Making Sense of Chaos: A Better Economics for a Better World

by J. Doyne Farmer

From a pioneer in the field of complexity science and chaos theory, a plan for solving the world’s most pressing problems

We live in an age of increasing complexity—an era of accelerating technology and global interconnection that holds more promise, and more peril, than any other time in human history. The fossil fuels that have powered global wealth creation now threaten to destroy the world they helped build. Automation and digitization promise prosperity for some, unemployment for others. Financial crises fuel growing inequality, polarization, and the retreat of democracy. At heart, all these problems are rooted in the economy, yet the guidance provided by economic models has often failed.

Many books have been written about J. Doyne Farmer and his work, but this is the first in his own words. It presents a manifesto for how to do economics better. In this tale of science and ideas, Farmer fuses his profound knowledge and expertise with stories from his life to explain how we can bring a scientific revolution to bear on the economic conundrums facing society.

Using big data and ever more powerful computers, we are now able for the first time to apply complex systems science to economic activity, building realistic models of the global economy. The resulting simulations and the emergent behavior we observe form the cornerstone of the science of complexity economics, allowing us to test ideas and make significantly better economic predictions—to better address the hard problems facing the world.

Genres: Economics, Nonfiction, Science, Business, Finance, Audiobook, Philosophy

384 pages, Paperback

First published April 25, 2024

Reviews

[Brian Clegg · Rating 5 out of 5]

This is a remarkable book, pulling together two key threads - chaos theory and economics. Doyne Farmer has a reputation as someone who breaks the mould: famously, he dropped out of studying physics at graduate level, working with a handful of others to put together a wearable computer (back in the 70s, when such a thing would have seemed pretty much impossible) to enable them to successfully beat the odds at casinos, picking up on the slight biases in roulette wheels.

Now, he presents a powerful case for applying chaos theory to economics, modelling economies in a totally different, agent-driven way rather than the traditional approach taken by economists. This combines for me the impact of two books I've read and greatly admired, but in both cases had felt that there needed to be a next step. The first of these was Chaos by James Gleick, which got me all fired up about chaos theory, but proved a bit of a let down as it was great to explain why, for example, it's difficult to predict the weather, but didn't give much of an idea of how to practically apply chaos theory.

Then there was Economyths by David Orrell, which demonstrates powerfully why traditional economics is so inconsistent from expert to expert and non-scientific - how, after all could a true science keep giving Nobel-ish prizes to totally incompatible theories? Economists seemed to be using the methods and mathematics of science rather in the fashion of a cargo cult, and Orrell exposes this beautifully... but doesn't give too much help on how to fix things. Farmer sets out to do just that with a ground-level reform.

I'm not qualified to say whether or not Farmer's ideas will truly deliver - but surely it is time for such a total overhaul of economics, and this could be the starting point. As a result this is an important, and fascinating book. It is, to be honest, quite heavy going - except when telling stories like the casino attempts, Farmer isn't great at making his topic accessible to a non-technical audience. Yet despite this, it is hard not to be impressed by the totally different take on economics, and the already demonstrated success with some limited modelling already.

Almost without doubt, most economists are going to hate this book - but history may well show that Farmer was on the side of the angels.

[Richard Marney · Rating 5 out of 5]

I read this book once, scribbled my notes, then read it again. Wow, this first plunge into Complexity Economics reminded me of the experience of diving into Behavioral Economics decades ago. What an eye opener.

As a graduate economics student, I was forced to journey through the mind-numbing landscape of the rational expectations school, consistently groaning the words “as if”, but never being able to convince myself much of it made sense once I left the classroom. The agent-based modeling (bounded rationality, heterogeneity, heuristics based strategies) discussed here requires highly advanced mathematical skills and computational power beyong the capacity of mere mortals, but as an approach to the economic world in which we live there is a connection to our reality.

The desciption of the economic analysis (prediction of the impact) of COVID-19 is an easily-digested and compellling example of the relevance and value of the author’s message.

A must read.

