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September 2 - October 13, 2019
Smith’s great insight was that the secret to wealth creation was improving the productivity of labor.
Jacques Turgot was a minister in the government of Louis XV and a famous proponent of laissez-faire, or the philosophy that governments should minimize their interference in the workings of markets.
The law of diminishing returns is a critical force in helping the economy achieve balance. Given a price in the market, a producer will keep adding more inputs and expanding output until the payoff is no longer worth it, that is, until the incremental cost of producing the next unit of output is greater than the incremental revenue one would receive for it.
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.
Pareto argued that since it takes two consenting people to trade and people aren’t stupid, they would only engage in trades that were either win-win or at least win-no-lose, both of which raise the total welfare of the participants. These trades later came to be called Pareto superior trades, and Pareto contended that in free markets, people would keep trading until they had exhausted all the Pareto superior trades. At that point trading would stop since any further trades would make someone worse off, and the market would reach an equilibrium point that later economists called Pareto optimal.
Knowledge is what economists refer to as an increasing returns phenomenon. As discussed earlier, in the eighteenth century Jacques Turgot showed that most production processes exhibit the opposite quality of decreasing returns. For most types of production processes, whether it is farming, manufacturing, or services, as one inputs more and more resources, the marginal returns get smaller and smaller.
Romer created a positive feedback loop in his model, a virtuous circle, in which the more society invests in technology over time, the richer the society gets, and the greater the payoffs to further investments in technology. The result is unbounded, exponential growth.
This can be illustrated using a well-worn joke about an old economist and a young economist walking down the street. The young economist looks down and sees a $20 bill on the street and says, “Hey, look a twenty-dollar bill!” Without even looking, his older and wiser colleague replies, “Nonsense. If there had been a twenty-dollar bill lying on the street, someone would have already picked it up by now.”
The point is not that the basic idea behind the law of one price is wrong; of course people have incentives to arbitrage price differences “in the absence of barriers.” But in the real world, barriers of some kind almost always exist, whether it is the fact that no one has the time to search all the stores in an area for the lowest-priced ketchup, or whether there are still various transaction costs and transport, legal and other issues affecting trade in the European Union. In fact, the scientifically interesting question around price convergence is the dynamic interplay over time between the
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The economy is not a closed equilibrium system; it is an open disequilibrium system and, more specifically, a complex adaptive system.
In addition to developing the concept of Pareto optimality, the Italian economist was also very interested in the distribution of wealth in society. In 1895, he collected income data from a number of countries and fit the data with a distribution curve that became known as the Pareto distribution. Rather than being a bell-shaped normal distribution, the Pareto curve has a lot of people at the bottom end of the wealth distribution, a wide range in the middle class, and then a few superrich. The Pareto distribution is where the so-called 80-20 rule comes from, as roughly 80 percent of the wealth
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Thus, while supply and demand balance in economics textbooks, the real world is full of inventories, excess production capacity, and other stocks to buffer disequilibrium. Sterman postulated that the differences in adjustment speeds of these various buffer stocks might ultimately be what lay behind the dynamics of commodity cycles.
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First, you have an inventory of widgets. The inventory acts as a buffer between the uncertain demand from your customers and the production from your factory.
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Second, there is the stock of immediately available productive capacity. If you start running low on your widget inventory, you can ask the factory to boost production.
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The third and final stock is the total amount of long-term production capacity. Once all production lines are running at maximum speed and utilization is at 100 percent, the only way to expand output is to build additional production lines or add another factory.
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Sterman’s model thus has a structure of three feedback loops all running at different adjustment speeds.
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Spock is a good example of how Traditional Economics portrays human behavior.
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.
FRAMING BIASES. Exactly how an issue is framed can affect how we think about it. Compare, for example, the two questions “Should Britain adopt the euro?” and “Should Britain abolish the pound?” Under perfect rationality, this framing should not matter.
REPRESENTATIVENESS. People have a bad habit of drawing big conclusions from very small and biased samples. For example, we might talk to three friends in the office, each of whom has coincidentally had a bad day, and conclude that the company is falling apart.
AVAILABILITY BIASES. People tend to make decisions based on data that is easily available as opposed to finding the data that is really needed to make a good decision. This is, in effect, “looking for your lost keys u...
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DIFFICULTIES JUDGING RISK. Most people have a tough time reasoning with probabilities and assessing risks. In October 2000, a train crashed in Hatfield, England, tragically killing four people and injuring thirty-four. In response, the British government proposed to invest an additional $3 billion in rail safety. However, as The Economist newspaper pointed out, the real probability of dying in a train crash is quite low compared with other forms of transportation, and this expend...
