The Origin of Wealth
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Highlights
- 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. (Location 276)
- This book will argue that wealth creation is the product of a simple, but profoundly powerful, three-step formulaâdifferentiate, select, and amplifyâthe 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 âeconosphere.â (Location 388)
- We are accustomed to thinking of evolution in a biological context, but modern evolutionary theory views evolution as something much more general. 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 âsubstrateâ of DNA, but in any system that has the right information-processing and information-storage characteristics.26 In short, evolutionâs simple recipe of âdifferentiate, select, and amplifyâ is a type of computer programâa program for creating novelty, knowledge, and growth. Because evolution is a form of information processing, it can do its order-creating work in realms ranging from computer software to the mind, to human culture, and to the economy. (Location 393)
- This is because researchers believe that there are general laws of evolutionary systems. (Location 412)
- 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. (Location 443)
- Evolution is a method for searching enormous, almost infinitely large spaces of possible designs for the almost infinitesimally small fraction of designs that are âfitâ according to their particular purpose and environment. As Dennett puts it, evolution is a search algorithm that âfinds needles of good design in haystacks of possibility.â (Location 449)
- 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. (Location 476)
- 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. (Location 478)
- The stories of the agricultural, industrial, and information revolutions are all largely stories of the reciprocal dance between Physical and Social Technologies. (Location 488)
- 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. (Location 492)
- As we will see, the primary inspiration for economists from the late nineteenth through the mid-twentieth centuries was not biology, but physics, in particular the physics of motion and energy. 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 mainstream paradigm of economics over the past hundred years has portrayed the economy as a system that moves from equilibrium point to equilibrium point over time, propelled along by shocks from technology, politics, changes in consumer tastes, and other external factors. (Location 526)
- While economists were pursuing their vision of the economy as an equilibrium system, during the latter half of the twentieth century, physicists, chemists, and biologists became increasingly interested in systems that were far from equilibrium, that were dynamic and complex, and that never settled into a state of rest. Beginning in the 1970s, scientists began to refer to these types of systems as complex systems. This is a term we will look at in detail later, but in brief, a complex system is a system of many dynamically interacting parts or particles. In such systems the micro-level interactions of the parts or particles lead to the emergence of macro-level patterns of behavior. For example, a single water molecule sitting in isolation is rather boring. But if one puts a few billion water molecules together and adds some energy in the right way, one gets the complex macro pattern of a whirlpool. (Location 531)
- 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. (Location 546)
- While at Glasgow he came to the attention of a wealthy young Scottish duke who took him on as his well-paid private tutor. Smith traveled with the duke to France, where the young tutor was exposed to the economic ideas being debated on the Continent at the time, in particular by the Physiocrats, a group of intellectuals who held the radical idea that governments should limit their interference in the economy and let markets do most of the work. Financially secure with his income from the duke, he returned to Kircaldy, where he lived with his mother in relative isolation for six years working on the manuscript for his Wealth of Nations. The book was published in 1776 and was instantly recognized as a great work. (Location 676)
- Smithâs great insight was that the secret to wealth creation was improving the productivity of labor. (Location 686)
- Walrasâs 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 869)
- Not only did the mathematics of economics seem like a blast from the past, but the physicists were also surprised by the way the economists used simplifying assumptions in their models. Ever since the days of Galileo, scientists have used simplifications such as perfect spheres and ideal gases to make their models easier to analyze. But scientists are generally careful to ensure that while their assumptions might simplify reality, their simplifications donât actually contradict it. And scientists also carefully test whether their assumptions matter to the answers given by their theories. In the view of the scientists at the workshop, the economists had taken the use of assumptions to an extreme. (Location 1158)
- One assumption that got the scientists particularly exercised was what economists refer to as perfect rationality. Traditional Economics simplifies human behavior by assuming that people know everything possible about the future and crunch all that information through incredibly complex calculations to make such basic decisions as whether to buy a pint of milk. Even without being fully aware of the long history of debate on this subject, the physical scientists vociferously objected to the use of a model so clearly at odds with day-to-day reality. (Location 1162)
- But, as with the assumptions on behavior, it is all but impossible to create models that combine equilibrium with complex dynamics and real-world timescales. (Location 1302)
- The economy is not a closed equilibrium system; it is an open disequilibrium system and, more specifically, a complex adaptive system. (Location 1670)
- The real voyage of discovery consists, not in seeking new landscapes, but in having new eyes. âMarcel Proust (Location 1785)
- A convenient way to describe a dynamic system is in terms of stocks and flows.3 A stock is an accumulation of something, such as the balance in a bank account or water in a bathtub. The rate at which a stock changes over time is known as a flow, for example, the rate of money flowing into or out of a bank account, or water flowing into and out of a bathtub. (Location 2168)
- Dynamic systems also have a third ingredientâtime delays. You have probably had the experience of taking a shower in an unfamiliar place such as a hotel room, turning on the hot water, noticing it isnât hot enough, turning it up some more, and then it turns scalding, so you turn it down, it is still too hot, so you turn it down some more, then it is freezing, and so on. The problem is that there is a small time delay between your actions on the water knobs and the feedback from the shower temperature. The delay causes you to overshoot and oscillate around the desired temperature. Eventually, you figure it out and the oscillations get smaller and smaller until you hit the desired temperature. The longer the time delay, however, the harder it is to control the shower and the more oscillations you get. (Location 2196)
- The fact that we can get such widely varying behaviors simply from tweaking one variable demonstrates an important characteristic of nonlinear dynamic systems: sensitivity to initial conditions. Imagine that a golfer hits a putt on a very tricky green with lots of dips and bumps and ridges on it. If the golfer hits two putts, but they are ever-so-slightly different in terms of the starting position of the ball, or the angle and force of the swing, the two balls will diverge, follow very different paths, and end up far apart on the green. Nonlinearities cause small differences in initial conditions to be magnified over time, and thus unless you know the beginning state of the system with almost infinite precision, you cannot know the end state. (Location 2267)
- This dynamic story is the essence of Stermanâs model. When he ran it, he found that it generated commodity cycles that were statistically similar in important ways to real-world cycles.18 The model shows that the combination of the different timescales in the feedback loops and human fallibility make such cycles almost inevitable. (Location 2411)
- One of the implications of Stermanâs model and experiments is that the only way to mitigate the cycles is to change the structure of the system itself. For example, one could reduce the time delays in the system (e.g., time to add new capacity), make capacity less chunky (e.g., building âmini-millsâ rather than big factories), get more forward visibility on customer orders, or increase transparency on how much capacity is actually in the industry and how much is under construction. (Location 2417)
New highlights added October 24, 2022 at 11:24 PM
- The basic structure of Holland and companyâs model is as follows: AGENT. There is an agent interacting with other agents and its environment. GOALS. The agent has some goal or goals it is trying to achieve, and thus the agent can perceive gaps between its current state and its desired state, for example, âIâm hungryâ or âIâm in danger.â The agentâs job is to make decisions that bring it closer to its goals. RULES OF THUMB. The agent has rules of thumb that map the current state of the world to actions. These are called condition-action rules, or better known as IF THEN rules. For example, IF <hot stove> THEN <do not touch>. An agentâs collection of rules of thumb at any point in time is referred to as the agentâs mental model. FEEDBACK AND LEARNING. The agentâs mental model keeps track of which rules have helped it achieve its goals and which rules have moved the agent farther from its goals. Historically successful rules are used more often than unsuccessful rules. Feedback from the environment thus causes the agent to learn over time. (Location 2746)
- Even the lowly bacterium uses inductive problem solving; as it encounters varying concentrations of food, it moves in the direction of higher concentrations, thus making the implicit prediction that more of the molecules it likes lie in that direction. The bacteriumâs DNA provides a model of the bacterial world that says, if the concentration of food is going up, then it is likely to keep going up (if food molecules were distributed completely randomly, this would not be true). The bacterium has a goal (food), recognizes a pattern (chemical gradient), makes a prediction (food that way), and acts on it (wiggle flagellum). The bacterium then gets very direct feedback from its environmentâif the rule works it lives and reproduces, if not it dies. (Location 2762)
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In our rule market, we can think of the rules in this chain as suppliers and customers to each other. For example, the <stick out tongue> rule buys from the <sit still> rule, which buys from the <approach smelly stuff> rule. Thus, when the <stick out tongue> rule gets the reward, it must pay its supplier rules, which pay their suppliers, and so forth. Thus, rules that make a profitâi.e., they contribute to the chainâs ability to get a rewardâgrow in strength over time and get used more frequently. As rules get farther and farther back in the chain, however, the trickle-down of payments diminishes, owing to all the middlemen. This resulting lower payment puts a natural limit on the distance between cause and effect. Such a structure is consistent with experimental evidence showing that we are able to act strategically to some extent, but have a difficult time reasoning through long, complex chains of cause and effect. Frog Poetry? (Location 2817)
- We can begin to see how a relatively simple system of competing rules, scores, and feedback from the environment can result in a flexible pattern-recognition system that learns over time. We will now make one more assumption about the system to greatly enhance its performance. We will assume that over time, the rules in the system self-organize into hierarchies. Because there is regularity in Kermitâs world, there will be regularities in the firing patterns of the rules in Kermitâs mental model. For example, rules dealing with <flying> <blue> things will tend to fire together. When rules often fire together, they become associated, and we can think of them as organized in a category. So the various rules that fire when Kermit encounters <blue> things can be put under the category <fly>; likewise things that fire <large> <flying> <flapping> rules might go under the category <bird>. Kermitâs mental models will have a host of experiences and responses associated with flies and birds. (Location 2825)
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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 âtipping pointâ where a random network suddenly goes from being sparsely connected to densely connected. (Location 3013)
- We tend to think of someone as being well connected if he or she knows a particular world very well. But Watts and Newmanâs research shows that the best-connected people are really the ones who have the most diverse group of contacts. We all know people who seem to be able to talk to just about anyone and pick up friends from all walks of life and circumstancesâthese are the people who are truly well connected. (Location 3088)
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After more than thirty years of research on Boolean networks, their properties are fairly well understood. While Boolean networks can do amazing things such as form the World Wide Web, build your body, and create your mind, they are in fact simple creatures at heart. Basically, three variables guide the behavior of such networks. The first is the number of nodes in the network. The second is a measure of how much everything is connected to everything else. And the third is a measure of âbiasâ in the rules guiding the behavior of the nodes. Letâs look at each of these in turn and their implications for economic and other types of organizations. (Location 3124)
- 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. Human economic organizations have in fact been growing in size over time. In particular, jumps in organization size have corresponded with changes in technology. The development of settled agriculture enabled the creation of villages that were significantly larger than the hunter-gatherer bands, the previous unit of organization. Likewise, the Industrial Revolution resulted in the creation of large-scale factories and industrial cities, while the information revolution of the late twentieth century has enabled the creation of enormous global companies. In a virtuous circle, technology change enables larger units of economic cooperation, which in turn can leverage greater informational scale, which in turn creates more potential for future innovations. We will explore this theme further in part 3. (Location 3157)
- 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. This, then, is where the problems start to arise. As the number of interdependencies grows, changes in one part of the network are more likely to have ripple effects on other parts of the network. As the potential for these knock-on effects grows, the probability that a positive change in one part of the network will have a negative effect somewhere else also increases. (Location 3174)
- 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 3203)
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Complexity catastrophes help explain why bureaucracy seems to grow with the tenacity of weeds. Many companies go through bureaucracy-clearing exercises only to find it has sprung back a few years later. No one ever sits down to deliberately design a bureaucratic muddle. Instead, bureaucracy springs up as people just try to optimize their local patch of the network: finance is just trying to ensure that the numbers add up, legal wants to keep us out of jail, and marketing is trying to promote the brand. The problem isnât dumb people or evil intentions. Rather, network growth creates interdependencies, interdependencies create conflicting constraints, and conflicting constraints create slow decision making and, ultimately, bureaucratic gridlock. (Location 3207)
