Opening Framework: Purpose and Audience

• Why Write the Book?
  Farmer wanted to communicate economic problems to society at large, making the work accessible to a general audience:

  “I just kind of wrote it because I felt like I had to. ...I tried to make it accessible to as wide an audience as I possibly could at a high level.”
  — J. Doyne Farmer (03:04)

Complexity Economics vs. Standard Models

• Contrast with Mainstream Economics:
  • Standard Economics: Top-down, assumes ‘rational expectations’ (everyone knows everything and optimizes perfectly), models are often overly simplified and mathematically intractable for real-world complexity.
  • Complexity Economics: Bottom-up, uses ‘bounded rationality’ (realistic, limited-information agents), actual decision processes, and computer simulations to represent realistic economic behaviors.

    "Complexity economics...takes a bottom up approach...agents who use bounded rationality...models what happens is that instead of mathematical equations, we have computer simulation in which we have virtual agents who each time period make a decision."
    — Farmer (03:44)

Central Concepts and Terms

• Bounded Rationality:
  Our decision-making is rational but limited; crucial for making models tractable and closer to reality.

  "We're not infinitely smart and that's the boundary part. ...[It] makes a difference in a couple of ways. One is that the models become more tractable...you can model much more complex situations."
  — Farmer (07:34)

• Chaos:
  Explains how complex systems can generate unpredictable outcomes and endogenous shocks:

  “Chaos is the phenomenon where...nearby states move exponentially apart. ...It’s a geometric property of models that is very prominent in the weather...and in economics, it explains why we have recessions, booms and busts.”
  — Farmer (09:37)

• Emergent Phenomena:
  The whole is more than the sum of its parts:

  "Emergent phenomena are situations where the property of the whole is qualitatively different than the components of the whole. The economy as a whole is an emergent phenomenon."
  — Farmer (13:23)

• Verisimilitude (As Is vs. As If):
  Realistic modeling aligns logic and mechanics with real systems, instead of assuming people act "as if" they're perfectly rational:

  “The idea is to replace as if with as is. And I believe that that can lead us to more realistic models of the world that we’ll be able to rely on better.”
  — Farmer (16:35)

• Endogenous Shocks:
  Shocks that originate within the system (like market crashes), not just from external events—something standard models struggle to capture.

  “Mainstream economics has a hard time dealing with [endogenous shocks]. ...Our bounded rationality is a really important feature because it...causes us to collectively overshoot and undershoot.”
  — Farmer (20:26)

Case Studies & Practical Successes

• COVID-19 Economic Modeling:
  Farmer’s group built a bottom-up simulation that accurately predicted the UK’s economic trajectory during the pandemic, outperforming standard models by focusing on supply chains and dynamic industry relationships.

  “We were quite tickled because we predicted in advance of things happening what the economic [impact] would be right on the money.”
  — Farmer (22:58)

• 2008 Housing Crisis:
  Their agent-based model of the Washington D.C. housing market revealed subprime lending, not interest rate policy, was the main driver of the bubble, showing how detailed, realistic simulations can clarify policy debates.

  “We saw very strongly that...if we had just stuck with the old-fashioned 30 year loans at fixed interest rates, no subprime lending, we wouldn’t have had much of a housing bubble.”
  — Farmer (22:58)

• Traditional Fed Models vs. Complexity Models:
  Mainstream macro models assume a world of ‘risk’ (known probabilities), not ‘uncertainty’ (unknown futures), whereas real markets and crises are dominated by uncertainty.

  “Traditional economic models only assume risk...whereas, you know, the way we make decisions in the real world, we've developed heuristics that allow us to cope in situations where there are a lot of uncertainty.”
  — Farmer (28:43)

Heuristics, Gut Science, & Behavioral Approaches

• Use of Heuristics:
  Farmer argues heuristics often outperform models in truly uncertain environments; traditional economics has yet to seriously integrate them.

  “Our approach takes heuristics seriously and uses them inside the models.”
  — Farmer (32:03)

Disciplinary Tensions and Methods

• Approach to Modeling:
  Physicists (and complexity economists) start with data and build theory from it; economists often start with theory and test against data.

  “Physicists took to understanding the financial system [by]...look[ing] at the data first and then build[ing] a model.”
  — Farmer (33:57)

• Resistance in Academia:
  Despite successes and endorsements (notably from Larry Summers), mainstream economics departments remain resistant, due to ingrained traditions and fears about theoretical displacement.

  “They have to get well out of their box to appreciate it. And so it's only more open minded people...that usually embrace this.”
  — Farmer (36:17)

Interdisciplinary Analogies and Insights

• Economy as Biology/Ecology:
  Complexity economics uses ecological and evolutionary models to understand the ‘market ecology’—how diverse strategies and agents interact and evolve.

  “I think of the economy as the metabolism of civilization. ...We're in an ecosystem of specialists...So complexity economics embraces that ecological perspective.”
  — Farmer (39:41)

  • Adaptation and Evolution:

    “The economy is evolving all the time. I mean we're in the AI evolutionary period right now...”
    — Farmer (43:30)

• Predator-Prey Analogies:
  The equations used in biology (e.g., Lotka-Volterra equations) echo those in Farmer’s models of market dynamics, clarifying how different financial strategies interact.

