A naïve view of public policy (like mine, for example) might be that a body of public servants gets a set of options from the parliament, studies them and their effects, and then returns a report replete with recommendations about what the outcomes of each of the legislative options might be. A good report would say clearly “If you do this, you get A. If you do that, you get B.” The parliament’s job then is to reflect on this information and decide on the tradeoffs that the nation should accept.

In reality, however, I feel that instead of choosing from a set of options, a lot of public policy seems to be looking at options one at a time, instead of choosing the best one from a set. Suppose the economy is doing badly, and we would need either to get that back on the track, or cut costs from government. An exchange might go like this.

- Parliament: So maybe we can raise taxes?
- Right wing: NO!

- Parliament: So cut benefits to lower costs?
- Left wing: NO!

- Parliament: Reduce work legislation to increase efficiency?
- Unions: NO!

…and so on. Instead of going “OK, we have to do something, and we have options A, B, C and D”, politics employs a method I call piecewise running into a wall: evaluating one option at a time, with each being rejected by some advocacy group.

Since there is an advocacy group for almost anything, presenting options in this piecewise fashion means they will all get rejected. Following the rule “don’t do anything someone might object to” is not good policy-making: it just ensures nothing at all will be done. What is needed is a comparison of options, and then deciding which of them is the best one.

On the other hand, presenting options as a list and saying we need to choose one of them – well, that’s one of the oldest political tricks in the world. There’s nothing better than creating a false dilemma, asking a voter to pick whether for cutting taxes or reducing prosperity. Or whether he supports corporate rights or human rights. And so on.

A crucial question emerging from this is: who generates the options, and how? Letting a small group generate them invites the false dilemma trap. Getting to choose the options means you have a lot of power. Your decision may surprisingly much depend on the options that you are given. However, letting the public generate the options directly is unlikely to work, either. Most people do not know enough about the complexities of law to be able to do that. If you asked me how unemployment benefits should be structured, I would have some kind of opinion, but the opinion is way too vague to be an option directly. That’s why we need public servants and assistants in the parliament: somebody needs to generate the actual legal text.

But one thing seems clear: openness and direct communication about our options would be good for democracy. Lobbying is small in Finland, but likely to increase in the future. The more opaque the process of option generation, the more power is given to the lobbies. If politics would be more transparent, it would be harder for lobbies to slant the option set badly. But not knowing the option set, or pretending there are no other options – that’s no good. Not for us, not for the nation, not for anyone.

So, I’m finally home from the trip to San Francisco and Palo Alto. Experiencing the US culture was again quite intriguing. The differences are pretty notable in comparison to Finland: the expectation of sociability and extraversion, the lunch spots that only do takeaway, and the enthusiasm around baseball and football. I spent a few evenings watching college football matches – and I have to say that they were quite exciting! If it wasn’t for the ubiquitous commercial breaks, I’d say football is one of the most intense and captivating sports on TV. What also caught my eye, however, was the lack of statistical sophistication of the commentators.

During a match, a team might make two or three really awesome plays in a row. For example, in Ohio State vs. Minnesota, Ohio made a few awesome touchdowns with long passes and and an over 80 yard run. After this streak of successes, the game got more even, with Minnesota actually managing to even the score. What’s special about this is that the commentators spent a lot of time arguing about momentum. In their view, Minnesota managed to “get the momentum to their side” with one interception and a few hard tackles, like this one: 

Well, I’m not so sure.

My statistical gut instinct says that this is just regression to the mean. That means that after a few lucky successes (or a streak of bungles), what’s likely to happen is that the game returns to the mean. And in professional sports, the mean is that teams are pretty evenly matched. So instead of talking about momentum, the more likely explanation is that Ohio St just wasn’t so lucky anymore.

Regression to the mean is especially tricky since we tend to see patterns everywhere, including places where there are none. Kahneman describes the famous case, in which he was working for the Israeli air force. The air force trainers had a habit of dressing down cadets who made mistakes harshly. In their experience, this helped the cadets to get a grip and concentrate, so they wouldn’t make an error the next time. Kahneman decided to look into this intuition. At first, it looks like that was the case: a failed training flight that included harsh criticism was usually followed by a better flight. Isn’t this evidence that harsh negative feedback caused improvements?

Well, not necessarily. Basing that conclusion on the data would be a case of a fallacy called post hoc, ergo propter hoc, or what it’s more commonly called the post hoc fallacy. What the Latin name means is “after this, hence because of this”. It’s a conclusion of the form “since B came after A, B must have been cauded by A”. This is of course rarely true. My waking is followed by a sunrise – but that doesn’t say I’m causing the sunrise! Of course, this example is so ridiculous that we never think I would be causing the sunrise. But the same principle applies in other cases.

So what happened in Kahneman’s air force case? Well, they considered that the air force trainers might be falling for the post hoc fallacy. Instructors believed that improvement after a bad flight was due to the harsh feedback. In fact, it was simple regression to the mean. An average training flight is the most likely case, so that is usually going to follow a bad training flight. In fact, in being the average it usually follows any kind of training flight!

To take this back to sports, I think regression to the mean is often at play in the sports domain. Exceptional performances are followed by average performances, and the same is the case in moving from bad to average performance. Especially in sports that contain many sequences – like American football or tennis, for example – are likely to contain divergence from the mean, followed by regression to the mean. Someone might make a few awesome plays, but that’s unlikely to last long no matter what the other player or team does. Tactical changes do have some effect, depending on sports, but I think regression is much more important than we usually think. And regression is the reason why a Rookie of the Year is unlikely to perform as well the next year, or why an awful batting season tends to be followed by a better one. It all comes back towards the mean.