- 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 3213)
- Although IBM executives probably realized at some point that Dell was a threat, and they surely would have liked to recapture Dellâs share of the market, the interdependencies of IBMâs business system meant that there were many opportunities for people to say no. The more interactions required to get something done, the higher the probability of a conflict or a constraint. The reality in most organizations is that if enough people say no, it wonât happen. In the early days of the IBMâDell battle, IBM had far more degrees of possibility than Dell. For example, IBM could have penetrated the corporate market with the direct-order model far faster than Dell did, but the older company had fewer degrees of freedom to take advantage of those opportunities.24 (Location 3235)
- This tension between interdependencies and adaptability is a deep feature of networks and profoundly affects many types of systems. Software designers see it when a program becomes so complex that any enhancement or bug fix introduces five new bugs. Architects see it when a client asks them to move a wall just one foot, and it has knock-on effects that send the projectâs cost sky-high. Some biologists, such as Stuart Kauffman, believe that this tension creates upper limits on the complexity of organisms.25 In economic organizations, there is a clear trade-off between the benefits of scale and the coordination costs and constraints created by complexity. The next question then is, what can be done about it? (Location 3241)
- In an organizational context, the conventional wisdom is that hierarchy is a feature of bureaucracy that reduces adaptability. Managers are told that they should de-layer and flatten their organizations. But counterintuitively, hierarchy can serve to increase adaptability by reducing interdependencies and enabling an organization to reach a larger size before gridlock sets in. (Location 3256)
- In an organizational context, we can think of bias as being a measure of predictability. If there is predictability in the decision making of an organization (the equivalent of the light bulbsâ rules), then the organization can function effectively with a more densely connected network. If, however, decision making is less predictable, then less-dense connections, more hierarchy, and smaller spans of control are needed. Thus, for example, in an army, where regular, predictable behavior of troops is highly valued, it might be possible to get away with larger unit sizes than, say, in a creative advertising agency. (Location 3299)
- For reasons we will look at more closely later in the book, evolutionary systems work best when their sensitivity to change is in a medium, in-between range. If an evolutionary system is too insensitive to change, then the system will not be able to keep up with the pace of change in its environment. However, if a system is overly sensitive to change, then small changes can have large consequences. This oversensitivity is a problem because if a system has been successful in the past, then few major changes are likely to improve it. Rather, the odds are that the vast majority of possible major changes will harm it. (Location 3322)
- If we combine Kauffmanâs original result with the later results on hierarchy and bias, however, the phase transition shifts to the range of six to nine nodes. Interestingly, the numbers that come out of the analysis of Boolean networks are quite close to what we typically see for the size of effective working groups in human organizations. (Location 3332)
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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. If we lower the temperature of the water, the molecules interact in a different way, forming the crystal structure of ice, losing its emergent characteristic of wetness and taking on the characteristic of hard. Similarly, what we call a symphony is a pattern of sound that emerges out of the playing of individual instruments, and what we call a kidney is a pattern of cells working together to provide a higher-level function that none of the cells could do on its own. (Location 3471)
- Over the years, there has been a great deal of study of technology development as an evolutionary process.36 This is a topic we will return to in chapter 11. But two key observations from this work are relevant to the discussion of punctuated equilibrium. The first is that no technology is developed in isolation. All technologies depend on a web of relationships with other technologies; the invention of the mobile phone, for example, drew not only on radio technology, but on many other areas, such as computer technology and coding technology.37 These interrelationships are not just technological, but economic. The economic web that has grown around the automobile, for example, includes industries ranging from steelmaking to oil, hotels, and fast food.38 (Location 3632)
- The second observation is that, as Kim Clark of Harvard Business School has noted, technologies are inherently modular: a car, for example, is made up of an engine, a transmission, a body, and so on.39 Modules are then assembled into âarchitectures,â in this case, the design of the car itself. Innovations in modules can enable new architectures (e.g., the microchip enabling the PC), but it is innovations in architectures (e.g., the PC revolution itself) that tend to have the big catalyzing ripple effects on innovation. We thus have two of the key features that led to the punctuated equilibrium pattern in Jain and Krishnaâs modelâsparse-dense networks of interaction, and catalyzing effects from individual nodes. We will explore this in more detail in chapter 11, but one can see how technology webs might be subject to cascades of change leading to the emergent pattern of punctuated equilibrium, and that certain technologies could play the role of keystones in those webs. (Location 3639)
- In many ways, the lessons of the Beer Game and the Farmer teamâs model are the same. Complex emergent phenomena such as business cycles and stock price movements are likely to have three root causes. The first is the behavior of the participants in the system. As we have seen, real human beings have real behavioral regularities, whether it is the anchor and adjust rule of the Beer Game participants, or the yet to be understood regularity that leads to student distributions in stock ordering. Second, the institutional structure of the system makes a big difference. In the case of the Beer Game, the structure of the supply chain between the manufacturer and retailer created dynamics, that when combined with participant behavior, led to oscillations. In the case of the stock market, the structure of the limit order system, when combined with trader behavior, led to power law volatility. Third and last, are exogenous inputs into the system. In the case of the Beer Game it was the onetime jump in customer orders, and in the case of the stock market it is news. These exogenous shocks undoubtedly initiate and help drive the dynamics of the system. While exogenous factors play a role, the equilibrium straitjacket of Traditional Economics has unfortunately led to an overemphasis on this factor at the expense of the other two. (Location 3819)
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One of the strongest claims of Complexity Economics is that this language is no mere metaphorâorganizations, markets, and economies are not just like evolutionary systems; they truly, literally are evolutionary systems. In this chapter, we will see that evolution is not just about biology. Rather, evolution is a general-purpose and highly powerful recipe for finding innovative solutions to complex problems. It is a learning algorithm that adapts to changing environments and accumulates knowledge over time. It is the formula responsible for all the order, complexity, and diversity of the natural world. And as we will see in part 3, it is the same formula that lies behind all the order, complexity, diversity, and, ultimately, wealth in the economic world. (Location 3837)