  “I was able to derive equations that are essentially the same as the [Lotka] Volterra equations...we need the whole ecology of financial participants, and they have to be in balance for the financial system to work right.”
  — Farmer (44:36)

• Turbulence in Fluids & Finance:
  Financial markets are turbulent and unpredictable in the same statistical way as fluid dynamics—similar models can predict market volatility and flow.

  “The statistical properties of financial markets are strikingly similar to the statistical properties of fluid turbulence.”
  — Farmer (51:16)

Agent-Based Models and Networks

• Networks as Skeletons:
  Modern complexity models treat the economy as a network—nodes and links describe the critical structure of people, banks, contracts, supply chains, etc.

  “A network is a mathematical simplification...nodes could be banks...the links could be loans...So...networks are in a sense the skeleton on which you build agent based models of the type that we use...”
  — Farmer (49:30)

Real-World Predictions and Applications

• Stock Market Models—Real Experience:
  Farmer describes his success co-founding Prediction Company, which used statistical and computational methods for market prediction. Moving forward, he is building new agent-based complexity models for macroeconomic forecasting through his new venture, Macrocosm.

  “[At Prediction Company]...none of our models were very good, but we made enough bets that we made very, very consistent profits.”
  — Farmer (46:31) “Now...we're simulating the economy. So...we're actually trying to understand how it works through complexity economics models.”
  — Farmer (46:31)

• Climate Economics and the Green Transition:
  Complexity economics can guide difficult transitions to a low-carbon economy, modeling real-world adaptations and policy effects:

  “We're undergoing a rapid change...the devil's in the details of how should we make the transition, which energy sources should we invest in...So...complexity economics is well suited to help chart the course.”
  — Farmer (53:29)

• Universal Laws and Unexpected Parallels:
  Farmer reflects on the surprising regularities (e.g., the ‘inverse square law’ for market impact) in interdisciplinary fields—sometimes economics does have robust laws like physics.

  “It's a very beautiful thing to see really. And as a scientist, a lot of fun to try and understand properly.”
  — Farmer (55:00)

The Future: AI, Computation, and Prospects

• AI & Computational Methods:
  Increased data and computational power are key. But in economics, agent-based, evolutionary approaches will be more effective than pure deep learning due to limited data and intractable complexity.

  “AI is going to drive progress in all fields of science and economics among them...We use evolution to effectively train agents that can operate well in the economy.”
  — Farmer (56:06)

• Mainstream Recognition:
  Farmer predicts within the next 5–10 years, complexity economics will surpass standard models in predictive power, spurring its wider adoption and perhaps Nobel recognition.

  “I think within the next five to 10 years we're going to see real breakthroughs in complexity economics because we're going to start beating standard metrics...once that happens, we'll start to get more mainstream attention.”
  — Farmer (57:37)

Notable Quotes & Memorable Moments

• On Bounded Rationality:
  "We’re not dumb...But we're not infinitely smart and that's the boundary part."
  — Farmer (07:34)

• On Market Bubbles:
  "If we had just stuck with the old-fashioned 30 year loans at fixed interest rates, no subprime lending, we wouldn’t have had much of a housing bubble."
  — Farmer (22:58)

• On Academic Resistance:
  "If we're right and they're wrong, the legacy of existing mainstream economists will be diminished and something else will take over."
  — Farmer (36:17)

• On the Economy as Ecology:
  "The financial system as a whole is the outcome of all these different specialists interacting. And in that vein, it becomes much easier to understand why markets malfunction."
  — Farmer (39:41)

• On the Power of Interdisciplinary Science:
  “There are things in economics that really are like the laws that we have in physics. It's a remarkably good quantitative law of markets.”
  — Farmer (55:00)

• On the State of Prediction:
  "None of our models were very good, but we made enough bets that we made very, very consistent profits."
  — Farmer (46:31)

Timestamps for Crucial Segments

• 03:04 – Why Farmer wrote the book & target audience
• 03:44 – Major differences between complexity & standard economics
• 07:34 – Bounded rationality, chaos, and agent-based modeling
• 13:23 – Emergent phenomena and bottom-up modeling
• 16:35 – Verisimilitude: ‘as if’ versus ‘as is’ analysis
• 20:26 – Endogenous shocks and why standard models miss them
• 22:58 – COVID and housing crisis modeling: real-world modeling successes
• 28:43 – Risk vs. uncertainty in economics
• 32:03 – Heuristics in business and economics
• 36:17 – Resistance within economics and academia
• 39:41 – Market ecology, economic metabolism, and biological parallels
• 44:36 – Predator/prey equations in financial markets
• 46:31 – Farmer’s experience with Prediction Company and new ventures
• 49:30 – Economy as a network: the skeleton for agent-based models
• 51:16 – Financial turbulence and its parallels in hydrodynamics
• 53:29 – Climate economics and the utility of complexity approaches
• 55:00 – Universal laws in economics and physics
• 56:06 – Role of AI and computation in the future of economics
• 57:37 – Will a complexity economist win a Nobel Prize?

Conclusion

Farmer’s discussion offers a compelling vision: modern economics must reckon with the limits of rationality, the unpredictability of real systems, and the value of computational, interdisciplinary tools. Complexity economics, he argues, is poised to move from the fringe to the mainstream—shaping everything from crisis forecasting to climate policy to the design of financial systems. Anyone interested in how the new science of economics is emerging from the “chaos” of the real world will find this episode thought-provoking and energizing.