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Attributes of Superforecasting The Art and Science of Prediction PDF
Everyone would benefit from seeing further into the future, whether buying stocks, crafting policy, launching a new product, or simply planning the week’s meals. Unfortunately, people tend to be terrible forecasters. As Wharton professor Philip Tetlock showed in a landmark 2005 study, even experts’ predictions are only slightly better than chance. However, an important and underreported conclusion of that study was that some experts do have real foresight, and Tetlock has spent the past decade trying to figure out why. What makes some people so good? And can this talent be taught?
In Superforecasting, Tetlock and coauthor Dan Gardner offer a masterwork on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament. The Good Judgment Project involves tens of thousands of ordinary people—including a Brooklyn filmmaker, a retired pipe installer, and a former ballroom dancer—who set out to forecast global events. Some of the volunteers have turned out to be astonishingly good. They’ve beaten other benchmarks, competitors, and prediction markets. They’ve even beaten the collective judgment of intelligence analysts with access to classified information. They are “superforecasters.”
In this groundbreaking and accessible book, Tetlock and Gardner show us how we can learn from this elite group. Weaving together stories of forecasting successes (the raid on Osama bin Laden’s compound) and failures (the Bay of Pigs) and interviews with a range of high-level decision makers, from David Petraeus to Robert Rubin, they show that good forecasting doesn’t require powerful computers or arcane methods. It involves gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course.
Superforecasting offers the first demonstrably effective way to improve our ability to predict the future—whether in business, finance, politics, international affairs, or daily life—and is destined to become a modern classic.
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The Writers
Philip E. Tetlock is the Annenberg University Professor at the University of Pennsylvania and holds appointments in the psychology and political science departments and the Wharton School of Business. He and his wife, Barbara Mellers, are the co-leaders of the Good Judgment Project, a multi-year forecasting study. He is also the author of Expert Political Judgment and (with Aaron Belkin) Counterfactual Thought Experiments in World Politics.
Dan Gardner is a New York Times bestselling author, speaker, and consultant. His three books on psychology and decision-making—published in 25 countries and 19 languages—have been praised by everyone from The Economist to Nobel laureate Daniel Kahneman. Prior to becoming an author, Gardner was a newspaper columnist, talking head, and investigative journalist who won or was nominated for every major award in Canadian newspaper journalism. He is an honorary senior fellow at the University of Ottawa’s Graduate School of Public Policy and International Affairs and lives in Ottawa, Canada.
Proportions of Superforecasting The Art and Science of Prediction PDF
- Identification Number : 0804136718
- Publisher : Crown; Illustrated edition (September 13, 2016)
- Language : English
- Paperback : 352 pages
- International Standard Book Number-10 : 9780804136716
- International Standard Book Number-13 : 978-0804136716
- Item Weight : 8 ounces
- Dimensions : 5.1 x 0.77 x 7.96 inches
Reviews From Customers
James Taylor
Scientific approach to prediction
October 3, 2018
I really enjoyed this book a few years ago, and I have come back to offer a review based on my notes at the time and how the insights have settled for me over time. I took away many key concepts for successfully forecasting uncertain events and also some areas I noted for further exploration. Many of the following notes are structured from the authors’ insight into the demonstrated practices of repeatedly successful forecasters.
The book mentions repeatedly the importance of measurement for assessment and revising forecasts and programs. Many people simply don’t create any metrics of anything when they make unverifiable and chronologically ambiguous declarations.
The book emphasizes the importance of receiving this feedback on predictions that measurement allows, as there is a studied gap between confidence and skill in judgment. We have a tendency to be uninterested in accumulating counterfactuals, but we must know when we fail to learn from it. If forecasts are either not made or not quantified and ambiguous, we can’t receive clear feedback, so the thought process that led to the forecasts can’t be improved upon. Feedback, however, allows for the psychological trap of hindsight bias. This is that when we know the outcome, that knowledge of the outcome skews our perception of what we thought at the time of the prediction and before we knew the outcome.
