From Forecasting to Learning:
How Economics Can Become a More Experimental Science
The Prediction Problem
Economics has always aspired to become a predictive science. Governments rely on economic forecasts to prepare budgets, central banks adjust interest rates based on predictions of inflation and employment, and businesses make investment decisions based on forecasts of growth, wages and consumer demand. Yet economic predictions repeatedly disappoint us. Financial crises arrive unexpectedly, inflation behaves differently from models, housing markets refuse to follow forecasts, and recessions often appear after economists have declared the economy fundamentally sound.
This is not a criticism of economists. Economics has been one of humanity’s greatest intellectual achievements. The study of markets, incentives, trade and competition has transformed living standards across much of the world. Economists have developed increasingly sophisticated mathematical models and statistical techniques, and many now recognise that economies are not simple machines but complex adaptive systems made up of millions of independent people, businesses and institutions.
So why is prediction still so difficult?
The answer is not that economists are using the wrong mathematics. The problem is that they are attempting to understand one of the most complex systems that exists. Every day millions of people make billions of decisions. Those decisions influence one another, change market behaviour, and alter the very conditions that economists are trying to predict. Unlike the physical sciences, the system itself continually adapts to its own past.
Perhaps, then, we are asking too much of economic forecasting. We expect economists to predict the future of a system that is constantly changing because the people within it are constantly learning, adapting and responding to one another. As our societies become larger, wealthier and more interconnected, the number of possible interactions grows beyond anything that any individual, government or computer can completely understand.
That does not mean economics has failed. It means the challenge has changed. The question is no longer simply how we improve our forecasts. The more important question is whether we can design economic institutions that learn continuously as circumstances change, just as science itself learns through observation, experimentation and feedback.
That is the idea explored in this article.
Science Learns by Being Wrong
One of the remarkable features of science is that it does not advance because scientists are right. In fact, it advances because scientists expect to be wrong from time to time. The scientific method is not about proving theories to be true. It is about testing them against reality and improving them when they fail.
Scientists develop theories about how they believe the world works and then use those theories to make predictions. Those predictions are compared with what actually happens. If the prediction turns out to be wrong, the theory is not defended simply because it has worked in the past. Instead, scientists ask why it failed, modify their understanding and test the new theory again. Every unsuccessful prediction becomes another opportunity to improve our understanding of the world.
Karl Popper described this as the process of falsification. No scientific theory can ever be proved to be permanently correct. It survives only because repeated attempts to show it is wrong have failed. Every new experiment therefore becomes another test of our current understanding, and every unexpected result becomes an opportunity to learn something new.
Science is therefore not really about prediction at all. Prediction is simply the tool that enables learning. The real objective of science is to build better explanations of how the world works. Theories are continually refined because reality continually provides new evidence.
That raises an interesting question for economics. If science progresses by continually testing and improving its theories, can economics do the same when the system it is trying to understand is itself constantly changing?
Economics Studies a Moving Target
Economists also develop theories and use them to make predictions, but the systems they study are fundamentally different from those studied by the physical sciences. Physicists study gravity, chemists study chemical reactions, and astronomers study the movement of planets. Those systems do not change their behaviour because someone has published a forecast.
People do.
Consumers change their spending when interest rates rise. Businesses alter investment plans when they expect a recession. Investors react to one another’s expectations. Governments respond to changing economic conditions with new policies. New technologies emerge, international events reshape supply chains and societies themselves continually evolve. Every participant observes others and adapts their own behaviour accordingly.
This means economists are trying to understand a system that is constantly changing because the people within it are constantly learning. Every decision changes the environment in which future decisions are made. Unlike a physical experiment carried out under controlled conditions, the economy never stands still long enough for the same experiment to be repeated.
Many economists already recognise this. Friedrich Hayek argued that knowledge is dispersed throughout society and cannot be gathered into a single central authority. Herbert Simon showed that people make decisions with limited information rather than perfect knowledge. Elinor Ostrom demonstrated that local communities often manage shared resources more effectively than distant governments, while W. Brian Arthur and other complexity economists have shown that economies evolve through continual adaptation rather than moving smoothly towards equilibrium.
