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Overcome definition is - to get the better of: surmount. How to use overcome in a sentence. Synonym Discussion of overcome.
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The weariness of Castell had overcome him again, however, for he snored at his side. Lady Delacour was overcome by the tenderness with which Belinda spoke. KO beat beat up better blank blast bulldoze clobber conquer cream deck drub exceed excel flax floor get the better of knock off lambaste let have it lick master outclass outdo outshine outstrip overcome prevail put away shoot down shut down surpass take care of take down tan thrash top total transcend trash triumph triumph over trounce wallop waste wax whip whomp whop wipe wipe out wipe the floor with zap.

One reason the problem of noise is invisible is that people do not go through life imagining plausible alternatives to every judgment they make. The expectation that others will agree with you is sometimes justified, particularly where judgments are so skilled that they are intuitive. High-level chess and driving are standard examples of tasks that have been practiced to near perfection.

The same is true of drivers.


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Negotiating traffic would be impossibly dangerous if we could not assume that the drivers around us share our understanding of priorities at intersections and roundabouts. There is little or no noise at high levels of skill. High skill develops in chess and driving through years of practice in a predictable environment, in which actions are followed by feedback that is both immediate and clear.

Unfortunately, few professionals operate in such a world. We offer this aphorism in summary: Where there is judgment, there is noise—and usually more of it than you think. As a rule, we believe that neither professionals nor their managers can make a good guess about the reliability of their judgments. The only way to get an accurate assessment is to conduct a noise audit.

And at least in some cases, the problem will be severe enough to require action. The most radical solution to the noise problem is to replace human judgment with formal rules—known as algorithms—that use the data about a case to produce a prediction or a decision. People have competed against algorithms in several hundred contests of accuracy over the past 60 years, in tasks ranging from predicting the life expectancy of cancer patients to predicting the success of graduate students.

Algorithms were more accurate than human professionals in about half the studies, and approximately tied with the humans in the others. The ties should also count as victories for the algorithms, which are more cost-effective. In many situations, of course, algorithms will not be practical. The application of a rule may not be feasible when inputs are idiosyncratic or hard to code in a consistent format.

Algorithms are also less likely to be useful for judgments or decisions that involve multiple dimensions or depend on negotiation with another party. Even when an algorithmic solution is available in principle, organizational considerations sometimes prevent implementation. The replacement of existing employees by software is a painful process that will encounter resistance unless it frees those employees up for more-enjoyable tasks.

But if the conditions are right, developing and implementing algorithms can be surprisingly easy. The common assumption is that algorithms require statistical analysis of large amounts of data. For example, most people we talk to believe that data on thousands of loan applications and their outcomes is needed to develop an equation that predicts commercial loan defaults.

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Very few know that adequate algorithms can be developed without any outcome data at all—and with input information on only a small number of cases. The construction of a reasoned rule starts with the selection of a few perhaps six to eight variables that are incontrovertibly related to the outcome being predicted. If the outcome is loan default, for example, assets and liabilities will surely be included in the list.

The next step is to assign these variables equal weight in the prediction formula, setting their sign in the obvious direction positive for assets, negative for liabilities. The rule can then be constructed by a few simple calculations. For example, you can build a reasoned rule that predicts loan defaults quite effectively without knowing what happened to past loans; all you need is a small set of recent loan applications.

Here are the next steps:. Select six to eight variables that are distinct and obviously related to the predicted outcome. Assets and revenues weighted positively and liabilities weighted negatively would surely be included, along with a few other features of loan applications. Take the data from your set of cases all the loan applications from the past year and compute the mean and standard deviation of each variable in that set.

Distraction

With standard scores, all variables are expressed on the same scale and can be compared and averaged. This is the output of the reasoned rule. The same formula will be used for new cases, using the mean and standard deviation of the original set and updating periodically. Order the cases in the set from high to low summary scores, and determine the appropriate actions for different ranges of scores.

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You are now ready to apply the rule to new cases. The algorithm will compute a summary score for each new case and generate a decision. The surprising result of much research is that in many contexts reasoned rules are about as accurate as statistical models built with outcome data. Standard statistical models combine a set of predictive variables, which are assigned weights based on their relationship to the predicted outcomes and to one another. In many situations, however, these weights are both statistically unstable and practically unimportant.

A simple rule that assigns equal weights to the selected variables is likely to be just as valid.

How to overcome distractions at work

The bottom line here is that if you plan to use an algorithm to reduce noise, you need not wait for outcome data. You can reap most of the benefits by using common sense to select variables and the simplest possible rule to combine them. Studies show that algorithms do better than humans in the role of decision maker. Of course, no matter what type of algorithm is employed, people must retain ultimate control. Algorithms must be monitored and adjusted for occasional changes in the population of cases.

Managers must also keep an eye on individual decisions and have the authority to override the algorithm in clear-cut cases. For example, a decision to approve a loan should be provisionally reversed if the firm discovers that the applicant has been arrested. Algorithms are sometimes used as an intermediate source of information for professionals, who make the final decisions.

One example is the Public Safety Assessment, a formula that was developed to help U. Uncomfortable as people may be with the idea, studies have shown that while humans can provide useful input to formulas, algorithms do better in the role of final decision maker. If the avoidance of errors is the only criterion, managers should be strongly advised to overrule the algorithm only in exceptional circumstances.

Replacing human decisions with an algorithm should be considered whenever professional judgments are noisy, but in most cases this solution will be too radical or simply impractical. An alternative is to adopt procedures that promote consistency by ensuring that employees in the same role use similar methods to seek information, integrate it into a view of the case, and translate that view into a decision.

A thorough examination of everything required to do that is beyond the scope of this article, but we can offer some basic advice, with the important caveat that instilling discipline in judgment is not at all easy.

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Training is crucial, of course, but even professionals who were trained together tend to drift into their own way of doing things. Firms sometimes combat drift by organizing roundtables at which decision makers gather to review cases. Unfortunately, most roundtables are run in a way that makes it much too easy to achieve agreement, because participants quickly converge on the opinions stated first or most confidently.

Such roundtables will effectively provide an audit of noise, with the added step of a group discussion in which differences of opinion are explored. As an alternative or addition to roundtables, professionals should be offered user-friendly tools, such as checklists and carefully formulated questions, to guide them as they collect information about a case, make intermediate judgments, and formulate a final decision. Unwanted variability occurs at each of those stages, and firms can—and should—test how much such tools reduce it. Ideally, the people who use these tools will view them as aids that help them do their jobs effectively and economically.

Unfortunately, our experience suggests that the task of constructing judgment tools that are both effective and user-friendly is more difficult than many executives think. Controlling noise is hard, but we expect that an organization that conducts an audit and evaluates the cost of noise in dollars will conclude that reducing random variability is worth the effort.

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