Doyne Farmer, a physicist and expert in complexity economics, has been making waves in the field of economics with his groundbreaking work on applying complex systems science to economic models. In a recent interview with EconTalk, Farmer shared insights from his book, “Making Sense of Chaos: A Better Economics for a Better World,” where he delves into the concept of complexity economics.
Complexity economics, as Farmer explains, is the application of complex systems science and methods to economics. This approach involves simulating the economy rather than relying on traditional economic models that are based on utility maximization and mathematical equations. Farmer emphasizes the need for quantitative models that make reliable predictions and take into account the richness and detail of real-world institutions.
One of the key differences between complexity economics and traditional economics lies in the level of detail and complexity of the models. While traditional economic models may provide qualitative insights, complexity economics aims to build quantitative models that can generate numerical predictions that policymakers can trust. By incorporating all essential features of real-world problems, such as climate change and macroeconomics, complexity economics offers a more robust framework for addressing complex economic challenges.
Farmer acknowledges that mainstream economics has its strengths and that complexity economics is not necessarily a critique of traditional economic theory. Instead, he advocates for allowing room for alternative ideas to compete and for empirical data to determine which approach is most effective in different circumstances. Complexity economics, he argues, excels in situations where multiple factors interact in a messy and complicated manner, making it difficult to make accurate predictions using traditional economic models.
In conclusion, Farmer believes that the success of any economic framework ultimately lies in its ability to make accurate predictions. Complexity economics offers a promising alternative to traditional economic models by providing a more nuanced and detailed understanding of complex economic systems. By embracing complexity economics, policymakers and economists can gain valuable insights into the dynamics of the economy and make more informed decisions to create a better world for all.
From a complexity economics perspective, however, we would argue that understanding those “strange preferences” is crucial to making accurate predictions about the housing market. In a complex system like the housing market, individual decisions and behaviors can have ripple effects that impact the overall market dynamics. Ignoring these nuances can lead to flawed predictions and ineffective policies.
For example, if a significant number of sellers in a particular city have a strong emotional attachment to their homes and are unwilling to sell to certain buyers, this could create bottlenecks in the market and lead to price fluctuations that traditional economic models might not be able to account for. By incorporating these behavioral insights into our models, we can better understand how these factors influence market outcomes and make more informed predictions.
Complexity economics offers a more flexible and adaptable framework for modeling economic systems. By focusing on individual behaviors and interactions, rather than assuming rationality and equilibrium, we can capture the rich dynamics of real-world markets and make more accurate predictions. This approach allows us to better understand emergent phenomena, such as bubbles and crashes, and develop more effective policies to mitigate their impact.
In conclusion, while traditional economic models have their place, complexity economics provides a valuable alternative for understanding and predicting economic phenomena. By embracing the complexity of human behavior and interactions, we can make better predictions and improve our understanding of the world around us. As we continue to refine and expand our models, we can hope to make more accurate predictions that benefit individuals, businesses, and societies as a whole.
J. Doyne Farmer: So, you would gain several things. First of all, I don’t think the example you gave at the beginning of somebody getting angry is–I mean, I wouldn’t know–in our models, we wouldn’t be able to understand who the angry people were and how they would get angry. So, that’s not the kind of thing that we’re trying to do.
But the big difference comes with things like: How do you set housing prices? Right? In all standard economics models, housing prices are set through market clearing–meaning you equate supply and demand. You can write that down mathematically. You can solve the equations.
But, how do housing prices really get set? They get set by what’s called ‘Aspiration-Level Adaptation.’ That is, the seller when they’re selling their house, goes to the real estate agent. The real estate agent helps them find some comparables. They between them decide on a price that they think is more or less the appropriate price they could hope for if everything goes well. They put that house on the market. If it doesn’t sell after a month or two, they mark it down. If it still doesn’t sell, they market down again. They keep marking it down until either the seller says, ‘This price is too low. I don’t want to go lower than this. I’m just not going to do it.’, or the house sells.