These ideas have transformed the way many economists think about the economy. They recognise that economies are complex adaptive systems rather than mechanical machines. The next step is to ask what follows from that insight. If the economy is continually learning and adapting, should our economic institutions be designed to do the same?
That question leads naturally to cybernetics, because cybernetics is concerned not simply with prediction but with how complex systems learn, adapt and remain stable in a changing world.
Prediction and Control Are Different Problems
There is another important distinction economics has not always made clearly enough. Predicting the future and controlling the future are not the same problem.
We might predict that inflation is likely to rise over the next year, but that does not tell us how to prevent it. We might predict that housing will become less affordable, but prediction alone does not explain how to improve affordability. Knowing what is likely to happen is valuable, but influencing what actually happens is a different challenge altogether.
This distinction is important because economic forecasts are often used to justify policy decisions. We tend to assume that if we can predict the future accurately enough, we will automatically know how to manage it. Unfortunately, that is not how complex systems behave. A forecast tells us something about the direction in which a system may be moving. It does not necessarily tell us how that system will respond once we begin trying to change it.
As economies become more complex, the challenge increasingly shifts from prediction to adaptation. Instead of asking how governments can anticipate every possible future, we should ask how our economic institutions can respond quickly and intelligently to unexpected events. That is a very different way of thinking about economic policy, and it leads naturally to the science of cybernetics.
What Cybernetics Teaches Us
Cybernetics is the science of communication, control and learning in complex systems. It grew out of the observation that living organisms, machines and organisations all face the same fundamental problem. They must continually respond to changing conditions if they are to remain stable and survive.
One of the central principles of cybernetics is W. Ross Ashby’s Law of Requisite Variety. Put simply, only variety can absorb variety. If a system is highly complex, then the system trying to control it must possess at least the same degree of flexibility and adaptability. Otherwise, it will always react to events rather than effectively manage them.
This principle is easy to see in everyday life. A thermostat can control a room’s temperature because there are relatively few variables involved. An aircraft remains stable because its control systems are constantly making thousands of small adjustments as conditions change. The human body survives because billions of individual cells continually respond to their own local environment while cooperating with the rest of the body. No single part of the body directs every activity. Stability emerges because learning and adaptation occur throughout the system.
The same principle applies to an economy. Every day millions of people make billions of decisions about what to buy, what to produce, where to invest and how to respond to changing circumstances. Every one of those decisions alters the environment faced by everyone else. Governments, however, have only a relatively small number of policy instruments available to them, such as taxation, interest rates, regulation and public spending. These are powerful tools, but they are vastly fewer than the number of independent decisions taking place across society.
The implication is not that governments should stop governing. It is that no central authority can ever possess enough information or enough flexibility to manage every aspect of a modern economy directly. As societies become larger and more interconnected, the variety within the economy grows faster than the variety available to the institutions attempting to guide it. That is not a failure of governments or economists. It is simply a consequence of the mathematics of complexity.
Why Economists and Politicians Are Not to Blame
This is why I do not believe economists deserve the criticism they often receive, nor do I believe politicians are primarily responsible for the problems we see in modern economies. Both are working within institutions that ask them to solve problems that have become too complex for any central authority to solve completely.
Modern politics is built on the assumption that governments should know the answers. Every election asks voters to choose leaders who appear most capable of managing economic growth, housing affordability, inflation, healthcare, education, energy, defence, infrastructure and countless other challenges. Once elected, those governments are expected to deliver successful outcomes across all these areas, even though each policy interacts with many others in ways that are often unpredictable.
Economists are then asked to provide advice to make these decisions successful. They develop increasingly sophisticated models, gather enormous amounts of data and continually refine their understanding of how economies work. Yet the economy itself continues to evolve. New technologies emerge, international events reshape markets, businesses innovate, consumers change their behaviour and societies continually adapt. The problem is therefore not that economists fail to improve their models. It is that the complexity of the system they are trying to understand continues to grow.
