So, last week we did a survey with Tuomas Lahtinen about the difficult decisions people face in the coming year. We got a total of 22 responses. What now follows is some analysis of those responses. Tuomas has yesterday already done a fantastic job of looking at the open question responses, and also categorizing the results. You can see from the figure below (copied from Tuomas’s analysis) that most decisions are related to one’s career. Given that we promoted the questionnaire on Facebook, and like our friends, we’re just the age of finishing up our studies and entering or having just entered work life – it’s hardly a surprise.

Since Tuomas already took a good look at the responses, I’m not going to repeat that. Instead, I’ll do what any analyst always does: wrangle the data for any other useful nuggets of information. So if you’re interested in the responses to the open questions, I direct you to Tuomas’s analysis.

The finding here seems to be that alternatives are better known than objectives, consequences or time. I guess this makes sense, since often alternatives are mostly a matter of browsing the internet and finding out what’s available. On the other hand, shouldn’t objectives be even easier? After all, to find your objectives, you just need to take a look inside your own thinking and find out what you value. Well, it seems that is not the easiest part of the problem.

What I find interesting is the comparison of career and family. Career alternatives and decision time seem to be relatively well known, but objectives not so much. With family, the situation is the exact opposite: objectives are clear, but consequences and decision time are very vague. This is probably due to the fact that many family decisions we can still afford to put off for several years, whereas many career choices at this point demand our choice by a certain date.

What other interesting patterns can we find? Well, here’s one:

So, what the correlation matrix shows, is that knowing when the decision should be made seems to go together with better knowledge of alternatives and consequences. Of course, with the data I can’t know whether the causality runs this way – but relying on the fact that people are prone to procrastination – I believe that it’s at least plausible on the face. Deadlines make us focus on the problem, which quite likely helps us to find out better what kinds of alternatives and consequences there are.

All in all, I’m surprised how low the values  in Table 1 are, especially since the measure was a self-report one. It looks to me as if respondents are comparing themselves to a perfect world, which is a tad unfair. We will never have perfect information, and uncertainty is something we’ll just have to tolerate to a degree. Of course, when there are information-laden variables that can help you, you ought to measure. But even after all the measurements you could do, there’s still going to be uncertainty. So what’s one to do? Well, I recommend heeding the advice of Reid Hastie and Robyn Dawes:

“[--] our advice is to strive for systematic external representations of the judgment and decision situations you encounter: Think graphically, symbolically, and distributionally. If we can make ourselves think analytically, and take the time to acquire the correct intellectual tools, we have the capability to think rationally.” – Rational Choice in an Uncertain World, p. 334

So, in short. Recognize that you can’t have it all. Decide what it is that you want, and then apply focused, analytical thinking to reach that. 

Ah, information. The One Thing that will solve all your problems, and make all the hard decisions for you. Or so many keep thinking: if I only had more information… Of course, in many ways, this is exactly right. More information does equal better decisions, as long as the information is – sorry for the pun – informative. Unfortunately, in many cases we either acquire the wrong information, or pass by getting the right kind of data, thinking that it’s too costly.

Thinking about that, I have a hypothesis about why the feeling “we need more information” persists:

1. Even with information, hard decisions are still hard
   
2. Information is of the wrong kind
   
3. Thinking information costs too much
   

Even with information, hard decisions are still hard

This is really not very surprising, but there’s a common thread linking all hard decisions: they are hard. If they were easy, you wouldn’t be sitting there, thinking about the problem. No, you’d be back at home, or enjoying a run, or whatever. Decisions are hard for two main reasons: uncertainty and tradeoffs. Uncertainty makes decisions hard, but it can be mitigated with measurements. But what about those pesky cases when you can’t measure? Well, I’m going to say it flat out: there are no such cases. Sure, you can rarely get perfect certainty, but usually you can reduce uncertainty by a whole lot.

The second problem, that of tradeoffs, is the true culprit for hard decisions’ existence. Often we’re faced with situations, in which one option is more certain, but another has more potential profit. For example, when I run a race, I can start with a slower pace or harder pace. The slower pace is safer: I’ll definitely finish. The hard start pace, in contrast, is more risky: my reachable time at finish is better, but I run the risk of cramps and might not finish at all. Tradeoffs are annoying in the sense that there’s often nothing you can do about it, no measurement will save you. If you’re thinking between a cheap but ugly car, and an expensive but fancier one, what could you measure? No, you’ll just have to make up your mind about what you value.

Information is of the wrong kind

According to a management joke, there are two kinds of information: what we need, and what we have. I think there’s some truth in this. 

A fundamental problem with information is that not all things are equally straightforward to measure. It’s quite a lot more difficult to measure employee motivation, and a lot easier to measure the number of defect products in a batch. For this reason, a lot of companies end up measuring just the latter. It’s just so much easier, so shouldn’t we focus our efforts on that? Well, not necessarily. It’s not the cheapest measurements you ought to do, but the ones with the most impact. In his book How To Measure Anything Doug Hubbard tells that he was shocked by companies measurements: many were measuring the easy things, and had left several variables with a large impact completely unquantified! As Hubbard explains (p. 111):

Thinking information costs too much

It’s an honest mistake, thinking that if you have a lot of uncertainty, you need a lot of information to help you. But, in fact, the relationship is exactly the inverse. The more uncertainty you have, the less information you need to improve the situation. If you’re Jon Snow, just spending a moment looking around will improve things!

I think this mistake has to do with looking for perfect information. Sure, the gap to perfect information is much larger here. But the point is that if you know next to nothing, you get to pick the low-hanging fruit and improve the situation with those very cheap pieces of information, while in a more advanced situation with less uncertainty, you’d need more and more complex and expensive measurements.

For example, many startups face the following question in the beginning: Is there demand for our product? In the beginning, they know almost nothing. They probably feel good about the product, but that’s not really much data. An expensive way of getting data would be to hire a marketing research firm, do a study or two about the demand, burning tens of thousands in the process. A cheaper way: call a few potential customers, or go to the market and set up a stand. You won’t have perfect information, but you’ll know a lot more than you did just a while ago! It’s good to see that the entrepreneurship literature has taken this to heart, and guys like Eric Ries are teaching also bigger companies that more costly doesn’t always equal better. Or even if it would, maybe it’s still unnecessary. Simple measurements go a long way.