Judgment under Uncertainty: Heuristics and Biases. Amos Tversky; Daniel Kahneman. Science, New Series, Vol. , No. (Sep. Thinking Fast and Slow Daniel Kahneman. Teshome Engda. A E= mc 2 This eBook is provided by 1 soeprolrendiele.gq ∑ Plenty of eBooks is a. was offered by Christopher Chabris and Daniel Simons in their book The. Invisible Gorilla. They constructed a short film of two teams passing basketballs, one.
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PDF | Book Review. Understanding how and why we make choices is important for everybody. If you are a scientist or aspire to be one in the future. The Framing of Decisions and the. Psychology of Choice. Amos Tversky and Daniel Kahneman. Explanations and predictions of people's choices, in everyday . Prospect Theory: An Analysis of Decision under Risk. Author(s): Daniel Kahneman and Amos Tversky. Source: Econometrica, Vol. 47, No. 2 (Mar., ), pp.
This phenomenon is the result of anchoring, one of the strongest and most prevalent of cognitive biases. In one experiment that revealed the power of anchoring, people were asked for the last four digits of their Social Security number.
They were then asked whether the number of physicians in Manhattan is larger or smaller than the number formed by those four digits. Finally, they were asked to estimate what the number of Manhattan physicians actually is. The correlation between the Social Security number and the estimate was significantly positive.
The subjects started from a random series of digits and then insufficiently adjusted their estimate away from it.
Noise: How to Overcome the High, Hidden Cost of Inconsistent Decision Making
Anchoring can be especially pernicious when it comes to forecasting the cost of major capital projects. When executives set budgets for such initiatives, they build in contingency funds to cover overruns. Often, however, they fail to put in enough. Many of the plants had their performance expectations permanently lowered, and the owners never realized a return on their investments.
Competitor Neglect. Here, again, the result is an underestimation of the potential for negative events—in this case, price wars, overcapacity, and the like. When a company identifies a rapidly growing market well suited to its products and capabilities, it will often rush to gain a beachhead in it, investing heavily in production capacity and marketing.
The effort is often justified by the creation of attractive pro forma forecasts of financial results. But such forecasts rarely account for the fact that many other competitors will also target the market, convinced that they, too, have what it takes to succeed.
As all these companies invest, supply outstrips demand, quickly rendering the new market unprofitable. Even savvy venture capitalists fell into this trap during the recent ill-fated Internet boom. Organizational Pressure. Every company has only a limited amount of money and time to devote to new projects. Competition for this time and money is intense, as individuals and units jockey to present their own proposals as being the most attractive for investment.
Because forecasts are critical weapons in these battles, individuals and units have big incentives to accentuate the positive in laying out prospective outcomes. This has two ill effects. First, it ensures that the forecasts used for planning are overoptimistic, which, as we described in our discussion of anchoring, distorts all further analysis.
Second, it raises the odds that the projects chosen for investment will be those with the most overoptimistic forecasts—and hence the highest probability of disappointment. Other organizational practices also encourage optimism. Senior executives tend, for instance, to stress the importance of stretch goals for their business units. This can have the salutary effect of increasing motivation, but it can also lead unit managers to further skew their forecasts toward unrealistically rosy outcomes.
And when these forecasts become the basis for compensation targets, the practice can push employees to behave in dangerously risky ways. Organizations also actively discourage pessimism, which is often interpreted as disloyalty.
The bearers of bad news tend to become pariahs, shunned and ignored by other employees. The optimistic biases of individual employees become mutually reinforcing, and unrealistic views of the future are validated by the group.
The Outside View For most of us, the tendency toward optimism is unavoidable.
Still, optimism can, and should, be tempered. Simply understanding the sources of overoptimism can help planners challenge assumptions, bring in alternative perspectives, and in general take a balanced view of the future. Companies can introduce into their planning processes an objective forecasting method that counteracts the personal and organizational sources of optimism. In , one of us was involved in a project to develop a curriculum for a new subject area for high schools in Israel.
The project was conducted by a small team of academics and teachers. When the team had been operating for about a year and had some significant achievements under its belt, its discussions turned to the question of how long the project would take. Everyone on the team was asked to write on a slip of paper the number of months that would be needed to finish the project—defined as having a complete report ready for submission to the Ministry of Education.
The estimates ranged from 18 to 30 months. Try to recall as many such projects as you can. Think of them as they were in a stage comparable to ours at present. How long did it take them at that point to reach completion? Of the remaining, I cannot think of any that completed their task in less than seven years, nor of any that took more than ten.
Indeed, my impression is that we are slightly below average in terms of resources and potential. Instead, the members ignored the pessimistic information and proceeded with the project. They finally completed the initiative eight years later, and their efforts went largely for naught—the resulting curriculum was rarely used. In this example, the curriculum expert made two forecasts for the same problem and arrived at very different answers.
We call these two distinct modes of forecasting the inside view and the outside view. The inside view is the one that the expert and all the other team members spontaneously adopted.
They made forecasts by focusing tightly on the case at hand—considering its objective, the resources they brought to it, and the obstacles to its completion; constructing in their minds scenarios of their coming progress; and extrapolating current trends into the future. Not surprisingly, the resulting forecasts, even the most conservative ones, were exceedingly optimistic.
The outside view, also known as reference-class forecasting, is the one that the curriculum expert was encouraged to adopt. Instead, it examined the experiences of a class of similar projects, laid out a rough distribution of outcomes for this reference class, and then positioned the current project in that distribution.
The resulting forecast, as it turned out, was much more accurate. The contrast between inside and outside views has been confirmed in systematic research. Recent studies have shown that when people are asked simple questions requiring them to take an outside view, their forecasts become significantly more objective and reliable.
For example, a group of students enrolling at a college were asked to rate their future academic performance relative to their peers in their major. Most individuals and organizations are inclined to adopt the inside view in planning major initiatives.
The natural way to think about a complex project is to focus on the project itself—to bring to bear all one knows about it, paying special attention to its unique or unusual features. The curriculum expert, for example, did not take the outside view until prompted—even though he already had all the information he needed. Even when companies bring in independent consultants to assist in forecasting, they often remain stuck in the inside view.
If the consultants provide comparative data on other companies or projects, they can spur useful outside-view thinking. But if they concentrate on the project itself, their analysis will also tend to be distorted by cognitive biases. When both forecasting methods are applied with equal intelligence and skill, the outside view is much more likely to yield a realistic estimate.
But for most projects, the outside view will produce superior results. The outside view is more likely to produce accurate forecasts and much less likely to deliver highly unrealistic ones. It is in the planning of such de novo efforts that the biases toward optimism are likely to be great. Ironically, however, such cases are precisely where the organizational and personal pressures to apply the inside view are most intense. Indeed, the preference for the inside view over the outside view can feel almost like a moral imperative.
The inside view is embraced as a serious attempt to come to grips with the complexities of a unique challenge, while the outside view is rejected as relying on a crude analogy to superficially similar instances. Yet the fact remains: The outside view is more likely to produce accurate forecasts and much less likely to deliver highly unrealistic ones. Of course, choosing the right class of analogous cases becomes more difficult when executives are forecasting initiatives for which precedents are not easily found.
Imagine that planners have to forecast the results of an investment in a new and unfamiliar technology. Or should they look at how other companies carried out projects involving similar technologies? Neither is perfect, but each will provide useful insights—so the planners should analyze both sets of analogous cases. This effort is best organized into five steps:1 1. Select a reference class.
Identifying the right reference class involves both art and science. You usually have to weigh similarities and differences on many variables and determine which are the most meaningful in judging how your own initiative will play out. But you may actually get better results by looking at other chemical plants built with new processing technologies.
In forecasting an outcome in a competitive situation, such as the market share for a new venture, you need to consider industrial structure and market factors in designing a reference class. The key is to choose a class that is broad enough to be statistically meaningful but narrow enough to be truly comparable to the project at hand.
Once the reference class is chosen, you have to document the outcomes of the prior projects and arrange them as a distribution, showing the extremes, the median, and any clusters. But you can still arrive at a rough distribution by calculating the average outcome as well as a measure of variability. Based on your own understanding of the project at hand and how it compares with the projects in the reference class, predict where it would fall along the distribution.
Because your intuitive estimate will likely be biased, the final two steps are intended to adjust the estimate in order to arrive at a more accurate forecast.
Assess the reliability of your prediction. Some events are easier to foresee than others. This step is intended to gauge the reliability of the forecast you made in Step 3.
The goal is to estimate the correlation between the forecast and the actual outcome, expressed as a coefficient between 0 and 1, where 0 indicates no correlation and 1 indicates complete correlation. In the best case, information will be available on how well your past predictions matched the actual outcomes. You can then estimate the correlation based on historical precedent.
Delusions of Success: How Optimism Undermines Executives’ Decisions
In the absence of such information, assessments of predictability become more subjective. You may, for instance, be able to arrive at an estimate of predictability based on how the situation at hand compares with other forecasting situations. To return to the movie example, say that you are fairly confident that your ability to predict the sales of films exceeds the ability of sportscasters to predict point spreads in football games but is not as good as the ability of weather forecasters to predict temperatures two days out.
Through a diligent statistical analysis, you could construct a rough scale of predictability based on computed correlations between predictions and outcomes for football scores and temperatures. You can then estimate where your ability to predict film scores lies on this scale.