Perhaps we have reached the limits of what can reasonably be expected from centralised decision-making. Modern nation states are vastly more interconnected than the societies for which many of our political and economic institutions were originally designed. We continue to expect governments to solve problems for entire nations, even though those problems increasingly arise from the independent decisions of millions of people every day.
Rather than expecting politicians and economists to know all the answers, perhaps we can ask a different question. How can we design institutions that enable society itself to learn continuously? Instead of concentrating all responsibility at the centre, can we distribute learning, adaptation and problem-solving throughout the economy in much the same way that living systems distribute intelligence throughout the body?
Markets Are One of Economics’ Greatest Achievements
One of the greatest contributions economics has made to human progress is the understanding of markets. Well-functioning markets allow millions of people to make independent decisions without anyone needing to understand the entire economy. Prices communicate information, competition rewards innovation and consumers continually decide which goods and services create the greatest value. No central planner could ever possess all the information needed to make those decisions, yet markets coordinate them remarkably well.
In many ways, markets already behave like living systems. Thousands of businesses try different ideas; new products emerge; unsuccessful businesses disappear; and successful ideas spread throughout the economy. Every purchase is a small experiment. Every new business is a hypothesis about what customers want. Competition provides the feedback that determines which ideas survive. Markets are therefore not simply mechanisms for buying and selling. They are one of society’s most effective learning systems.
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This is one of the reasons economists have generally preferred competitive markets to central planning. Competitive markets encourage diversity, innovation and experimentation. They allow people to respond to local knowledge and local circumstances in ways that no central authority could ever achieve. Friedrich Hayek recognised this many years ago, arguing that knowledge is dispersed throughout society and that markets provide a remarkably effective way of bringing it together.
The difficulty is that markets do not always remain competitive. Successful organisations often grow larger, merge with competitors and gradually acquire sufficient market power to reduce competition. As barriers to entry increase, fewer new ideas reach the market, and the learning process slows. Markets that were once highly competitive gradually become dominated by oligopolies or monopolies whose primary objective often shifts from creating value to protecting market share and extracting economic rents.
This is not an argument against markets. Quite the opposite. It is an argument for preserving the conditions that allow markets to continue functioning as learning systems. Competitive markets remain one of the best mechanisms we have ever discovered for encouraging innovation and adapting to changing circumstances. The challenge is to ensure that the institutions operating within those markets continue to learn, adapt and remain competitive rather than gradually concentrating power in fewer and fewer organisations.
The Answer Is Not Bigger Models
If the economy is a complex adaptive system, it is natural to think that better forecasting will come from larger models, more data and faster computers. Advances in artificial intelligence reinforce this belief because AI can identify patterns in enormous quantities of information that no human being could ever analyse. These technologies will undoubtedly improve our understanding of many aspects of the economy, but they do not remove the fundamental limitation identified by cybernetics.
The difficulty is not simply a shortage of information. The economy is continually changing while we try to understand it. Every new technology, every political decision, every business innovation and every change in consumer behaviour alters the relationships that existed only a short time before. A larger model may describe yesterday’s economy more accurately, but tomorrow’s economy is already becoming something different.
This is why we should be careful not to confuse prediction with learning. Better models may improve forecasts, but they do not automatically create institutions that can respond effectively when circumstances change. The question is therefore not simply how we build more accurate economic models. It is how we build an economy that can continually adapt as new information becomes available.
Living systems solved this problem billions of years ago. They did not become more resilient by creating a larger brain to control every cell. They became more resilient by distributing learning throughout the organism. Every cell responds to its local environment while operating within a shared set of rules. Stability emerges because learning occurs everywhere rather than only at the centre.
Perhaps our economic institutions need to evolve in the same direction.
The Economy as a Learning System
This is the idea behind Cellular Economics. It begins with a simple observation. If markets work because they distribute decision-making throughout society, perhaps our institutions can also distribute learning throughout society.
