# The Origin of Wealth  ## Metadata - Author: [[Eric Beinhocker]] - Full Title: The Origin of Wealth - Category: #books ## Highlights - Yet, as Smith showed in his Wealth of Nations, wealth is not a fixed concept; the value of something depends on what someone else is willing to pay for it at a particular point in time. ([Location 202](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=202)) - To summarize 2.5 million years of economic history in brief: for a very, very, very long time not much happened; then all of a sudden, all hell broke loose. It took 99.4 percent of economic history to reach the wealth levels of the Yanomamö, 0.59 percent to double that level by 1750, and then just 0.01 percent for global wealth to leap to the levels of the modern world. ([Location 337](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=337)) - 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. ([Location 354](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=354)) - If the economy is truly an evolutionary system, and there are general laws of evolutionary systems, then it follows that there are general laws of economics—a controversial notion for many. ([Location 383](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=383)) - Evolution creates designs, or more appropriately, discovers designs, through a process of trial and error. ([Location 409](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=409)) - As Dennett puts it, evolution is a search algorithm that “finds needles of good design in haystacks of possibility.” ([Location 416](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=416)) - Our intentionality, rationality, and creativity do matter as a driving force in the economy, but they matter as part of a larger evolutionary process. ([Location 441](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=441)) - Businesses fuse Physical and Social Technologies together and express them into the environment in the form of products and services. ([Location 459](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=459)) - Just as Turgot showed that there were diminishing benefits to increased production, Gossen showed that there were also diminishing benefits to increased consumption. ([Location 737](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=737)) - Also, just as diminishing marginal returns keep farmers from growing an infinite quantity of crops, diminishing marginal utility keeps consumers from consuming an infinite quantity of doughnuts. ([Location 744](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=744)) - 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. ([Location 834](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=834)) - Just as a ball in a bowl seeks its minimum energy state within the constraints of the sides of the bowl, human beings will seek their maximum happiness state within the constraints of their finite resources and will trade their way to get there. ([Location 873](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=873)) - Note: Early economic theory took inspiration from physics. - Adam Smith postulated that human self-interest drives markets to a form of balance, a stable state where prices are agreed on, trades are made, and the market clears. ([Location 876](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=876)) - He reasoned that there are four kinds of trades that people can make. First, there are win-win trades, in which both parties gain; in this case it is clear that welfare has gone up. Second, there are trades, in which one party gains, but no one loses, and again welfare has unambiguously gone up. Third, there are trades, in which no one gains, but someone loses, and in this case welfare has unambiguously gone down. Fourth and finally, there are trades, in which some parties win and some lose, but without the ability to directly measure utility, it is impossible to determine what the net impact is. ([Location 890](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=890)) - Note: Getting around the problem of measuring ‘utility’ in economics. The trades that arise due to these tendencies are called Pareto Trades. - The Pareto optimal is thus the point at which no further trades can be made without making someone worse off. ([Location 898](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=898)) - Without a way to precisely measure utilities and a dictator to force trades that reduce the welfare of some for the benefit of others, the Pareto optimal is the best that one can do in a free society. ([Location 900](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=900)) - for growth to occur, there must be “a source of energy within the economic system which would of itself disrupt any equilibrium that might be attained.”67 For Schumpeter, that source of energy was the figure of the entrepreneur, whom he wrote about in almost heroic terms. ([Location 991](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=991)) - The origin of wealth, according to Schumpeter, lies in the heroic efforts of individual entrepreneurs. ([Location 997](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=997)) - it is not how much capital a country has that makes it rich; it is how productive that capital is, and according to Solow the key to productivity is technology. ([Location 1026](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1026)) - the more we invest in knowledge cumulatively over time, the higher the payoffs. An hour of R&D invested in microchips and biotech today has a higher payoff than an hour of R&D invested in steam locomotives and telegraphs in 1900. ([Location 1044](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1044)) - Arrow-Debreu general equilibrium theory ([Location 1104](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1104)) - Note: In mathematical economics, the Arrow–Debreu model suggests that under certain economic assumptions (convex preferences, perfect competition, and demand independence) there must be a set of prices such that aggregate supplies will equal aggregate demands for every commodity in the economy. - coarse- and fine-grained maps (and theories) must agree with each other and the observations of underlying reality. ([Location 1195](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1195)) - Of all the assumptions in Traditional Economics, perhaps the strongest and most obviously unrealistic is its model of human behavior, ([Location 1204](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1204)) - The standard model, often referred to as perfect