And, I can say that’s how they do it because we looked at millions of sales in Washington, D.C., because we had access to a decade-and-a-half worth of housing data where we could see every price a house was offered at and whether or not it sold and what price it sold at.
Now, that might sound like a small thing, but it actually makes a big difference. Because it means that prices react very sluggishly to changes in the housing market.
It also means that the market can be far from clearing. You can have 20 times as many buyers as sellers, or vice versa. And, during something like the housing bubble that popped in 2008, leading up to the bubble, you had far more buyers and sellers. And then when the bubble popped, you had far more sellers than buyers. And that makes a big difference in the way the prices actually moved.
But, the second thing, if you look at our model–maybe the second and third thing–a second thing is that we could really look at the details. Because: what caused the housing bubble? The housing bubble was caused by a shift in lending policy by banks. Banks got a lot looser in who they were giving loans to. And, in our model, we actually looked through all the loans that were given in Washington, D.C. area. We looked at the criteria of the buyers behind those houses. And we could just see the way the lending policy shift by seeing what the characteristics of those loans were. And, so, it was a combination of several things: that you went from the old-fashioned vanilla 30 year loan, fixed interest rate, like the one I had on the first house I bought–
Russ Roberts: 20% down–
J. Doyne Farmer: 20% down. Oh, sorry, yeah, 20% down. Fixed interest rate like the one I had, to much more complicated loans with balloon payments, smaller amounts down.
And, so, because we were doing a simulation and not a mathematical model, we could put all that detail in. We could, you know, put the kinds of loans that were actually given and see how changing lending policy in all of its detail affected the bubble.
And, part of what we saw in our simulations was that that was really the dominant effect. That’s what fueled the bubble. We could compare it to, say, interest rates, which had a little bit to do with the bubble. But we could see that these much more complicated loan types, which were much looser, was a thing that fueled the bubble.
Then, the final advantage of the way we did it is–that has not been fully exploited in complexity economics yet–is that we had both a micro-model and a macro-model. That is: We were really simulating the behavior of individual house sales. And there is the capability to really match that up one-to-one with the world to advise: ‘Well, on this block things are different than they are on this other neighborhood over here,’ or ‘These kind of buyers are affected differently than those kind of buyers,’ or ‘These kind of sellers and those kind of sellers’
So, we had rich textural detail in our model that you just can’t get in a mainstream model.
So, those were the really three biggest factors I think that made the difference and really allowed our model to be much more realistic and accurate and useful than the mainstream models. The conversation between J. Doyne Farmer and Russ Roberts sheds light on the limitations of traditional economic models and the importance of incorporating individual behaviors and dynamics into economic analysis. Farmer points out that economists often rely on statistical models that abstract from the level of detail necessary to fully understand complex economic systems. This can lead to blind spots and failures to predict major economic events, such as the 2008 housing market crash.
One of the key criticisms of traditional economic models is their failure to account for the dynamic nature of markets and the stickiness of prices. While mainstream models assume that markets will eventually settle into equilibrium, they often lack an understanding of the process by which this occurs. Farmer’s agent-based modeling approach offers a more dynamic and realistic depiction of economic systems, capturing the frictions and dynamics that traditional models overlook.
One of the strengths of agent-based models is their ability to incorporate heterogeneity into economic analysis. Traditional models often rely on simplifying assumptions about homogenous agents, whereas agent-based models can account for differences in behavior based on factors such as income, race, gender, and geography. This allows for a more nuanced understanding of how different groups within the economy may behave and interact, leading to more accurate predictions and policy recommendations.
Overall, the conversation highlights the importance of moving beyond traditional economic models and embracing more dynamic and realistic approaches to economic analysis. By incorporating individual behaviors, dynamics, and heterogeneity into economic models, economists can gain a deeper understanding of complex economic systems and improve their ability to predict and respond to economic events. Complexity economics is a relatively new field that is gaining attention for its innovative approach to modeling economic behavior. Unlike traditional econometric models that rely solely on historical data and statistical analysis, complexity economics focuses on building models from first principles, incorporating causal relationships and making predictions based on those relationships.
