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.

This special issue explores the impacts of behavioral strategy on management practice. Behavioral strategy can best contribute to management practice by shifting its focus from individual decision biases to the design of behaviorally informed decision processes at the level of the firm. This introduction identifies three types of organizational decision processes, shows how they interact with individual and group biases, and proposes a model showing how managers can design and deploy these processes to shape the strategy of the firm. It then introduces the articles in this special issue and discusses their contributions to the future of behavioral strategy.

The ability to sense valuable strategic options and then to organize effectively and efficiently to embrace them is at the core of a company's dynamic capabilities. This article identifies and discusses a specific type of sensing that we call "generative sensing." Companies and executives that display generative sensing capabilities proactively generate hypotheses about observed events and then test these hypotheses to generate new data in a recursive process. Borrowing from design cognition research, we discuss the two microfoundations of these capabilities- framing and abduction - and provide examples of how they are embedded in companies to enhance option generation.

This is an MIT Sloan Management Review article. Managers often must make decisions about complex strategic issues, and they are expected to make choices carefully and objectively. A retailer, for example, may need to decide whether to bid more in a highly competitive auction. Or a manufacturer may want to determine how long to hold onto a money-losing plant as the economy sinks into a recession. In boom times, deals are often in demand and expensive (and acquirers tend to know it); but when the economy cools off, acquisitions fall out of favor and prices decline. Conventional capital budgeting methods for valuing acquisitions and investments (such as discounted cash flow) may result in overpricing in "hot"deal markets and underpricing in "cold"deal markets. By setting potential deals in the context of real options theory and behavioral economics, authors Han Smit and Dan Lovallo write, executives can compensate for potential biases. Investor exuberance, the positive sentiments of boards and interest by rivals can cause executives to view acquisition opportunities as more attractive than they actually are in "hot"deal markets. Loss aversion and a narrow perspective that does not consider long-term growth options, meanwhile, can subdue acquisition behavior during "cold"markets. The article is designed to improve the use of valuation methods and help mitigate decision biases. Treating acquisition decisions as simple go/no-go choices based on expected cash flows, the authorswrite, creates an unhealthy dynamic. Because it's difficult for executives to recognize their own biases, the authors suggest using a formalized process to de-bias the decision-making team. First, managers must determine whether they are facing an investment in a "hot"or "cold"deal market (something that can often be revealed by the number of deals), after which the authors propose taking a broader view, supported by checklists.

Most businesses rely on traditional capital-budgeting tools when making strategic decisions such as investing in an innovative technology or entering a new market. These tools assume that decision makers have access to remarkably complete and reliable information--yet most strategic decisions must be made under conditions of great uncertainty. Why are these traditional tools used so often even though their limitations are widely acknowledged? The problem is not a lack of alternatives. Managers have at their disposal a wide variety of tools--including decision analysis, scenario planning, and information aggregation tools--that can help them make smart decisions under high degrees of uncertainty. But the sheer variety can be overwhelming. This article provides a model for matching the decision-making tool to the decision being made, on the basis of three factors: how well you understand the variables that will determine success, how well you can predict the range of possible outcomes, and how centralized the relevant information is. The authors bring their framework to life using decisions that executives at McDonald's might need to make--from the very clear-cut (choosing a site for a new store in the United States) to the highly uncertain (changing the business in response to the obesity epidemic).

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.

Pursuing a merger or acquisition is inherently difficult. Things get even harder when executives are blind to their own faulty assumptions, say Lovallo--a professor at the University of Western Australia Business School and a senior adviser to McKinsey--and three of his McKinsey colleagues. The authors identify biases that can surface at each step of the M&A process and provide practical tips for rising above them--an approach they call targeted debiasing. During the preliminary due-diligence stage, biases abound. To overcome the confirmation bias, aggressively seek evidence that challenges your initial hypothesis about a deal. The best medicine for overconfidence in identifying revenue and cost synergies is to learn from precedents at your firm and others. Avoiding underestimation of cultural differences between your company and the target requires understanding the differences in the ways people interact at each organization. Misjudging the time and resources you need is at the core of the planning fallacy, which you can elude by formally identifying best practices and continually revisiting them. Finally, dilute conflict of interest by soliciting dispassionate external expertise. The bidding phase is vulnerable to the winner's curse, a phenomenon common in auctions. To avoid paying too much for a target, actively generate alternatives to the deal under consideration and develop a set of bidding cutoff rules. After offering an initial bid, deal makers are susceptible to anchoring, whereby they remain attached to their original price estimate, and to the sunk cost fallacy that they've invested too much to stop now. The secret to overcoming both: Use your newly available access to the target's books to better assess the investment case--and change your tune accordingly.

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.