When the calculations are complex, it may help to bring in a skilled statistician. Correct the intuitive estimate. Due to bias, the intuitive estimate made in Step 3 will likely be optimistic—deviating too far from the average outcome of the reference class. In this final step, you adjust the estimate toward the average based on your analysis of predictability in Step 4. But if applying formulas is politically or operationally infeasible, companies can still set up procedures and practices that will guide employees to make more-consistent decisions.
Idea in Brief The Problem Many organizations expect consistency from their professional employees. However, human judgment is often influenced by such irrelevant factors as the weather and the last case seen.
More important, decisions often vary from employee to employee. The chance variability of judgments is called noise, and it is surprisingly costly to companies. The Starting Point Managers should perform a noise audit in which members of a unit, working independently, evaluate a common set of cases.
The degree to which their decisions vary is the measure of noise. It will often be dramatically higher than executives anticipate. The Solution The most radical solution to a severe noise problem is to replace human judgment with algorithms. In such instances, companies should establish procedures to help professionals achieve greater consistency. At a global financial services firm we worked with, a longtime customer accidentally submitted the same application file to two offices.
Though the employees who reviewed the file were supposed to follow the same guidelines—and thus arrive at similar outcomes—the separate offices returned very different quotes. Taken aback, the customer gave the business to a competitor. From the point of view of the firm, employees in the same role should have been interchangeable, but in this case they were not.
Unfortunately, this is a common problem. Professionals in many organizations are assigned arbitrarily to cases: appraisers in credit-rating agencies, physicians in emergency rooms, underwriters of loans and insurance, and others. Organizations expect consistency from these professionals: Identical cases should be treated similarly, if not identically.
The problem is that humans are unreliable decision makers; their judgments are strongly influenced by irrelevant factors, such as their current mood, the time since their last meal, and the weather.
We call the chance variability of judgments noise. It is an invisible tax on the bottom line of many companies. Some jobs are noise-free. Clerks at a bank or a post office perform complex tasks, but they must follow strict rules that limit subjective judgment and guarantee, by design, that identical cases will be treated identically.
In contrast, medical professionals, loan officers, project managers, judges, and executives all make judgment calls, which are guided by informal experience and general principles rather than by rigid rules. But often noise is far above the level that executives would consider tolerable—and they are completely unaware of it. The prevalence of noise has been demonstrated in several studies. Academic researchers have repeatedly confirmed that professionals often contradict their own prior judgments when given the same data on different occasions.
Performing a Noise Audit
When pathologists made two assessments of the severity of biopsy results, the correlation between their ratings was only. Judgments made by different people are even more likely to diverge. The unavoidable conclusion is that professionals often make decisions that deviate significantly from those of their peers, from their own prior decisions, and from rules that they themselves claim to follow.
Noise is often insidious: It causes even successful companies to lose substantial amounts of money without realizing it. How substantial? Aggregated over the assessments made every year, the cost of noise was measured in billions—an unacceptable number even for a large global firm.
The value of reducing noise even by a few percentage points would be in the tens of millions. Remarkably, the organization had completely ignored the question of consistency until then. It has long been known that predictions and decisions generated by simple statistical algorithms are often more accurate than those made by experts, even when the experts have access to more information than the formulas use.
It is less well known that the key advantage of algorithms is that they are noise-free: Unlike humans, a formula will always return the same output for any given input.
Superior consistency allows even simple and imperfect algorithms to achieve greater accuracy than human professionals. Of course, there are times when algorithms will be operationally or politically infeasible, as we will discuss. In this article we explain the difference between noise and bias and look at how executives can audit the level and impact of noise in their organizations.
We then describe an inexpensive, underused method for building algorithms that remediate noise, and we sketch out procedures that can promote consistency when algorithms are not an option. Noise vs. Bias When people consider errors in judgment and decision making, they most likely think of social biases like the stereotyping of minorities or of cognitive biases such as overconfidence and unfounded optimism. The useless variability that we call noise is a different type of error.Economists would argue that the low success rate reflects a rational assessment of risk, with the returns from a few successes outweighing the losses of many failures.
Thinking is done with almost no effort from the person and there is no feeling of control.
Kahneman explains that the relationship between the two systems can be seen in the effects of random anchors. You usually have to weigh similarities and differences on many variables and determine which are the most meaningful in judging how your own initiative will play out. Further, they find cognitive effort unpleasant and avoid it as much as possible. System one suggested the incorrect intuition, and System two endorsed it and expressed it in a judgment.
The brain does this to save on effort, substituting easier questions for harder questions. Sometimes, in fact, they will explicitly deny the role of chance in the outcome of their plans.
It wants large events to cause effects, and it wants effects to have causes.