The main qualities for successful forecasting are being open-minded, careful, and undertaking self-critical thinking with focus, which is not effortless. Commitment to self-improvement is the strongest predictor of long-term performance in measured forecasting. This can basically be considered as equivalent to the popular concept of grit. Studies show that individuals with fixed mindsets do not pay attention to new information that could improve their future predictions. Similarly, forecasts tend to improve when more probabilistic thinking is embraced rather than fatalistic thinking in regards to the perspective that certain events are inevitable.
A few interesting findings that the authors expand upon in more detail in the book: experience is important to have the tacit knowledge essential to the practice of forecasting, and that grit, or perseverance, towards making great forecasts is three times as important as intelligence.
Practices to undertake when forecasting are to create a breakdown of components to the question that you can distinguish and scrutinize your assumptions; develop backwards thinking as answering the questions of what you would need to know to answer the question, and then making appropriate numerical estimations for those questions; practice developing an outside view, which is starting with an anchored view from past experience of others, at first downplaying the problem’s uniqueness; explore other potential views regarding the question; and express all aspects and perspectives into a single number that can be manipulated and updated.
Psychological traps to be aware of discussed in the book include confirmation bias, which is a willingness to seek out information that confirms your hypothesis and not seek out information that may contradict it, which is the opposite of discovering counterfactuals; belief perseverance, also known as cognitive dissonance, in which individuals can be incapable of updating their belief in the face of new evidence by rationalization in order to not have their belief upset; scope insensitivity, which is not properly factoring in an important aspect of applicability of scope, such as timeframe, properly into the forecast; and thought type replacement, which is replacing a hard question in analysis with a similar question that’s not equivalent but which is much easier to answer.
Researched qualities to strive for as a forecaster: cautious, humble, nondeterministic, actively open-minded, reflective, numerate, pragmatic, analytical, probabilistic, belief updaters, intuitive psychologists, growth mindset.
The authors then delve into a bit of another practical perspective on forecasting, which involves teams. Psychological traps for teams include the known phenomenon known as groupthink, which is that small cohesive groups tend to unconsciously develop shared illusions and norms that are often biased in favor of the group, which interfere with critical thinking regarding objective reality. There is also a tendency for members of the group to leave the hard work of critical thinking to others on the team instead of sharing this work optimally, which when combined with groupthink, leads the group towards tending to feel a sense of completion upon reaching a level of agreement. One idea to keep in mind for management of a group is that the group’s collective thinking can be described as a product of the communication of the group itself and not the sum of the thinking of the individual members of a group.
There are some common perceived problems with forecasting, which receive attention in the book: the wrong side of maybe fallacy, which is the thinking that a forecast was bad because the forecast was greater than 50% but the event didn’t occur, which can lead to forecasters not willing to be vulnerable with their forecasts; publishing forecasts for all to see, where research shows that public posting of forecasts, with one’s name associated with the forecast, creates more open-mindedness and increased performance; and the fallacy that because many factors are unquantifiable due their real complexity, the use of numbers in forecasting is therefore not useful.
Some concepts that I took note of for further research from the book were: Bayesian-based application for belief updating, which is basically a mathematical way of comparing how powerful your past belief was relative to some specific new information, chaos theory, game theory, Monte Carlo methods, and systematic intake of news media. These are concepts that I was particularly interested in from the book based on my own interests and that I have continued to explore. This book was very valuable for cohesively bringing together the above concepts in the context of a compelling story, based on the DARPA research project which was compellingly won by the author’s team as a product of the research that led to this groundbreaking book.
60 people found this helpful
Valuable insights in too verbose wrapping
January 29, 2016
The book offers insights into one of the most important aspects of professional performance, being able to predict the outcome of future events – which is of course impossible in most cases. The book does, however, come with very valuable insights – and unless you are familiar with these already – could make you a better forecaster.
The key points I found from this book is to force yourselves to explicitly state your forecast, preferably in numerical probability, so that you can easily judge and learn from them when the facts are given. Keep an open mind (ridiculously obvious?). Separate known statistical content from case specific guess work – ie what is general in a case and what needs specific analysis?
Although the information provided might be very useful, and I do not regret the read – I think the text is far too verbose and much of the content either repetitive or not very interesting. I kept thinking about Nassim Nicholas Taleb’s entertaining books on randomness when reading, until his name appeared in an endless chapter on the critics of the author’s work. If you haven’t read Taleb’s books, read them first – and save this one to your retirement days.
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