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The appearance of common features and Good Tricks in the population of interactors leads us to another important point. Complex designs are inherently modular. (Location 4032)
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Once the information-processing medium is established, the processes of differentiation, selection, and replication can begin. Good replicators replicate, and what is fit is defined by the environment, which includes competition with other replicators. Evolution then starts its march through design space, seeking out designs that are better and better replicators. (Location 4135)
- According to Crutchfield, the flat spots, Swiss cheese holes, and portal routes characteristics of the landscape also contribute to punctuated equilibrium by creating a nonlinearity in the impact of genetic changes. Most changes have little or no effect, but some changes have a big impact on fitness (for good or ill) and thus may have a disproportionate effect on the web of species relationships. (Location 4208)
- 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. This is an important point, because it is this rough-correlated characteristic that makes evolution the ideal algorithm for searching fitness landscapes. (Location 4211)
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Interestingly, Holland has shown that evolution automatically strikes the right balance between exploration and exploitation. When things are good, when evolution has found a high plateau, evolution will devote proportionally more population resources to exploiting. But when things are bad, when the population is down in the valley, proportionally more resources will be devoted to exploring. Every time evolution occupies a new part of the fitness landscape, it is placing bets to sample the unknown. But like any bettor, as evolution gets more information, it wants to double up on the bets that look most promising. Holland has worked out the optimal formula for balancing exploitation and exploration and has shown that evolution comes very close to achieving the optimal balance.33 Evolution is a gambler, but one that plays the odds very well. (Location 4303)
- Physical Technologies are what we usually think of when we think of the word technology. Physical Technologies are designs and processes for transforming matter, energy, and information in ways that are useful for human purposes, for example, turning sand into glass or into silicon chips. Social Technologies are equally important, but often less at the forefront of our minds. They are the designs, processes, and rules that humans use to organize themselves. Villages, armies, matrix organizations, paper money, the rule of law, and just-in-time inventory management are all examples of social technologies. (Location 4785)
- Physical Technologies (PTs) are methods and designs for transforming matter, energy, and information from one state into another in pursuit of a goal or goals. (Location 4868)
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At its core, evolution is an iterative process of experimentation, selection, and then amplification of things that work. The random part of the process in biological evolution is the creation of variety for selection to act on. (Location 4988)
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The Post-it Note was not discovered by deductive logic alone; serendipitous tinkering played a major role as well. As Henry Petroski, an engineering professor at Duke University, has put it, âForm follows failure.â (Location 5005)
- In the 1980s, my colleague at McKinsey & Company, Richard Foster, developed a theory of the natural life cycle of technologies and described it in his book Innovation: The Attackerâs Advantage.26 Fosterâs theory was based on observations and case histories ranging from sailing ships to microprocessors. He found a strikingly consistent pattern in the cases and data he studied. In the early days of a new technology, performance is poor and progress slow. However, after a period of investment and tinkering with various designs, the performance of the technology suddenly takes off on an exponential improvement curve. During this period, each dollar invested in R&D yields substantial gains in performance from the technology. But as the technology matures, the curve of performance improvement begins to taper off. Diminishing returns on investment begin to set in. Foster called this pattern the S-curve, because a graph of the level of effort invested in improving a technology versus its performance gives an S shape (figure 11-3). (Location 5075)
- The second part of Fosterâs theory was that once returns on investment in one technology begin to tail off, entrepreneurs begin to have an incentive to look for new technologies. Early progress tends to be slow but eventually reaches the takeoff point, and the new technology replaces the old one as the market âjumps S-curves.â (Location 5086)
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Christensen claimed that whether a technology is disruptive depends less on how radical a technological advance it is, and more on its specific effect on the S-curve. If a technology pushes performance up an existing S-curve, even very rapidly, then the technology tends to preserve the power of existing players. However, when a technology requires a new S-curve, particularly when it starts at a worse price-performance point than the current technology, the newer technology tends to be disruptive and to change the industry structure. This is because any executive in a successful incumbent company would have a difficult time justifying investing resources in a technology that offers, at least initially, worse price performance. (Location 5126)
- Almost by definition, architectural innovations require changing many things at once, whether the transition is from sailing ships to steamships, or racing bikes to mountain bikes. In a detailed study of the semiconductor equipment industry, Rebecca Henderson of MIT and Kim Clark of Harvard Business School showed that architectural innovations tend to be more disruptive of industry structure than innovations in individual components.28 (Location 5141)
- By viewing innovation as a search across a fitness landscape, we can see the common thread running through the work of Foster, Christensen, and Henderson and Clark: it is very difficult for successful, incumbent companies to make long jumps in the PT landscape. When you are on top of a local peak, there are far more ways to go down in fitness than up, and leaping to a new architecture appears highly risky. From the perspective of an entrepreneur or a new entrant starting in the low valley of a new architecture, there are lots of ways up and many new, untried peaks to explore. Most attempts up from the entrepreneurial valley will wind up in dead-end canyons or on disappointing short peaks. But with enough explorers working away, someone will eventually find an attractive route up. (Location 5145)
- One might assume that the major determinants of national wealth include factors such as the existence of natural resources, the competence of government policies, and the relative sophistication of a countryâs Physical Technologies. Easterly and Levine found that while these factors all mattered to a degree, the most significant factor was the state of a nationâs Social Technology. The rule of law, the existence of property rights, a well-organized banking system, economic transparency, a lack of corruption, and other social and institutional factors played a far greater role in determining national economic success than did any other category of factors. (Location 5183)
- Social Technologies (STs) are methods and designs for organizing people in pursuit of a goal or goals. (Location 5208)
- In fact, the agricultural, industrial, and information revolutions can each be viewed as coevolutionary merry-go-rounds of advances in PTs leading to new forms of STs, which in turn were crucial for further advances in PTs, and so on. (Location 5273)
- We can next ask, what drives humanityâs deductive-tinkering search through Social Technology space? What spurs us to constantly seek out new and better ways of organizing (Location 5275)
