Laplace demon

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pages: 208 words: 70,860

Paradox: The Nine Greatest Enigmas in Physics by Jim Al-Khalili

Albert Einstein, Albert Michelson, anthropic principle, Arthur Eddington, butterfly effect, clockwork universe, complexity theory, dark matter, Edmond Halley, Edward Lorenz: Chaos theory, Ernest Rutherford, Henri Poincaré, invention of the telescope, Isaac Newton, Johannes Kepler, Laplace demon, luminiferous ether, Magellanic Cloud, Olbers’ paradox, Pierre-Simon Laplace, Schrödinger's Cat, Search for Extraterrestrial Intelligence, The Present Situation in Quantum Mechanics, Wilhelm Olbers

Does this imply that we must discard our ideas of free will, since our fate is already sealed? And how then do we resolve the Paradox of Laplace’s Demon? We can briefly compare this situation with the time-travel paradoxes of the last chapter. In that case, our past was fixed and known to us, but we had to travel to it in order to change it and force paradox. Here, Laplace’s demon knows the future, but doesn’t require time travel; it just waits for the future to come to it, and while waiting it can meddle with the present to force a different future to evolve. One not very scientific way of ruling out time-travel paradoxes is to insist that time travel to the past is simply impossible. But in the case of Laplace’s demon, no time travel is necessary; the demon cannot escape the future, which is heading its way even if it does nothing, so it looks like we need another explanation to resolve the paradox.

Or it just might be that time travelers from the future are indeed among us, but choose to keep a low profile. 8 THE PARADOX OF LAPLACE’S DEMON Can the flapping of a butterfly’s wings rescue us from a predictable future? “Prediction is very difficult, especially about the future.” So said the Danish quantum physicist Niels Bohr. The quote might sound trite and frivolous, but hidden behind it, as was so often the case with the utterances of Bohr, are profound ideas about the nature of fate, free will, and our ability to determine how the future will unfold. Let me first set up the paradox. The French mathematician Pierre-Simon Laplace devised his own imaginary demon half a century before Maxwell proposed his. Laplace’s demon is far more powerful than Maxwell’s since it has the ability to know the exact position and state of motion not merely of every air molecule in a box, but of every particle in the Universe, and fully understands the laws of physics that describe how they interact with each other.

Q173.A395 2012 500—dc23 2012005011 eISBN: 978-0-307-98680-1 Illustrations: Patrick Mulrey Cover design: Kyle Kolker Cover photography: istockphoto v3.1 To Julie, David, and Kate Contents Cover Title Page Copyright Dedication Preface 1 The Game Show Paradox 2 Achilles and the Tortoise 3 Olbers’ Paradox 4 Maxwell’s Demon 5 The Pole in the Barn Paradox 6 The Paradox of the Twins 7 The Grandfather Paradox 8 The Paradox of Laplace’s Demon 9 The Paradox of Schrödinger’s Cat 10 Fermi’s Paradox 11 Remaining Questions Acknowledgments About the Author Preface Paradoxes come in all shapes and sizes. Some are straightforward paradoxes of logic with little potential for investigation, while others sit atop icebergs of entire scientific disciplines. Many can be resolved by careful consideration of their underlying assumptions, one or more of which may be faulty.


pages: 137 words: 36,231

Information: A Very Short Introduction by Luciano Floridi

agricultural Revolution, Albert Einstein, bioinformatics, carbon footprint, Claude Shannon: information theory, conceptual framework, double helix, Douglas Engelbart, Douglas Engelbart, George Akerlof, Gordon Gekko, industrial robot, information asymmetry, intangible asset, Internet of things, invention of writing, John Nash: game theory, John von Neumann, Laplace demon, moral hazard, Nash equilibrium, Nelson Mandela, Norbert Wiener, Pareto efficiency, phenotype, Pierre-Simon Laplace, prisoner's dilemma, RAND corporation, RFID, Thomas Bayes, Turing machine, Vilfredo Pareto

Unfortunately there are probably only around 1080 elementary particles in the Universe. Moreover, if the world were a computer, this would imply the total predictability of its developments and the resuscitation of another demon, that of Laplace. Pierre-Simon Laplace (1749-1827), one of the founding fathers of mathematical astronomy and statistics, suggested that if a hypothetical being (known as Laplace's demon) could have all the necessary information about the precise location and momentum of every atom in the universe, he could then use Newton's laws to calculate the entire history of the universe. This extreme form of determinism was still popular in the 19th century, but in the 20th century was undermined by the ostensibly probabilistic nature of quantum phenomena. Science has moved from being based on necessity and laws to being based on probability and constraints.

Science has moved from being based on necessity and laws to being based on probability and constraints. Nowadays, the most accepted view in physics is that particles behave indeterministically and follow the uncertainty principle. To the best of our knowledge - that is, at least according to the Copenhagen interpretation of quantum mechanics, which is the most widely accepted among physicists - computational determinism is not an option, Laplace's demon is a ghost, and digital physics shares its fate. A digital reinterpretation of contemporary physics may still be possible in theory, but a metaphysics based on informationtheoretic grounds seems to offer a more promising approach. Following Wiener and Wheeler, one might interpret reality as constituted by information, that is, by mind-independent, structural entities that are cohering clusters of data, understood as concrete, relational points of lack of uniformity.

Games are said to be sequential when there is some predefined order according to which players move, and at least some players have information about the moves of players who preceded them. The presence of a sequence of moves is insufficient without some access to them, for in that case the game is effectively simultaneous and the difference in time has no strategic import. If all players have information about all the previous moves or states of all the other players then the sequential game is one of perfect information. Maxwell's demon and Laplace's demon (Chapter 6) may be described as complete- and perfect-information single-player games. If only some players have perfect information, then we shall see below that the sequential game is one of imperfect information. Examples in this case include Scrabble, a game in which each player is not informed about what tiles are held by another player, and poker, for the same reason. In sequential games, agents with incomplete or imperfect information are lacking something precious, either some information about features (a)-(c) or some information about feature (d) of the game.


pages: 634 words: 185,116

From eternity to here: the quest for the ultimate theory of time by Sean M. Carroll

Albert Einstein, Albert Michelson, anthropic principle, Arthur Eddington, Brownian motion, cellular automata, Claude Shannon: information theory, Columbine, cosmic microwave background, cosmological constant, cosmological principle, dark matter, dematerialisation, double helix, en.wikipedia.org, gravity well, Harlow Shapley and Heber Curtis, Henri Poincaré, Isaac Newton, Johannes Kepler, John von Neumann, Lao Tzu, Laplace demon, lone genius, low earth orbit, New Journalism, Norbert Wiener, pets.com, Pierre-Simon Laplace, Richard Feynman, Richard Stallman, Schrödinger's Cat, Slavoj Žižek, Stephen Hawking, stochastic process, the scientific method, wikimedia commons

Laplace didn’t know about computers, so he imagined a vast intellect. His later biographers found this a bit dry, so they attached a label to this hypothetical intellect: Laplace’s Demon. Laplace never called it a demon, of course; presumably he had no need to hypothesize demons any more than gods. But the idea captures some of the menace lurking within the pristine mathematics of Newtonian physics. The future is not something that has yet to be determined; our fate is encoded in the details of the current universe. Every moment of the past and future is fixed by the present. It’s just that we don’t have the resources to perform the calculation.105 There is a deep-seated impulse within all of us to resist the implications of Laplace’s Demon. We don’t want to believe that the future is determined, even if someone out there did have access to the complete state of the universe.

But that wouldn’t be nearly the puzzle it appears to be if it weren’t for the fact that the underlying laws of physics seem perfectly reversible; as far as Laplace’s Demon is concerned, there’s no difference between reconstructing the past and predicting the future. Reversing time turns out to be a surprisingly subtle concept for something that would appear at first glance to be relatively straightforward. (Just run the movie backward, right?) Blithely reversing the direction of time is not a symmetry of the laws of nature—we have to dress up what we really mean by “reversing time” in order to correctly pinpoint the underlying symmetry. So we’ll approach the topic somewhat circuitously, through simplified toy models. Ultimately I’ll argue that the important concept isn’t “time reversal” at all, but the similar-sounding notion of “reversibility”—our ability to reconstruct the past from the present, as Laplace’s Demon is purportedly able to do, even if it’s more complicated than simply reversing time.

Or, if we want to be even more comprehensive and realistic, we can admit that the state is really the position and momentum of every single particle constituting these objects. If it’s your boyfriend or girlfriend you are interested in, all you need to do is precisely specify the position and momentum of every atom in his or her body. The rules of classical mechanics give unambiguous predictions for how the system will involve, using only the information of its current state. Once you specify that list, Laplace’s Demon takes over, and the rest of history is determined. You are not as smart as Laplace’s Demon, nor do you have access to the same amount of information, so boyfriends and girlfriends are going to remain mysterious. Besides, they are open systems, so you would have to know about the rest of the world as well. It will often be convenient to think about “every possible state the system could conceivably be in.” That is known as the space of states of the system.


pages: 348 words: 83,490

More Than You Know: Finding Financial Wisdom in Unconventional Places (Updated and Expanded) by Michael J. Mauboussin

Albert Einstein, Andrei Shleifer, Atul Gawande, availability heuristic, beat the dealer, Benoit Mandelbrot, Black Swan, Brownian motion, butter production in bangladesh, buy and hold, capital asset pricing model, Clayton Christensen, clockwork universe, complexity theory, corporate governance, creative destruction, Daniel Kahneman / Amos Tversky, deliberate practice, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, dogs of the Dow, Drosophila, Edward Thorp, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, fixed income, framing effect, functional fixedness, hindsight bias, hiring and firing, Howard Rheingold, index fund, information asymmetry, intangible asset, invisible hand, Isaac Newton, Jeff Bezos, Kenneth Arrow, Laplace demon, Long Term Capital Management, loss aversion, mandelbrot fractal, margin call, market bubble, Menlo Park, mental accounting, Milgram experiment, Murray Gell-Mann, Nash equilibrium, new economy, Paul Samuelson, Pierre-Simon Laplace, quantitative trading / quantitative finance, random walk, Richard Florida, Richard Thaler, Robert Shiller, Robert Shiller, shareholder value, statistical model, Steven Pinker, stocks for the long run, survivorship bias, The Wisdom of Crowds, transaction costs, traveling salesman, value at risk, wealth creators, women in the workforce, zero-sum game

Swarm Smarts Chapter 30 - Vox Populi The Accuracy of Crowds Needle in a Haystack Weighing the Ox with the Vox Estimating Printers with Populi And Now, For the Real World Chapter 31 - A Tail of Two Worlds Experience Versus Exposure Tell Tail What Fat Tails Mean for Investors Chapter 32 - Integrating the Outliers Bernoulli’s Challenge What’s Normal? St. Petersburg and Growth Stock Investing Integrating the Outliers Chapter 33 - The Janitor’s Dream Beyond Newton Sorting Systems The Stock Market as a Complex Adaptive System Using What You’ve Got Chapter 34 - Chasing Laplace’s Demon Evolution Made Me Do It Laplace’s Demon Interpreting the Market Investor Risks Chapter 35 - More Power to You Zipf It The More Things Change . . . Catch the Power Chapter 36 - The Pyramid of Numbers Firm Size, Growth Rates, and Valuation Why Big Fierce Animals Are Rare Find Your Niche Dear CEO: We’ve Made It to the Fortune 50! You’re Fired Extrapolative Expectations Chapter 37 - Turn Tale Hush Puppies and Dogs of the Dow Ah Choo Economists, Meet Mr.

Corporate managers see analyst reports that dwell on earnings and hence incorrectly assume that the market is a simple addition of these agents. Investors and corporate managers trying to understand the market must recognize that it’s a complex adaptive system. The market’s action reflects the interaction of many agents, each with varying knowledge, resources, and motivation. So a disproportionate focus on individual opinions can be hazardous to wealth creation. 34 Chasing Laplace’s Demon The Role of Cause and Effect in Markets [Our ancestors] must have felt uncomfortable about their inability to control or understand such [causeless] events, as indeed many do today. As a consequence, they began to construct, as it were, false knowledge. I argue that the primary aim of human judgment is not accuracy but the avoidance of paralyzing uncertainty. We have a fundamental need to tell ourselves stories that make sense of our lives.

Naturally, we make up stories to explain cause and effect. Why should investors care about cause and effect in the market? An appreciation of our need for explanation can be an inoculation against making mistakes. Investors who insist on understanding the causes for the market’s moves risk focusing on faulty causality or inappropriately anchoring on false explanations. Many of the big moves in the market are not easy to explain. Laplace’s Demon Two hundred years ago, determinism ruled in science. Inspired by Newton, scientists largely embraced the notion of a clockwork universe. The French mathematician Pierre Simon Laplace epitomized this thinking with a famous passage from A Philosophical Essay on Probabilities:An intellect which at any given moment knew all of the forces that animate Nature and the mutual positions of the beings that comprise it, if this intellect were vast enough to submit its data to analysis, could condense into a single formula the movement of the greatest bodies of the universe and that of the lightest atom: for such an intellect nothing could be uncertain; and the future just like the past would be present before its eyes.


pages: 247 words: 43,430

Think Complexity by Allen B. Downey

Benoit Mandelbrot, cellular automata, Conway's Game of Life, Craig Reynolds: boids flock, discrete time, en.wikipedia.org, Frank Gehry, Gini coefficient, Guggenheim Bilbao, Laplace demon, mandelbrot fractal, Occupy movement, Paul Erdős, peer-to-peer, Pierre-Simon Laplace, sorting algorithm, stochastic process, strong AI, Thomas Kuhn: the structure of scientific revolutions, Turing complete, Turing machine, Vilfredo Pareto, We are the 99%

