Title: The Origins of Wealth, Evolution, Complexity and the Radical Remaking of the Economics
Author: Eric D Beinhocker
Scope: 4.5 stars
Readability: 3.5 stars
My personal rating: 5 stars
See more on my book rating system.
Topic of Book
The economy is a complex, adaptive system that evolves in ways similar to how biological organism evolve in nature. This means that we have to reconceptualize economics.
- Traditional economics assumes that the economy is a closed-stable system. Because of this assumption, economist cannot answer the most basic question: where did the economy come from?
- The actual economy is an open, unstable system.
- Wealth creation is the product of a simple, but profoundly powerful, three-step formula— differentiate, select, and amplify.
- The three-way coevolution of Technology, Cooperation, and Business Models that accounts for the patterns of change and growth we see in the economy.
Important Quotes from Book
This book is the story of what I will call the Complexity Economics revolution: what it is, what it tells us about the deepest mysteries in economics, and what it means for business and for society at large.
The most startling empirical fact in economics is that there is an economy at all. The second most startling empirical fact is that day in and day out, for the most part, it works.
Cooperative trading between nonrelatives is a uniquely human activity. No other species has developed the combination of trading among strangers and a division of labor that characterizes the human economy.
If we use the appearance of the first tools as our starting point, it took about 2,485,000 years, or 99.4 percent, of our economic history to go from the first tools to the hunter-gatherer level of economic and social sophistication typified by the Yanomamô. It then took only 0.6 percent of human history to leap from the $90 per capita 102 SKU economy of the Yanomamô, to the $36,000 per capita 10 SKU economy of the New Yorkers.
Zooming in for a more granular look into the past 15,000 years reveals something even more surprising. The economic journey between the hunter-gatherer world and the modern world was also very slow over most of the 15,000-year period, and then progress exploded in the last 250 years.
We now have a greater sense of just what kind of a phenomenon we are dealing with and can add some additional questions to our inquiry:
- How can something as complex and highly structured as the economy be created and work in a self-organized and bottom-up way?
- Why has the complexity and diversity of the economy grown over time? And, why does there appear to be a correlation between the complexity of an economy and its wealth?
- Why has the growth in wealth and complexity been sudden and explosive rather than smooth?
Any theory that seeks to explain what wealth is and how it is created must answer these questions. Although we know the historical narrative of what has happened in the history of the economy, for example, the advent of settled agriculture, the Industrial Revolution, and so on, we still need a theory of how it happened and why it happened.
Modern science provides just such a theory. This book will argue that wealth creation is the product of a simple, but profoundly powerful, three-step formula— differentiate, select, and amplify—the formula of evolution. The same process that has driven the growing order and complexity of the biosphere has driven the growing order and complexity of the “econosphere.”And the same process that led to an explosion of species diversity in the Cambrian period led to an explosion in SKU diversity during the Industrial Revolution.
Evolution is an algorithm; it is an all-purpose formula for innovation, a formula that, through its special brand of trial and error, creates new designs and solves difficult problems. Evolution can perform its tricks not just in the “substrate” of DNA, but in any system that has the right information processing and information-storage characteristics. In short, evolution is a simple recipe of “differentiate, select, and amplify” is a type of computer program— a program for creating novelty, knowledge, and growth. Because evolution is a form of information processing, it can do its order-creating work in realms
Our intentionality, rationality, and creativity do matter as a driving force in the economy, but they matter as part of a larger evolutionary process.
Economic evolution is not a single process, but rather the result of three interlinked processes. The first is the evolution of technology, a critical factor in economic growth throughout history.
[There are] two types of technology that play a major role in economic growth. The first is Physical Technology; this is what we are accustomed to thinking of as technology things such as bronze-making techniques, steam engines, and microchips. Social Technologies, on the other hand, are ways of organizing people to do things.
In order for technologies to have an impact on the world, someone, or some group of people, needs to turn the Physical and Social Technologies from concepts into reality. In the economic realm, that role is played by business. Businesses fuse Physical and Social Technologies together and express them into the environment in the form of products and services.
One of the major themes of this book is that it is the three-way coevolution of Physical Technologies, Social Technologies, and business designs that accounts for the patterns of change and growth we see in the economy.
Traditional economic theory views the economy as being like a rubber ball rolling around the bottom of a large bowl. Eventually the ball will settle down into the bottom of the bowl, to its resting, or equilibrium, point. The ball will stay there until some external force shakes, bends, or otherwise shocks the bowl, sending the ball to a new equilibrium point.
The Marginalists and their successors fundamentally (though unwittingly) misclassified the economy. The economy is not a closed equilibrium system; it is an open disequilibrium system and, more specifically, a complex adaptive system.