rationality, is built on two fundamental assumptions. The first is that people pursue their self-interest in economic matters. ([Location 1205](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1205)) - The second part of the assumption is that people pursue their self-interest in fantastically complex and calculating ways. ([Location 1208](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1208)) - Economists regularly assume that we take into account factors such as inflation rates, estimates of future government spending, and the trade deficit in our daily decision making. Economists also assume that we process all this information using equations and calculations that they themselves find difficult to solve. ([Location 1209](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1209)) - Other typical assumptions about the world we live in include: There are no transaction costs (e.g., no fees, taxes, legal restrictions, or other costs or barriers to buying and selling) All products are pure commodities sold only on price (e.g., no brands or differences in product quality) Companies are always working as efficiently as possible Consumers can purchase insurance for any possible eventuality Economic decision makers only interact with each other through price, usually through an auction mechanism (when was the last time your supermarket held an auction?) This combination of assumptions has caused Axel Leijonhufvud, a macro-economist at the University of California, Los Angeles, to comment that Traditional Economics models “incredibly smart people in unbelievably simple situations,” ([Location 1217](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1217)) - Real people are actually quite poor at complex logical calculations, but are very good at quickly recognizing patterns, interpreting ambiguous information, and learning. Real people are also fallible and subject to biases in their decision making. Finally, they engage in what Herbert Simon called satisficing, whereby one looks for a result that is “good enough” rather than the absolute best. ([Location 1229](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1229)) - Science is full of examples of fields where researchers can explain phenomena and test the validity of their explanations, without necessarily being able to make accurate forecasts. ([Location 1367](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1367)) - As Sir Karl Popper showed in the 1930s, there is no “final proof” that a theory is correct, but one can say whether a theory is disproved by data, whether one theory fits the data better than another, and whether a theory has yet to be contradicted by data.34 For example, one cannot say that Einstein’s theory of relativity has been proven, but one can say that its predictions have been well tested, it has yet to be contradicted, and it fits the data better than any alternative explanation proposed thus far. ([Location 1371](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1371)) - In open systems, there is a never-ending battle between energy-powered order creation and entropy-driven order destruction. ([Location 1604](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1604)) - The earth thus imports energy and exports entropy. ([Location 1609](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1609)) - Thus, individual capabilities, circumstances at birth, and, even more importantly, the little twists and turns of fate at the individual level all combine to create a particular path for an agent through simulated life. The key is that small differences at one point (i.e., a lucky or a bad break somewhere) can lead to major differences down the road. This acceleration of small differences tends to send some individuals to riches and others to rags. ([Location 1912](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=1912)) - When scientists compare two theories, they do so according to the correspondence principle. According to the correspondence principle, a new theory should reproduce the successes of the old theory, explain the failures of the old theory, and offer new insights that the old theory does not. ([Location 2059](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=2059)) - Walras and later economists tried to justify ex post facto the lack of realism in the assumptions by arguing that even if the assumption of perfect rationality was not a good description of how people do behave (in economics lingo, a “positive model”), it could be interpreted as a description of how they should behave (a “normative” model). ([Location 2452](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=2452)) - Humans have strongly ingrained rules about fairness and reciprocity that override calculated “rationality.” ([Location 2523](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=2523)) - 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. ([Location 2524](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=2524)) - Stories are vital to us because the primary way we process information is through induction. Induction is essentially reasoning by pattern recognition. It is drawing conclusions from a preponderance of evidence. ([Location 2646](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=2646)) - We like stories because they feed our inductive thinking machine, they give us material to find patterns in—stories are a way in which we learn. ([Location 2651](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=2651)) - In random networks, the phase transition from small clusters to giant clusters happens at a specific point, when the ratio of segments of thread (edges) to buttons (nodes) exceeds the value of 1 (i.e., on average, one thread segment for every button).5 One can think of the ratio of one edge to one node as the “tipping point” where a random network suddenly goes from being sparsely connected to densely connected.6 ([Location 2984](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=2984)) - We tend to think of someone as being well connected if he or she knows a particular world very well. But Watts and Newman’s research shows that the best-connected people are really the ones who have the most diverse group of contacts. ([Location 3059](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3059)) - If Traditional economies of scale were all there were to the economic growth story, then we would simply be making stone tools more cheaply today than we did 2 million years ago. But if we think of human organizations as a kind of Boolean network (admittedly, with far more states than on or off), then we can see that as organizations grow in size, the space of possible innovations unfolds exponentially. ([Location 3128](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3128)) - The number of connections per node has an important effect on the behavior of a network. Kauffman and his colleagues at the Santa Fe Institute have studied this relationship in depth.22 One of their key findings derives from the simple observation that if a network has on average more than one connection per node, then as the number of nodes grows, the number of connections will scale exponentially with the number of nodes. This means that the number of interdependencies in the network grows faster than the network itself. ([Location 3143](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3143)) - 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. ([Location 3175](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3175)) - We thus have two opposing forces at work in organizations: the informational economies of scale from node growth, and the diseconomies of scale from the buildup of conflicting constraints. Taken together, these opposing forces help us understand why big is both beautiful and bad: as an organization grows, its degrees of possibility increase exponentially while its degrees of freedom collapse exponentially. ([Location 3184](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3184)) - When Kauffman and his colleagues did their original work, they found that nonhierarchical networks exhibit spontaneous order with one or two average connections per node, but went into chaos (thus creating cascades of change and the potential for a complexity catastrophe) at four connections per node or more. ([Location 3252](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3252)) - One can see a recipe for creating a dysfunctional organization: just mix unpredictable behavior, a flat hierarchy, and lots of dense interconnections—the chances of getting anything done would be roughly zero. ([Location 3277](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3277)) - Equilibrium economics says that such a contraction in the money supply eventually should self-correct—prices and wages should drop to reflect less money in circulation, and eventually everything should get back to normal full-employment equilibrium. In the Great Depression, prices and wages did indeed drop, but this deflation caused people to spend even less (in a deflationary environment your money is worth more in the future, so it is best to hang on to it), further exacerbating the downward spiral. Keynes argued that these dynamics could cause economies to get stuck out of equilibrium for very long periods. So to knock the economy back to full employment, Keynes advocated that the government play a role by injecting money into the system. ([Location 3398](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3398)) - Keynes’s ideas were widely accepted by Western governments in the postwar years, but remained controversial in economic circles in the decades that followed. The debate came to a head in the 1960s and 1970s, when Milton Friedman argued that the kind of government spending Keynes advocated would not lead to long-term growth, but only to higher inflation. ([Location 3405](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3405)) - The ultimate accomplishment of Complexity Economics would be to develop a theory that takes us from theories of agents, networks, and evolution, all the way up to the macro patterns we see in real-world economies. ([Location 3439](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3439)) - Emergence may seem mysterious, but it is actually something that we experience every day. For example, a single water molecule of two hydrogen atoms and an oxygen atom does not feel wet (assuming you could feel a single molecule). But a few billion water molecules in a cup feel wet. That is because wetness is a collective property of the slippery interactions between water molecules in a particular temperature range. ([Location 3446](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3446)) - Complexity Economics likewise views economic patterns such as business cycles, growth, and inflation as emergent phenomena arising endogenously out of the interactions in the system. Complex adaptive systems tend to have signature emergent patterns that are common across many types of systems. These patterns help us better understand the workings of those systems. We will look at three such signature patterns: oscillations, punctuated equilibrium, and power laws. ([Location 3453](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3453)) - Oscillations are a common feature in complex adaptive systems. ([Location 3461](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3461)) - If one calculates the costs generated in the experiments versus those in the theoretically rational case, the costs generated by the real humans are on average ten times the perfectly rational costs. ([Location 3518](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3518)) - Note: This is with respect to the beer experiment. Wild oscillations were not predicted as per the rational theory. But they happen anyway. Oscillations are an integral part of complex systems. - But unlike a jelly given a single tap, once the oscillations start in the Beer Game, the system never returns to equilibrium.23 This is because the ultimate source of the oscillations in the Beer Game is not the exogenous shock itself (it just gets things started), but the behavior of the participants and the feedback structure of the system. The system is not propagating exogenous dynamics; it is endogenously creating them. ([Location 3534](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3534)) - There are two ways to dampen the cycles in the Beer Game: one is to reduce the time delays, and the other is to give the participants more information (e.g., giving the brewer direct visibility into what is happening at the retail level). ([Location 3548](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3548)) - The information technology revolution that began in the 1960s has directly affected both factors. Data shows that the volatility of the U.S. business cycle has been dampening since 1959, with a particularly sharp reduction beginning in the 1980s that coincided with the widespread adoption of fast, cheap computers. ([Location 3550](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3550)) - Power laws have been discovered in a wide variety