An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes. This intellect came to be called “Laplace’s demon” (see http://en.wikipedia.org/wiki/Laplace’s_demon). The word “demon” in this context has the sense of “spirit,” with no implication of evil. Discoveries in the 19th and 20th centuries gradually dismantled this hope. The thermodynamic concept of entropy, radioactive decay, and quantum mechanics posed successive challenges to strong forms of determinism. In the 1960s, chaos theory showed that in some deterministic systems, prediction is only possible over short time scales, limited by the precision of measurement of initial conditions.

harmonics, Spectral Density hash function, Hashtables HashMap, Hashtables hashtables, Hashtables Hertz, Spectral Density Hist, Zipf’s Law histograms, Cumulative Distributions hoisting, Spectral Density holism, A New Kind of Model holistic model, Reductionism and Holism, Reductionism and Holism Homo economicus, The Axes of Scientific Models homogeneous, The Axes of Scientific Models hop, Stanley Milgram hurricane, Realism, Instrumentalism I id, Instrumentalism immutable objects, Representing Graphs implementing cellular automata, Implementing CAs implementing Game of Life, Implementing Life in operator, Analysis of Search Algorithms incompleteness, A New Kind of Thinking Incompleteness Theorem, A New Kind of Thinking indeterminism, A New Kind of Thinking indexing, Analysis of Basic Python Operations, Fast Fourier Transform infinite loop, Generators infinite sequence, Iterators inheritance, Representing Graphs, Representing Graphs __init__, Representing Graphs instantiate, CADrawer instrumentalism, A New Kind of Model, Instrumentalism interactions, minimizing, A New Kind of Engineering interface, CADrawer, CADrawer implementing, CADrawer specifying, CADrawer IPython, Summing Lists isolation of components, A New Kind of Engineering __iter__, Iterators iterator, Iterators iteritems, List Comprehensions itertools, Iterators J join, Analysis of Basic Python Operations K Kansas, Stanley Milgram kernel, Implementing Life KeyError, Hashtables Kosko, Bart, A New Kind of Thinking Kuhn, Thomas, Paradigm Shift?, A New Kind of Thinking, A New Kind of Thinking, Explanatory Models L label attribute, Representing Graphs labelling nodes, Dijkstra lambda calculus, Universality Langton, Chris, Turmites Langton’s ant, Turmites Laplace, Pierre-Simon, Determinism Laplace’s demon, Determinism leading coefficient, Order of Growth leading term, Order of Growth __len__, Hashtables Life, Implementing Life LifeViewer, Implementing Life line, Fractals linear algorithm, Summing Lists linear congruential generator, Randomness linear growth, Order of Growth linear search, Analysis of Search Algorithms linear system, The Axes of Scientific Models LinearMap, Hashtables list comprehension, List Comprehensions list methods, Analysis of Basic Python Operations list of tuples, List Comprehensions The Little Book of Semaphores, Dijkstra local connection, Stanley Milgram local information, Boids log-log plot, Zipf’s Law, Pareto Distributions, Zipf, Pareto, and Power Laws, Fractals, Pink Noise log-log scale, Summing Lists logarithm, Zipf’s Law logarithmic growth, Order of Growth logic, A New Kind of Thinking long tail, Pareto Distributions, Sand Piles long-tailed distribution, Reductionism and Holism, SOC, Causation, and Prediction M machine model, Analysis of Algorithms Mandelbrot, Benoit, Reductionism and Holism many-valued logic, A New Kind of Thinking Massachusetts, Stanley Milgram mathematical proofs, A New Kind of Science, Paradigm Shift?


pages: 327 words: 103,336

Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, Black Swan, business cycle, butterfly effect, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, coherent worldview, collapse of Lehman Brothers, complexity theory, correlation does not imply causation, crowdsourcing, death of newspapers, discovery of DNA, East Village, easy for humans, difficult for computers, edge city, en.wikipedia.org, Erik Brynjolfsson, framing effect, Geoffrey West, Santa Fe Institute, George Santayana, happiness index / gross national happiness, high batting average, hindsight bias, illegal immigration, industrial cluster, interest rate swap, invention of the printing press, invention of the telescope, invisible hand, Isaac Newton, Jane Jacobs, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, lake wobegon effect, Laplace demon, Long Term Capital Management, loss aversion, medical malpractice, meta analysis, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, Pierre-Simon Laplace, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, social intelligence, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize

Likewise, predictions that the United States will not go to war with Canada in the next decade or that the sun will continue to rise in the east are also likely to be accurate, but impress no one. The real problem of prediction, in other words, is not that we are universally good or bad at it, but rather that we are bad at distinguishing predictions that we can make reliably from those that we can’t. LAPLACE’S DEMON In a way this problem goes all the way back to Newton. Starting from his three laws of motion, along with his universal law of gravitation, Newton was able to derive not only Kepler’s laws of planetary motion but also the timing of the tides, the trajectories of projectiles, and a truly astonishing array of other natural phenomena. It was a singular scientific accomplishment, but it also set an expectation for what could be accomplished by mathematical laws that would prove difficult to match.

For many things lead me to have a suspicion that all phenomena may depend on certain forces by which particles of bodies, by causes not yet known, either are impelled toward one another and cohere in regular figures, or are repelled from one another and recede.6 A century later, the French mathematician and astronomer Pierre-Simon Laplace pushed Newton’s vision to its logical extreme, claiming in effect that Newtonian mechanics had reduced the prediction of the future—even the future of the universe—to a matter of mere computation. Laplace envisioned an “intellect” that knew all the forces that “set nature in motion, and all positions of all items of which nature is composed.” Laplace went on, “for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes.”7 The “intellect” of Laplace’s imagination eventually received a name—“Laplace’s demon”—and it has been lurking around the edges of mankind’s view of the future ever since. For philosophers, the demon was controversial because in reducing the prediction of the future to a mechanical exercise, it seemed to rob humanity of free will. As it turned out, though, they needn’t have worried too much. Starting with the second law of thermodynamics, and continuing through quantum mechanics and finally chaos theory, Laplace’s idea of a clockwork universe—and with it the concerns about free will—has been receding for more than century now.

Until it is actually realized, all we can say about the future stock price is that it has a certain probability of being within a certain range—not because it actually lies somewhere in this range and we’re just not sure where it is, but in the stronger sense that it only exists at all as a range of probabilities. Put another way, there is a difference between being uncertain about the future and the future itself being uncertain. The former is really just a lack of information—something we don’t know—whereas the latter implies that the information is, in principle, unknowable. The former is the orderly universe of Laplace’s demon, where if we just try hard enough, if we’re just smart enough, we can predict the future. The latter is an essentially random world, where the best we can ever hope for is to express our predictions of various outcomes as probabilities. Past Versus Future Stock Price PREDICTING WHAT TO PREDICT The distinction between predicting outcomes and predicting probabilities of outcomes is a fundamental one that should change our view about what kinds of predictions we can make.


pages: 829 words: 186,976

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver

"Robert Solow", airport security, availability heuristic, Bayesian statistics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Swan, Broken windows theory, business cycle, buy and hold, Carmen Reinhart, Claude Shannon: information theory, Climategate, Climatic Research Unit, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, computer age, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, Daniel Kahneman / Amos Tversky, diversification, Donald Trump, Edmond Halley, Edward Lorenz: Chaos theory, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, everywhere but in the productivity statistics, fear of failure, Fellow of the Royal Society, Freestyle chess, fudge factor, George Akerlof, global pandemic, haute cuisine, Henri Poincaré, high batting average, housing crisis, income per capita, index fund, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, invention of the printing press, invisible hand, Isaac Newton, James Watt: steam engine, John Nash: game theory, John von Neumann, Kenneth Rogoff, knowledge economy, Laplace demon, locking in a profit, Loma Prieta earthquake, market bubble, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, Monroe Doctrine, mortgage debt, Nate Silver, negative equity, new economy, Norbert Wiener, PageRank, pattern recognition, pets.com, Pierre-Simon Laplace, prediction markets, Productivity paradox, random walk, Richard Thaler, Robert Shiller, Robert Shiller, Rodney Brooks, Ronald Reagan, Saturday Night Live, savings glut, security theater, short selling, Skype, statistical model, Steven Pinker, The Great Moderation, The Market for Lemons, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, transfer pricing, University of East Anglia, Watson beat the top human players on Jeopardy!, wikimedia commons

The idea of scientific, technological, and economic progress—which by no means could be taken for granted in the centuries before then—began to emerge, along with the notion that mankind might learn to control its own fate. Predestination was subsumed by a new idea, that of scientific determinism. The idea takes on various forms, but no one took it further than Pierre-Simon Laplace, a French astronomer and mathematician. In 1814, Laplace made the following postulate, which later came to be known as Laplace’s Demon: We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes.13 Given perfect knowledge of present conditions (“all positions of all items of which nature is composed”), and perfect knowledge of the laws that govern the universe (“all forces that set nature in motion”), we ought to be able to make perfect predictions (“the future just like the past would be present”).

An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes.13 Given perfect knowledge of present conditions (“all positions of all items of which nature is composed”), and perfect knowledge of the laws that govern the universe (“all forces that set nature in motion”), we ought to be able to make perfect predictions (“the future just like the past would be present”). The movement of every particle in the universe should be as predictable as that of the balls on a billiard table. Human beings might not be up to the task, Laplace conceded. But if we were smart enough (and if we had fast enough computers) we could predict the weather and everything else—and we would find that nature itself is perfect. Laplace’s Demon has been controversial for all its two-hundred-year existence. At loggerheads with the determinists are the probabilists, who believe that the conditions of the universe are knowable only with some degree of uncertainty.* Probabilism was, at first, mostly an epistemological paradigm: it avowed that there were limits on man’s ability to come to grips with the universe. More recently, with the discovery of quantum mechanics, scientists and philosophers have asked whether the universe itself behaves probabilistically.

Perfect predictions are impossible if the universe itself is random. Fortunately, weather does not require quantum mechanics for us to study it. It happens at a molecular (rather than an atomic) level, and molecules are much too large to be discernibly impacted by quantum physics. Moreover, we understand the chemistry and Newtonian physics that govern the weather fairly well, and we have for a long time. So what about a revised version of Laplace’s Demon? If we knew the position of every molecule in the earth’s atmosphere—a much humbler request than deigning to know the position of every atomic particle in the universe—could we make perfect weather predictions? Or is there a degree of randomness inherent in the weather as well? The Matrix Purely statistical predictions about the weather have long been possible. Given that it rained today, what is the probability that it will rain tomorrow?


pages: 407 words: 116,726

Infinite Powers: How Calculus Reveals the Secrets of the Universe by Steven Strogatz

Albert Einstein, Asperger Syndrome, Astronomia nova, Bernie Sanders, clockwork universe, complexity theory, cosmological principle, Dava Sobel, double helix, Edmond Halley, Eratosthenes, four colour theorem, fudge factor, Henri Poincaré, invention of the telescope, Isaac Newton, Islamic Golden Age, Johannes Kepler, John Harrison: Longitude, Khan Academy, Laplace demon, lone genius, music of the spheres, pattern recognition, Paul Erdős, Pierre-Simon Laplace, precision agriculture, retrograde motion, Richard Feynman, Socratic dialogue, Solar eclipse in 1919, Steve Jobs, the rule of 72, the scientific method

To clarify what I mean by this somewhat apocalyptic warning, we need to understand how prediction is possible at all, what it meant classically, and how our classical notions are being revised by discoveries made in the past several decades in studies of nonlinearity, chaos, and complex systems. Early in the 1800s, the French mathematician and astronomer Pierre Simon Laplace took the determinism of Newton’s clockwork universe to its logical extreme. He imagined a godlike intellect (now known as Laplace’s demon) that could keep track of all the positions of all the atoms in the universe as well as all the forces acting on them. “If this intellect were also vast enough to submit these data to analysis,” he wrote, “nothing would be uncertain and the future just like the past would be present before its eyes.” As the turn of the twentieth century approached, this extreme formulation of the clockwork universe began to seem scientifically and philosophically untenable, for several different reasons.

All others from then on would be non-integrable, meaning that their dynamics would be impossible to solve with Newtonian-style formulas. It wasn’t a matter of insufficient cleverness; she proved that there simply couldn’t be any formulas of a certain type (in the jargon, a meromorphic function of time) that could describe the motion of the top forever. In this way, she put limits on what calculus could do. If even a spinning top could defy Laplace’s demon, there was no hope—even in principle—of finding a formula for the fate of the universe. Nonlinearity The unsolvability that Sofia Kovalevskaya discovered is related to a structural aspect of the equations for a top: the equations are nonlinear. The technical meaning of nonlinear need not concern us here. For our purposes, all we need is a feel for the distinction between linear and nonlinear systems, which we can get by considering some homey examples from everyday life.

