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The Future of Everything: The Science of Prediction
For centuries, scientists have strived to predict the future. But to what extent have they succeeded? Can past events-Hurricane Katrina, the Internet stock bubble, the SARS outbreak-help us understand what will happen next? Will scientists ever really be able to forecast catastrophes, or will we always be at the mercy of Mother Nature, waiting for the next storm, epidemic, or economic crash to thunder through our lives? In "The Future of Everything," David Orrell looks back at the history of forecasting, from the time of the oracle at Delphi to the rise of astrology to the advent of the TV weather report, showing us how scientists (and some charlatans) predicted the future. How can today's scientists claim to anticipate future weather events when even thee-day forecasts prove a serious challenge? How can we predict and control epidemics? Can we accurately foresee our financial future? Or will we only find out about tomorrow when tomorrow arrives?
- GenresScienceNonfiction
476 pages, Kindle Edition
First published January 7, 2006
20 people are currently reading
263 people want to read
About the author
David Orell
6 books15 followersMe llamo David Orell, aunque es posible que ya me conocieras desde hace unos años como @Kassius9 y, si no es así, te lo resumiré en pocas palabras: fue un apodo que usé en Twitter y en este blog hasta 2014, cuando publiqué mi primer libro de relatos breves. Bien, a partir de entonces, y viendo que mi carrera como escritor comenzaba a despegar, decidí tomármelo en serio y trabajar en mi propia marca personal, con más o menos acierto.
No voy a empezar diciendo lo que muchos cuentan sobre sus inicios en la escritura durante la infancia porque no sería nada original, tampoco me ayudaría a destacar.
Pues yo era de esos que, durante la hora del patio en el colegio, a veces se quedaba en el aula para escribir historias. ¿Cuando comencé a escribir? No lo sé. ¿Ves? Por eso no voy a decirlo. Pero lo que sí recuerdo es que a los diez años le pedí a mi madre que me ayudara a encuadernar un relato de 100 páginas. Hoy en día ese relato no existe, y si ahora lo tuviera en las manos creo que me moriría de vergüenza.
Leí muchos libros, de esos que eran lectura obligatoria y luego tenías que presentar un trabajo para sacar nota. No obstante, también leí otros títulos que me absorbían por completo, y la culpa la tenían Sherlock Holmes, Stephen King y Jules Verne, entre otros. Recuerdo que años más tarde, en la época del instituto, escribí una novela tipo «Diez negritos» de Ágatha Christie, inspirandome en los compañeros para los personajes. Al acabar, hice veinte copias y las vendí para sacar algo de dinero.
En 2000, el amor y el desamor invocaron al poeta que llevaba dentro y me pasé vomitando versos hasta llenar cuatro cuadernos de anillas con dibujos o pegando con cinta adhesiva todo tipo de recuerdos entre las páginas. Sí, todo fue muy dramático… ¡En fin! Cuando me recuperé un poco de aquellas bobadas que tanto se magnifican a esa edad, quemé los dichosos cuadernos bajo la promesa de no volver a cometer tal apostasía.
Después de eso, comencé a leer a Murakami, Katzenbach, Kerouac y la generación Beat, Luca de Tena, Truman Capote, Khaled Hosseini y un largo etcétera. Devoraba libros, fumaba, trabajaba y siempre pensando en escribir la novela de mi vida, pero cuando me sentaba frente al portátil llegaba el vacío y el implacable silencio.
Ocho años tuvieron que pasar para que se me ocurriera una idea y no dejé de escribir hasta poner el punto y final. Ahora es otro proyecto archivado esperando su oportunidad.
Y a lo tonto, en 2011, abrí un blog para escribir mis relatos y otras tonterías. Al poco, entré a formar parte de la asociación de blogueros Mallorcablogs. He participado y colaborado en varios blogs con artículos sobre las diferentes facetas del miedo y las leyendas de terror. Poco después, en 2014, publicaba mi primer libro de relatos Ya lo dijo Thomas, obteniendo favorables reseñas de los lectores. Tras esto me entrevistaron en Radio Marratxí. Seguidamente llegaron dos años de intenso trabajo, escribiendo y reescribiendo varios borradores de una novela que todavía sigue en pañales. ¡Tiempo al tiempo! Lo bueno es que este año he ganado el certamen de microrrelatos Índigo Crea con Jardín de papel, fue alucinante, ¡no esperaba llegar a tanto!