- But while the benefits of cooperation in non-zero-sum games are substantial, as the Prisonerâs Dilemma showed us, there is often a tension between cooperating for the greater good and pursuing oneâs narrow self-interest.12 In his thought-provoking book, Non Zero, the journalist and science writer Robert Wright argues that much of human history can be viewed as the outcome of this central tension between cooperation and self-interest.13 Wright claims that the process of bootstrapping social complexity, from simple hunter-gatherer tribes to organized villages to nation-states and global corporations, has been the result of humans innovating new ways to cooperate across larger and larger scales and devising ways to play increasingly complex and profitable non-zero-sum games. He notes that in a world where resources are finite at any given moment, there are competitive pressures to cooperate. Over time, societies that are better able to organize themselves will socially, economically, and militarily dominate societies that are less successful at creating cooperative structures. Thus, it is the competition to cooperate that drives social innovation. (Location 5282)
- Recasting Wrightâs thesis in the language we have developed, we can view the deductive-tinkering search through the ST fitness landscape as a quest for STs that enable people to play and capture the benefits of non-zero-sum games. Social Technology fitness will therefore depend on three factors. First, the ST must provide the potential for non-zero-sum payoffs. Second, it must provide methods for allocating the payoffs in such a way that people have an incentive to play the game. And third, the ST must have mechanisms for managing the problem of defection. Letâs take a closer look at each factor. (Location 5292)
- This leads to our third critical factor in ST fitness; for STs to be fit, they must have mechanisms for dealing with those who donât play nice. Cheaters (Mostly) Never Win and Winners (Mostly) Never Cheat (Location 5337)
- Evolution has steered us in a direction whereby we are naturally inclined to be cooperative to capture the riches of non-zero-sum gains. Nevertheless, it has also equipped us with a sensitivity to cheating, expectations of fairness, and a willingness to mete out punishment to those we believe have crossed the line. In effect, evolution has programmed into our mental software sophisticated, intuitive âNash equilibrium findersâ and âfairness detectorsâ that enable groups of humans to form coalitions that are at least reasonably stable and resistant to attack by free riders and cheaters.19 (Location 5362)
- Thus, Social Technologies that are better at tapping into sources of non-zero-sum gains, finding cooperative Nash equilibriums for allocating those gains, and managing the defection problem will be higher on the fitness landscape than those that do not. (Location 5382)
- Once humans had the invention of hierarchy, it was then a simple step to the nested structure of hierarchy within hierarchy. We can just imagine the progression: at some point, a successful Big Man with a growing village to run does not have enough time to keep his eye on salmon trap production, so he appoints his younger brother or best friend to run that aspect of village lifeâand voilĂ , the business unit is born. The Big Man boss has reporting to him minibosses, who in turn have minibosses reporting to them. Hierarchy facilitates the division of labor and the processing of information. It is pervasive in all human social structures, ranging from hunter-gatherer tribes to neighborhood bowling leagues to big corporations.29 (Location 5429)
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Not only does competition within hierarchies need to be managed, but competition between members of different hierarchies also presents both threats and opportunities. Human groups need STs that enable cooperation between complete strangers. The first problem that strangers must overcome when they meet is that they donât know whether to trust each other. The parameters for their norms of cooperation and reciprocity behaviors might be very different, and one party might take advantage of the other in a transaction. Thus, people need STs for figuring out whom they can and cannot trust beyond their immediate kin. The first such ST extending beyond individual villages was undoubtedly tribal identity. By identifying âyour peopleâ versus âoutsiders,â you could efficiently find people with whom you were more likely to share social norms. And since your interactions in a tribe were likely to be repeated over time, you were less likely to get ripped off or to have destructive miscommunications. (Location 5449)
- Thus, a major breakthrough in ST was the development of an open protocol to enable strangers to cooperate: the rule of law. Laws enable complete strangers, with different backgrounds, histories, ethnicities, and social norms, to conduct business with each other with greatly reduced risk. (Location 5463)
- Just like an anthill, or the brain, human organizations exhibit a form of networked emergent intelligence. The University of California, San Diego, anthropologist and cognitive scientist Edwin Hutchins has studied the problem-solving capabilities of individuals versus organized groups in a variety of settings. He concluded that organizations are capable of having collective, emergent capabilities that do not exist individually within the group.33 In essence, not only is BP smarter than any one of its people, it is also smarter than the sum of its people. (Location 5500)
- The same principle applies to the differentiation of Business Plans. Managers do the best they can to rationally deduce what they hope will be a successful Plan. But then, as James Collins and Jerry Porras describe in their study of long-surviving companies, Built to Last, it comes down to âtrying a lot of stuff and keeping what works.â (Location 5673)
- The Big Man system of Business Plan selection has a further problem: Big Men distort the fitness function itself. One of the features of evolutionary algorithms is that they are brilliant at adapting to whatever fitness criteria they are given. As we saw earlier, when the computer researcher Karl Sims selected his artificial creatures for their fitness in swimming, he got an amazing array of clever swimmers. When he switched the fitness criteria to moving across land, fins and tails were dropped in favor of legs and snake bodies. When engineers employ artificial evolution to do things such as design semiconductors, software, or new drugs, they have to be extremely careful in how they specify the fitness function, because the wrong fitness function inevitably leads to the wrong design. In a Big Man system, the fitness function maximized is the wealth and power of the Big Man (and his cronies), rather than the overall economic wealth of the society. Thus, the creative, entrepreneurial, and deductive-tinkering energies of the population are directed toward pleasing the Big Man. The immense mansions and palaces dotting the world, from grand French chateaus to the Hermitage in Russia, that delight tourists with their extravagant displays of riches are testaments to the effectiveness of economic evolution in maximizing the fitness function of Big Man wealth. (Location 5720)
- But, sitting on top of these massive hierarchies is a thin, but crucial layer, in which the hierarchies meet the market. Market economies are systems of evolutionarily competing hierarchies. (Location 5757)
- The process of selection is nested and occurs at several levels, ranging from the mental simulations of individuals to the problem-solving activities of groups. Further selection occurs as Business Plans percolate up and down the hierarchies of organizations, but then at some point the plans are implemented and the market renders its judgment. (Location 5831)
- Beyond the basic machinery of the evolutionary algorithm, we would not expect any particular similarities between economic evolution and biological evolution.16 For example, the notion that units of selection follow âdescent with modificationâ through discrete generations in biological evolution, but hop around from Business Plan to Business Plan in economic evolution, does not make the process any less evolutionary, just different. Likewise, the ability of humans to use their brains and have foresight implies that the mechanisms for differentiation and selection are very different in economic systems versus biological systems, but again are evolutionary nonetheless. (Location 5840)
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In short, the reason that markets work so well comes down to what evolutionary theorists refer to as Orgelâs Second Rule (named after biochemist Leslie Orgel), which says, âEvolution is cleverer than you are.â Even a highly rational, intelligent, benevolent Big Man would not be able to beat an evolutionary algorithm in finding peaks in the economic fitness landscape. Markets win over command and control, not because of their efficiency at resource allocation in equilibrium, but because of their effectiveness at innovation in disequilibrium. (Location 5860)
- The American Revolution starting in 1776 (the same year that Adam Smith published his great work) was the second major boost to the development of market economies. The historian Paul Johnson notes that the economic approach of the British to their colonies was very different from the approach of the other major European powers.25 France, Spain, and Portugal each transplanted highly centralized and hierarchical systems of political, military, and religious command to their New World holdings, which in turn led to top-down control of their fledgling economies. The British, on the other hand, were too cheap to go to the trouble of investing in the large military resources needed to institute that kind of control. There were more pressing concerns elsewhere in the empire, such as Britainâs always-complex relationships with the Continent. Thus, the British American colonies evolved as scattered settlements of highly independent farmers and merchants. From its earliest days, British North America was an âanything goesâ kind of place of free trade, free ideas, and free religion. Whenever the British or local colonial authorities attempted to clamp down, the inhabitants would simply head for wilder lands farther away from any source of control. (Location 5908)
- The basic insight in The Entropy Law and the Economic Process is that economic activity is fundamentally about order creation, and that evolution is the mechanism by which that order is created. We will examine Georgescu-Roegenâs groundbreaking ideas, bring them up to date with current science, and integrate them with the evolutionary model outlined over the previous chapters. In doing so, we will arrive at our destination: a new perspective on the origin of wealth. (Location 5950)
- To add more precision to Georgescu-Roegenâs observations, I will restate them in more formal terms and refer to them collectively as the G-R Conditions. A pattern of matter, energy, and or information has economic value if the following three conditions are jointly met: IRREVERSIBILITY. All value-creating economic transformations and transactions are thermodynamically irreversible. ENTROPY. All value-creating economic transformations and transactions reduce entropy locally within the economic system, while increasing entropy globally. FITNESS. All value-creating economic transformations and transactions produce artifacts and or actions that are fit for human purposes. (Location 6024)
- Over time, the machine sorts the coffee and milk to different sides of the cup. The little nanobot is in effect increasing order (decreasing entropy) in the coffee cup by creating a lower-probability state than existed before. However, this order comes at an inescapable price. We would have to feed the nanobot energy to do its work, and in return the nanobot would give off heat. Thus, entropy would decrease locally within the coffee cup, but still increase in the wider universe around it. (Location 6062)
- The role of destruction as an intermediate step in value creation holds in biological systems as well. Before your body can create new order in its cells and systems, your digestive system must break down the nicely ordered packages of chemicals and energy that are in your food. Again, the adage that you canât make an omelet without breaking some eggs holds true. (Location 6119)
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It depends on your point of view. This is a key point in understanding the concept of order. What constitutes order versus disorder must be measured relative to something. (Location 6138)
- Taken together, the three G-R Conditions say that economic activity is fundamentally about order creation. Faced with the disorder and randomness of the world, humans spend most of their waking hours ordering their environment in various ways to make it a more hospitable and enjoyable place. We order our world by transforming energy, matter, and information into the goods and services we want, and we have discovered the evolutionary Good Trick that by cooperating, specializing, and trading, we can create even more order than we otherwise could on our own. (Location 6278)
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If wealth is indeed fit order, then we can use another more familiar word to describe it. In physics, order is the same thing as information, and thus we can also think of wealth as fit information; in other words, knowledge. Information on its own can be worthless. Knowledge on the other hand is information that is useful, that we can do something with, that is fit for some purpose. So we have come full circle; the founder of Traditional growth theory, Robert Solow, was right. The origin of wealth is knowledge. Yet rather than treating knowledge as an assumption, an exogenous input, a mysterious process outside the bounds of economics, the Complexity-based view I have outlined puts the creation of knowledge at the endogenous heart of the economy. (Location 6348)
- While Complexity Economics strips away our illusions of control over our economic fate, it also hands us a leverâa 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. (Location 6425)
- The key to doing better is to âbring evolution insideâ and get the wheels of differentiation, selection, and amplification spinning within a companyâs four walls. Rather than thinking of strategy as a single plan built on predictions of the future, we should think of strategy as a portfolio of experiments, a population of competing Business Plans that evolves over time. (Location 6668)
- What Gates created was not a focused big bet, but a portfolio of strategic options. One way of interpreting what Gates did was that he set a high-level aspirationâto be the leading PC software companyâand then he created a portfolio of strategic experiments that had the possibility of evolving toward that aspiration. (Location 6709)
- The message is not to tear up your strategy books, but to think of the tools of conventional strategy analysis as having a different purpose. The purpose is not to get to the âanswerâ of a single focused five-year plan based on predictions of the future, but rather to create âprepared minds.â This requires thinking about whose minds it is important to prepare and how one can best go about doing that. (Location 6749)
- We will define a social architecture as having three components: The behaviors of the individual people in the organization. The structures and processes that align people and resources in pursuit of an organizationâs goals. The culture that emerges from the interactions of people in the organization with each other and their environment. (Location 7004)
- We can update Coaseâs idea for our evolutionary framework and say that not only might organizations be cheaper mechanisms for cooperation in some circumstances, but they might also enable people to reach parts of Business Plan design space unobtainable just by contracting. In short, organizations are better vehicles for exploiting Business Plan space and thus better vehicles for wealth creation. There are four reasons for this. (Location 7058)
