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Jerker Denrell

Publications and source records attributed to Jerker Denrell.

3 recordsLinked to original sources

Adaptive learning and risk taking.

Humans and animals learn from experience by reducing the probability of sampling alternatives with poor past outcomes. Using simulations, J. G. March (1996) illustrated how such adaptive sampling could lead to risk-averse as well as risk-seeking behavior. In this article, the author develops a formal theory of how adaptive sampling influences risk taking. He shows that a risk-neutral decision maker may learn to prefer a sure thing to an uncertain alternative with identical expected value and a symmetric distribution, even if the decision maker follows an optimal policy of learning. If the distribution of the uncertain alternative is negatively skewed, risk-seeking behavior can emerge. Consistent with recent experiments, the model implies that information about foregone payoffs increases risk taking.

Adaptation, Psychological↗

Why most people disapprove of me: experience sampling in impression formation.

Individuals are typically more likely to continue to interact with people if they have a positive impression of them. This article shows how this sequential sampling feature of impression formation can explain several biases in impression formation. The underlying mechanism is the sample bias generated when the probability of interaction depends on current impressions. Because negative experiences decrease the probability of interaction, negative initial impressions are more stable than positive impressions. Negative initial impressions, however, are more likely to change for individuals who are frequently exposed to others. As a result, systematic differences in interaction patterns, due to social similarity or proximity, will produce systematic differences in impressions. This mechanism suggests an alternative explanation of several regularities in impression formation, including a negativity bias in impressions of outgroup members, systematic differences in performance evaluations, and more positive evaluations of proximate others.

Humans↗

Selection bias and the perils of benchmarking.

To find the secrets of business success, what could be more natural than studying successful businesses? In fact, nothing could be more dangerous, warns this Stanford professor. Generalizing from the examples of successful companies is like generalizing about New England weather from data taken only in the summer. That's essentially what businesspeople do when they learn from good examples and what consultants, authors, and researchers do when they study only existing companies or--worse yet--only high-performing companies. They reach conclusions from unrepresentative data samples, falling into the classic statistical trap of selection bias. Drawing on a wealth of case studies, for instance, one researcher concluded that great leaders share two key traits: They persist, often despite initial failures, and they are able to persuade others to join them. But those traits are also the hallmarks of spectacularly unsuccessful entrepreneurs, who must persist in the face of failure to incur large losses and must be able to persuade others to pour their money down the drain. To discover what makes a business successful, then, managers should look at both successes and failures. Otherwise, they will overvalue risky business practices, seeing only those companies that won big and not the ones that lost dismally. They will not be able to tell if their current good fortune stems from smart business practices or if they are actually coasting on past accomplishments or good luck. Fortunately, economists have developed relatively simple tools that can correct for selection bias even when data about failed companies are hard to come by. Success may be inspirational, but managers are more likely to find the secrets of high performance if they give the stories of their competitors'failures as full a hearing as they do the stories of dazzling successes.

Administrative Personnel↗