Instead of expecting governments to solve every problem from the centre, we can create many thousands, and eventually millions, of relatively small organisations, or cells, each with a clearly defined purpose and the freedom to improve the way it achieves that purpose. Some cells might provide affordable housing. Others might invest in renewable energy, improve biodiversity, support local enterprises or build community infrastructure. Each would operate within an agreed framework, but each would also be free to learn from its own experience.
The important point is that every cell continually measures its own performance and adjusts its own rules when experience suggests a better way of operating. Learning therefore becomes part of the institution itself rather than something imposed from outside. Successful innovations can be adopted by other cells, while unsuccessful approaches remain local and can be changed without disrupting the wider economy. Instead of relying on a single large experiment conducted across an entire nation, society benefits from thousands of small experiments conducted simultaneously.
Markets continue to fulfil the role they have always played by distributing decision-making and encouraging competition. Cellular Economics adds another layer by distributing learning. Markets tell us which organisations are creating value. Cells continually improve the way those organisations operate. Together, they create a system capable not only of responding to change but also of becoming progressively better at doing so.
In that sense, Cellular Economics is not an alternative to markets. It is an attempt to build institutions that learn in the same way that successful markets already do. Markets distribute decisions. Science distributes learning. Cellular Economics brings those two ideas together by creating institutions that continually learn from experience and continually improve how they serve their members and communities.
Profit Without Extraction
One of the assumptions built into modern capitalism is that profits are continually extracted from the people who use goods and services. Investors provide capital, businesses generate profits and those profits are distributed to owners. Each new generation of customers therefore contributes to paying returns to earlier investors.
There is another way to think about profit. Some forms of wealth disappear when they are used. Coal is burned once. Oil is consumed once. Once they have created value, they are gone.
Other forms of wealth become more valuable precisely because they continue to be used. Knowledge does not disappear when it is shared. Software is copied without being consumed. A well-maintained bridge, railway, library or public building can provide value for generations. A solar farm continues producing electricity year after year without consuming the sunlight that powers it.
These assets create value through reuse rather than consumption. Cellular Economics applies the same principle to capital.
When a social enterprise earns a surplus, that surplus is not continually extracted from the organisation. Instead, it remains part of the enterprise’s productive capacity. The organisation becomes progressively more valuable because its accumulated knowledge, infrastructure, relationships and financial strength remain available for future users.
Members still receive the benefit of these profits, but at different times. While they use the enterprise’s services, they help build its long-term value. When they eventually leave, sell their ownership, or transfer it to someone else, they receive the value that later users are willing to pay. The profits have not disappeared. They have remained within the enterprise until ownership changes hands.
Profit therefore comes from recycling value rather than extracting value. This is similar to the way renewable energy differs from fossil fuels. Fossil fuels create value through consumption. Renewable energy creates value by continually harvesting the same natural process. The sun does not become smaller because yesterday’s electricity is generated from it. The same resource continues creating value every day.
Community capital can work in the same way. Instead of continually extracting wealth from productive organisations, the wealth remains attached to the productive assets themselves. Every generation pays for the benefits it receives from previous generations while also adding value for those who follow. The enterprise becomes progressively stronger, and the accumulated value is passed from one generation of members to the next rather than continually leaving the organisation.
This produces profit without extraction. Capital behaves more like renewable energy or knowledge than like a consumable resource. The same assets continue creating value, and that value is continually recycled through successive generations of users.
Today’s users build tomorrow’s wealth, and tomorrow’s users pay yesterday’s builders. Wealth is not extracted from the system; it is continually recycled through it.
Why Every Cell Is a Scientific Experiment
The idea behind Cellular Economics is not simply to divide economic activity into smaller organisations. The more important idea is that organisations can become learning organisations. In science, we improve our understanding by proposing explanations, testing them against reality and changing them when the evidence tells us we can do better. There is no reason why our economic institutions cannot work in the same way.
Every cell begins with a clear purpose and a set of operating rules. Those rules represent the members’ best understanding of how to achieve that purpose. In effect, they are hypotheses. The cell then measures its own performance against its objectives. If experience shows that different rules would produce better outcomes, the members change those rules and continue learning. The revised rules become new hypotheses, which are again tested through experience.