See technology inverse-square law of gravity, 195, 232–33 irrational numbers, discovery of, 33, 312n33 isochronism, 72 J Jefferson, Thomas, xxi–xxii, 239–40, 326n239 Jobs, Steve, 51 Johnson, Katherine, xxii, 237–38 Josephson, Brian, 73–74 Jupiter, 65, 80, 84 Jyesthadeva, 193 K Kasparov, Garry, 291–92 Kepler, Johannes awe of, xix background, 78 Cosmic Mystery, 78–81, 79 first law of planetary motion, 81–82 functions of one variable, 124–25 vs Galileo, 85–86 geometry and, 59–60 Harmonies of the World, 84 legacy of, 86–88, 234 logarithms, use of, 133 packing spheres problem, 293–94 on planetary motion, 79 Pythagorean fever, xiv second law of planetary motion, 82–84 third law of planetary motion, 84–85, 232–33 volumes, 92–93 Khwarizmi, Muhammad Ibn Musa al-, 92 King Hiero’s crown, 86 Koestler, Arthur, 86 Komodo, 292 Kovalevskaya, Sofia, 277–79, 280, 290 L La Chambre, Marin Cureau de, 116 Lagrange, Joseph Louis, 260 Laplace, Pierre Simon, 277 Laplace’s demon, 277, 279 lasers, xxii law of inertia, 71 law of odd numbers, 66–69 law of the lever, 28, 46 laws of motion Aristotelian understanding of, 60–64 circular motion, sine waves and, 108–12 first law of planetary motion, 81–82 force, 230–31, 252, 258 planetary motion, 81–85 second law of planetary motion, 82–84 third law of planetary motion, 84–85, 232–33 Le Blanc, Antoine-August (as pseudonym), 261–62 Leibniz, Gottfried Wilhelm approach to fundamental theorem, 211–18, 213 as co-inventor of calculus, 201–2 death of, 167–68 differentials, use of, 208–9 fundamental theorem, with differentials, 209–11 infinitesimals, 202–5, 324n203 integral sign, 211 Newton correspondence, 197, 199, 200–201, 323n199 levers, 28, 44–46 light bending of, xix, 114–18, 195, 209 composition of white light, 195, 197, 259 as electromagnetic wave, xi–xiii quantum electrodynamics, 296–97 speed of, 23, 263, 328n262 limits concept of, 8–9 in decimals, 9–11 of determinism, 278–80 infinity and, 13–14 Paradox of the Arrow, 19–21 linear relationships, 95–96, 126, 146–49, 173 Littlewood, John, 284–85 Liu Hui, 91 local operations, 185–86 logarithms, 131–34, 192, 196, 221 longitude, 74–75 M Madhava of Sangamagrama, 193 magic numbers, 217–18 magnetrons, 263–64 Marconi, Guglielmo, xi Mars period of, 84 retrograde motion, 61–62, 61, 62 sector areas, 82–83 Tycho and Kepler on, 78, 80 Mathematical Principles of Natural Philosophy (Newton), 229, 234, 236, 240 mathematics analysis vs synthesis, 102–3 discovery of, 49 Galileo on, 60 See also algebra; calculus; geometry Maxwell, James Clerk, xi, xii–xiii, 77, 264 “Measurement of a Circle” (Archimedes), 7 medical field CT scanning, 265–69 DNA, 273–76 facial surgery, 53–56 hepatitis C, 225 HIV progress, 218–25 Hodgkin-Huxley equations, 289 PET scans, 298 Mercator, Nicholas, 196 Mercury, 79, 80 Mersenne, Marin, 100–101 Method, the (of Archimedes), 42–50, 93, 313n47 method of exhaustion, 32, 47, 93, 102 method of least squares, 111 method of power series, 188–93 microwave ovens, 262–64, 328n262 Middle Ages, 50, 62, 173, 174 military aircraft, 245–47 Archimedes and, 27–28 ballistic data, 285–87 GPS, 76–77 nonlinear dynamics and radar, 284–85 modes of vibration, 259–60 moon Aristotle on, 60–61 Galileo’s observations, 65 gravity and, 232–33 inverse-square law of gravity, 195 Newton on, 229, 232–34 of Saturn, 278 Tycho on, 80 motion, 123–39 Archimedes’s study of, 315n57 exponential functions, 127–28 exponential growth and decay, 137–39 functions, role of, 125–26 fundamental theorem, 169–72 logarithms, 131–34 at a molecular scale, 165–66 natural logarithm (e), 134–37 of planets, 78–81 power functions, 126–27 scientific notation, 128–31 struggle with, xx two-body problem, 234–37 xy plane and, 123–24 Munro, Alice, 278 music continuous vs discrete, 20–21 CT scanning, 268 harmony and, 48–49, 230 logarithmic perception of pitch, 134 Newton on, 192 Pythagoras on, xiii–xiv, 230 string theory, 252–56 vibration modes, 259–60 N Napier, John, 133 Napoleon, 260, 261 NASA’s two-body problem, 237–38 natural logarithm (e), 134–37 nature calculus as language of, vii–viii, xiii–xiv, xix–xx, 166 circles in, 2 Galileo on, 67, 69, 70 logic of, 229–34 nonlinearity, 279–80 optimization principle, 118, 120 predator-prey interactions, 159 quantum mechanics, 21 rates of change, 143, 258 negative powers, 130 Neptune, 237 nerve cell communication, 286–87, 289 Newton, Hannah, 187, 188 Newton, Isaac analysis vs synthesis, 102–3 area problem, 176–79 background, 186–88 constant acceleration, 172–75 correspondence, 320n169, 322n195, 323n199 De Analysi, 196, 199, 200 De Methodis, 196, 197, 201 death of, 167–68 on Descartes, 102 discoveries of, viii, 195–97, 199–201, 322n195 discrete vs continuous systems, 241 Einstein’s theory of relativity, 299 fundamental theorem, 169–72, 182–83 gravity, force and nature, 229–34 legacy and influence of, xxi–xxii, 238–40, 325n229, 326n239 as mash-up artist, 193–94 Mathematical Principles of Natural Philosophy, 229, 234, 236, 240 method of power series, 188–93, 321n188 notebook, 182 pendulums, 72 Principia, 229, 234, 236, 240 System of the World, The, 236–37 three-body problem, 229, 281, 288 two-body problem, 234–38 xy plane, 124 Newton Project, 192 Nobel Prize winners, 267, 269, 278, 287, 298, 300 nonlinear equations, 96–97, 149–54 nonlinearity, 279–80, 299–300 nuclear reactions, 138 O Obama, Barack, 238 Oldenburg, Henry, 199–200 On Analysis by Equations Unlimited in Their Number of Terms (Newton), 196, 199, 200 “On the Sphere and Cylinder” (Archimedes), 49 “On the Unreasonable Effectiveness of Mathematics in the Natural Sciences” (Wigner), xiii optics curved lenses, 87, 99 principle of least time, 114–18 reflecting telescopes, 195 optimization algorithm, 110–11 optimization problems, 103–7, 116–18 orbits area of, 82–83 gravity and, 232–33 period of, 84–85 shape of, 81–82 See also ellipses; moon; planetary motion ordinary differential equations, 242–44 Oresme, Nicole, 173 oscillations, 73–74, 158–59 overtones, 254–55 “paint-roller” proof, fundamental theorem, 175–79, 178 P parabolas, 35–39 equations for, 97, 150 projectiles and, 70 as slice of cone, 36 slope and, 207–9 thought experiment for, 150–53 parabolic segment, 36–39, 43–44 Paradox of the Arrow, 19–21 parallax, 63 partial differential equations, 242–48, 249–50 patterns, 111–12 pendulums, 71–77, 158, 282–84, 288 Perelson, Alan, 219–25, 242 period, 109–10 period, or orbit, 84–85 PET scans, 298 phase, 109–10 physics electricity and magnetism, xi gravitational waves, 300 heat flow, 249–52 laws of planetary motion, 81–85 NASA’s two-body problem, 237–38 Newton’s legacy, 229–34 pendulums, 71–77, 158, 282–84, 288 quantum mechanics, 21–25, 77, 295–97 See also Galilei, Galileo; Newton, Isaac pi Archimedes’s estimation of, 29–32, 52 Chinese contribution to, 91 historical view of as ratio, 33–35 infinite decimals, 24 power series method, 189 Picasso, 166 Pixar, 51, 52, 314n52 pizza proof, 4–8 Planck, Max, 23 Planck length, 23 planetary motion, 78–81 Einstein on, 248 elliptical orbits, 81–82 Kepler on, xix Newton on, 234–37 orbital period, 84–85 sector areas, 82–84 planets vs stars, 61 Plato, 17, 60, 91 Plutarch, on Archimedes, 27 Poincaré, Henri, 281, 282–84, 288–89 polygons, 11–12 positron, 297–98 power functions, 126–27, 182 power series, method of, 188–93 powers of ten, 128–31 predator-prey interactions, 158–59 predictability horizon, 281–82 predictions antimatter, 297–98 for calculus, 273 electromagnetic waves, 264 fundamental theorem, 183–84 Josephson effect, 73–74 of new planets, 237 of orbits, 235–37 of particles in a continuous media, 256 quantum electrodynamics, 296–97 relativity and, 300 Principia (Newton), 229, 234, 236, 240 principle of least action, 118 principle of least time, 113–18, 319n118 projectile motion, 69–70 proportions, as Greek way, 33–35, 48 protease inhibitors, 219, 220–22, 223 “proto-calculus,” 227 Ptolemaic system, 63 Ptolemy, 63, 91 Pythagoras, xiii–xiv, 86, 90, 230 Pythagorean dream, 78, 86, 230 Pythagorean theorem, 31–32, 90 Pythagorean theory of musical harmony, 48–49 Q quadratic equations, 96–97, 126 quadrature, 36, 168–69 Quadrature of the Parabola, The (Archimedes), 35–39, 41, 43 quantum electrodynamics (QED), 296–97 quantum mechanics, 21–25, 77, 295–97 quarter cycle, of sine waves, 109, 109, 154, 156, 157–59, 257–59 Quinto, Todd, 269 R radar, 263–64, 284–85 “radar ranges.”