Closed equilibrium systems do not spontaneously self-organize; they do not generate patterns, structures, and complexity; and above all, they do not create novelty over time.7 0 All the movement, buzz, organization, and activity of the economy outside your window cannot be the product of a closed equilibrium system.
Complexity Economics is a better approximation of economic reality than Traditional Economics, just as Einstein’s relativity is a better approximation of physical reality than Newton’s laws.
It is important to note that the key behavioral assumptions of Traditional Economics were not developed because anyone thought they were a good description of real human behavior; they were adopted to make the math work in the equilibrium framework.
In the complex adaptive system of the economy, understanding the micro-level behaviors of individuals is essential to understanding how the system as a whole behaves.
Networks are an essential ingredient in any complexadaptive system. Without interactions between agents, there can be no complexity.
Power laws, along with oscillations and punctuated equilibrium, are another signature characteristic of complex adaptive systems.
Complex designs are inherently modular.
Evolution is thus a process of sifting from an enormous space of possibilities. It tries a bunch of designs, sees what works, and does more of what works and less of what doesn’t, repeated over and over again. There is no foresight, no planning, no rationality, and no conscious design. There is just the mindless, mechanical grinding of the algorithm.
To recap the substrate-neutral version of evolution we have been building, here are the necessary conditions for evolution to do its work:
- There is a design space of possible designs.
- It is possible to reliably code those designs into a schema.
- There is some form of schema reader that can reliably decode schemata and render them into interactors. In endogenous evolution, schemata code for the building of their own readers.
- Interactors are made up of modules and systems of modules that are coded for by building blocks in the schemata
- The interactors are rendered into an environment. The environment place constraints on the interactors (e.g., the laws of physics, climate, or the LEGO Judge), any of which can change over time. A particularly important constraining factor is competition among interactors for finite resources.
- Collectively, the constraints in an environment create a fitness function whereby some interactors are fitter than others.
The algorithm conducts its search of the design space as follows:
- There is a process of variation of schemata over time. Schemata can be varied by any number of operators, for example, crossover and mutation.
- Schemata are rendered into interactors creating a population.
- Acting on the interactors is a process of selection, whereby some designs are deemed by the fitness function to be fitter than others.
- Less fit interactors have a higher probability of being removed from the population.
- There is a process of replication. Fit interactors have on average a greater probability of replicating, and more variants are made of them than of less-fit designs.
- Thus over time, building blocks that contribute to inter actor fitness are replicated more frequently and become more common in the population.
- Finally, the algorithmic process of variation, selection, and replication is conducted recursively on the population, with output from one round acting as the input for the next round.
When the algorithm is running in an appropriately setup information processing substrate with the right parameters, we can then expect to see the following results:
- The creation of order from randomness.
- The discovery of fit designs.
- Continuous adaptation.
- The accumulation of knowledge.
- The emergence of novelty.
- Growth in resources devoted to successful designs.
Evolution is highly effective at finding fit designs in massive design spaces with rough-correlated fitness landscapes because:
- Evolution employs parallel search. In effect, each member of the population is an individual experiment in design, so there are many hikers out looking for high peaks.
- Evolution creates a spectrum of jumps on the landscape. It doesn’t pursue just short, incremental jumps that could get stuck on local optima; nor does it pursue too many crazy long jumps that have a greater chance of failing than succeeding.
- Finally, evolution is a process of continuous innovation. The recursive nature of the algorithm never stops. This is essential, given the constantly changing nature of the landscape… The system has no equilibrium—in evolutionary systems, stasis is a recipe for extinction.
Evolution is a knowledge-creation machine—a learning algorithm.
All the order and complexity, all the knowledge, was created and assembled by the simplest of recipes: differentiate, select, replicate, and repeat.
Wealth is knowledge and its origin is evolution.
While Complexity Economics strips away our illusions of control over our economic fate, it also hands us a lever—a lever that we have always possessed but never fully appreciated. We may not be able to predict or direct economic evolution, but we can design our institutions and societies to be better or worse evolvers.
All competitive advantage is temporary.
A brutal truth about most companies. Markets are highly dynamic, but the vast majority of companies are not.
Human beings are neither inherently altruistic nor selfish; instead they are what researchers call conditional cooperators and altruistic punishers. Gintis and his colleagues refer to this type of behavior as strong reciprocity.
In essence, people try to follow the Golden Rule, but with a slight twist: do unto others as you would have them do unto you (i.e., conditional cooperation)—but if others don’t do unto you, then nail them, even at personal cost to yourself (i.e., altruistic punishment). People have a highly developed sense of whom they can trust and whom they cannot.