In this way every cell becomes a permanent scientific experiment. Instead of governments trying to discover a single solution that works for an entire nation, thousands of independent organisations are continually discovering better ways to solve problems under different local conditions. Successful ideas spread because other cells are free to adopt them. Less successful ideas remain local and can be modified or abandoned without affecting the stability of the wider economy.
This is one of the important differences between Cellular Economics and many previous proposals for economic reform. It does not claim to have discovered the perfect economic system. Instead, it provides a framework within which better systems can continually evolve. The objective is not to eliminate mistakes. The objective is to ensure that mistakes become opportunities for learning rather than failures that affect an entire economy.
Start Small and Learn
If there is one lesson that science teaches us, it is not to begin with grand theories that attempt to explain everything at once. Science progresses through carefully designed experiments. Ideas are tested on a small scale, evidence is gathered, and the results determine what happens next. If the evidence is encouraging, others repeat the experiment. If it is not, the theory is modified and tested again.
Economic reform can proceed in the same way. Cellular Economics does not require governments to redesign an entire national economy. In fact, that would contradict the very principles on which it is based. Instead, it begins with small pilot projects where results are measured carefully, and the rules improved over time.
A housing cell might be established to explore new ways of financing home ownership. An energy cell might test community ownership of renewable energy. A biodiversity cell might investigate whether environmental restoration can become economically self-sustaining. Other cells could focus on local enterprise, aged care, active transport, cultural activities or any number of other community objectives. Each would be free to learn from its own experience while sharing successful ideas with others.
The important point is that no single experiment determines the future of the entire economy. Society benefits from many independent experiments taking place simultaneously. Some will succeed, others will fail, but every one of them contributes to our understanding. That is how science progresses, how living systems evolve and how economies can evolve.
A Different Future for Economics
For more than two centuries, economics has helped us understand markets, trade, incentives and the way people respond to changing circumstances. Those insights have transformed living standards across much of the world, and there is every reason to expect economics to continue making important contributions in the future. The question is not whether economics remains valuable. The question is what the next stage in its development might look like.
As societies become larger and more interconnected, the limits of centralised prediction become increasingly apparent. No government, no central bank and no group of economists can ever possess enough information to predict or control every interaction taking place within a modern economy. That is not because economists lack intelligence or because governments lack commitment. It is because complexity itself has become one of the defining characteristics of the modern world.
Perhaps, then, the next great advance in economics will not come from building ever larger forecasting models. It will come from designing institutions capable of continuous learning. Markets already distribute decision-making across society. Science has shown us how learning occurs through experimentation and feedback. Living systems demonstrate how stability emerges when adaptation is distributed throughout the whole system rather than concentrated at the centre.
Cellular Economics is a framework to bring these insights together. It does not seek to replace markets because competitive markets remain one of humanity’s greatest discoveries. It does not ask governments to withdraw from the economy, but to become architects of institutions that encourage learning, cooperation and experimentation. It does not force profits to be distributed through taxation. Nor does it claim to have found the final answer. It proposes a different way of discovering better answers.
Whether Cellular Economics succeeds or fails cannot be decided by theory alone. Like every scientific idea, it can be tested against experience. Small pilot projects are established, their performance measured and their rules refined in the light of evidence. If they prove successful, others can adopt what has been learned. If they fail, the lessons will still improve our understanding of how economic institutions can work more effectively.
That, perhaps, is the most important lesson science tells us. The future will always surprise us because people will always continue to learn, innovate and adapt. Rather than trying to predict every outcome, we can build institutions that can learn faster than the world around them changes.
Cellular Economics is not a final answer. It is a better way of discovering answers. If it succeeds, it will be because the evidence supports it. If it fails, we can change it and try again. That is how science progresses, and it is also how economics becomes a science.
Markets distribute decisions. Science distributes learning. Living systems distribute adaptation. Reuse distributes profits. Cellular Economics brings these ideas together so that economics becomes not merely a science of forecasting, but a science of continual learning.