Como dije antes, decidí tomármelo en serio y empaparme de información mediante cursos online sobre productividad, gestión de blogs y todo tipo de competencias digitales. Mientras tanto, de forma paralela, mi mente hervía de nuevas ideas y conceptos que, en la actualidad, son novelas en potencia.
Creo que escribir es fácil, pero lo difícil es hacerlo bien. Además, la figura del escritor que solo se dedica a aporrear el teclado se ha vuelto obsoleta. Vivimos en un mundo lleno de competencia y estamos muy saturados de información, de novedades literarias diarias en las estanterías. Por eso mismo hay que aprender a manejar las herramientas que tenemos a nuestra disposición y utilizarlas bien para no ser uno más entre un millón. Nadie dijo que
No voy a empezar diciendo lo que muchos cuentan sobre sus inicios en la escritura durante la infancia porque no sería nada original, tampoco me ayudaría a destacar.
Pues yo era de esos que, durante la hora del patio en el colegio, a veces se quedaba en el aula para escribir historias. ¿Cuando comencé a escribir? No lo sé. ¿Ves? Por eso no voy a decirlo. Pero lo que sí recuerdo es que a los diez años le pedí a mi madre que me ayudara a encuadernar un relato de 100 páginas. Hoy en día ese relato no existe, y si ahora lo tuviera en las manos creo que me moriría de vergüenza.
Leí muchos libros, de esos que eran lectura obligatoria y luego tenías que presentar un trabajo para sacar nota. No obstante, también leí otros títulos que me absorbían por completo, y la culpa la tenían Sherlock Holmes, Stephen King y Jules Verne, entre otros. Recuerdo que años más tarde, en la época del instituto, escribí una novela tipo «Diez negritos» de Ágatha Christie, inspirandome en los compañeros para los personajes. Al acabar, hice veinte copias y las vendí para sacar algo de dinero.
En 2000, el amor y el desamor invocaron al poeta que llevaba dentro y me pasé vomitando versos hasta llenar cuatro cuadernos de anillas con dibujos o pegando con cinta adhesiva todo tipo de recuerdos entre las páginas. Sí, todo fue muy dramático… ¡En fin! Cuando me recuperé un poco de aquellas bobadas que tanto se magnifican a esa edad, quemé los dichosos cuadernos bajo la promesa de no volver a cometer tal apostasía.
Después de eso, comencé a leer a Murakami, Katzenbach, Kerouac y la generación Beat, Luca de Tena, Truman Capote, Khaled Hosseini y un largo etcétera. Devoraba libros, fumaba, trabajaba y siempre pensando en escribir la novela de mi vida, pero cuando me sentaba frente al portátil llegaba el vacío y el implacable silencio.
Ocho años tuvieron que pasar para que se me ocurriera una idea y no dejé de escribir hasta poner el punto y final. Ahora es otro proyecto archivado esperando su oportunidad.
Y a lo tonto, en 2011, abrí un blog para escribir mis relatos y otras tonterías. Al poco, entré a formar parte de la asociación de blogueros Mallorcablogs. He participado y colaborado en varios blogs con artículos sobre las diferentes facetas del miedo y las leyendas de terror. Poco después, en 2014, publicaba mi primer libro de relatos Ya lo dijo Thomas, obteniendo favorables reseñas de los lectores. Tras esto me entrevistaron en Radio Marratxí. Seguidamente llegaron dos años de intenso trabajo, escribiendo y reescribiendo varios borradores de una novela que todavía sigue en pañales. ¡Tiempo al tiempo! Lo bueno es que este año he ganado el certamen de microrrelatos Índigo Crea con Jardín de papel, fue alucinante, ¡no esperaba llegar a tanto!
Como dije antes, decidí tomármelo en serio y empaparme de información mediante cursos online sobre productividad, gestión de blogs y todo tipo de competencias digitales. Mientras tanto, de forma paralela, mi mente hervía de nuevas ideas y conceptos que, en la actualidad, son novelas en potencia.
