The Complexity Economics Revolution
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The Complexity Economics Revolution J. Doyne Farmer Baillie Gifford Professor, Mathematical Institute, University of Oxford Director of Complexity Economics, Institute for New Economic Thinking at the Oxford Martin School External Professor, Santa Fe Institute Acknowledgements: I would like to thank Shanny Peer, Vince Bielski, Edward Kastenmeier and Andrew Weber for help editing, and Penny Mealy, Sander Bais, Karen Lawrence, Steve Lawton, Robb Lentz, Fotini Markopoulou and Elizabeth Rozeboom for valuable comments. This book is dedicated to my mentor, Thomas Edward Ingerson (1938 – 2019).1 INTRODUCTION ................................................................................................................... 3 Part I: WHAT IS COMPLEXITY ECONOMICS? 1 MAKING RANDOMNESS PREDICTABLE 1.1 Can we make better economic predictions? 1.2 Roulette: Random number generator or predictable physical system? 1.3 Why does the economy change? 1.4 Making chaos predictable 1.5 Is the economy chaotic? 2 THE ECONOMY IS A COMPLEX SYSTEM 2.1 What is a complex system? 2.2 Why is the economy a complex system? 2.3 How ideas from biology help us understand the economy 2.4 Economics = accounting + behavior 3 MAKING PREDICTIONS WITH NETWORKS OF SPECIALISTS 3.1 The ecology of production networks 3.2 The ecology of the economy predicts long term growth 3.3 Understanding the ecology of jobs to predict occupational unemployment 4 HOW SIMULATION HELPS US UNDERSTAND THE ECONOMY 4.1 What is a simulation? 4.2 Emergent phenomena are nonlinear 4.3 A housing bubble from the bottom-up 1 The Everyday Practice of Physics in Silver City, New Mexico. Appeared in Curious Minds: How a Child Becomes a Scientist, edited by John Brockman, 2004. 4.4 Modeling the COVID pandemic Part II: HOW DOES COMPLEXITY ECONOMICS DIFFER FROM MAINSTREAM ECONOMICS? 5 MAINSTREAM ECONOMICS 5.1 Foundational assumptions of economic theory 5.2 The Lucas critique 5.3 Friction in the economy 5.4 Adding heterogeneity 5.5 The behavioral revolution 5.6 Mainstream alternatives to rational expectations 5.7 What is wrong with utility? 5.8 Uncertainty vs. risk 6 DECISION MAKING UNDER UNCERTAINTY 6.1 Heuristics 6.2 Modeling behavior directly 6.3 Simulation and ecological rationality 6.4 Exploring the wilderness of bounded rationality 7 WHY IS THE ECONOMY ALWAYS CHANGING? 7.1 How standard macroeconomics understands change 7.2 The hand of God vs. emergent behavior 7.3 From rocking horses to pole balancing 7.4 Spontaneous emergence of inequality and business cycles PART II: THE FINANCIAL SYSTEM 8 Inefficient markets 8.1 What does the financial system do? 8.2 Beating the stock market 9 THE SELF-REFERENTIAL MARKET 9.1 How well does the market do its job? 9.2 Does the market make its own news? 9.3 How complexity economics captures the moods of markets 9.4 The uncanny resemblance of financial and fluid turbulence 10 ECOLOGY AND EVOLUTION IN FINANCE 10.1 Market inefficiencies feed financial markets 10.2 The analogy to biological evolution 10.3 The ecology of overcrowding 10.4 Market profit webs 10.5 Dynamics of market ecologies 10.6 Market ecology and financial instability 11 SYSTEMIC RISK AND THE GREAT FINANCIAL CRISIS 11.1 The Great Recession 11.2 The key role of leverage 11.3 How leverage causes the market to make its own news 11.4 The evolving landscape of risk management 11.5 How prudent risk management fueled the crisis 11.6 The ecology of leverage Part III: VISION OF THE FUTURE 12 OFFICIAL MODELS 12.1 National accounting 12.2 Weather forecasting 12.3 The economics of climate change? 12.4 Lessons for economics 13 GLOBAL MICROECONOMICS 13.1 How the mainstream will attack the problems 13.2 Reconstructing the structure of the economy 13.3 Making the agent-based model 13.4 How granular? 13.5 How complexity economics can help overcome limited data 14 MAPPING THE ECONOMY 14.1 Why data is so important 14.2 “Google economics” 15 ECONOMIC GROWTH AND CLIMATE CHANGE 15.1 Predicting technological progress 15.2 The green energy transition is likely to be a net economic benefit 16 CHALLENGES 17 WHAT WILL THE BENEFITS BE? 17.1 The central bank of the future 17.2 Creating a world of equal opportunity 17.3 Transitioning to a sustainable economy INTRODUCTION We live in an age of increasing complexity – an era that holds more promise, and more peril, than any other in human history. As civilization becomes increasingly interconnected and technology accelerates the pace of change, our problems have become more complex than ever before. New forms of flourishing lie tantalizingly at our fingertips, but apocalypse is all too easy to imagine. The fossil fuels that power our economy have generated remarkable wealth, but they threaten to destroy it through climate change, which is like an immense Tsunami rolling toward us from the future. Automation