pages: 339 words: 94,769

Possible Minds: Twenty-Five Ways of Looking at AI by John Brockman

AI winter, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, Danny Hillis, David Graeber, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, finite state, friendly AI, future of work, Geoffrey West, Santa Fe Institute, gig economy, income inequality, industrial robot, information retrieval, invention of writing, James Watt: steam engine, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Loebner Prize, market fundamentalism, Marshall McLuhan, Menlo Park, Norbert Wiener, optical character recognition, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, RAND corporation, random walk, Ray Kurzweil, Richard Feynman, Rodney Brooks, self-driving car, sexual politics, Silicon Valley, Skype, social graph, speech recognition, statistical model, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, telemarketer, telerobotics, the scientific method, theory of mind, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, zero-sum game

Computers may be programmed to take on some of these problems (like recognizing faces), not to bother with others (like charming mates), and to take on still other problems that humans can’t solve (like simulating the climate or sorting millions of accounting records). The problems are different, and the kinds of knowledge needed to solve them are different. But instead of acknowledging the centrality of knowledge to intelligence, the dystopian scenarios confuse an artificial general intelligence of the future with Laplace’s demon, the mythical being that knows the location and momentum of every particle in the universe and feeds them into equations for physical laws to calculate the state of everything at any time in the future. For many reasons, Laplace’s demon will never be implemented in silicon. A real-life intelligent system has to acquire information about the messy world of objects and people by engaging with it one domain at a time, the cycle being governed by the pace at which events unfold in the physical world. That’s one of the reasons that understanding does not obey Moore’s Law: Knowledge is acquired by formulating explanations and testing them against reality, not by running an algorithm faster and faster.


pages: 404 words: 134,430

Why People Believe Weird Things: Pseudoscience, Superstition, and Other Confusions of Our Time by Michael Shermer

Albert Einstein, Alfred Russel Wallace, anesthesia awareness, anthropic principle, butterfly effect, cognitive dissonance, complexity theory, conceptual framework, correlation does not imply causation, cosmological principle, discovery of DNA, false memory syndrome, Gary Taubes, invention of the wheel, Isaac Newton, laissez-faire capitalism, Laplace demon, life extension, moral panic, Murray Gell-Mann, out of africa, Richard Feynman, Search for Extraterrestrial Intelligence, Silicon Valley, Stephen Hawking, Steven Pinker, The Bell Curve by Richard Herrnstein and Charles Murray, the scientific method, Thomas Kuhn: the structure of scientific revolutions

Shermer, M. 1991. Heretic-Scientist: Alfred Russel Wallace and the Evolution of Man. Ann Arbor, Mich.: UMI Dissertation Information Service. ......... 1993. The Chaos of History: On a Chaotic Model That Represents the Role of Contingency and Necessity in Historical Sequences. Nonlinear Science Today 2, no. 4:1-13. ......... 1994. Satanic Panic over in UK. Skeptic 4, no. 2:21. ......... 1995. Exorcising Laplace's Demon: Chaos and Antichaos, History and Metahistory. History and Theory 34, no. 1:59-83. ......... 1999. How We Believe: The Searchfor God in an Age ofScience. New York: W. H. Freeman. ......... 2001. The Borderlands ofScience: Where Sense Meets Nonsense. New York: Oxford University Press. ......... 2002. This View of Science: Stephen Jay Gould as Historian of Science and Scientific Historian.


pages: 698 words: 198,203

The Stuff of Thought: Language as a Window Into Human Nature by Steven Pinker

airport security, Albert Einstein, Bob Geldof, colonial rule, conceptual framework, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, Douglas Hofstadter, en.wikipedia.org, experimental subject, fudge factor, George Santayana, Laplace demon, loss aversion, luminiferous ether, Norman Mailer, Richard Feynman, Ronald Reagan, Sapir-Whorf hypothesis, science of happiness, social intelligence, speech recognition, stem cell, Steven Pinker, Thomas Bayes, Thorstein Veblen, traffic fines, urban renewal, Yogi Berra

The theory in the history of physics that is closest to intuitive force dynamics is the medieval notion of impetus, in which a moving object has been imbued with some kind of vim or zest that pushes it along for a while and gradually dissipates. So there is a big discrepancy between intuitive physics, with its discrete episodes of causing and helping and overcoming a tendency toward rest, and real physics, which is just a bunch of differential equations specifying how objects change their velocity and direction over time. Laplace’s Demon, the hypothetical imp that knows the instantaneous positions and velocities of every particle in the universe, was said to be able to calculate the entire future or past by plugging these values into the equations that express the laws of mechanics and electromagnetism. The concept of a “cause,” or even a discrete “event,” plays no role. The discrepancy between intuitive physics and classical physics has led some philosophers to suggest that the very concept of causation is scientifically obsolete, a holdover from an evolutionary past in which we dragged branches along the ground and threw rocks at giraffes.