Creo que escribir es fácil, pero lo difícil es hacerlo bien. Además, la figura del escritor que solo se dedica a aporrear el teclado se ha vuelto obsoleta. Vivimos en un mundo lleno de competencia y estamos muy saturados de información, de novedades literarias diarias en las estanterías. Por eso mismo hay que aprender a manejar las herramientas que tenemos a nuestra disposición y utilizarlas bien para no ser uno más entre un millón. Nadie dijo que
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Displaying 1 - 18 of 18 reviews
March 15, 2017
As the subtitle says, this is a book about the science of prediction. Very little of the book (the last chapter) is about specific predictions of the future. The book is divided into three parts. Part one talks about predicting in the past, a major focus of which is in mechanics. This includes Ptolmey’s model, the Copernican model, Galileo and Newton’s mechanics. Part two covers the science of prediction in the present. It focuses on the weather, biology (mainly genetics and its influence on health), and economics. David Orrell’s major claim in this part of the book is that prediction goes astray not because of chaos theory (sensitive dependence on initial conditions—the famous butterfly effect), but on model error because no model is exact enough to capture all the necessary elements. Part three then looks at the future of predicting with a dismal appraisal. But, he is not claiming that all prediction is worthless. In the final chapter he gives two sets of prediction. One in which we do nothing about carbon emissions and one where we do. There are three appendixes, which explain some of the mathematics (simplified) used in prediction models.
Here are some of the notes and comments I made on the text:
[page 9] The whole thing of “an insect flapping its wings [the butterfly effect]” and affecting a storm at a great distance seems pretty far-fetched to me. Orrell tends to agree with me, but his view comes from his work on model error [also see comment for page 145].
[page 70] He and his editor made a big mistake: “[Galileo] also discovered four moons around Saturn.” They were Jupiter’s moons. Galileo did discover Saturn’s rings, not its moons;
[page 95] “When Napoleon asked Laplace why God did not play a role in his calculations for the solar system, he is said to have replied, ‘I have no need of that hypothesis.’” This is an excellent summation of methodological naturalism, which most practicing scientists adhere to, at least in their scientific work. Some people do consider god a mathematician, but most of these people I feel are usually speaking figuratively.
[page 105] Speaking on non-local phenomena in quantum mechanics, he claims that “Einstein . . . was religious . . .” Einstein was not religious in the sense of practicing a religion. He might have had some nonstandard belief in god, or some sense of spirituality, but that is a far cry from being religious.
[page 109] He seems to defend free will by reference to unpredictability, which he is saying all our prediction models of complex systems exhibit. I claim unpredictability does not eliminate cause and effect. The only known area of the universe that is known to be noncausal is in the quantum sphere, but from there on up it is cause and effect. If cause and effect were not occurring, the world would be a pretty strange place as the quantum realm appears to be. He says that autonomy comes from this unpredictability. Autonomy should only be considered as being violated when someone does not determine his or hers actions, but is force to or prevented from doing something.
[page 114] “It is important that models not be confused with this [complex systems] far richer reality.” A good example of the basic principle of “the word is not the thing” or “the map is not the territory.”
[page 124] “[Aristotle’s] own view [of meteorology] was that thunder, lightening, and hurricanes were all caused by ‘windy exhalations,’ perhaps from quarreling philosophers.” Orrell shows this sense of humor in places throughout the book.
[page 145] In response to the butterfly effect as a perceived source of prediction error, not model error, he states: “But is the weather really so delicate and finely poised a system that an insect can stir up a hurricane—or knock it off track—with a beat of its tiny wings?” My sentiments actually.
As far as the second half of the book, I found nothing worthy of comment, except I admire his willingness to see metaphor as an integral part of science, and I say as it is with life.
I thought the book to be a fair appraisal of the state of the science of prediction, and what can be expected from it in the future. I am not sure how other scientists working on prediction models would rebut his arguments, that the main problem with these models is model error (for instance, not having enough parameters), not sensitive dependence on initial conditions (ala chaos theory); although, I do not think he believes that chaos theory does not play any role at all. Orrell is a good explainer and easily understood by someone conversant enough with science. The book was enjoyable to read, and I did learn some new stuff, not just stuff I already new explained in a different way, that I often run into
in reading popular science books.
If you are looking for what is going to happen in the future, you maybe disappointed. However, if you would like to know or know more about models used in prediction science, especially those involved with the weather, health, or economics, the book should be of interest to you. I will offer one word of caution. If you are looking for actual mathematics (except for the appendixes) and not just description, I feel you would be as disappointed as those that wish for predictions for what is going to occur in the future.
Here are some of the notes and comments I made on the text:
[page 9] The whole thing of “an insect flapping its wings [the butterfly effect]” and affecting a storm at a great distance seems pretty far-fetched to me. Orrell tends to agree with me, but his view comes from his work on model error [also see comment for page 145].
[page 70] He and his editor made a big mistake: “[Galileo] also discovered four moons around Saturn.” They were Jupiter’s moons. Galileo did discover Saturn’s rings, not its moons;
[page 95] “When Napoleon asked Laplace why God did not play a role in his calculations for the solar system, he is said to have replied, ‘I have no need of that hypothesis.’” This is an excellent summation of methodological naturalism, which most practicing scientists adhere to, at least in their scientific work. Some people do consider god a mathematician, but most of these people I feel are usually speaking figuratively.
[page 105] Speaking on non-local phenomena in quantum mechanics, he claims that “Einstein . . . was religious . . .” Einstein was not religious in the sense of practicing a religion. He might have had some nonstandard belief in god, or some sense of spirituality, but that is a far cry from being religious.
[page 109] He seems to defend free will by reference to unpredictability, which he is saying all our prediction models of complex systems exhibit. I claim unpredictability does not eliminate cause and effect. The only known area of the universe that is known to be noncausal is in the quantum sphere, but from there on up it is cause and effect. If cause and effect were not occurring, the world would be a pretty strange place as the quantum realm appears to be. He says that autonomy comes from this unpredictability. Autonomy should only be considered as being violated when someone does not determine his or hers actions, but is force to or prevented from doing something.
[page 114] “It is important that models not be confused with this [complex systems] far richer reality.” A good example of the basic principle of “the word is not the thing” or “the map is not the territory.”
[page 124] “[Aristotle’s] own view [of meteorology] was that thunder, lightening, and hurricanes were all caused by ‘windy exhalations,’ perhaps from quarreling philosophers.” Orrell shows this sense of humor in places throughout the book.
[page 145] In response to the butterfly effect as a perceived source of prediction error, not model error, he states: “But is the weather really so delicate and finely poised a system that an insect can stir up a hurricane—or knock it off track—with a beat of its tiny wings?” My sentiments actually.
As far as the second half of the book, I found nothing worthy of comment, except I admire his willingness to see metaphor as an integral part of science, and I say as it is with life.
I thought the book to be a fair appraisal of the state of the science of prediction, and what can be expected from it in the future. I am not sure how other scientists working on prediction models would rebut his arguments, that the main problem with these models is model error (for instance, not having enough parameters), not sensitive dependence on initial conditions (ala chaos theory); although, I do not think he believes that chaos theory does not play any role at all. Orrell is a good explainer and easily understood by someone conversant enough with science. The book was enjoyable to read, and I did learn some new stuff, not just stuff I already new explained in a different way, that I often run into
in reading popular science books.
If you are looking for what is going to happen in the future, you maybe disappointed. However, if you would like to know or know more about models used in prediction science, especially those involved with the weather, health, or economics, the book should be of interest to you. I will offer one word of caution. If you are looking for actual mathematics (except for the appendixes) and not just description, I feel you would be as disappointed as those that wish for predictions for what is going to occur in the future.
May 15, 2008
A lot of words to reach an underwhelming conclusion.
January 15, 2017
The book considers three main areas:
The introduction to the book is an interesting history of forecasting - starting with ancient history (Delphi, Greek attempts to understand astronomy, astrology) then Renaissance developments (Kepler, Galileo, Newton, Descartes) then through Darwin, Malthus and Freud and into quantum theory and chaos and more importantly complexity). This is probably the strongest part of the book. A key premise of the book is that many models are not just chaotic; they are extremely sensitive to parameters. Further some systems are simply not predictable; even if a model can be built there is no way to predict outcomes other than by running the models. Another strong view is that biologists (Dawkins is explicitly mentioned) still have an over strong belief in determinism and in a mechanical world model of the type discarded by physicists a long time ago.
The next section considers three areas in turn and explores three areas of forecasting - weather/climate, genetic (the most complex to follow but most original section and most thought provoking - showing the complexities of the genetic process like those of the weather and economy), economic. The conclusions are similar - unlike say planetary motion, the systems are complex, model and parameterisation error dwarfs the butterfly effect, models can easily be used to fit past data but are unreliable going forwards and also cannot be predicted without running them (often in not far off real time).
The third section brings some of the thoughts together including discussing the psychology of the climate change debate. This is the weakest section including a rather over long Gaia section and incongruously making some future long term predictions.
A mixed "curate's egg" of a book - definitely with some interesting ideas and worth retaining but not one to recommend to others.
The introduction to the book is an interesting history of forecasting - starting with ancient history (Delphi, Greek attempts to understand astronomy, astrology) then Renaissance developments (Kepler, Galileo, Newton, Descartes) then through Darwin, Malthus and Freud and into quantum theory and chaos and more importantly complexity). This is probably the strongest part of the book. A key premise of the book is that many models are not just chaotic; they are extremely sensitive to parameters. Further some systems are simply not predictable; even if a model can be built there is no way to predict outcomes other than by running the models. Another strong view is that biologists (Dawkins is explicitly mentioned) still have an over strong belief in determinism and in a mechanical world model of the type discarded by physicists a long time ago.
