Book Summary Of Origin Of Wealth By Eric Beinhocker

Book Summary Of Origin Of Wealth By Eric Beinhocker



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Amazon Summary

Over 6.4 billion people participate in a $36.5 trillion global economy, designed and overseen by no one. How did this marvel of self-organized complexity evolve? How is wealth created within this system? And how can wealth be increased for the benefit of individuals, businesses, and society? In The Origin of Wealth, Eric D. Beinhocker argues that modern science provides a radical perspective on these age-old questions, with far-reaching implications. According to Beinhocker, wealth creation is the product of a simple but profoundly powerful evolutionary formula: differentiate, select, and amplify. In this view, the economy is a "complex adaptive system" in which physical technologies, social technologies, and business designs continuously interact to create novel products, new ideas, and increasing wealth. Taking readers on an entertaining journey through economic history, from the Stone Age to modern economy, Beinhocker explores how "complexity economics" provides provocative insights on issues ranging from creating adaptive organizations to the evolutionary workings of stock markets to new perspectives on government policies. A landmark book that shatters conventional economic theory, The Origin of Wealth will rewire our thinking about how we came to be here--and where we are going.

About Author: Eric D. Beinhocker

Eric D. Beinhocker is a Senior Fellow at the McKinsey Global Institute. Fortune magazine named him a Business Leader of the Next Century, and his writings on business and economics have appeared in a variety of publications, including the Financial Times.


Technology Types

  • Social technologies
  • Physical technologies

My Favorite Highlights

Marrying Biology And Economics

The notion that the economy is an evolutionary system is a radical idea, especially because it directly contradicts much of the standard theory in economics developed over the past one hundred years. It is far from a new idea, however. Evolutionary theory and economics have a long and intertwined history.38 In fact it was an economist who helped spark one of Charles Darwin’s most important insights. In 1798, the English economist Thomas Robert Malthus published a book titled An Essay on the Principle of Population, as It Affects Future Improvements of Society, in which he portrayed the economy as a competitive struggle for survival and a constant race between population growth and humankind’s ability to improve its productivity. It was a race that, Malthus predicted, humankind would lose. Darwin read Malthus’s work and described his reaction in his autobiography: In October 1838, that is fifteen months after I had begun my systematic enquiry, I happened to read for my amusement “Malthus on Population”, and being well prepared to appreciate the struggle for existence which everywhere goes on from long-continued observation of the habits of animals and plants, it once struck me that under these circumstances favorable variations would tend to be preserved and unfavorable ones to be destroyed. The result of this would be the formation of new species. Here then I had at last got a theory by which to work.39 Darwin’s great insight into the critical role of natural selection in evolution was thus inspired by economics.40 It was not long after Darwin published his Origin of Species that the intellectual currents began to flow back the other way from evolutionary theorists to economists. In 1898, the economist Thorstein Veblen wrote an article that still reads remarkably well today arguing that the economy is an evolutionary system.41 Not long afterward, Alfred Marshall, one of the founders of modern economic theory, wrote in the introduction to his famous Principles of Economics, “The Mecca of the economist lies in economic biology.”42 Over the following decades, a number of great economists, including Joseph Schumpeter and Friedrich Hayek, delved into the relationship between economics and evolutionary theory.43 In 1982, Richard Nelson and Sidney Winter published a landmark book titled An Evolutionary Theory of Economic Change. It was the first major attempt to marry evolutionary theory, economics, and the then recently developed tool of computer simulation.44

Economic wealth and biological wealth are thermodynamically the same sort of phenomena, and not just metaphorically. Both are systems of locally low entropy, patterns of order that evolved over time under the constraint of fitness functions. Both are forms of fit order. And the fitness function of the economy - our tastes and preferences - is fundamentally linked to the fitness function of the biological world - the replication of genes. The economy is ultimately a genetic replication strategy.


Each invention opens up new niches for future inventions, and components from one invention are often recycled into new forms. As discussed earlier in the book, some inventions, such as the automobile, set off major avalanches of change, and some set off only small avalanches (the size of which Kauffman believes follow a power law). Nevertheless, all inventions have ripple effects, no matter how small.11
In a world governed by the Second Law of Thermodynamics, successful exploitation of one’s niche in the current environment is a necessary condition for survival—calories in must be greater than calories out, and money in must be greater than money out. But, as we also know, the shelf life of strategies in evolutionary systems can be quite short, so one must continuously explore new strategies, or risk finding oneself stuck in a poor position when the environment inevitably changes. But because the processes of executing and adapting each require resources, there is a natural tension in the system. How much should be invested in executing for today versus adapting for tomorrow? In business organizations, there is a constant competition for money, people, and senior management time between the need to perform in the short run and the equally critical need to invest and innovate for the long run.
In a world of 6.4 billion people, why is there so much similarity in preferences? Why, much to the chagrin of nutritionists and antiglobalization protesters, do people around the world like fizzy, sweet drinks such as Coca-Cola? Why do teens from Russia to Brazil crave the latest Nike sneakers? And why was the television show Bay Watch a big hit in both the suburbs of the United States and small villages in rural China?


The appearance of common features and Good Tricks in the population of interactors leads us to another important point. Complex designs are inherently modular.13 Our bodies have a bewildering array of systems, subsystems, and other components, from the cardiovascular system, to the heart, to an individual red blood cell, which in turn has its own systems and subsystems. Complex human-made designs have the same characteristic, such as a car’s braking system, the brakes themselves, and an individual brake pad. A complex design can be viewed as a hierarchical collection of modules and submodules. In an evolutionary system, each of these systems, subsystems, and component parts has corresponding pieces of code for it in the schema. Thus, the schema for an evolutionary construction is full of such building blocks combined into higher-level building blocks that are combined into still higher-level building blocks. In biology, the building blocks of our DNA schemata are individual genes that code for things ranging from eye color to the chemical cycle that converts toxic ammonia into less dangerous urea.

Have A Bushy Strategy

Typical strategic planning processes focus on chopping down the branches of the strategy decision tree, eliminating options, and making choices and commitments. In contrast, an evolutionary approach to strategy emphasizes creating choices, keeping options open, and making the tree of possibilities as bushy as possible at any point in time. Options have value.40 An evolving portfolio of strategic experiments gives the management team more choices, which means better odds that some of the choices will be right, or, as my McKinsey colleague Lowell Bryan calls it, “loading the dice.”41 The objective is to be able to make lots of small bets, and only make big bets as a part of amplifying successful experiments when uncertainties are much lower.
An evolutionary approach to strategy emphasizes keeping the strategy tree bushy and the options open for as long as possible. But irreversibility—the first G-R Condition of wealth creation—cannot be avoided forever, and at some point, difficult-to-reverse commitments must be made. Companies are hierarchies, and senior management must ultimately make commitments and allocate finite resources. How do we make such decisions in an evolutionary context?