of phenomena, including the sizes of biological extinction events, the intensity of solar flares, the ranking of cities by size, traffic jams, cotton prices, the number of fatalities in warfare, and even the distribution of sex partners in social networks. ([Location 3681](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3681)) - Gaussian, random walks almost never have fluctuations greater than five standard deviations, yet in real economic data, such as stock market crashes, five-standard-deviation events, and even larger-deviation ones, do in fact occur. ([Location 3704](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=3704)) - Complex designs are inherently modular. ([Location 4016](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=4016)) - 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. ([Location 4019](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=4019) - 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. ([Location 4039](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=4039)) - the information-processing medium that gave evolution a foothold in the economy was spoken language and, later, writing. ([Location 4119](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=4119)) - Just as with the Library of LEGO, most of what is here is junk—genetic nonsense that, if it were ever built, would produce at best a stillborn mutant. While there are unimaginably large numbers of designs for things that could live successfully in their environment, relative to the even more unimaginable size of the design space, they are exceedingly rare. ([Location 4133](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=4133)) - Any design space for which most small changes in schemata lead to small or no changes in fitness, but some small changes have large effects, will have the rough-correlated shape of the biological fitness landscape. ([Location 4196](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=4196)) - 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 places 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 process of evolution can then be thought of as an algorithm that searches the design space for designs that are fit, given the fitness constraints of the environment. 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 interactor 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. ([Location 4329](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=4329)) - 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:37 The creation of order from randomness. From simple random beginnings, the algorithm creates complex designs that are “ordered” from the point of view of the fitness function. All evolutionary processes operate in open systems, so in effect the algorithm harnesses energy to decrease local entropy and turn randomness into order. The discovery of fit designs. The algorithm provides a fast and efficient way of searching the enormity of design space for fit designs. In endogenous evolution, designs are fit if they survive and replicate under the constraints of their environment (“good replicators get replicated”). Continuous adaptation. The algorithm “learns” what the fitness function wants and seeks out designs that meet those criteria. If the fitness function changes, evolution produces designs that reflect the new selection pressures. The accumulation of knowledge. The evolutionary process accumulates knowledge over time. If we were to freeze the LEGO evolutionary process and analyze the schema cards for all the toys at a particular point in time, we could say that the information in those cards reflected learning or knowledge about the fitness environment in which the toys had historically evolved. Likewise, DNA contains immense amounts of information about which biological designs have worked in the past. If you were an alien from another planet and had never seen the earth, but somehow obtained a piece of DNA from an earthly organism, you could learn much about the earth’s environment just from that piece of code (assuming you had a DNA reader). Schemata are like the hard drives of the evolutionary process; they fill up with information over time. The emergence of novelty. During the evolutionary process, the algorithm continuously creates new variants of designs. In a theoretical sense, all possible designs already exist in the design space, but by discovering and rendering them, evolution introduces “new” designs into the real world. In our LEGO example, the evolutionary algorithm would undoubtedly churn out some designs that the children themselves had not thought of beforehand, and experiments using computer-simulated evolution to design things from jet-engine fan blades to computer chips have also resulted in novel designs.38 Growth in resources devoted to successful designs. Populations of successful designs grow, and populations of unsuccessful designs shrink as successful designs win in the competition for resources. The larger populations mean that successful schemata “control” more resources in terms of matter, energy, and information than do unsuccessful schemata. Growth, however, may not follow a smooth pattern, but may follow a pattern of punctuated equilibrium due to a combination of network effects from coevolution and the shape of the fitness landscape itself.… ([Location 4349](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=4349)) - But the strategy that won the tournament was extremely simple. It was submitted by Anatol Rapoport, a professor of psychology and mathematics at the University of Toronto. Rapoport’s strategy was memorably called Tit for Tat, and its first move was to cooperate, and from then on, it simply looked at the last move its opponent made and repeated the opponent’s move. If its opponent cooperated, Tit for Tat cooperated; if its opponent defected, it defected. Axelrod was surprised at the success of this simple strategy and ran a second, larger tournament to test it further. This time there were sixty-two entries from leading scholars in economics, mathematics, physics, computer science, and evolutionary biology. Tit for Tat won again. ([Location 4489](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=4489)) - Economies rely on the existence of two factors: Physical Technologies to enable people to create products and services that are worth trading, and Social Technologies that smooth the way for cooperation in creating and trading those products and services among nonrelatives. ([Location 4837](https://readwise.io/to_kindle?action=open&asin=B07KK2CMVF&location=4837))