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.
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.
Okay, so with 22 responses the data is of course not exactly scientific quality, but let’s look at some averages nevertheless. Below are averages to the questions “How well have you figured out the objectives/alternatives/consequence/when to make the decision” by category of the problem. To make the data a little more robust, I’ve combined Education under Career, and took one of the three questions under Other and also put it under Career.
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:
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.
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.
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