I think a quantified, connected society will have some interesting consequences for businesses based on managing uncertainty. As we get better at prediction, the economics of amortization get worse.
Big data is fundamentally trying to make a prediction. It wants to reduce uncertainty by finding patterns, fitting to a curve, and correlating things. If I can use data to predict your chance of an accident, or the flu, or your likelihood of a terrorist act, I can mitigate it. So big data increases certainty.
But consider, for a moment, that there are entire industries predicated on amortizing risk across populations. Insurance is a good one; socialized medicine is another. There are plenty more: travel ticket pricing, credit cards, crop futures, and so on.
A mortgage is—literally—a bet on death. The word comes from the French “mort” and “gage.” It’s a bet that you’ll die before you pay something off.
Update: Chris Bidmead corrects me on this.
A gage isn’t a bet, it’s an undertaking, an “engagement”. And mort refers to the death of the gage: it will expire when a particular condition defined in the mortgage is fulfilled.
Prediction and amortization are fundamental opposites. If you know nothing about a population’s risk factors, you use a socialized medicine model where everyone is taxed equally. Risk is shared. If you know more, you have differentiated pricing based on pre-existing conditions, smoking, etc.
In any amortized model, even with some prediction and correlation, there is an element of uncertainty. Among the population of, say, fast drivers, or smokers, you don’t know which will have an accident or die of throat cancer.
So the “perfect” prediction would be an insurance policy tailored to one person. If I knew with absolute certainty the chance that you’d have an accident and the economic impact of that accident, then your monthly insurance payment would simply be monthly deposits into a savings account whose value would equal the cost of the accident on the day of the accident. Plus, of course, the insurer’s administration and profit margins.
In other words, a tax on your life.
Obviously, no prediction system is perfect. But as we use data to make more and more accurate decisions based on the latest information (thanks to a connected, sensor-equipped world and Bayesian probability calculations) things get absurd. In the split-second before a collision, when someone slams on the brakes, we have a very good idea about their chances of being in an accident. Can we revoke their coverage or up their premiums?
Ultimately, industries that deal with mitigating uncertainty run head-on into the near-certainty of a more quantified, more instrumented, more accurately predicted world.
Economists have talked about the “perfect market” of a commodity good where supply and demand drive pricing for decades, but we know that it’s not a very useful model in the real world. Branding, human whim, cognitive bias and more play a far greater role in price elasticity and market share than we had thought.
At some point, arbitrage markets get deflated by data. The billions of dollars of taxi cab medallion speculation in New York—in some cases, a family inheritance handed from part to child—are being rapidly devalued by a service like Über that remove the “tax” a taxi dispatcher could extract.
I suspect that we’ll soon up-end many economic theories that have been accepted as true when we are able to collapse the inherent risk in an industry using data. Initiatives like Lean Startup are attempts to collapse the risk up front, leaving less reward for subsequent investors because the certainty of market demand exists.
Consider one more example of risk removal: Kickstarter. The company has already funneled $200M to new projects, and should fund half a billion dollars to projects by next year. But none of these projects get funding until they have proven both consumer demand and a compelling message. So the old build-it-and-see-if-they come model—and the investment returns and profit margins needed to justify the resulting risk—is somewhat outdated.
Does this mean that as we get better at predicting outcomes, we’re less likely to pool our resources because of inherent uncertainties? Is a predicted world an individualistic, Libertarian world?
Comments
14 responses to “The selfish economics of Big Data”
Hey, good start on capturing ideas. Here’s a few thoughts:
On the bit about Uber, I think the old taxi system was an artificial barrier to entry that allowed the taxies to extract “rent” not tax.
“Arbitrage markets get deflated by data” this may be correct, however you give no discussion of arbitrage. There’s no arbitrage in the taxi example. It might be worth looking up arbitrage.
I kinda feel like a pedantic jerk in writing these comments. I think you are fumbling with some very good ideas. However you’re misusing specific terms of art from economics. This lose use of words muddles your points.
Keep up the good thoughts!
Having grown up in the UK through a time of settled consensus on the need for a national health service, my starting point is that there’s a strong moral case for socialising life’s biggest risks. It’s a matter of Rawlsian “justice as fairness” as well as a pragmatic means of spreading catastrophic lightning strikes across a timespan and population base that can bear them more easily.
Big data adds an interesting new dimension to this.
When we join together as a community to meet our needs through a shared service, whether publicly or privately procured, we enable the aggregation of data, creating new value which could not exist if each of us met our own needs in isolation.
There are two broad types of value that could be extracted from that data.
The first arises from using the data to predict and prevent, or at least minimise bad outcomes, and promote the good. If health data pinpoints a gene that makes some people susceptible to a disease, doctors might be able to target preventative care towards those people. If a fire service notices a spike in late night kitchen fires it might run a campaign discouraging people from deep frying while tired and drunk. More people living longer, healthier lives, in less smoke-blackened homes – everyone wins.
The second type of value, which you refer to around “arbitrage” exploits asymmetries of information. Unfortunately there’s nothing inherent in big data to discourage this, and much that might reinforce it. When it comes to putting a price on a risk, an insurer’s actuaries know the histories of millions of people, while each individual has only a hazy grasp of her own and the anecdotal experiences of a few relatives, neighbours and friends.
The problem with this second kind of value is that it doesn’t actually increase the sum total of human happiness – the suffering from illnesses and house fires remains unchanged. And likely as not it sucks value down into the pockets of the few with a godlike view of the dataset at the expense of the many whose collective participation made the data big in the first place.
So it seems to me a good idea to build social and economic incentives around big data that reduce information asymmetry between the subjects and processors of data. This in turn ought to reward positive value creation over zero-sum outcomes like your “arbitrage” examples.
I enjoyed this post a lot.
We have previously established, through other discussions, that there is an upward limit on predictability. Complex systems generate an asymptote. Physics impose another one. Market incentives to generate uncertainty also produce noise.
Setting the asymptote aside – and looking at the trend here – you’re right to ask these two questions:
Does this mean that as we get better at predicting outcomes, we’re less likely to pool our resources because of inherent uncertainties?
The answer depends on the definition of ‘we’. For some people, the ability to better predict the future doesn’t mean that they’ll change current behavior in an effort to create a better future. If anything, it will cause some people to exploit others that much more efficiently. The skill of ‘prospection’ is not distributed evenly across societies or people. To that end, I think that it will cause some people to behave more selfishly, and for disproportionate benefit. But, if by ‘we’, you mean ‘everybody’, then no. Anybody can benefit. Not everybody will.
Is a predicted world an individualistic, Libertarian world?
Many winners from globalization don’t really attribute much credit to globalization for what they’ve achieved. They attribute 100% of the credit to themselves. And, as such, they become much more individualistic. That line of reasoning assumes that you know the correlation between self-attribution and being individualistic, or really, the way that blame is dispersed and credit is owned. The predictive trend is almost as disruptive as globalization, and, as such, most of us won’t credit much of the success back. As such, sure, we’re going to be much more Libertarian.
This is a good post. It’s a provocative thesis. Thank you for sharing it in this rawer form.
Hi Alistair,
Great post, agree in the main with the points you have made. I did notice, however, that you seem to be conflating the terms “uncertainty” and “risk”. These are two different notions: http://www.quora.com/What-is-the-difference-between-risk-and-uncertainty-in-economics
In the scheme of things it’s a quibble but a lot of people on the quant side take the distinction quite seriously.
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[…] Companies will compete based on their ability to guess what’s going to happen. We’re simply taking the inefficiency out of the way we’ve dealt with risk in the past. Algorithms can be wrong. Prediction is only a problem when we cross the moral Rubicon of […]
“A mortgage is—literally—a bet on death. The word comes from the French “mort” and “gage.” It’s a bet that you’ll die before you pay something off.”
This is an error. A gage isn’t a bet, it’s an undertaking, an “engagement”. And mort refers to the death of the gage: it will expire when a particular condition defined in the mortgage is fulfilled.
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Chris
Thanks for disabusing me of that, Chris. Interesting. I learned French in my native Québec, and was always told “gager” meant to bet. Appreciate the correction; will note it in the body of the post.
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Think again when big data will be influence by blockchain economics. Where risk is not dependent on on the stability of central governments and institutions and also when contracts can become dynamics and time and condition driven based on a complex set of disparate variables