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Bias Beware

Bias Beware

Advertisements get under the skin of professor and human behaviour expert Maurice Schweitzer

It's commonly believed that the more time we devote to a project, the better the results. Not so. Wharton professor Maurice Schweitzer tells Stephanie Overby how CIOs can correct "input bias" and stop confusing quantity with quality.

Advertisements get under the skin of professor and human behaviour expert Maurice Schweitzer. There's the beer commercial that brags about its slow brewing process. And the billboard from a luxury car manufacturer that boasts about how its engineers haven't taken a vacation in years. "Three hundred thousand people vacationed in the south of France last year, and none of them was a Lexus engineer. Who cares? That's not very informative to me," says Schweitzer. "And I'm not drinking a beer because of how long it was in a vat. I drink it because of how it tastes."

Schweitzer, who specializes in behavioural decision research as assistant professor in operations and information management at the Wharton School at the University of Pennsylvania, uses these advertisements as examples of what he calls "input bias". According to his research, people automatically associate input related to quantity (how long it takes to make a car) with output quality (how well it performs). While in many cases, input information does directly correspond to outcome, in some cases it does not. Yet humans are hardwired to automatically associate input and output. And people can prey on your input bias, causing you to make poor decisions or judgments to their advantage.

It's no surprise that advertisers exploit this basic fact of human nature. But CIOs, Schweitzer says, fall victim to the same input bias. Employees, vendors and fellow business leaders all take advantage of these natural biases in manipulating IT decisions. Fortunately, Schweitzer says, there are ways to guard against making mistakes based on bias.

CIO: Can you explain what your research has revealed about input bias - that is, how information on the quantity of something is often misused to infer quality?

Maurice Schweitzer: In general, input quantities are positively related to the quality of outcome. The more you invest in a project, the better that outcome will be. Companies that spend a lot of money on R&D typically produce the most innovative products. The more time an employee spends in the office, the more productive she is. Students who study the most do better on exams. It's a natural assumption that's usually right.

However, there are many cases where that direct relationship does not exist. For example, people assume that longer hospital stays are better and propose legislation that women who give birth should spend a certain length of time in the hospital. They figure the longer you're in the hospital, the better care you'll receive. But in fact, there are so many sick people in a hospital that it's actually not a great place to be unless you have to be there.

We live our lives mostly on automatic pilot, and we have heuristics - decision rules or shortcuts - to make a lot of our decisions. Assuming that input quantity directly correlates to outcome quality is one of those. These heuristics can lead us afoul. There are times when we need to step back and give decisions some extra attention. That means participating in a deliberate thought process that takes our natural biases into account.

CIO: Certainly we all use a lot of shortcuts in making decisions every day. Why is the input bias particularly dangerous?

Schweitzer: It's a social bias that relies on information from people around you and is thus much more dangerous because people can manipulate you. The classic example is face time. Someone puts in a lot of time in the office; you assume they're working hard. Or he says: "I had this many people working on this project", or "This program I created has so many lines of code". He's giving you some measure of the input in a way that might skew your judgment of the outcome.

The input information they give you may be accurate. But of particular importance to managers and business decision-makers is the fact that people can manipulate or misrepresent input to prey on this bias.

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