Keysar, Boaz Khrushchev, Nikita kill killing Kim Jong Il, kinship metaphors Kipling, Rudyard Kitcher, Patricia Klein, Devrah knowing: a priori and a posteriori as having mutual knowledge Korean language Korff, Baruch Krebs, John Kripke, Saul Lakoff, George Lambek, Jim Langan, Michael language: combinatorial power of components of concreteness of as digital medium expanding perfect for reasoning as window into human nature see also language learning; semantics; syntax language acquisition, see language learning Language Instinct, The (Pinker) language learnability language learning as induction problem Linguistic Determinism and language of thought languages, see American Sign Language, Arabic language, Aymara language, Berber language, Chichewa language, Chinese language, Czech language, Danish language, Djirbal language, Dutch language, English language, French language, German language, Greek language, Hebrew language, Hungarian language, Igbo language, Indonesian language, Inuit languages, Italian language, Japanese language, Korean language, Papuan language, Portuguese language, Québecois French language, Russian language, Shona language, Spanish language, Tamil language, Tlingit language, Turkish language, Tzeltal language, Tzotzil language, Yiddish language, Yupik language Laplace’s Demon Larkin, Philip law Law & Order Lederer, Richard Lee, Peggy legalese Lehrer, Tom Leibniz, Gottfried Wilhelm Levin, Beth Levinson, Stephen Leviticus Lewis, C,S, Lewis, David Lexicon Branding Li, Peggy Lieberson, Stanley liff, meaning of Lillie, Beatrice limbic system Linguistic Determinism (Whorfian hypothesis) arguments against banal versions of and count-mass distinction defined on Eskimo words for snow interesting versions of radical versions of requirements for demonstrating linguistic relativity, see Linguistic Determinism (Whorfian hypothesis) literally literary metaphors conceptual metaphors contrasted with Lloyd, John locative construction gestalt-shift theory of idiosyncratic uses of learnability paradox universals and variation locative rule Locke, John logic “Logic and Conversation” (Grice) “love is a journey” metaphor “Love Me Two Times” (Doors) McCartney, Paul McCawley, James D.


pages: 677 words: 121,255

Giving the Devil His Due: Reflections of a Scientific Humanist by Michael Shermer

Alfred Russel Wallace, anthropic principle, anti-communist, barriers to entry, Berlin Wall, Boycotts of Israel, Chelsea Manning, clean water, clockwork universe, cognitive dissonance, Colonization of Mars, Columbine, cosmological constant, cosmological principle, creative destruction, dark matter, Donald Trump, Edward Snowden, Elon Musk, Flynn Effect, germ theory of disease, gun show loophole, Hans Rosling, hedonic treadmill, helicopter parent, hindsight bias, illegal immigration, income inequality, invisible hand, Johannes Kepler, Joseph Schumpeter, laissez-faire capitalism, Laplace demon, luminiferous ether, McMansion, means of production, mega-rich, Menlo Park, moral hazard, moral panic, More Guns, Less Crime, Peter Singer: altruism, phenotype, positional goods, race to the bottom, Richard Feynman, Ronald Coase, Silicon Valley, Skype, social intelligence, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, the scientific method, The Wealth of Nations by Adam Smith, transaction costs, WikiLeaks, working poor, Yogi Berra

“The Moral Landscape Challenge.” "http://bit.ly/2f2cc1f 13. Pinker, Steven. 2012. “The False Allure of Group Selection.” Edge.org "http://bit.ly/2m7pU8t Chapter 22 How Lives Turn Out 1. Shermer, Michael. 1993. “The Chaos of History: On a Chaotic Model that Represents the Role of Contingency and Necessity in Historical Sequences.” Nonlinear Science, 2:4., 1–13; Shermer, Michael. 1995. “Exorcising Laplace’s Demon: Chaos and Antichaos, History and Metahistory.” Invited paper for History and Theory. Wesleyan University. 34:1, 59–83; Shermer, Michael. 1997. “The Crooked Timber of History: History is Complex and Often Chaotic. Can We Use This to Better Understand the Past?” Complexity, 2:6, 23–29. 2. The White House. 2012. “Remarks by the President at a Campaign Event in Roanoke, Virginia.” Office of the Press Secretary.


pages: 317 words: 100,414

Superforecasting: The Art and Science of Prediction by Philip Tetlock, Dan Gardner

Affordable Care Act / Obamacare, Any sufficiently advanced technology is indistinguishable from magic, availability heuristic, Black Swan, butterfly effect, buy and hold, cloud computing, cuban missile crisis, Daniel Kahneman / Amos Tversky, desegregation, drone strike, Edward Lorenz: Chaos theory, forward guidance, Freestyle chess, fundamental attribution error, germ theory of disease, hindsight bias, index fund, Jane Jacobs, Jeff Bezos, Kenneth Arrow, Laplace demon, longitudinal study, Mikhail Gorbachev, Mohammed Bouazizi, Nash equilibrium, Nate Silver, Nelson Mandela, obamacare, pattern recognition, performance metric, Pierre-Simon Laplace, place-making, placebo effect, prediction markets, quantitative easing, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, scientific worldview, Silicon Valley, Skype, statistical model, stem cell, Steve Ballmer, Steve Jobs, Steven Pinker, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!

So much of our reality is this predictable, or more so. I just Googled tomorrow’s sunrise and sunset times for Kansas City, Missouri, and got them down to the minute. Those forecasts are reliable, whether they are for tomorrow, the day after, or fifty years from now. The same is true of tides, eclipses, and phases of the moon. All can be predicted from clocklike scientific laws with enough precision to satisfy Laplace’s forecasting demon. Of course each of these pockets of predictability can be abruptly punctured. A good restaurant is very likely to open its doors when it says it will, but it may not, for any number of reasons, from a manager sleeping late, to fire, bankruptcy, pandemic, nuclear war, or a physics experiment accidentally creating a black hole that sucks up the solar system. The same is true of anything else.


Blueprint: The Evolutionary Origins of a Good Society by Nicholas A. Christakis

agricultural Revolution, Alfred Russel Wallace, Amazon Mechanical Turk, assortative mating, Cass Sunstein, crowdsourcing, David Attenborough, different worldview, disruptive innovation, double helix, epigenetics, experimental economics, experimental subject, invention of agriculture, invention of gunpowder, invention of writing, iterative process, job satisfaction, Joi Ito, joint-stock company, land tenure, Laplace demon, longitudinal study, Mahatma Gandhi, Marc Andreessen, means of production, mental accounting, meta analysis, meta-analysis, microbiome, out of africa, phenotype, Pierre-Simon Laplace, placebo effect, race to the bottom, Ralph Waldo Emerson, replication crisis, Rubik’s Cube, Silicon Valley, social intelligence, social web, stem cell, Steven Pinker, the scientific method, theory of mind, twin studies, ultimatum game, zero-sum game

