Chasing the expert

“Chasing the expert” is the search for a single supposedly decisive authority when a problem could instead be addressed by combining many people’s judgments. The book presents the phrase as a warning against mistaking concentrated individual brilliance for the most reliable way to know: experts can contribute valuable information, but seeking the one smartest person is often less effective than building a group whose knowledge and errors can be pooled.

Meaning and Central Claim

The expression captures a common assumption about knowledge: that a good decision depends on finding the right person, whether a consultant, executive, or specialist. Even when a crowd reaches a strikingly accurate result, observers may credit a few unusually smart members rather than the group’s combined judgment. The book reverses that instinct, arguing that the crowd itself can know more than any one participant, including its experts.

The phrase “chase the expert” is attributed to sociologists Richard Larrick and Jack B. Soll. Their formulation gives the book’s argument a practical edge: the problem is not simply that people admire expertise, but that organizations can waste time and resources trying to identify a lone answer-giver. A group’s judgment can be consistently better than most of its members’ judgments, while an individual expert’s performance may vary substantially from one problem to another.

Why the Search Misleads

The book does not claim that informed analysts are useless or that crowds should replace trained specialists in every task. Rather, it argues that expertise should be pooled. A specialist may possess information others lack, but combining that contribution with independent judgments can make the overall result more dependable than choosing one person and relying on that person alone.

One obstacle is an intuition that averaging advice means compromising or diluting it. Faced with several experts, people tend to select the one they believe is best instead of combining their estimates. That preference reflects a broader belief that intelligence resides in individuals and that a single distinguished consultant or chief executive can transform an organization. In this view, the crowd is blind to its own potential wisdom.

Confidence offers no dependable shortcut for finding the best forecaster. Experts are not more confident in their abilities than average people, and an expert’s self-assessment has little connection to actual performance. The distinction between knowing something and knowing how much one knows makes confidence a poor credential for choosing a sole authority.

Nor does a conspicuous record of past success settle the question. When many people make predictions, a few will accumulate impressive records through luck as well as skill. Their past results do not establish that they will continue to outperform others. The book’s conclusion is deliberately qualified: finding smart people need not lead decision-makers astray, but trying to find the single smartest person can.

A Crowd at Work

The search for the lost submarine Scorpion shows why the alternative is not merely a theoretical preference for groups. After the submarine disappeared in May 1968 while returning to Newport News from duty in the North Atlantic, the navy knew its last reported location but did not know what had happened or how far it might have traveled. The initial search covered a circle twenty miles wide and thousands of feet deep.

Naval officer John Craven did not simply ask a handful of leading submarine and ocean-current experts to name a location. He devised alternative scenarios and assembled people with different kinds of knowledge, including mathematicians, submarine specialists, and salvage men. Each made separate estimates of how likely the scenarios were, wagering bottles of Chivas Regal on questions such as the submarine’s speed toward the ocean bottom and the steepness of its descent. Craven combined their estimates using Bayes’s theorem to produce a collective estimate of the submarine’s final location. No single piece of information supplied the answer; the method made their partial judgments useful together.

The same principle appears in the book’s account of the market’s response to the Challenger disaster. Traders did not need individually to know that Thiokol was responsible for the shuttle’s O-ring failure. Each trader could contribute bits of information or interpretation, and the market’s aggregated judgment could bring those pieces close to the truth. The broader lesson is conditional, not magical: a wise crowd needs diverse private information, independent judgments, decentralized access to local knowledge, and a mechanism for aggregating opinions. Averaging can cancel individual errors, but only if the judgments contain relevant information to begin with.

Collaboration Beyond the Lone Expert

The international search for the cause of SARS offers a scientific counterpart to the expression’s argument. In March 2003, the World Health Organization brought eleven research laboratories into a collaborative project. The labs held daily teleconferences, shared photographs and test results online, and exchanged virus samples. Their parallel work helped them rule out possible causes, identify a coronavirus in samples from SARS patients, and establish that it caused the disease. The labs announced that conclusion on April 16, about a month after their collaboration began.

Cynthia Goldsmith first spotted the coronavirus, but that observation alone could not establish it as the cause of SARS. The evidence accumulated through work across the laboratories: other possible causes were investigated, the virus was detected in varied patient samples, and infected monkeys in a Dutch laboratory developed SARS. The book therefore credits the laboratory network collectively, rather than treating the first observation as the completed discovery. The case shows what seeking the expert can miss: a crucial contribution may be individual, while the finding that makes it consequential depends on many people’s work and scrutiny.

Scientific knowledge also depends on exchange beyond formal collaborations. Researchers publish results and make data available so that colleagues can scrutinize conclusions, build new hypotheses, and design further experiments. Most scientists do not reproduce every experiment for themselves; they rely on other researchers’ work and on the community’s judgment about which findings are credible. Trust and scrutiny thus operate together. Scientific claims enter the common body of knowledge not merely because one expert asserts them, but because a wider community examines and provisionally accepts the evidence.

Where the Principle Applies—and Where It Does Not

The book’s argument is about how to make the most of expertise, not about abolishing it. Its examples distinguish tasks in which separate, informed judgments can be combined from tasks that require specialized practical competence. The advice is not to replace surgeons or pilots with crowds of amateurs; it is to pool informed predictions and assessments when doing so can improve a decision.

That distinction gives “chasing the expert” its lasting force. It challenges the idea that authority should be concentrated in a visible individual simply because an answer is needed. In the Scorpion search, the SARS investigation, and the analysis of markets, the decisive resource is not a lone mind but a structure that lets people contribute separate knowledge and brings their judgments together. The expert remains part of the crowd; the mistake is assuming that the crowd’s best answer must already reside in one person.