- We will look at four reasons for poor individual adaptability: First, people have a bias toward overoptimism, which can reduce the felt need for change. Second, people also have a natural bias toward loss aversion, making them less likely to take certain kinds of risks. Third, the way we categorize the world and structure our mental models can get in the way of change. And fourth, the punctuated-equilibrium nature of change tends to favor leadership styles that are more rigid than ones that are more flexible.23 (Location 7148)
- Performing norms PERFORMANCE ORIENTATION. Always do your best, go the extra mile, take initiative, and continuously improve yourself. HONESTY. Be honest with others, be honest with yourself, be transparent and face reality. MERITOCRACY. Reward people on the basis of merit. Cooperating norms Cooperating norms 4. MUTUAL TRUST. Trust your colleaguesâ motivation, and trust in their skills to get the job done. 5. RECIPROCITY. Live the golden rule; do unto others as you would have them do unto you. 6. SHARED PURPOSE. Put the organizationâs interests ahead of your own, and behave as if everyone is in it together. Innovating norms 7. NONHIERARCHICAL. Junior people are expected to challenge senior people, and what matters is the quality of an idea, not the title of the person saying it. 8. OPENNESS. Be curious, open to outside thinking, and willing to experiment; seek the best, wherever it is. 9. FACT-BASED. Find out the facts; it is facts, not opinions, that ultimately count. 10. CHALLENGE. Feel a sense of competitive urgency; it is a race without a finish line. (Location 7452)
- We have touched on a number of factors that might explain why organizations generally donât adapt as quickly or successfully as markets do: Individuals Human mental models tend to be biased toward optimism, dulling our ability to recognize the need for change. A natural bias toward loss aversion causes people to systematically underinvest in experimentation. As our mental models gain experience in a stable environment, they become more effective in that environment, but at the price of reduced flexibility. In a stable environment, organizational hierarchies tend to favor the promotion of people with experience and skills executing the current business model (Rigids) over people whose skill sets are more oriented toward exploring and adapting (Flexibles). Structure The challenges of executing complex production and service processes drive organizations to develop deep, densely connected hierarchies. Yet these structures are not well suited to the tasks of exploration, which require flatter, more autonomous organizational structures. The coevolutionary relationship between Business Plans and resources constrains the space of Business Plans that a firm can explore, and path dependence in resources limits the ability and speed with which an organization can shift to a new Business Plan. Culture Strong cultural norms are critical to adaptability because they enable organizations to reduce centralized hierarchical control without sacrificing execution performance. There are, however, inherent tensions in cultural norms that encourage individual performance, cooperation, and innovation; these tensions must be actively managed by the organizationâs senior leadership. (Location 7517)
- Communications is also naturally important, and cognitive science tells us that most corporate change programs are 180 degrees backward.48 Humans are fairly stubborn creatures and donât just immediately change their mental models and behaviors in responses to speeches from their bosses, or because of PowerPoint presentations or plastic cubes with inspirational messages. Instead of trying to appeal to our fact-based, deductive sides, corporate change programs need to address the story-loving, pattern-recognizing, more emotional, inductive side of human cognition. People need to be jolted from their existing mental models and see an urgent and personal gap between the way things are and the way things need to be. A change program needs to have a fact-based argument underlying it, but the emphasis in communications should be on stories, analogies, and patterns to help people see the issues. Most change programs are also very passive, with lots of communications cascading down from on high. But learning is interactive, and thus a change program needs to get people to personally grapple with the issues. (Location 7575)
- If true, this means that we are operating at a level vastly below our human potential. The great cognitive scientist and artificial intelligence researcher Marvin Minsky observed that what we call âintelligenceâ is not a singular thing; rather, it is an emergent phenomenon that arises from the collective interactions of many individual parts. The magic of intelligence is that when those parts are organized in a particular way, they can do things that no individual part could do on its own. Minsky called this description of intelligence âthe society of mind.â (Location 7611)
- We can think of human organizations as passing through three stages of development. The first stage was the evolution of STs that enabled strangers to reliably cooperate with each other. The second stage was the creation of large-scale organizations that could exploit the PTs of the industrial revolution. But perhaps now we are at the beginning of a third stage. We are only just now learning how to create âsocieties of mindsâ in our largest organizations. Most organizations tap only a small fraction of the brainpower of their people. The hierarchical command-and-control organizations that arose out of the industrial age enabled humans to achieve cooperation on scales once unimaginable and execute Business Plan designs of immense complexity. Nevertheless, the inherent rigidities of these structures ultimately limit them to being evolutionary fodder in the churnings of markets. But through the lever of culture, companies can create true societies within their organizations and free the minds of their people. As in any society, along with freedom comes responsibility, and the culture must support performance and execution, as well as exploration and adaptation. Creating such a culture is truly âsocial engineeringâ with all the consequent risks and unpredictability associated with the phrase. But for companies that succeed, the reward is the creation of institutions that can be engines of wealth creation for generations to come. (Location 7616)
- Embracing the dual objectives of endurance and growth forces management to fully confront the inherent tensions in executing and adapting and creates pressure to strike a more equal balance between the two. In a competitive evolutionary environment, âendure and growâ is the what, and âadapt and executeâ is the how. Enduring and growing are the timeless demands placed on designs in an evolutionary system. (Location 8324)
- Experiments show, however, that real people care not only about outcomes, but about whether the process itself was fair. Second, as the ultimatum game shows, people will punish unfair behavior, even at a cost to themselves, and even if they have no hope of recovering that cost in the future. In other words, when people feel as if theyâve really been cheated, they can do some pretty crazy stuff. That is certainly a departure from self-interested rationality. (Location 8440)
- But the antigovernment free marketers forget that economies donât exist in isolation. The economic evolutionary system is constructed out of a vast array of Social Technologies, many of which rely on government.20 Market-based evolution requires a careful balance between cooperation and competition, and governments play a vital role in enabling their societies to strike this balance. Social Technologies such as contract law, consumer protection regulations, worker safety rules, and securities law all serve to engender cooperation and trust, while antitrust regulations serve to maintain healthy levels of competition. (Location 8553)