From an early age, we categorize objects according to fundamental commonalities, discriminate between these categories, and assign each category a basic essence. P. Bloom, How Pleasure Works: The New Science of Why We Like What We Like (New York: W. W. Norton, 2010); S. A. Gelman, The Essential Child: Origins of Essentialism in Everyday Thought (New York: Oxford University Press, 2010). 40. In the end, we would get Laplace’s Demon. For “such an intellect,” French mathematician Pierre-Simon Laplace argued in 1814, “nothing would be uncertain and the future just like the past would be present before its eyes.” P. S. Laplace, A Philosophical Essay on Probabilities, 6th ed., trans. F. W. Truscott and F. L. Emory (New York: Dover, 1951), p. 4. 41. There is actually much debate about whether the idea that the world obeys natural laws and is predictable can be reconciled with the idea that humans can truly have free will.


pages: 1,034 words: 241,773

Enlightenment Now: The Case for Reason, Science, Humanism, and Progress by Steven Pinker

3D printing, access to a mobile phone, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Albert Einstein, Alfred Russel Wallace, anti-communist, Anton Chekhov, Arthur Eddington, artificial general intelligence, availability heuristic, Ayatollah Khomeini, basic income, Berlin Wall, Bernie Sanders, Black Swan, Bonfire of the Vanities, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, clockwork universe, cognitive bias, cognitive dissonance, Columbine, conceptual framework, correlation does not imply causation, creative destruction, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, decarbonisation, deindustrialization, dematerialisation, demographic transition, Deng Xiaoping, distributed generation, diversified portfolio, Donald Trump, Doomsday Clock, double helix, effective altruism, Elon Musk, en.wikipedia.org, end world poverty, endogenous growth, energy transition, European colonialism, experimental subject, Exxon Valdez, facts on the ground, Fall of the Berlin Wall, first-past-the-post, Flynn Effect, food miles, Francis Fukuyama: the end of history, frictionless, frictionless market, germ theory of disease, Gini coefficient, Hans Rosling, hedonic treadmill, helicopter parent, Hobbesian trap, humanitarian revolution, Ignaz Semmelweis: hand washing, income inequality, income per capita, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), invention of writing, Jaron Lanier, Joan Didion, job automation, Johannes Kepler, John Snow's cholera map, Kevin Kelly, Khan Academy, knowledge economy, l'esprit de l'escalier, Laplace demon, life extension, long peace, longitudinal study, Louis Pasteur, Martin Wolf, mass incarceration, meta analysis, meta-analysis, Mikhail Gorbachev, minimum wage unemployment, moral hazard, mutually assured destruction, Naomi Klein, Nate Silver, Nathan Meyer Rothschild: antibiotics, Nelson Mandela, New Journalism, Norman Mailer, nuclear winter, obamacare, open economy, Paul Graham, peak oil, Peter Singer: altruism, Peter Thiel, precision agriculture, prediction markets, purchasing power parity, Ralph Nader, randomized controlled trial, Ray Kurzweil, rent control, Republic of Letters, Richard Feynman, road to serfdom, Robert Gordon, Rodney Brooks, rolodex, Ronald Reagan, Rory Sutherland, Saturday Night Live, science of happiness, Scientific racism, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Simon Kuznets, Skype, smart grid, sovereign wealth fund, stem cell, Stephen Hawking, Steven Pinker, Stewart Brand, Stuxnet, supervolcano, technological singularity, Ted Kaczynski, The Rise and Fall of American Growth, the scientific method, The Signal and the Noise by Nate Silver, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, total factor productivity, union organizing, universal basic income, University of East Anglia, Unsafe at Any Speed, Upton Sinclair, uranium enrichment, urban renewal, War on Poverty, We wanted flying cars, instead we got 140 characters, women in the workforce, working poor, World Values Survey, Y2K

Intelligence is a contraption of gadgets: software modules that acquire, or are programmed with, knowledge of how to pursue various goals in various domains.24 People are equipped to find food, win friends and influence people, charm prospective mates, bring up children, move around in the world, and pursue other human obsessions and pastimes. Computers may be programmed to take on some of these problems (like recognizing faces), not to bother with others (like charming mates), and to take on still other problems that humans can’t solve (like simulating the climate or sorting millions of accounting records). The problems are different, and the kinds of knowledge needed to solve them are different. Unlike Laplace’s demon, the mythical being that knows the location and momentum of every particle in the universe and feeds them into equations for physical laws to calculate the state of everything at any time in the future, a real-life knower has to acquire information about the messy world of objects and people by engaging with it one domain at a time. Understanding does not obey Moore’s Law: knowledge is acquired by formulating explanations and testing them against reality, not by running an algorithm faster and faster.25 Devouring the information on the Internet will not confer omniscience either: big data is still finite data, and the universe of knowledge is infinite.


Engineering Security by Peter Gutmann

active measures, algorithmic trading, Amazon Web Services, Asperger Syndrome, bank run, barriers to entry, bitcoin, Brian Krebs, business process, call centre, card file, cloud computing, cognitive bias, cognitive dissonance, combinatorial explosion, Credit Default Swap, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Debian, domain-specific language, Donald Davies, Donald Knuth, double helix, en.wikipedia.org, endowment effect, fault tolerance, Firefox, fundamental attribution error, George Akerlof, glass ceiling, GnuPG, Google Chrome, iterative process, Jacob Appelbaum, Jane Jacobs, Jeff Bezos, John Conway, John Markoff, John von Neumann, Kickstarter, lake wobegon effect, Laplace demon, linear programming, litecoin, load shedding, MITM: man-in-the-middle, Network effects, Parkinson's law, pattern recognition, peer-to-peer, Pierre-Simon Laplace, place-making, post-materialism, QR code, race to the bottom, random walk, recommendation engine, RFID, risk tolerance, Robert Metcalfe, Ruby on Rails, Sapir-Whorf hypothesis, Satoshi Nakamoto, security theater, semantic web, Skype, slashdot, smart meter, social intelligence, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, telemarketer, text mining, the built environment, The Death and Life of Great American Cities, The Market for Lemons, the payments system, Therac-25, too big to fail, Turing complete, Turing machine, Turing test, web application, web of trust, x509 certificate, Y2K, zero day, Zimmermann PGP

(This isn’t a new idea, going back at least as far as 17 th-century geeks like Gottfried Leibniz, who hoped that “it is always possible to terminate that part of a controversy that can be determined from the data […] so that it will suffice for two debaters to say to each other: let us calculate” [233]. This concept was later taken up by PierreSimon Laplace, who hoped that everything could be determined from Newtonian mechanics [234], an idea that became known as Laplace’s demon). The same thing happens outside the courtroom. The user doesn’t click through to the display of an X.509 certificate and say “The X.500 Distinguished Name of the certificate owner matches the name of the organisation with which I want to communicate and the certificate issuer is [and so on in this vein for another three pages] and therefore I can hand over my credit card details”. Instead they’ll look at the web page, which looks about right, possibly briefly check the URL, and maybe glance at the padlock (whether it’s present in the browser chrome, as the favicon, or as a GIF in the page content [235]), balance it against how badly they want to do whatever it is that they’re doing on the page, and make a decision based on the available evidence.