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SUPERSTITIOUS REASONING. We tend to only look for the most proximate causes of things and often confuse random chance with cause and effect. Examples range from sports stars wearing their “lucky socks” to governments trying to reduce unemployment by simply making it more difficult to fire people.
MENTAL ACCOUNTING. Traditional Economics treats all money the same. However, people tend to put money into different mental compartments. For example, many people make a monthly contribution to a retirement plan even if they have outstanding credit-card balances. This is not economically rational, because the return on investment in the retirement account (even after tax savings) will likely be less than the credit-card interest. Nevertheless, people often view their retirement contribution as sacred and wall it off from current spending. This mental compartmentalizing of different types of
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In 1985, a mathematician named Alain Lewis used some sophisticated techniques from the theory of computation to prove that no one, not even the smartest arbitrageur, could actually make the calculations described by perfect rationality.
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What Lewis proved was that perfect rationality as Traditional Economists define it is not computable by a Turing Machine.
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Cognitive science is the label given to the field that studies the “software” of the human mind (as opposed to the “hardware” of the brain).
As Plato said, “Those who tell the stories rule society.”
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Stories are vital to us because the primary way we process information is through induction. Induction is essentially reasoning by pattern recognition.
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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.
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The opposite of induction is deduction. Deduction is a process of reasoning in which the conclusions must logically follow from a set of premises, for example, “Socrates is a man, and all men are mortal. Therefore, Socrates is mortal.”
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A good illustration of how our inductive and deductive sides work together is the game of chess. Top chess players use induction to look globally at the patterns on the board, while they use deduction to analyze specific local positions.
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Deduction only works on very well-defined problems such as chess moves; for deduction to work, the problem cannot have any information missing or ambiguity. Deduction is thus a powerful method of reasoning, but inherently brittle. While induction is more error prone, it is also more flexible and better suited for the incomplete and ambiguous information that the world throws at us. It thus makes evolutionary sense that we would be built this way.
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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 “tipping point” where a random network suddenly goes from being sparsely connected to densely connected.
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 circumstances—these are the people who are truly well connected.
The structure of social networks is not only important for us as individuals, but also makes a big difference in the functioning of large organizations. If an organization keeps people in strict career ladders and has silo-like business units and divisions, then the social network will be overly structured, with insufficient randomness. This, in turn, means long chains of hops for information to be transmitted around, resulting in poor communications and slow decision making.
Networks of nodes that can be in a state of 0 or 1 are called Boolean networks, after the mathematician George Boole, who invented them.
Traditional economists have usually thought of economies of scale as a function relating cost and volume, for example, as the quantity of widgets produced rises, the cost per widget declines. The laws of Boolean networks, however, cause us to think of another kind of economy of scale. As the size of a Boolean network grows, the potential for novelty increases exponentially.
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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.
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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.
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Rather, network growth creates interdependencies, interdependencies create conflicting constraints, and conflicting constraints create slow decision making and, ultimately, bureaucratic gridlock.
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Kauffman argues that an effective way to increase adaptability and avoid conflicting constraints is to break things up.
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The move by many companies in the 1980s and 1990s to more autonomous business units with their own profit-and-loss accountability was to a large degree a response to the complexity that came with organizational growth.
While New Keynesian economics remains somewhat controversial among economic theoreticians, in the practical worlds of government and Wall Street, people generally accept that government management of factors such as interest rates and budget deficits do have an impact on economic performance.
Jay Forrester of MIT invented a game called the Beer Distribution Game, which demonstrates how a combination of human behavior and dynamic structure can interact to produce oscillations in a simple economic system.19 Four volunteers are asked to play a game simulating the manufacture and distribution of a commodity.
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This supply chain of manufacturer, distributor, wholesaler, and retailer is, of course, common to many industries.
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Players incur costs of $0.50 per case for holding inventory (e.g., the cost of storing and securing the beer), and costs of $1.00 per case for running out of beer (e.g., angry customers and lost sales).
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The game starts out in equilibrium, with each player getting an order for four cases of beer and shipping exactly that many.
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Then, suddenly, on one turn, the consumer-order card jumps from four to eight.
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MIT professor John Sterman and others have conducted the Beer Game experiment hundreds of times with people from all around the world, including MBA students, businesspeople, and people selected at random.20 They have even tried it with professional inventory managers and highly rational economists. Yet the result is always the same—wild oscillations.
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