The past few days I’ve been spending at INFORMS Annual Meeting at the beautiful San Francisco. Due to the planning fallacy, I never had the time to write something new and proper for this week - so you if you’ll excuse me, I’ll just use this week’s post to share a few interesting insights from the talks I’ve seen here during the past few days.

Statistics.com

I’ve never heard of them before, but apparently these guys offer stats courses on crisply defined domains. Each course costs a small amount of money, and lasts for four weeks. So completing one of them may be a good idea if you want to know about one specific area, rendering full-semester MOOCs somewhat useless.

Cost Transparency Increases Purchase Intention

Bhavya Mohan, from Harvard Business School, showed in their group’s experiments that – at least currently – a company can increase customers’ willingness to buy in an online store by providing information on the product’s costs.

Emotional-Motivational Responses Predicting Choices

Outi Somervuori, a colleague of mine from Aalto University, showed that negative emotions and frontal lobe asymmetry predict the endowment effect.

My Own Talk

My own presentation (available here) was about using a linear value function model to predict choices when choosing student apartments. As it turns out, whether a subject is consistent with a linear value function had very limited impact on how well the model can predict choices – meaning that assuming a linear value function is an ok starting point for a model.

ProbabilityManagement.org

These guys have a fantastic, completely free SIPmath standard for communicating uncertainty between Excel and different pieces of software. It for example enables easy calculations with distributions in native Excel, something that’s notoriously difficult. For example, in the screenshot I’ve just calculated U+U, U*U, and U^cos(U) with  a sample of 10 000 for the uniform distribution. I think this system is nifty enough to maybe warrant a full post in the future.

Decision Analysis in Life and Business

Larry Neal told about his personal experience on how business and life decisions differ. A decision analyst at Chevron (one of the most decision analysis –friendly companies out there), he faced cancer a few years ago, and ended up helping several other patients to analyze and decide on their treatment. He concluded that DA in personal decisions is much harder, because the goals are more ambiguous and the situation much more complex. Moreover, in his view our behavior is heavily influenced by cultural assumptions, and it is often impossible to recognize them yourself – but that’s exactly something as an analyst he was able to help with.

MOOCs

A panel by four professors from Stanford, MIT and Columbia was talking about MOOCs. Their response was very favorable. They all agreed that the traditional way of teaching with a professor speaking and the students listening is not adequate. MOOCs can be used to deliver value to a large audience, and even though the completion rate is low (less than 10 %), they still reach thousands of students per course.

Teaching Decision Analysis

There has been several amazing sessions about Decision Analysis. I just came back from a session about teaching and the legacy of Ron Howard, one of the giants of DA. It was certainly very inspiring and there were some good points about how DA teaching need to stay connected to the actual practice of decision making. After all, we don’t want to produce just new researchers who produce papers – the point of research is to impact the world in a larger way.

Well, that's a summary of the past couple of very exciting days! I'll be back on track with more decision making thoughts next week.

One of the dismissals sometimes offered against rationality is that we don’t need to make optimal decisions, because most decisions don’t really matter that much. So what if you don’t pick the optimal job – if your pick is a job that’s “good enough”, well, that’s probably good enough. The difference between rationality and near-optimality, the argument goes, is not big enough to warrant all this fuss.

Admittedly, in many cases this argument is a good one. It probably makes no sense to spend a lot of energy optimizing the choice of lunch. As for myself, I’ll just pick something that’s tasty enough and healthy enough, without worrying whether there might be a better alternative. In fact, there is considerable evidence that this kind of behavior is better in the long run, when small choices are being considered. For a thorough argument, see for example Barry Schwartz’s book The Paradox of Choice.

But there is a fatal flaw in the argument, which renders it unsuitable for the assumed role of a magic bullet. The argument assumes that all decisions are independent of each other. If this were true, then the differences of rationality and good enough choices – assuming they’re small – don’t build up to a very large extent. A sum of small differences is still a small number. Unfortunately, the independence assumption is not warranted. For example, consider the case of choosing a job. It is plain that this choice has a heavy effect on your future choices concerning family life, work-life balance and career development. An optimal choice in this respect will maximize the opportunities you have in the future, and enable better decision opportunities then. 

This idea makes it clear why rationality is important in smaller decisions, too. Firstly, it generates benefits in the long term due to the cumulative effect. Secondly, cultivating rationality whenever possible makes it a habit, a stable way of behavior, and thus also ensures beneficial outcomes over the long period. And habits – if one believes modern psychology – are the things that we usually resort to. As I’ve already argued in relation to weight loss, we have limited willpower. After a hard day at work, we usually don’t have the energy anymore to think very rationally. But – and this is crucial – if we’ve cultivated a habit of rationality, good decisions will be much easier to make. And the more we follow this habit, the easier it becomes.

The habitual behavior is in some way giving hope. I know from the literature – and even more so from personal experience – that we cannot change all our ways at once. Trying to eat better, exercise more, facebook less and read more books at the same time is a plan that’s doomed from the start. There’s just too much to remember, too much to focus on. The same goes for better thinking: it doesn’t make any sense to expect that we’ll be able to optimize all our decisions as soon as we decide to try. No – we’ll still get tired, lose our focus and tons of other things that prevent a good, reflective decision. But by cultivating the habit of rationality, we slowly but surely go towards better decision making – one decision at a time. And after a while, we hopefully notice that the habit has become almost automatic, and making good decisions is not so hard anymore.

And that’s when we’ll really start reaping the benefits from the fact that rationality is cumulative.