[Filip Lubinski · Rating 5 out of 5]

J. Doyne Farmer needs no introduction to anyone interested in complexity economics. He is widely known for his achievements in this field. The list goes from bold and groundbreaking ideas that he implemented as a student to scientific articles that he publishes up to this day as a professor at Oxford University (Farmer et al. 2015, 2016, 2017, 2018). Yet, we had to wait thirty years for him to publish the book. That's how long his manager "nagged" him to write it (p. ix). Finally, 'Making Sense of Chaos' came to European bookstores this year in April, just to get followed by the US edition (Yale University Press) in August. It was worth waiting for and might be the most important popular science book in complexity economics since Eric Beinhocker's 'The Origin of Wealth' (2006).

[Cheenu · Rating 1 out of 5]

The premise of the book is that "standard" economics is out of date held back by core concepts developed at a time of no computers and almost no empirical data.

It makes the claim that the economy is a "chaotic" system. Hence, the system is nonlinear where small perturbations have can oversized effects.

(I really enjoyed reading James Gleick's book Chaos: Making A New Science on this topic and recommend reading it before starting this book. )

Thereby, the field is best served by "complexity" economics - based on computer simulations rather than mathematical formulas as the latter is not capable of modeling nonlinear systems.

While I love a contrarian book, I found this book pretty underwhelming.

Rather than diving deep into the nitty gritty of "agent based modeling", it was more of disjointed assortment of claims based on some modeling the author's group had done.

Interspersed with disparaging comments of mainstream economics.

[Alexandre Pittet · Rating 5 out of 5]

Brilliant book, the best introduction so far on complexity economics I’ve read! Ties together everything from a criticism of standard economics, an analysis of financial crises à la Mehrling/Pozsar and an analysis of climate change modeling. The type of book that makes you want to do a PhD !

[Andreas · Rating 4 out of 5]

I had a lot of “Yes, thank you!” moments reading this book, especially when it concerns utility maximizers with perfect knowledge about the future, using a discount rate to make decisions between consuming and saving. I always had my doubts about microfounded macro-economic models, not only because they are not micro-founded anyway (a firm is not a proper microfoundation: it consists of individuals making decisions). I always assumed I was just not smart enough to understand, and that if smarter people than me could live with these implausible assumptions, then why couldn’t I? In classes on DSGE modelling, I was already suspicious that New Keynesian frictions could only be modelled symmetrically, meaning general rigidity in setting prices instead of downward rigidity, which aligns much better with psychology. This book has taught me a lot about agent-based modelling, which sounds a lot more promising than what mainstream DSGE has to offer, without falling prey to equally implausible heterodox models, like MMT. Also happy that the book confirms some of my priors (aren’t we all?) on nuclear energy for example (just too expensive to be useful). Highly recommended!

[Jacob · Rating 5 out of 5]

Farmer’s book is truly remarkable, making a compelling case for complexity economics over conventional models.

Traditional economics assumes rational agents and market equilibrium through elegant
mathematical models, while complexity economics views the economy as a dynamic, evolving
system where diverse agents with limited rationality interact to create emergent patterns. With complexity economics, we often need to run agent-based models and observe these emergent properties in simulations, which may differ from what we expect.

Put differently, I view traditional economics as trying to define laws – universal principles like supply and demand, rational choice, and market efficiency – perhaps inspired by Newtonian physics (f = ma). Complexity economics seeks to understand adaptive processes and pulls more from fields like ecology, statistical physics, and climate science.

Farmer provides many examples where complexity economics can be valuable. These include modeling the economic effects of climate change, housing bubbles, leverage regulations, etc. While some challenges still remain, eg more granular data collection and better tooling, I’m hopeful that we’ll begin to use it more widely in the future. It has already been shown to capture phenomena not captured by traditional models and, at least to me, is a much more faithful representation of how the economy actually works.

[Josephene · Rating 4 out of 5]

This book got me really excited about complexity economics by providing me with an intuitive understanding of how it works, why it makes sense, and how it can be a force of good. I enjoyed everything up to the deep dive into examples financial markets, which isn’t an area of economics I’m particularly interested in. I did feel underwhelmed with the lack of technical content. I would have liked a wider range of examples, along with technical details of their implementation and the theory that drives them. For example, I would be interested in reading the source code or documentation the modular approach of the Climate Policy Laboratory. But, that’s not what this book set out to do, and if the goal was to convince the general public that complexity economics is a good idea, I think it did well. I think ultimately I just need to mess around with agent-based modeling myself. I also ended up getting a copy of Growing Artificial Societies by Joshua Epstein.

