If you’re even slightly familiar with the last decade’s deluge of pop science books in psychology, you probably have heard of the phenomenon that sudden events and life events like marriage, death of a spouse, winning the lottery, etc. don’t matter that much for happiness. Instead, you have a set point that you bounce back to in a couple of years, if not even faster. This has been quoted at least in Gilbert’s Stumbling on Happiness, and in Kahneman’s Thinking Fast and Slow. Here’s a typical pattern:

Source: Kahneman & Krueger (2006). Developments in the Measurement of Subjective Well-Being. Journal of Economic Perspectives, 20(1), pp. 3-24.
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They all claim the same thing: even if you become a quadriplegic – or win the lottery – this has no impact on your happiness in the long term. In a few months or years, you’ll adapt and be right back to your happiness set point.

However, it’s not like that.
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A meta-analysis from 2012 Luhmann, Hofmann, Eid & Lucas in the Journal of Personality and Social Psychology goes through a swathe of research about the impact of life events. Crucially, they look at longitudinal studies, which in this case are much better than just cross-sectional designs. Anyway, technicalities aside, let’s dive right into the main findings.
Here’s a picture from the paper:

Here, the event (childbirth) happens at time t=0. The points are effect sizes, which compare the difference in emotional or affective well-being (AWB) to the value at the event. The CWB; LS and CWB: RS reflect to effect sizes for life satisfaction and relationship satisfaction. In the middle, the black straight line is the estimated level of AWB before the event, while the dashed straight line is the same, but for life satisfaction. The curves are just log model estimates for how the effect sizes for AWB, RS and LS develop over time.

The crucial point is that, even after 100 months (9 years!) the life satisfaction still hasn’t returned to the baseline. Since the estimated level before the event is negative, we know that LS is typically lower before the event than at t=0. This makes sense, since having a child is an exciting experience, and creates a good sense of achievement for many. For the life satisfaction to reach that baseline of before anticipating a child, it would have to reach the dashed straight line. This would then mean it’s that much lower than at childbirth, ie. the same as before anticipation.
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Similar graphs can be found for marriage, divorce, losing one’s job, and rehiring. For example, the marriage one looks pretty similar:

​Once again, the story is similar. Even after 10 years, life satisfaction is still not at the baseline, but slightly above the EPL. Emotional well-being seems to be unaffected by marriage, though there are only five estimated effect sizes.

For me, this is shocking. The adaptation hypothesis seemed to fit in together with everything I had read about happiness. (Of course it did, since all books referenced the same phenomenon.) Now, by golly, it looks like losing your partner does in fact make you unhappier. Even if this might be “common sense”, it’s prudent to remember that the same meme has been all over the place. Even in books and talks that have appeared after the meta-analysis.

If you’re a research psychologist who specializes in happiness, this is probably no news. However, if you’re anybody else, chances are that the happiness adaptation meme has found its way to your mind and entrenched itself deep. It certainly did that for me. I mean, you keep seeing the same thing in every book, so it must be true! But like so many memes, this one if false too.

What are the implications? Well, for me, this definitely decreases my confidence on the whole in the happiness set point hypothesis. I used to think that the set point was probably generated through some interaction of genetics, early life experiences and social environment. I used to think that it was very robust to changes in your personal wealth, job situation etc. Now, I’m not so sure. It could still be that the set point hypothesis still holds. Maybe the set point is just not as robust as I used to think.

However, what the meta-analysis seems to imply is that the set point itself can be changed. If the life events can impact your set point over the course of several years, it makes more sense to talk about change in the set point, instead of lags of several years.
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P.S. On a tangent, finding out about this article was interesting validation for reading outside my own field (commonly called procrastinating). Wellbeing psychology is generally interesting, but I’m definitely not an expert on it. If I tried to read the journals in the field, I’d never get any work done – and would suffocate myself with what are (to me) irrelevant papers. There’s just too much stuff. But how do you separate the wheat from the chaff? Blogs can help:  this gold nugget came through a psych/science blog, which had mentioned the finding (thanks, Scott). Call me out on the N=1 if you want, but now I feel again that this blog-reading is useful (and not just pointless PhD procrastination).

​The past two months, I’ve been completing University of Michigan’s fantastic Model Thinking course, available for free on Coursera. There’s so much to love about the modern world: you can learn interesting things through quality teaching, no matter where you are (well, you need a wifi), no matter when. And it doesn’t cost a cent!