creates prosperity for some and unemployment for others. Financial crises and growing inequality have ripped civilization apart, causing socio-political polarization, stoking terrorism and putting democracy in retreat. As I write, we are experiencing the worst global pandemic in more than a century. Many of these problems are rooted in the economy, and as a result, public policy- making relies on economic theories and the economic models that support them. While political leaders do not make decisions based on models and theories by themselves, they have a significant impact, framing debates, narrowing options, and providing arguments for and against policy proposals. Perhaps even more importantly, economic models and theories shape our intuition and affect how decision-makers think about the world. Unfortunately, the guidance provided by economists has thus far failed as often as it has helped. The last global economic crisis provides a good example. In 2006 top economists at the New York Federal Reserve Bank became concerned about the possible dangers of a crash in the housing market. They asked the Federal Reserve Bank US model (FRB/US), one the most well-developed economic models in the world, “What would happen to the U.S. economy if the average house price drops by 20%?” The model came back with a clear answer: “Not much.” The economists’ concerns were remarkably prescient: Between January 2007 and January 2009, the average price of a house sold in the U.S. dropped by about 23%. But the FRB/US prediction was horribly wrong: It under-estimated the effect on the U.S. economy by a factor of twenty. The Great Financial Crisis that ensued was the worst period of global economic decline since the Great Depression of the 1930s. In the U.S., five million people lost their jobs, unemployment went to 10%, and it cost the U.S. economy about 10 trillion dollars, roughly equivalent to a half a year of work for everyone in the country. The effect on the rest of the world was even bigger. Perhaps if the FRB/US model had given a better answer, the Fed might have taken timely action before the crisis occurred, and some of the pain and suffering could have been avoided. The failure of economic models was universal. As the president of the European Central Bank, Jean-Claude Trichet said, “As a policy-maker during the crisis, I found the available models of limited help. In fact, I would go further: In the face of the crisis, we felt abandoned by conventional tools.” Modern science and technology provide us with powerful tools to understand and solve the world’s problems in ways that differ from the kinds of models proposed by mainstream economic theory. We are now able to collect data on economic activity at an unprecedented scale. Modern computers potentially allow us to model the global economy with a remarkable level of fidelity and detail. Better science could guide us through the crises we face and help us create an economy that is sustainable, fair and prosperous. This is not happening. While the conceptual framework of mainstream economics is useful for well-specified small problems, like designing a more efficient auction, it is ill- suited to understanding and solving big, messy real-world problems. The core assumptions do not match reality and the methods based on them do not take full advantage of modern technology. For important issues, such as climate change, this has resulted in wrong answers that have provided a rationale for inaction, leading us to the brink of a global catastrophe. Nobel Prizes have been given to economists whose theories were based on idealized arguments with no empirical support, providing fodder for neoliberal political philosophies. These theories were used by Reagan and Thatcher to justify economic policies that caused extreme inequality and socio-political polarization. It is time to try something new. The science of complexity economics that I write about here offers an alternative. Complexity economics uses ideas and methods from the science of complex systems, which is an interdisciplinary movement spanning the physical, biological and social sciences. This movement emerged in the eighties and I was one of its founders. The science of complex systems studies emergent phenomena. This means that the behavior of a system as a whole is qualitatively different from that of its individual parts. Perhaps the most dramatic example is the brain: An individual neuron is a relatively simple device that takes in stimuli from other neurons and produces new stimuli. An individual neuron is not conscious, but somehow the 85 billion neurons in the human brain combine to produce abstract thinking and consciousness – something that would be impossible to even imagine from observing a single neuron in isolation. Adam Smith, who is a founder of economics, was also perhaps the first person to describe the idea of a complex system .