Popular Highlights Of All Kindle Readers

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.
Evolution creates designs, or more appropriately, discovers designs, through a process of trial and error. A variety of candidate designs are created and tried out in the environment; designs that are successful are retained, replicated, and built upon, while those that are unsuccessful are discarded. Through repetition, the process creates designs that are fit for their particular purpose and environment. If the conditions are right, competition between designs for finite resources drives the emergence of greater structure and complexity over time, as evolution builds on the successes of the past to create novel designs for the future.32 Then as the world changes, so too do the designs that evolution creates, often in brilliant and sometimes surprising ways. Evolution is a method for searching enormous, almost infinitely large spaces of possible designs for the almost infinitesimally small fraction of designs that are “fit” according to their particular purpose and environment. As Dennett puts it, evolution is a search algorithm that “finds needles of good design in haystacks of possibility.”33
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. Most notably, the sharp bend in economic growth around 1750 coincides with the great technological leap of the Industrial Revolution. But the evolution of technology is only part of the story. The evolutionary economist Richard Nelson of Columbia University has pointed out that there are in fact two types of technology that play a major role in economic growth.35:
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. Examples include settled agriculture, the rule of law, money, joint stock companies, and venture capital.
Nelson notes that while Physical Technologies have clearly had an immense impact on society, the contributions of Social Technologies have been equally important and in fact, the two coevolve with each other.36 During the Industrial Revolution, for example, Richard Arkwright’s invention of the spinning frame (a Physical Technology) in the eighteenth century made it economical to organize cloth-making in large factories (a Social Technology), which in turn helped spur numerous innovations in the application of water power, steam, and electricity to manufacturing (back to Physical Technologies).37 The stories of the agricultural, industrial, and information revolutions are all largely stories of the reciprocal dance between Physical and Social Technologies.
Yet the coevolution of Physical and Social Technologies is only two-thirds of the picture. Technologies alone are nothing more than ideas and designs. The Physical Technology for a cloth-spinning frame is not itself a cloth-spinning frame—someone actually has to make one. Likewise, the Social Technology for a factory is not a factory—someone actually has to organize it. 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.
There are two fundamental questions that economists have grappled with throughout the history of their field: 1. how wealth is created and how wealth 2. How wealth is allocated. Smith addressed both in The Wealth of Nations.20 His answer to the first question was simple but powerful: economic value is created when people take raw materials from their environment and then, through their labor, turn those materials into something that people want. For example, a pottery might take clay from the ground and use it to create a bowl. Smith’s great insight was that the secret to wealth creation was improving the productivity of labor. The more bowls a potter can make in an hour, the richer he or she will be. The secret to greater productivity in turn was the division of labor and the specialization that it enables.21 Smith famously cited the example of a pin factory, where he observed ten men at work, each of whom specialized in one or two steps of the pin-making process.22 Smith noted that this specialization and cooperation enabled the group to make 48,000 pins per day, or 4,800 pins per man. Without this division of labor, he estimated, the factory would have only been able to make twenty pins per man per day, or in the case of the less-skilled men, none.
While Walras’s ideas were novel, what was truly revolutionary was his use of sophisticated mathematics borrowed from physics. If one accepted Walras’s assumptions that people had different utilities, and that they were rational and self-interested in maximizing those utilities, then one could predict with mathematical precision how they would trade and the relative prices that would be set in the economy. There were a few minor details, like the existence of his godlike auctioneer and questions as to how one could observe and measure individuals’ utilities, but these issues could be addressed in the future, a small price to pay for the ability to make mathematically precise, scientific predictions about things like prices in the economy for the first time.48 Walras’s willingness to make trade-offs in realism for the sake of mathematical predictability would set a pattern followed by economists over the next century. Walras and Jevons were not familiar with the Second Law and thus were not aware of the distinction between open and closed systems, or the existence of complex adaptive systems. In fact, a detailed understanding of open systems emerged only gradually during the twentieth century, accelerating with the work of the Russian-born chemist Ilya Prigogine in the 1960s and 1970s. The Traditional model, then, was created with the implicit assumption that the economy is a thermodynamically closed equilibrium system, even though, at the time, Walras, Jevons, and their fellow Marginalists did not know that they were building this assumption into their theories. For the next one hundred years, as economics and physics each went their separate ways, this assumption lay buried in the mathematical heart of Traditional Economics.68 Unfortunately for economic theory, this unawareness of the Second Law meant that 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. The proof for this lies just outside your window, right under your nose. It is so obvious, in fact, that it has escaped the attention of most economists until relatively recently.69 If the economy were a closed equilibrium system, its defining characteristic would be a trend toward less order, complexity, and structure over time, as entropy sends any closed equilibrium system inevitably toward a featureless stasis. 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.70 All the movement, buzz, organization, and activity of the economy outside your window cannot be the product of a closed equilibrium system. As we saw in the first chapter, the defining characteristic of the economy has been an immense rise in economic complexity throughout its journey from the 101 SKU economy of Homo habilis to the 1010 SKU economy of the modern world. The growth of economic activity from the Stone Age until now has been one long story of fighting entropy on a grand scale—something that could only happen in an open disequilibrium system.
Scientists refer to parts or particles that have the ability to process information and adapt their behavior as agents and call the systems that agents interact in complex adaptive systems.46 Other examples of complex adaptive systems include the cells in your body’s immune system, interacting organisms in an ecosystem, and users on the Internet. With the advent of inexpensive, high-powered computers in the 1980s, scientists began to make rapid progress in understanding complex adaptive systems in the natural world and to see such systems as forming a universal class, with many common behaviors. In fact, many biologists have come to view evolutionary systems as just one particular type, or subclass, of complex adaptive systems.
The Traditional model implicitly assumes that people only care about the outcome of economic decisions, and not the process that people go through in making them. Things like bargaining, fairness, and coercion don’t enter the picture. In addition, the model assumes that people only care about what they personally gain and lose and don’t look at the results for other people. Experiments with the ultimatum game are direct evidence against those assumptions. In a survey of recent behavioral research, Herbert Gintis, of the University of Massachusetts and the Santa Fe Institute, and a group of colleagues note that Adam Smith portrayed humankind as a selfish, materialistic creature in his Wealth of Nations.14 Gintis and company comment that many people forget that Smith also wrote another book, The Theory of Moral Sentiments, in which Smith presented a more nuanced view of human nature, portraying it as capable of both selfishness and generosity. Evidence from the ultimatum game and other experiments shows that the second Smith was right. Humans have strongly ingrained rules about fairness and reciprocity that override calculated “rationality.” Gintis and his colleagues observe that humans are “conditional cooperators” who will behave generously as long as others are doing so, and “altruistic punishers” who will strike back at those perceived to behave unfairly, even at the expense of their own immediate interests.
This kind of interdependency in a network creates what Kauffman calls a complexity catastrophe. The effect occurs because as the network grows, and the number of interdependencies grows, the probability that a positive change in one part of the network will lead to a cascade resulting in a negative change somewhere else grows exponentially with the number of nodes. This in turn means that densely connected networks become less adaptable as they grow.

Top Quotes From Michael Magoon

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.

Thought Leaders References

  • Stuart Kauffman