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healthy group, neither group predicted that the other one felt the way it did. For example, when imagining they had kidney failure, healthy people predicted being in a happy mood much less frequently. This misprediction fits a pattern of healthy people underestimating the well-being of people with chronic illness or disability. Moreover, dialysis patients imagining their prior health overinflated how often they felt happy, as reflected in their assessment of their predictions for the control healthy group. We all have stereotypes in our mind of sickness and health, and these stereotypes cause us to mispredict the moods of people whose circumstances differ from our own.

Daniel Kahneman, the Nobel laureate who, with Amos Tversky, identified many cardinal cognitive biases, concluded that the fundamental paradigm of utility assessment in health is flawed. Kahneman compared assigning a single number to a patient’s utility with the flawed concept in the field of physics of an ether surrounding the earth in the nineteenth century: Daniel Kahneman, “A different approach to health state valuation,” special issue, Value in Health 12 (2009), pp. S16–S17.

97 The data given to patients on impact of side effects come from studies like Jim C. Hu et al., “Utilization and outcomes of minimally invasive radical prostatectomy,” JCO 26 (2008), pp. 2278–2284; Wesley M. White et al., “Quality of life in men with locally advanced adenocarcinoma of the prostate: An exploratory analysis using data from the CaPSURE database,” Journal of Urology 180 (2008), pp. 2409–2414.

There is growing literature on cognitive pitfalls in forecasting and in assigning numbers to quality of life. Gretchen Chapman, the psychologist at Rutgers, reviewed scores of studies and concluded, “Biases that affect utility assessment mean that the utilities incorporated in decision analyses often do not reflect patients’ true preferences” (from D. Koehler and N. Harvey (eds.), Blackwell Handbook of Judgment and Decision Making [Oxford, UK: Blackwell Publishing, 2004], chapter 29, “The Psychology of Medical Decision Making,” pp. 585–603). She points out that sick people need to imagine what life would be without their illness—that is, in “perfect health”—so “opening the door for the same sorts of biased predictions that affect utility valuations provided by non-patients.” In that regard, Dr. Peter Ubel, formerly of the University of Michigan and now at Duke University, studied patients with kidney failure and asked them to predict life after a successful transplant, and colostomy patients who later had their colostomies reversed. The utility of better health imagined by these patients when ill did not correspond to the reality of their health state experienced later: Dylan M. Smith, Stephanie L. Brown, Peter A. Ubel, “Are subjective well-being measures any better than decision utility measures?” Health Economics, Policy and Law 3 (2008), pp. 85–91; Dylan M. Smith et al., “Misremembering colostomies? Former patients give lower utility ratings than do current patients,” Health Psychology 25 (2006), pp. 688–695.

98 Daniel Gilbert, the professor of psychology at Harvard University who has extensively studied forecasting and its biases, considers Bernoulli’s formula and concludes that such calculations drawn from economic decision making are “beautiful useless abstractions” since no one can reliably assign a numerical value and predict his or her level of contentment and pleasure as life changes (Daniel Gilbert, Stumbling on Happiness [New York: Vintage Books, 2005]). For research studies or biases in forecasting, see Daniel T. Gilbert, Timothy D. Wilson, “Prospection: Experiencing the future,” Science 317 (2007), pp. 1351–1354; Yoav Bar-Anan, Timothy D. Wilson, “The feeling of uncertainty intensifies affective reactions,” Emotion 9 (2009), pp. 123–127; Sarit A. Golub, Daniel T. Gilbert, “Anticipating one’s troubles: The costs and benefits of negative expectations,” Emotion 9 (2009), pp. 277–281; Carey K. Morewedge, Daniel T. Gilbert, Timothy D. Wilson, “The least likely of times: