In theory, companies create value for stakeholders by making risky investments. In practice, however, managers in large corporations routinely quash risky ideas in favor of marginal improvements, cost-cutting, and "safe" investments. Why are managers in large, hierarchical organizations so risk-averse? Corporate incentives and control processes actively discourage managers from taking risks. Whereas CEOs consider each investment in the context of a greater portfolio, managers essentially bet their careers on every investment they make--even if outcomes are negligible to the corporation as a whole. This article explains how loss aversion works, presents an analysis of just how much value manager attitudes toward investment risk leave on the table, and offers suggestions for changes in practices and systems.

Many decisions about strategy require that senior executives make evaluative judgments on the basis of extensive, complex information. A disciplined, sequential approach can mitigate common errors and improve the quality of both one-off and recurrent decisions in an array of business domains. The process described in this article is easy to learn, involves little additional work, and (within limits) leaves room for intuition.

Organizations expect to see consistency in the decisions of their employees, but humans are unreliable. Judgments can vary a great deal from one individual to the next, even when people are in the same role and supposedly following the same guidelines. And irrelevant factors, such as mood and the weather, can change one person's decisions from occasion to occasion. This chance variability of decisions is called "noise," and it is surprisingly costly to companies, which are usually completely unaware of it. Nobel laureate Daniel Kahneman, a professor of psychology at Princeton, and Andrew M. Rosenfield, Linnea Gandhi, and Tom Blaser of TGG Group explain how organizations can perform a "noise audit" by having members of a professional unit evaluate a common set of cases. The degree to which their assessments vary provides the measure of noise. If the problem is severe, firms can pursue a number of remedies. The most radical is to replace human judgment with algorithms. Unlike people, algorithms always return the same output for any given input, and research shows that their predictions and decisions are often more accurate than those made by experts. Although algorithms may seem daunting to construct, the authors describe how to build them with input data on a small number of cases and some simple commonsense rules. 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.

In legal disputes, contested insurance claims, and similarly adversarial negotiations, one party is likely to open with an inflated claim or a lowball offer. And if the other side's position is unreasonable, it may make little sense to be reasonable yourself. But if everyone routinely came to a dispute with a realistic starting position, the offers would be more or less aligned, and any negotiation that followed would most likely be relatively civil, speedy, and fair. How can a negotiator who wants to be fair from the start ensure that his or her counterpart will be reasonable as well? The authors propose the "final-offer arbitration" challenge, which leverages an approach first applied in labor negotiations in the 1960s. You can employ this tactic by opening with a demonstrably fair offer and then--if the other party is unreasonable--extending a challenge to take the competing offers to an arbitrator who must choose one or the other rather than a compromise between them (the usual outcome of conventional arbitration). The authors describe how AIG used the approach and how other companies can begin to adopt it.

When an executive makes a big bet, he or she typically relies on the judgment of a team that has put together a proposal for a strategic course of action. After all, the team will have delved into the pros and cons much more deeply than the executive has time to do. The problem is, biases invariably creep into any team's reasoning-and often dangerously distort its thinking. A team that has fallen in love with its recommendation, for instance, may subconsciously dismiss evidence that contradicts its theories, give far too much weight to one piece of data, or make faulty comparisons to another business case. That's why, with important decisions, executives need to conduct a careful review not only of the content of recommendations but of the recommendation process. To that end, the authors-Kahneman, who won a Nobel Prize in economics for his work on cognitive biases; Lovallo of the University of Sydney; and Sibony of McKinsey-have put together a 12-question checklist intended to unearth and neutralize defects in teams' thinking. These questions help leaders examine whether a team has explored alternatives appropriately, gathered all the right information, and used well-grounded numbers to support its case. They also highlight considerations such as whether the team might be unduly influenced by self-interest, overconfidence, or attachment to past decisions. By using this practical tool, executives will build decision processes over time that reduce the effects of biases and upgrade the quality of decisions their organizations make. The payoffs can be significant: A recent McKinsey study of more than 1,000 business investments, for instance, showed that when companies worked to reduce the effects of bias, they raised their returns on investment by seven percentage points. Executives need to realize that the judgment of even highly experienced, superbly competent managers can be fallible. A disciplined decision-making process, not individual genius, is the key to good strategy.

The evidence is disturbingly clear: Most major business initiatives--mergers and acquisitions, capital investments, market entries--fail to pay off. 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. But two distinguished scholars of decision making, Dan Lovallo of the University of New South Wales and Nobel laureate Daniel Kahneman of Princeton University, provide a very different explanation. They show that a combination of cognitive biases (including anchoring and competitor neglect) and organizational pressures lead managers to make overly optimistic forecasts in analyzing proposals for major investments. By exaggerating the likely benefits of a project and ignoring the potential pitfalls, they lead their organizations into initiatives that are doomed to fall well short of expectations. The biases and pressures cannot be escaped, the authors argue, but they can be tempered by applying a very different method of forecasting--one that takes a much more objective "outside view" of an initiative's likely outcome. This outside view, also known as reference-class forecasting, completely ignores the details of the project at hand; instead, it encourages managers to examine the experiences of a class of similar projects, to lay out a rough distribution of outcomes for this reference class, and then to position the current project in that distribution.