[Debbie Notkin · Rating 4 out of 5]

This fascinating book about complexity economics is readable, comprehensible, comprehensive, and often funny. Farmer's history includes early manipulation of casino roulette, and a physics education which eventually led him to economics. His scientist's mind has yet, after many decades, to come to terms with how easily (most) economists feel free to ignore the real world in favor of their theories.

Unfortunately, the use of complexity economics that interests Farmer the most is beating the stock market--which is probably the one that interests me the least. And his short section on complexity economics and climate change lays out the problems of climate change on his term but in the end fails to make concrete suggestions for better models. The book is best explaining the underlying concepts, and much weaker in its real-world advantages to what is clearly (to my mind) a much more sensible way to think about economies large and small. Worth your time if you care about the subject; skim the stock market chapters unless that's your jam.

[Yushi · Rating 5 out of 5]

Got introduced to agents modeling at work, this book was recommended. Excellent read to get a good grasp of what is happening. I am surprised this is not as widely adopted in industry.

[Saurabh Varanasi · Rating 3 out of 5]

Passable and strictly superficial, the book maybe does a decent job of glossing over some of key differences between the standard economic model and the complexity-based/agent-based economic model but fails to weave these concepts into a compelling narrative whatsoever.

[Sam · Rating 3 out of 5]

Chaos be chaosing

[Mohammad Ahsan · Rating 4 out of 5]

“Making Sense of Chaos” is a fascinating book about Complexity Economics written by Doyne Farmer, who is a complex systems scientist, and is currently the Director of the Complexity Economics Programme and Professor of Complex Systems Science at the Oxford University.

In the book, Doyne Farmer has explained in details the concept of Complex Economics and why it is more useful in analyzing economic issues and developments, compared to standard economics. The extensive use of data, technology and computing power is the main engine that empowers Complexity Economics and gives it an ede. The author has given numerous applications of Complexity Theory in the fields of Economics, Finance, Climate Change and Disaster Planning (Weather Forecasting and Pandemic mapping). He has also challenged some traditional economic & finance theories and hypothesis such as Utility Maximization and Efficient Markets.

For me the chapters covering Standard Economics - Complexity Economics, and Financial System were the most informative. The way he explained the difference between Risk and Uncertainty was elegant and clear.

The book also uncovers some interesting debates (shouting matches) between traditional economists and econophysicists (a term used for scientists covering complex economics) that took place over the years. The last section gives recommendations on how Complexity Economics can help in finding viable solutions to some of the current global issues.

I recommend the book to all those who are interested in Physics, Chaos Theory, Social issues, Economics and Financial Markets.

[Mischa Kasperan · Rating 5 out of 5]

Wow! Economics is not my strongest subject by far but the author kept me captivated from start to finish with personal stories and relatable examples. If you have even the slightest interest in econ., data science, finance or how complexity economics could lead us to a better and brighter future, I highly recommend this book!

[Jeroen Ruissen · Rating 2 out of 5]

Could have been an essay and makes basic agent theory sound more interesting than it is

[Thomas · Rating 4 out of 5]

The book details an interesting approach to understanding economics; create a simulation, with high verisimilitude, of economic actors, institutions, and policies and then run A/B tests to see the effects of variations in policy/institutions/behavioral assumptions. The idea is very appealing to anyone who is dissatisfied with the (underwhelming) performance of conventional macroeconomic methods. Personally, I am excited to add agent-based modeling to my economic toolkit, augmenting the purely qualitative methods (behavioral and Austrian) that I employ with a potentially powerful quantitative program.

In addition to describing the approach and some interesting findings, the book highlights what is increasingly becoming obvious to practitioners but perhaps unknown outside of the data science community: high quality data is frequently the limiting factor in model-building enterprises. This might impact the arguments for the inevitability of a Singularity event; intelligence is only one factor in the production of additional intelligence, so super-human intelligence might not produce an unchecked positive feedback loop.