Anyway, the course had a section about Random Walks, and it got me thinking. A while back I wrote about how the nonlinear life and our linear emotions aren’t exactly optimally suited to each other. Your brain craves signs of progress, so it could reward you with a burst of feel-good chemicals. Unfortunately, the nonlinear life doesn’t work like that. Often, you can spend days or weeks slaving away at the office/studio/whatever, not really moving forward – or even taking two steps back for each move forward. Despite the hours that you put in, the article/thesis/design never seems to be finished, making you question whether you’re really cut out for this kind of job. Perhaps you’d do the world a favor by setting your sights lower and working as a sales clerk instead.

Now, while watching one of the course lectures, I suddenly realized that the creative nonlinear work is exactly a random walk! I don’t claim this to be a unique insight or anything – I’m sure many of you have realized this before. But for the fun of it, it might be a nice exercise to show with a random walk model how the nonlinear life functions. At least in my own case, models often help to see the bigger picture, and forget about the noise in the short term. And who knows, maybe this will help to quell those linear emotions, too.

So, a random walk is very simple. In this case, let’s assume that we have a project that has a goal we’re trying to reach. Arbitrarily, let’s say that the completion means we reach a threshold of 100 points. Of course, these numbers are completely make-believe and I pulled them from my magical hat. Now, further, let’s assume that each unit of time – say 1 unit equals 1 day – means we have three possibilities: make progress, stay where we are, or take steps backward. In my personal experience, this is an ok model for work: sometimes you’re actually making progress, and things move smoothly. Sometimes, though, you’re actually hurting your project, for example by programming bugs into the software, which need to be fixed later on (just happened to me two weeks ago). Most often, though, you’re trying your best, but nothing seems to work. Maybe you’re stuck in a dead end with your idea, and need to change tack. Maybe you’re burdened with silly tasks that have nothing to do with the project. Well, I’m sure we all have these kinds of days.
So let’s again use my magical hat and pull out some probabilities for these options. Let’s say you have a 5% chance of making a great jump forwards (10 points), 25% chance of making 3 points of progress, 55% chance of getting stuck (0 points), 10% chance of making a mistake (-2 points), and a 5% chance of doing serious damage (-6 points). Now we just simulate these across and get a graph that shows your cumulative progress towards the goal (yes I'm doing this in Excel):

​So, in the graph there are several periods when it’s just going downhill, or plateauing for several time periods. Even though the numbers are really made up, I feel the above graph is actually a pretty decent example of how the nonlinear work often feels. However, there’s still the additional complication: the emotions.

Suppose that our emotions work as follows. If you’re making progress, you feel good. And this is mostly irrespective of how much progress you’re making. Suppose the same holds for drawbacks – it hurts, but it hurts almost as much to look for a bug for two hours or the full day. Finally, I’ll assume that if you’re not moving anywhere, you inherit the feeling from the day before. Now, I realize this is probably not how emotions really work (we’re often annoyed by our administrative duties, for example). But on the other hand, when I have a day I have spent at a dull seminar, I seem to find myself looking back a bit to evaluate the progress. The “inherit from t-1” rule tries to describe this: I feel good if the past has been good, and I feel annoyed if the past wasn’t successful. Why just t-1 and not the actual level? Well, I’ve also found that it’s really hard to evaluate how far the project actually is, which makes that option unrealistic. And when looking back, our memories are much stronger from the immediate past than the long-gone part. In short, I’m modeling here the short-sightedness. The actual progress-emotions payoff table looks like this:

So with these assumptions, we get the following graph portraying emotions:

Now this is pretty interesting! You can see how 1) there’s a lot of fluctuations back and forth, and 2) how there’s still “runs”, ie. the same emotional state tends to linger for a while. If you run the numbers, with this particular string of successes and failures you get 99 positive time periods and 51 negative ones, out of the total 150 periods I ran the simulation for. I think the above graph is quite a good summary of how the nonlinear life often feels: you love you’re job, but you’re not above hating it when things are not going well.

A final word of warning: this was of course just one simulated outcome. With the exact same parameters, you can get project outcomes that never finish, that run into negative progress, that finish in less than 30 periods, etc. They are not very nice for terms of a presentation, but also capture the great amount of uncertainty in a nonlinear project. Sometimes it just falls apart, and after 50 periods you’re back to exactly where you started. Or that a project you thought takes 6 weeks takes 16 weeks instead. Well, I’m sure everyone has had these experiences.