- Complexity Economics thus changes our perspective not just on the ideological positions of Left and Right, but also on the two great institutions that they battle over: states and markets. The economic role of the state is to create an institutional framework that supports the evolutionary workings of markets, strikes an effective balance between cooperation and competition, and shapes the economic fitness function to best serve the needs of society. Consistent with norms of strong reciprocity, the state also has an obligation to ensure that all its citizens have an equal opportunity to participate in the economic system, and to provide a basic level of support for those who do not succeed in that system. The economic role of markets is to provide incentives for the discovery and differentiation of Business Plans, apply the fitness function shaped by consumers, technology, and the state in selection, and channel resources to selected plans for amplification. The question is not states versus marketsâit is how to combine states and markets to create an effective evolutionary system. (Location 8597)
- In the first category are norms related to individual behavior. These include norms that support a strong work ethic, individual accountability, and a belief that you are the protagonist of your own life and not at the whim of gods or Big Men. Fatalism greatly reduces personal incentives. It is also important to believe that there is a payoff to hard work and a moral life in this world, and not just in the next. Finally, economically successful cultures appear to strike a balance between optimism that improvement is possible, and realism about oneâs current situation. (Location 8660)
- In the second category are norms related to cooperative behavior. Foremost is a belief that life is a non-zero-sum game and that there are payoffs to cooperation. Societies that believe in a fixed pie of wealth have a difficult time engendering cooperation and tend to be low in mutual trust. Consistent with our discussion of strong reciprocity, it is important that the culture have norms that value generosity and fairness, but also sanction those who free ride and cheat. (Location 8664)
- The third category contains norms related to innovation.31 Deductive-tinkering is much more effective if the deductive part is strong, and thus cultures that look to rational scientific explanations of the world rather than religious or magical explanations tend to be more innovative. Likewise, a culture needs to be tolerant of heresy and experimentation, as strict orthodoxy stifles innovation. Finally, it is important that the culture be supportive of competition and celebrate achievement, since overly egalitarian cultures reduce the incentives for risk taking. (Location 8667)
- One final norm is important to all three categories: how people view time. Cultures that live for today (or, conversely, are mired in the past) have problems across the board, ranging from low work ethic, to an inability to engage in complex cooperation and low levels of investment in innovation. Why work hard, and invest in cooperation and innovation if tomorrow doesnât matter? In contrast, cultures that have an ethic of investing for tomorrow tend to value work, have high intergenerational savings rates, demonstrate a willingness to sacrifice short-term pleasures for long-term gain, and enjoy high levels of cooperation. (Location 8672)
- Given the structural and largely irreversible nature of the causes of the Great Disruption (e.g., few would advocate women returning to the home, and people wonât give up their cars), the question of what can be done is difficult. Putnam advocates an âagenda for social capitalistsâ that includes the following components:50 Greater individual commitment to social involvement (and watching less TV) Programs in schools to help build norms of trust and social capital in the next generation Reforms to make workplaces more family friendly Better public transport and the rewriting of zoning laws Efforts to encourage voting and political involvement (Location 8847)
- Miller translates his Rawlsian logic into four specific policy proposals. The first is to create universal health coverage by providing tax subsidies to enable people to buy health insurance from competing private health plans. The second is to raise the quality of public education, particularly for poorer children, by dramatically raising teacher salaries in exchange for union agreement to allow pay for performance. The third is a proposal to further reform education through a âgrand bargainâ between liberals and conservatives that would allow competition via a voucher system in exchange for de-linking education spending from property taxes (which ensure the richest kids get the best schools), and raising overall investment in education. Fourth, and finally, is a federal guarantee of a âminimum living wageâ to put a floor under peopleâs incomes. Miller proposes to pay for these new initiatives by redirecting 2 percent of GDP to these priorities. (Location 8993)
- Throughout this book, I have claimed that two institutions have provided the foundations of economic evolution: markets and science. To that score, we should add a third: democracy. Democracy is itself an evolutionary system of policy ideas. As E. M. Forster exclaimed, âTwo cheers for democracy: one because it admits variety and two because it permits criticism.â71 Over the coming years, Complexity Economics will inject a new burst of variety into debates about politics and policy. It will be up to the evolutionary workings of the democratic process to select and amplify those ideas that will best serve society. (Location 9082)
- The continuing evolution and spread of Physical and Social Technologies thus give us reasons to be optimistic. But some would argue that the real risk to humankind is not that we will fall off the growth curve, but rather that we will stay on it. Three issues stand out. First, like Dr. Frankensteinâs monster, our global economic creation may already be in the process of turning on its creator, devouring the resources of the earth and polluting its lands, seas, and sky. As mentioned before, the environment is a complex adaptive system itself, and the potential for tipping points, radical change, and even collapse are very real. Second, since the eighteenth century, the pace of PT evolution has far outstripped ST evolution. We are already wrestling with the effects of nuclear and genetic technology, and in the next generationâs lifetime, artificial intelligence and nanotechnology will likely be added to the list. If our Social Technologies do not catch up, the risks of global catastrophe will continue to grow. Third, and finally, is the clash of cultures.5 In earlier times, cultures tended to collide only at their geographic frontiers. Today they collide on a daily basis on television, over the Internet, and in our great multicultural cities. Unfortunately, humanityâs record of sensitively managing cultural clashes, from the landing of CortĂŠs in Mexico to the current tensions between Islam and the West, is not encouraging. (Location 9134)
- Undoubtedly many of the actions needed to address these issues will require vision and wisdom from political and business leaders. But we should remember that in evolutionary systems, power comes not from the top down, but from the bottom up. Evolution is a blind process, and the evolutionary algorithm will respond to whatever fitness function it is given. If, as individual consumers, workers, and voters, we ask the economy and our political institutions to maximize our short-term needs, to fill materialistic lives with ever more stuff, and to do so without regard for the health of our planet or the lives of future generations, then that is what we will get. (Location 9147)
- But there is an alternative. Through the ways in which we spend our money, whom we choose to work for, our votes, and our voices, we can create a fitness function that requires our businesses, governments, and scientific institutions to take a longer-term view and to address the needs of global society in a broader and more sustainable way. If we create such a fitness function, then those institutions and our economy will by necessity adapt and respond to that call. Edmund Burke once said that a society is a âpartnership not only between those who are living, but between those who are living, those who are dead, and those who are [yet] to be born.â7 We are all a part of the global society of minds, and how that society evolves is up to each of us. (Location 9151)