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  • The change in the world today is the availability of immense data. As a matter of fact every

  • individual has a digital footprint. And by looking at that footprint you can pretty much

  • tell what drives them. That's new. When I went to school it was all about I have so

  • little data therefore let me come up with the fanciest tools to get as much information

  • out of it. Today it is about I have so much data how do I get to figuring out what does

  • that data tell you? And I think that's the shift. Those are the new sciences. And here's

  • the problem with data. The problem with data is I can do a lot of garbage in garbage out.

  • I can look at data and say here's the relationship between this and this. And that doesn't mean

  • the one thing causes the other, it's just that the two are linked. So a lot of the data

  • sciences are about this is correlated with this and therefore I'm going to assume they're

  • related. I'm going to assume they're correlated and that one drives the other.

  • But that's not the case. That can be pretty dangerous. So the best new techniques get

  • at the constant of causality, what causes what? That's what I really want to know in

  • data. And when you get to thinking about causality, causality is usually some human behavior that's

  • driving that. And so the confluence of behavioral economics and behavioral sciences that tells

  • you this is how humans behave and taking that, together with data, and trying to create these

  • behavioral causal models is really the Holy Grail of data analysis. And that's an art.

  • You can have lots of machines and people can do it, do it to a great extent, but the last

  • mile is always about understanding that human behavior. So today it's still an art. It's

  • a very powerful art. But the benefit of that is that you could actually aid individuals

  • in saying this is what's driving this behavior or this is what's driving what you're doing.

  • And by the way, do you want to correct that or keep doing what you're doing? That's so

  • important in the world of finance because people tend to procrastinate, sometimes they

  • don't need to look so far ahead as they should. These kind of sciences can add a lot to helping

  • individuals and their financial needs as an example.

  • When you look at data, when you look at what drives people and how they behave, there's

  • so many different types. The one thing we can be sure of is that it's a rarity to find

  • sort of the rational Adams Smith individual. There are fewer of those than people think.

  • Everybody has some different ways of behaving, but there are so many of them. For example,

  • pick a number and you may say 50. And if I follow up that with a question of how many

  • people do you think are in this building? It turns out your mind gravitate to 50 and

  • the answer is pretty close to 50. That's called anchoring. What you just heard influences

  • what you say and what your judgments are all about. That's a big bias. The other kind of

  • biases you see is people have invariably huge impatience factors, meaning that you value

  • that coffee right now a lot more than ten coffees tomorrow. So it's another factor,

  • which is instant gratification is something people want to pay for and they pay for a

  • lot and they will defer things like savings, as an example, as a result of that. You know

  • you get into confirmatory biases, when you believe in something and you don't say you

  • believe in it but it's implicit in what you're doing. When you go do analysis you search

  • for the data that confirms what you believe and then you say I've got this data that shows

  • this is right. Well, it turns out, well you were looking for it. So there are eight, nine,

  • ten of these things, there are many of these things. It doesn't mean that somebody is doing

  • something wrong, it's just the way we're wired sometimes and just the way we think. And the

  • more you expose it the better off we're going to be as individuals to think about whether

  • we should counter it or shouldn't counter it. At least we need to have that choice.

  • So, knowing what the biases are I think is important. Knowing which individuals have

  • which bias and making that clear to them and transparent, I think that's important too.

  • Having said that, people need to choose to counter these biases doesn't always happen.

  • So you've got all kinds of the health apps that tell you this is what's happening. Should

  • you be doing this? And guess what, people still do it sometimes. So modification of

  • behavior is a tougher challenge than just knowing that your behavior is driving certain

  • things. But having said that, there's some very clever ways of doing it and that's where

  • this art of behavioral science and economics come in. It's not about saying hey you're

  • a rational person, this is irrational behavior; you shouldn't be doing this, if it was you

  • would have probably already done it. It's about how you create the right nudges and

  • the right kind of incentives to drive people to do the right thing. Sometimes it's about

  • is this behavior that you have right for your kids? And that sometimes tilts how people

  • start behaving. So the art still lies in how you take that information and turn that into

  • nudges and other prompts that get people to counter the behavior.

The change in the world today is the availability of immense data. As a matter of fact every

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A2 初級

ビッグデータを利用して自分自身をより良くすることができる (We Can Use Big Data to Make Ourselves Better)

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    richardwang に公開 2021 年 01 月 14 日
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