The next section considers three areas in turn and explores three areas of forecasting - weather/climate, genetic (the most complex to follow but most original section and most thought provoking - showing the complexities of the genetic process like those of the weather and economy), economic. The conclusions are similar - unlike say planetary motion, the systems are complex, model and parameterisation error dwarfs the butterfly effect, models can easily be used to fit past data but are unreliable going forwards and also cannot be predicted without running them (often in not far off real time).
The third section brings some of the thoughts together including discussing the psychology of the climate change debate. This is the weakest section including a rather over long Gaia section and incongruously making some future long term predictions.
A mixed "curate's egg" of a book - definitely with some interesting ideas and worth retaining but not one to recommend to others.
August 14, 2018
A knowledgable book that covers the study of prediction since the ancient times to now. Discusses the error of current models and provides counterarguments for common arguments, etc. I can definitely say I learned (a lot) from the book, though I couldn't hold on word-for-word and towards the end only read the summaries, bullet points, asides/case studies, etc
January 14, 2023
read this as a wall street journal copy so not sure if this is the same book.
that said, at the time it was great insight to future tech. is outdated now.
that said, at the time it was great insight to future tech. is outdated now.
February 7, 2008
The title of this book is a little over-reaching, since it mostly just discusses the prediction of weather, the economy, and an individual's health over the course of his lifetime. That said, I thought this book was so interesting. It starts with a historical overview of the tools the ancients used to predict events in their world. For example, how the model of the earth as the center of the universe was so entrenched in their mind-set, and how that prevented them from moving forward even when observations were inconsistent with the model. When Isaac Newton invented calculus and was actually able to predict the movement of the planets with real accuracy, it caused scientists in all disciplines to believe that all science should be tools of this kind of clean and precise prediction. But, physics is an anomaly in that way. Systems like the weather, organisms and the economy can't be predicted from given starting conditions, because, as the system moves forward, new conditions emerge as the result of the system moving forward, and that can't be captured by mathematical equations.
From this book I learned (1) a precipitation forecast is only accurate for 24 hours, and (2) invest in index funds and forget the rest of the stock market. The author was able to explain concepts in such a way that I could understand even though I am a product of the public school system and my math skills are shameful. This book wasn't a quick, easy read. There were several parts where I had to read the same paragraph about 40 times before what he was saying came together, and I finally grasped where he was coming from. This book made me want to learn more about chaos and emergence theory.
From this book I learned (1) a precipitation forecast is only accurate for 24 hours, and (2) invest in index funds and forget the rest of the stock market. The author was able to explain concepts in such a way that I could understand even though I am a product of the public school system and my math skills are shameful. This book wasn't a quick, easy read. There were several parts where I had to read the same paragraph about 40 times before what he was saying came together, and I finally grasped where he was coming from. This book made me want to learn more about chaos and emergence theory.
June 19, 2012
This book is about why it's so hard to predict certain things. In particular, it focuses on models and methods for predicting weather, health and wealth. The book begins with a historical overview of major scientific advances, from the Greeks to the 20th century, that improved our ability to predict things (like the motions of the planets) and shaped our expectations for our ability to control the future. Then it looks at methods for making short-term predictions and long-term predictions of its three main topics. Weather covers tomorrow's weather as well as climate change. Health looks at individual genetic profiles and global pandemics. Wealth looks at predicting stock and commodity prices over a few weeks or months and longer-term effects like recessions and bubbles.
In all these cases, the author argues that the systems are so inherently complex that they cannot be modeled. That is, they cannot be reduced to equations that can be solved to predict future states. They are uncomputable. The best we can do is create models that simulate these systems. But our models have so many parameters that even minor adjustments to a few of them can cause huge changes in the predictions the models make. All of which means that the future is inherently unpredicatble. Faster computers or better models won't help.
The author concludes with some thoughts about the usefulness of models and how to prepare for an unpredictable future. He believes that, even if we can't be sure about major events like climate change, it seems foolhardy not to prepare for them, just in case. The cost of preparing for catastrophe and being wrong is less than the cost of NOT preparing for catastrophe and being wrong.
In all these cases, the author argues that the systems are so inherently complex that they cannot be modeled. That is, they cannot be reduced to equations that can be solved to predict future states. They are uncomputable. The best we can do is create models that simulate these systems. But our models have so many parameters that even minor adjustments to a few of them can cause huge changes in the predictions the models make. All of which means that the future is inherently unpredicatble. Faster computers or better models won't help.
The author concludes with some thoughts about the usefulness of models and how to prepare for an unpredictable future. He believes that, even if we can't be sure about major events like climate change, it seems foolhardy not to prepare for them, just in case. The cost of preparing for catastrophe and being wrong is less than the cost of NOT preparing for catastrophe and being wrong.
February 16, 2009
Thought-provoking study of the limits of forecasting
This book is a fascinating, very readable look at the accuracy of modern forecasting. David Orrell begins with an overview of the history of telling the future, including humanity’s inherent need to try to decipher what tomorrow holds. He covers the current state of forecasting in fascinating detail, dwelling on weather and climate, economics and medicine. He points out the shortcomings in experts’ current ability – or lack thereof – to predict the future accurately in any of these realms. Finally, he discusses the future of forecasting, and makes a case for using the limited models available to become better prepared for future events, particularly climate-related ones, even those that are impossible to forecast with precision. The fiscal and commercial relevance of his advice is startlingly clear in the light of recent natural disasters. getAbstract highly recommends this well-structured overview of forecasting and the author’s cautionary message that the planet’s health will govern much of what lies ahead.
This book is a fascinating, very readable look at the accuracy of modern forecasting. David Orrell begins with an overview of the history of telling the future, including humanity’s inherent need to try to decipher what tomorrow holds. He covers the current state of forecasting in fascinating detail, dwelling on weather and climate, economics and medicine. He points out the shortcomings in experts’ current ability – or lack thereof – to predict the future accurately in any of these realms. Finally, he discusses the future of forecasting, and makes a case for using the limited models available to become better prepared for future events, particularly climate-related ones, even those that are impossible to forecast with precision. The fiscal and commercial relevance of his advice is startlingly clear in the light of recent natural disasters. getAbstract highly recommends this well-structured overview of forecasting and the author’s cautionary message that the planet’s health will govern much of what lies ahead.
April 13, 2012
This book is a fascinating, very readable look at the accuracy of modern forecasting. David Orrell begins with an overview of the history of telling the future, including humanity’s inherent need to try to decipher what tomorrow holds. He covers the current state of forecasting in fascinating detail, dwelling on weather and climate, economics and medicine. He points out the shortcomings in experts’ current ability – or lack thereof – to predict the future accurately in any of these realms. Finally, he discusses the future of forecasting, and makes a case for using the limited models available to become better prepared for future events, particularly climate-related ones, even those that are impossible to forecast with precision. The fiscal and commercial relevance of his advice is startlingly clear in light of recent natural disasters
August 6, 2014
This is definitely something that you should read before you start putting a lot of trust into any long-term forecasting involving weather, genetic outcomes, and economics. As you would expect, lots of variables and assumption errors in models can - and do - produce wildly different results.
The only thing that I didn't care for was all the quotes that he used. Many of them were unnecessary for him to get his point across.
The only thing that I didn't care for was all the quotes that he used. Many of them were unnecessary for him to get his point across.
Read
January 12, 2010Literally just started this book. It was the only thing I asked for for Christmas (nerd I know).
I received a forward of the abstract from a Sr. Director from work who handles our analytics and promotions team. It explores the history & evolution of forecasting as well as the intriguiing limits of prediction, so far anyway. Looking forward to the rest of the read.
I received a forward of the abstract from a Sr. Director from work who handles our analytics and promotions team. It explores the history & evolution of forecasting as well as the intriguiing limits of prediction, so far anyway. Looking forward to the rest of the read.
July 28, 2013
I thought the book was going to have more substance to it than what it did. I guess my expectations were a little higher than they should have been, but I was wooed in by the title and guess I was expecting some profound revelations revealed in the book. I guess in the long run, we're all dead! lol
July 9, 2008
Excellent Book! Combines the knowledge of the ancients with the science of the present and the future.
July 10, 2010
The bottom line - there are and always will be limits in making predictions about future outcomes. This is applicable to weather, human events, and medicine.
June 18, 2012
A vastly underrated book about the limits of what we can know about the future.
February 8, 2016
A thoughtful book that gives an its own unique insight into current questions of the day. There is quite a bit of historical data and lots of models at the end of the book.
May 29, 2017
really enjoy the topic and I got some good history, but it was a lot of history. it didn't feel very pointed.
Displaying 1 - 18 of 18 reviews