One of the key differences between complexity economics and traditional econometric models is the ability to consider counterfactual situations. This means that complexity economics models can be used to simulate the effects of policy changes or other interventions, even in situations where historical data may not be available. By understanding the underlying causal relationships in the economy, complexity economics can provide insights into how different factors interact and influence economic outcomes.
In a recent interview, J. Doyne Farmer, a leading figure in complexity economics, emphasized the importance of building theoretical models that include causal relationships. He explained that while econometric models can be useful for analyzing historical data, they may not be well-suited for predicting the effects of policy changes or other future events. This is where complexity economics shines, offering a new perspective on economic modeling that takes into account the complexity and interconnectivity of economic systems.
Despite its potential benefits, complexity economics has not yet gained widespread acceptance in the mainstream economics profession. Farmer attributes this to the newness of the field and the relatively small number of researchers working in this area. However, he points to examples such as a model of COVID-19 that accurately predicted a decline in GDP in the United Kingdom, as evidence of the effectiveness of complexity economics models.
As complexity economics continues to evolve and grow, it may offer new insights and perspectives on economic behavior that could complement traditional econometric approaches. By focusing on causal relationships and building models from first principles, complexity economics has the potential to provide a more comprehensive understanding of economic systems and how they respond to changes in policy, technology, and other factors. The field of economics has long been dominated by mainstream models that have been developed over decades by hundreds of researchers. However, there is a growing interest in complexity economics, which offers a more realistic and nuanced understanding of human decision-making and economic behavior. While complexity economics models are relatively new and have been developed by a small group of researchers, they are beginning to show promise in various applications.
One notable example is the Washington housing model, which is an agent-based macro-model that has performed well in predicting housing market trends. This model, along with other complexity economics models, is challenging the dominance of mainstream models in the field. As these complexity economics models are scaled up and tested in real-world scenarios, it is becoming increasingly clear that they can offer valuable insights and predictions.
J. Doyne Farmer, a leading researcher in complexity economics, predicts that over the next decade, we will see more examples where these models outperform mainstream models. Farmer is also the chief scientist and director of Macrocosm, a company focused on scaling up complexity economics techniques and putting them into practice. The goal of Macrocosm is to accumulate a track record of real predictions and to run head-to-head comparisons with mainstream models.
One of the challenges facing complexity economics is the resistance from mainstream economics departments. The field of economics has become somewhat closed off to alternative perspectives, and researchers who pursue complexity economics may face challenges in securing academic positions. However, Farmer believes that by focusing on commercial applications and engaging with Central Banks, where there is more openness to new ideas, complexity economics can gain greater acceptance in the field.
Russ Roberts, a prominent economist, suggests that part of the challenge for complexity economics is its lack of elegance compared to mainstream models. Mainstream economics, with its emphasis on mathematical precision and simplicity, has a certain appeal that complexity economics may lack. However, Farmer points out that complexity economics also incorporates math and theoretical approaches, drawing on ideas from other disciplines to provide a more qualitative understanding of economic phenomena.
While complexity economics may not have the same level of elegance as mainstream models, it offers a richer and more nuanced perspective on human decision-making and economic behavior. As researchers continue to develop and test complexity economics models, it is likely that they will gain greater traction in the field of economics. With a focus on practical applications and real-world predictions, complexity economics has the potential to revolutionize the way we understand and analyze economic systems. Supply and demand are fundamental concepts in economics that play a crucial role in determining prices and quantities of goods and services in a market economy. The basic premise is that the price of a product or service is determined by the balance between its supply, the quantity of goods or services that producers are willing to offer for sale at a given price, and its demand, the quantity of goods or services that consumers are willing to purchase at a given price.
In most cases, supply and demand tend to reach an equilibrium where the quantity of goods or services supplied by producers matches the quantity demanded by consumers. This equilibrium price is where the forces of supply and demand are in balance, and it signals the most efficient allocation of resources in the market.
However, there are instances when supply does not match demand, leading to situations where prices may fluctuate or quantities may be limited. These out-of-equilibrium situations can have significant implications for the economy and can result in market inefficiencies.
One example of how supply and demand can be disrupted is illustrated in the story of J. Doyne Farmer and his experience beating the roulette table. By using a wearable digital computer to predict the outcome of the roulette ball, Farmer and his team were able to gain an edge over the house and make profitable bets.
This adventure taught Farmer valuable lessons about predictive modeling and the nature of randomness. It demonstrated that with the right information and tools, seemingly random events can be predicted and influenced. This insight can be applied to economics, where better models and data can lead to more accurate predictions and a deeper understanding of market dynamics.
Despite their success in beating the roulette table, Farmer and his team faced challenges that prevented them from becoming wealthy. Limited resources, technical failures, and concerns about retaliation from the casino industry all contributed to their decision to pursue other endeavors.
The story of Farmer’s roulette adventure highlights the complexities of supply and demand in real-world markets. While economic theory provides a framework for understanding the forces of supply and demand, the practical application of these concepts can be influenced by a variety of factors, including technology, regulation, and market dynamics.
Overall, supply and demand remain essential components of economics, shaping the way goods and services are produced, distributed, and consumed in a market economy. By studying these concepts and their real-world applications, economists can gain valuable insights into the mechanisms that drive economic activity and inform policy decisions to promote efficiency and growth.
The issue is not necessarily that people are harder to predict than clouds, but rather that the behavior of people can be influenced by predictions themselves. In other words, if we were able to accurately predict economic outcomes, individuals and institutions would adjust their behavior in response to those predictions, which would then change the outcome. This is known as the Lucas Critique, named after economist Robert Lucas, who pointed out that policies based on models that do not account for individual responses to policy changes are likely to be ineffective.
So, the challenge in predicting economic outcomes is not just a matter of complexity, but also a matter of reflexivity – the idea that predictions can change the behavior of individuals and institutions, thereby altering the outcome. This is a fundamental difference between predicting the weather and predicting economic outcomes.
However, that does not mean that we should give up on trying to improve economic forecasting. Just as with weather forecasting, there have been significant advances in economic modeling and forecasting techniques in recent years. From the use of big data and machine learning algorithms to agent-based modeling and network theory, economists are exploring new ways to model the complex interactions that drive economic outcomes.
And, much like with weather forecasting, there is a growing recognition of the need for collaboration and data-sharing among researchers and institutions to improve the accuracy of economic forecasts. Just as weather forecasts benefit from international collaboration, so too could economic forecasts benefit from a more collaborative and data-driven approach.
While predicting economic outcomes may never be as reliable as predicting the weather, there is certainly room for improvement. And, as J. Doyne Farmer suggests, perhaps we should invest more resources and effort into developing better economic forecasting models, much like we did with weather forecasting. The potential benefits – from more accurate policy decisions to better investment strategies – could be well worth the investment.
So, while predicting economic outcomes may always be a challenging and uncertain endeavor, it is a challenge worth pursuing. Just as we have made great strides in predicting the weather, perhaps we can make similar strides in predicting economic outcomes, improving our understanding of the complex systems that drive our global economy.
In a recent interview, J. Doyne Farmer discussed the challenges of predicting economic events, drawing on the work of economist Friedrich Hayek. Hayek argued that the complexity of human behavior and the multitude of variables involved in economic events make accurate predictions difficult.
Farmer acknowledges the validity of Hayek’s concerns, noting that there are always unknown factors that can impact economic outcomes. However, he believes that despite these challenges, there are salient features of the economy that can be identified and used to make more accurate predictions.
