What it argues
Daniel Gilbert is a Harvard psychologist whose central finding, after decades of studying affective forecasting, is that humans are systematically wrong about what will make them happy. Stumbling on Happiness, published in 2006, is his account of that finding — what causes our errors, why we are so confident despite being so wrong, and what, if anything, we can do about it.
The problem Gilbert describes is not that we are unhappy but that we are bad at predicting the future states of our own minds. We imagine events in detail, attach an emotional value to those imagined states, and then use that estimate to make decisions. But the imagined future is typically too vivid and too stable. We fail to imagine the full context around the event — the adaptation, the surrounding circumstances, the ways our attention will shift. This failure has a name: impact bias. We overestimate how much good events will improve our lives and how much bad events will hurt us.
What it gets right
- 1.
Impact bias is the systematic tendency to overestimate how much future events — good or bad — will affect our emotional state. We expect more from outcomes than they deliver.
- 2.
The psychological immune system rationalizes and reframes bad outcomes, generating good feelings about situations that did not go as planned. We consistently underestimate its power.
- 3.
Imagination fills in missing details of future scenarios automatically and usually incorrectly. We imagine the event but not the context around it.
What it covers
Who wrote it
Daniel Gilbert is Edgar Pierce Professor of Psychology at Harvard University, where he has taught since 1996. He is one of the most cited social psychologists working on affective forecasting and well-being. His TED talk on the surprising science of happiness is among the most widely viewed. Beyond Stumbling on Happiness he has contributed research on cognitive bias, choice, and mental simulation to dozens of peer-reviewed publications. He also co-created the television series This Emotional Life.