Unhappy ending

Will we be happy right before we die?

What will we say to ourselves, how will we feel about our days, our past, our life as a whole?

Especially in the developed world, conditions improved with respect to just a few decades ago. The life expectancy has increased dramatically. We live healthier for longer.

But what about our final self-evaluation?

It seems that we might not die happy after all. Two possible reasons are the peek-end-rule (interesting post here) and the duration neglect (interesting post here).

Peek-end-rule is about what we remember after a certain experience. It turns out, extreme events and final episodes stay with us the most. Life will possibly bring us both positive and negative extremes, but final moments will probably not consist of the finest ones. Also, recurrent negative health shocks will likely drag our happiness down in our later years.

Duration neglect, on the other hand, is about what we don’t remember and don’t take into account. It turns out, 20 years of happy and healthy life does not give us twice the satisfaction as 10 years of happiness and health. In fact, we pretty much neglect the whole 10 years difference. When we evaluate our experiences, we do not sum up all the happy moments. This tendency suggests that a longer and healthier youth will not affect much our happiness at deathbed.

We tend to discount individual, happy days. We do not include them in our mental accounting. Maybe this is useful, as we tend to do the same for bad days as well. Still, the aging process guarantees that our evaluation will suffer from the weight of many health issues we will face and overcome in our later years.

Perhaps the primitive man, who lived thousands (or hundreds) of years ago, died much younger, but happier.

As for final words, the question we’ll ask ourselves right at the end is likely to be: “What the hell happened to me?”

Transparency

Transparency of a description denotes how correctly it is perceived and accurately understood by people. If I tell you that, as a side effect, the use of a certain drug increases the probability of say, becoming completely bold by 100%, you would have second thoughts about even touching the pill. But you are missing a crucial piece of information there: the base on which this statistic was calculated. For instance, an increase from one in a million to two in a million would also constitute a 100% increase. The drug seems less scary now, doesn’t it? Hence, the description that includes the base rates is more transparent to the human mind in this case.

Gerd Gigerenzer, Wolfgang Gaissmaier, Elke Kurz-Milcke, Lisa M. Schwartz and Steven Woloshin, in their 2007 report published in Psychological Science for Public Interest (downloadable here), dug deeper into the issue. At one point, they talk about why abortions in England and in Wales increased dramatically around 1995. The reason was that the birth control pills were rumored to have an undesirable side effect, expressed in a non-transparent way, which created an overreaction against their use. Ironically, it was found that the abortion procedure increases even further the probability of facing the same side effect, hence the importance of transparency of a description, especially in medicine.

The most curious cases are medical tests. They can make two types of mistakes: finding a sick guy healthy or a healthy guy sick. They are commonly designed to avoid more the former one (finding a sick guy healthy). On the other hand, one can observe more cases where they find a healthy person sick, especially if there are much more healthy people than sick ones. Consider a test that always correctly identifies a sick person and fails to find a disease (correctly) in a healthy person 90% of the time, i.e. 10% of the time it erroneously claims that a healthy guy is sick. Say that the probability that a random person in the population has the disease is 1%. We grab a random guy from the population and test him. The test says he has the disease. What is the probability that he is really sick then?

Answer: Take the test again.