Criticisms
A not insignificant amount of the book was either a complaint about being misunderstood/underappreciated/alienated or a funding pitch (Humans need these methods to deal with inequality and climate change).

Early on, the author states that homo economicus would not benefit from specialization since they can do everything well, which completely ignores skill development and opportunity cost.

The author, and economists in general, do not seem to understand the meaning of one of the most commonly used terms in their field: rationality. Rationality is 'acting in accordance with reason and logic' , yet economists continually imply that humans are not rational because they make sub-optimal decisions. Apparently, in this field rationality = omniscience + infinite compute + no time constraints + acting according to logic + perfect execution + no time preference. Might I suggest using optimal for this type of decision making, allowing us to use the term rational to distinguish between 'acting according to logic' and 'not acting according to logic'? The Star Trek character Spock is perfectly rational but his decisions are highly unlikely to be optimal in any but the most trivial of circumstances. Rationality is a (frequently) necessary but not sufficient condition for optimal decision making.

[Javier H · Rating 3 out of 5]

Parte 1: Cuestionando las suposiciones básicas

Farmer inicia el libro desafiando los fundamentos de la economía tradicional, especialmente la noción de que los mercados siempre tienden hacia un equilibrio donde la oferta iguala la demanda. Argumenta que esta perspectiva simplista no refleja la realidad de los mercados, que suelen ser impredecibles y desordenados.
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Parte 2: El ecosistema económico

En esta sección, se introduce la idea de la economía como un sistema complejo, similar a un ecosistema natural. Se enfatiza la importancia de reconocer la diversidad de agentes económicos y sus interacciones, lo que puede conducir a comportamientos emergentes que los modelos tradicionales no pueden predecir.
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Parte 3: Rompiendo con la teoría estándar

Farmer presenta la economía de la complejidad como una alternativa a la teoría económica convencional. Este enfoque utiliza modelos basados en agentes y herramientas computacionales avanzadas para simular y analizar sistemas económicos reales, permitiendo una mejor comprensión de fenómenos como las crisis financieras y la desigualdad.
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Parte 4: La conmoción desde dentro

Se exploran las limitaciones de los modelos económicos actuales para prever y gestionar eventos disruptivos, como la pandemia de COVID-19. Farmer argumenta que la economía de la complejidad ofrece herramientas más robustas para enfrentar tales desafíos, al incorporar la incertidumbre y la dinámica no lineal en sus análisis.
pitchforkeconomics.com

Parte 5: Modelando un futuro mejor

La última parte del libro se centra en cómo la economía de la complejidad puede contribuir a diseñar políticas más efectivas y sostenibles. Al utilizar simulaciones detalladas y datos empíricos, es posible prever el impacto de diferentes políticas y tomar decisiones más informadas para abordar problemas globales como el cambio climático y la automatización.
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[Matt Douglas · Rating 3 out of 5]

I learned a lot from this book. It’s written by a sidelined technocrat looking to influence how mainstream technocrats formulate their views.

To do this, Farmer outlines a common critique of mainstream economic techniques and sketches of a different, more scientific approach to economics in the right amount of detail. The book is engaging and includes excellent references for further reading.

A key part of the motivation for the new approach is to establish objective truths in social science. Farmer argues that we now have the computing power to find an objectively correct answer to questions like “how should we deal with climate change.” This is exciting and clearly worthwhile. However, it’s also clear that we need more than just a correct answer to solve these problems.

For example, Farmer notes debates about austerity after the GFC as an example where his approach can help. Current economic techniques allow pundits to pick their preferred model based on their ideology, so we’re stuck. Farmer thinks “We can break out of this stalemate only by finding models whose predictions are consistently good enough to gain a wide level of credibility.”

This is not convincing. Society has difficulty agreeing on basic facts. The advice of formidable scientists on topics ranging from climate change to pandemics is ridiculed, regardless of the scientific basis for this advice. A new approach that is even more technical than the current one is unlikely to address this issue. Technocrats will not save us from the problems Farmer outlines.

