The Futile Pursuit of Happiness

The NY Times magazine has a fascinating piece titled The Futile Pursuit of Happiness:

A professor in Harvard's department of psychology, Gilbert likes to tell people that he studies happiness. But it would be more precise to say that Gilbert -- along with the psychologist Tim Wilson of the University of Virginia, the economist George Loewenstein of Carnegie-Mellon and the psychologist (and Nobel laureate in economics) Daniel Kahneman of Princeton -- has taken the lead in studying a specific type of emotional and behavioral prediction. In the past few years, these four men have begun to question the decision-making process that shapes our sense of well-being: how do we predict what will make us happy or unhappy -- and then how do we feel after the actual experience? For example, how do we suppose we'll feel if our favorite college football team wins or loses, and then how do we really feel a few days after the game? How do we predict we'll feel about purchasing jewelry, having children, buying a big house or being rich? And then how do we regard the outcomes? According to this small corps of academics, almost all actions -- the decision to buy jewelry, have kids, buy the big house or work exhaustively for a fatter paycheck -- are based on our predictions of the emotional consequences of these events.
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The problem, as Gilbert and company have come to discover, is that we falter when it comes to imagining how we will feel about something in the future. It isn't that we get the big things wrong. We know we will experience visits to Le Cirque and to the periodontist differently; we can accurately predict that we'd rather be stuck in Montauk than in a Midtown elevator. What Gilbert has found, however, is that we overestimate the intensity and the duration of our emotional reactions -- our ''affect'' -- to future events. In other words, we might believe that a new BMW will make life perfect. But it will almost certainly be less exciting than we anticipated; nor will it excite us for as long as predicted. The vast majority of Gilbert's test participants through the years have consistently made just these sorts of errors both in the laboratory and in real-life situations. And whether Gilbert's subjects were trying to predict how they would feel in the future about a plate of spaghetti with meat sauce, the defeat of a preferred political candidate or romantic rejection seemed not to matter. On average, bad events proved less intense and more transient than test participants predicted. Good events proved less intense and briefer as well.

Gilbert and his collaborator Tim Wilson call the gap between what we predict and what we ultimately experience the ''impact bias'' -- ''impact'' meaning the errors we make in estimating both the intensity and duration of our emotions and ''bias'' our tendency to err.
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Gilbert does not believe all forecasting mistakes lead to similar results; a death in the family, a new gym membership and a new husband are not the same, but in how they affect our well-being they are similar. ''Our research simply says that whether it's the thing that matters or the thing that doesn't, both of them matter less than you think they will,'' he says. ''Things that happen to you or that you buy or own -- as much as you think they make a difference to your happiness, you're wrong by a certain amount. You're overestimating how much of a difference they make. None of them make the difference you think. And that's true of positive and negative events.

Two observations:

1) It often seems to me that many of the things social scientists study -- "impact bias" and whatnot -- are really just principles of human nature that have always been obvious to anyone of average intelligence with the slightest capacity for introspection and self-awareness. How can anyone ever fail to notice that the new car didn't make them as happy as they thought while on the car lot?

2) I don't know how much impact this "impact bias" really has. The general point I take from the article is that we all tend to overestimate the impact of both positive and negative events. But does this really affect our choices? Why would this matter if the proportionality of utility estimations remains intact? That is, if I'm choosing between two options that I think will be positive, and I overestimate the positive impact of both options to the same extent, I will make the same choice regardless of the overestimation.

What would be more interesting would be a showing that we tend to overestimate the positive impact of certain types of events or choices, or correspondingly that we tend to underestimate the negative impact of certain types of events or choices. Or even better, a showing that we estimate certain events as having a positive impact when in reality they turn out to have a negative impact.