One example Farmer cites is the COVID model developed by his team. This model focused on fundamental economic factors such as labor availability, input supply, and demand for products. By tracking these key variables, the model was able to predict the impact of the pandemic on different industries with a high degree of accuracy.
The success of the COVID model, according to Farmer, demonstrates that it is possible to make reliable predictions even when not all details are known. By focusing on essential elements of the economy and leveraging data on industry-specific factors, it is possible to anticipate shocks and fluctuations.
Farmer also points to the concept of universality in physics, where certain criteria determine the behavior of systems regardless of specific details. He suggests that similar principles may apply to macroeconomic models, with certain universal classes dictating economic outcomes.
While challenges remain, Farmer is optimistic about the potential for improving economic predictions through the identification of salient features and the application of universal principles. By focusing on key variables and understanding how they interact, researchers can enhance their ability to forecast economic events with greater accuracy. In the world of academia, there exists a divide between physicists and economists. While physicists are often seen as having little to teach economists, and vice versa, there are some who believe that dialogue and collaboration between the two fields can lead to a better understanding of complex systems.
One such individual is J. Doyne Farmer, a physicist who has made significant contributions to the field of economics. In a recent interview with Russ Roberts, Farmer discussed the challenges of bridging the gap between the two disciplines. He mentioned the difficulty of communicating with economists due to the different jargon used in each field. Despite this, Farmer has made an effort to understand mainstream economics and has even formed relationships with economists who are open-minded and willing to engage in dialogue.
One of Farmer’s key points is the importance of paying attention to what others are doing in order to stay competitive. He believes that economists can learn from physicists, just as physicists can learn from economists. Farmer’s book, “Making Sense Chaos: A Better Economics for a Better World,” explores how the principles of chaos theory can be applied to economic systems to create a more accurate and sustainable model.
While Farmer acknowledges the challenges of getting mainstream economists to pay attention to his work, he remains optimistic about the potential for collaboration in the future. He recognizes that change takes time and is willing to publish in lesser-known journals in order to make his research accessible to a wider audience.
Overall, Farmer’s insights highlight the importance of interdisciplinary collaboration in academia. By breaking down barriers between fields and embracing new ideas, researchers can work together to create a more comprehensive understanding of complex systems. Farmer’s work serves as a testament to the power of collaboration and the potential for innovation when different disciplines come together. The world of technology is constantly evolving, with new innovations and advancements being made every day. From artificial intelligence to virtual reality, there is no shortage of exciting developments to keep an eye on. One area that has seen significant growth in recent years is the field of robotics.
Robotics is the branch of technology that deals with the design, construction, operation, and application of robots. These machines are capable of performing a variety of tasks, from simple repetitive actions to complex movements that require a high level of precision. Robots can be found in a wide range of industries, from manufacturing and healthcare to transportation and entertainment.
One of the most exciting developments in robotics is the use of artificial intelligence (AI) to enhance the capabilities of robots. AI allows robots to learn from their experiences and adapt to new situations, making them more versatile and efficient. This has opened up new possibilities for the use of robots in a variety of fields, from autonomous vehicles to robotic surgery.
Another area of innovation in robotics is the development of humanoid robots. These robots are designed to mimic the appearance and behavior of humans, with the goal of being able to interact with people in a more natural way. Humanoid robots can be used in a variety of applications, such as customer service, entertainment, and even companionship for the elderly.
In addition to AI and humanoid robots, there are also exciting developments in the field of swarm robotics. Swarm robotics involves the coordination of multiple robots working together to achieve a common goal. This approach is inspired by the collective behavior of animals like ants and bees, and has the potential to revolutionize industries such as agriculture and disaster response.
As robotics technology continues to advance, there are sure to be even more exciting developments on the horizon. From robots that can think and learn like humans to swarms of robots working together in perfect harmony, the future of robotics is full of endless possibilities. It’s an exciting time to be a part of this rapidly evolving field, and the potential for innovation and growth is truly limitless.