[Alice · Rating 4 out of 5]

dense, but also impressively easy to follow given my utter lack of background in econ and finance. in moments gave me this good book feeling that im being exposed to really important compelling new ideas. felt a bit of resistance to the overall claim that detailed, handcrafted agent-based models are the solution to market prediction, financial crises, climate change and etc. maybe because of my bitter lesson AI bias.

some unedited notes from my reading:
- roulette is predictable but small differences in starting state can lead to drastically different outcomes.
- impact of climate change + automation is influenced by job change networks
- emergent behaviors and chaotic dynamics in closed form games / rock-paper-scissors
- classic economics solves for equilibrium assuming utility maximization, complexity economics mimics human decision making and observes step by step outcomes
- is increasing size of financial sector faff?
- prediction company and the idea that you cant predict the stock market if it is already efficient (informational efficiency vs allocational efficiency)
- market crashes are driven by inverted demand functions
- classic economics bad complexity economics good
- two researchers studying the laws governing pace of technological advancement mysteriously disappear
- i would think the economy is much harder to predict than the weather if individual human behavior also demonstrates complicated chaos
- exponential increase in production and decrease in cost of renewables

[Bruno Freitas · Rating 2 out of 5]

The elevator pitch could be: most economic principles don’t work, and the author asserts he has a better solution for a better world.

Basic economics fail because models are too simplistic (yes, they are, so what?) and markets are inefficient (well yes, mostly inefficient in the short term, but mostly efficient in the long term).

The author’s solution? Build agent based models! And what is that? Basically try to model the behavior of every household, every company, etc (all economic agents). And with this, the world will be bright, the sun will shine and climate change will end (well, the assertiveness of the author’s solutions for climate change makes you think whether he actually understands the scientific principles…). Does the author understand what is chaos? Does he think that modeling makes chaos understandable? This is a huge paradox coming from an actual physicist…

And if we follow his solution, it will work, it’s the only hope. Otherwise, degrowth… Oh we go again, yet another book with fancy analyses used as makeup of an ideological agenda, usually promoted by people that never left their big cities to see the real world.

[Răzvan · Rating 5 out of 5]

citEști business „Making Sense of Chaos- A Better Economics for a Better World”, J.Doyne Farmer: POV
„Economia complexității este încă îm primele faze și mai sun multe de făcut pentru a obține toate avanajele pe care le promite” p. 270 J.Doyne Farmer „Making Sense of Chaos- A Better Economics for a Better World”, Penguin, 2025
https://www.youtube.com/watch?v=xepET...
Puține cuvinte reușesc să îți spună complet altceva de câte ori le auzi. Dar când îți spune cineva ”economie” imediat te uiți cine a zis, în ce context și la ce ar putea să se refere. Cum funcționează această investigație vezi în „Making Sense of Chaos- A Better Economics for a Better World”. Cartea lui J.Doyne Farmer insistă asupra modurilor în care vorbim despre economie la întâlnirea dintre contabilitate și comportamente. Iar punctele de vedere umane încep să-și dispute întâietatea în colaborarea tot mai strânsă cu perspectiva inteligențelor artificiale în domeniu.
„Folosim știința și modelele științifice din ce în ce mai mult ca să fim pregătiți pentru viitor și ca să evităm amenințările pentru mediu”. p.277 J.Doyne Farmer

[Charles Reed · Rating 5 out of 5]

90%
5:53 PM
As a scientist that undergoes novel research In some very difficult to monitor Regions In example inflammation and water retention throughout your body. This is an excellent book because it reflects directly on chaos, which I'm studying and studying through even some very. I'm using ways commercially. I was watching Wand Division while reading this. I watched Doctor Strange and the Multiverse of Madness. I really empathize well with water, because I'm like, I also work with chaos. Wanda, I feel like I feel a kinship here Without, you know, being sanity? So, Doctor Strange is another one that I really enjoy and work with because he, so we could all get along so well because he's a doctor type, but that, I guess, causes. He's part of the reason why we cause Friction among each other. It's all it's like, oh, we all work together, and we all love each other, and we all Freaking want to punch each other in the face sometimes, So this book actually tackles Financial systems and other things like weather, just. There's a bunch of things that tackles practically through life.

[Yohan · Rating 5 out of 5]

Complexity theory is a really interesting topic and this book does a great job applying its principles to economics.

The main premise of the book is essentially that traditional economic theory has many flaws, especially since behavioural economics has come to the fore. But most importantly it has no proven consistent predictive power.

However there has not been any new comprehensive theory to take its place which has left traditional economics as the dominant policy shaper around the world.

And that’s where complexity economics comes in, as a new theory to become mainstream, taking into account the learnings from behavioural economics and bringing predictive power (with the use of simulations as its main tool).

Overall a really good book, goes into some tangents such as weather forecasting and fluid dynamics but all quite interesting learnings.

If you like complexity theory and you like economics it’s the perfect book for you!

[Gary Cohen · Rating 4 out of 5]

This is a very important book to read if you are interested in improving economic policies and decision-making. The author shows how moving from classical equilibrium models to agent-based models, we can create better models that are more likely to help us make beneficial and informed policy decisions.

This book is also of interest to those who are interested in complexity science and how complex systems (like the economy) actually work. There is also a section on climate economics that would also be of interest to those interested in how we deal with climate change.

One small critique of the book is that at times, it seems like the author focuses on the accomplishments of him and his teams of colleagues. I think the book would be more powerful if it didn't feel a bit like a brag. I also think the book got repetitive at the end and probably could have been 50-75 pages shorter.

[Hamilton Carvalho · Rating 4 out of 5]

This a great book but I think Farmer is too optimistic about energy transition. Hidrogen-based fuels can't replace entirely fossil fuels when it comes to cement and plastics, in particular, and even in other uses (steel, fertilizers) it will take many and many decades to accomplish it (partially). He focus too much on electric energy, by the way. I also disagree with his stance on degrowth: consider, dear reader, that there are many more consequences of endless economic growth, including microplastics generation, water and soil degradation and others. Moreover, we live in a world of Putins, Trumps and other geopolitical bullies; the overall picture should produce alarm instead of (empty) hope. Going back to the book, the bulk of it is worthwhile and, in a sense, a must-read.

[Harry Fox · Rating 5 out of 5]

My overall take is that this is more or less everything that is good about pop science. Whereas I have a gripe with someone in physics like Rovelli who sensationalises his work without grounding it in an understandable reality, Farmer is really out to convince by providing analogy and explanation that the reader can really get to grips with. As a result, you come away feeling not like you've been to some crazy museum of spectacles, but rather that you've been a part of that scientific journey.

I would recommend this book to all my friends, particularly the ones who want an insight into the weird stuff I will (hopefully) be doing with the next 3 years of my life!;)

[Bradley Hughes · Rating 5 out of 5]

I absolutely LOVED this book! I've had some murky thoughts that were along the lines of what Dr. Farmer was saying, regarding the ability to used agent-based modelling to get better predictive powers, whether it be in economics, or how to impact innovation. But it's clear that Farmer has thought so deeply and so far beyond what others in economics have thought in this way that I felt grateful to be able to learn from someone so well thought out and able to articulate the ideas clearly. Absolutely one of my top ten favorite books now!

[James Evans · Rating 2 out of 5]

i love that it's about complexity economics, but the i find that the author spends too much time talking about himself and his interjections of personal anecdotes pull away from making the topic coherent and approachable. The back half of the book is a pretty easy skip as well. This feels like ~40 pages of context spread over ~250 pages of writing. Those are personal preferences though and I greatly appreciate the soft benefit of interjecting so many anecdotes b/c it goes to show how foundational assumptions we're taught about econ are not laws and that it is still an ever-evolving field.

[Simon Mcleish · Rating 3 out of 5]

The book is about the relatively recent work in economics that uses large scale modelling techniques. As far as that goes, it should be a decent read to me, as someone with a mathematics doctorate. It is simple enough to understand, but I found that it was far to sketchy; it really is a whistle stop tour, with no real depth. I would have preferred to have read a longer version, with more details on the mathematics side of things.

[Shane Fitzsimons · Rating 5 out of 5]

Excellent overview of the concept of complexity and the shortcomings of mainstream economics and economic modelling. So much extra reading noted too. The breadth of sources here is impressive - though I suppose that makes sense give the analogical nature of the study of complexity. FYI for those interested the Santa Fe Institute offers free courses in systems and complexity studies, including getting into the detail of ABM.