Another Reason We Need Open Government Data: To Avoid Information Asymmetries

from the database-of-intentions dept

Can the future aggregate actions of people be predicted from relevant sets of data that describe them? That, of course, is what Isaac Asimov’s invented mathematical discipline of psychohistory was supposed to do. Some Japanese researchers claim to have made some progress towards that goal:

These guys have used ideas from statistical mechanics to model the behaviour of humans influenced by word-of-mouth interactions and advertisements. In this paper, Ishii and co derive a bunch of equations that they use to model the number of people who’ll turn up to see a movie or visit an art show.

Inspired by this work, Nicklas Lundblad has written an interesting speculative piece about what the rise of predictability through the analysis of huge data sets might mean for society and openness. He notes that one of the “theorems” of psychohistory is that for it to be effective the data sets and the predictions derived from them must be kept secret from the populations involved – the idea being that if they were able to analyze that same data themselves, they might change their actions and thus nullify the predictions.

He points out that this creates a tension between predictability and openness:

There is an assumption here that is worth highlighting. And that is that for a democracy to remain open it can not be predictable by only a few. That is a complex and perhaps provocative assumption that I think we should examine. I believe this to be true, but others will say that our democracy already is predictable, in some sections and instances, only to a few and that they build their power base on that information asymmetry, but that it is reasonably open still. Maybe. But I think that those asymmetries are not systematic to our democracy, but confined to those phenomena, like stock markets, where they are certain to be important, but where they also do not threat the nature of democracy as such.

In summary, if we share the data and allow everyone to use it, then predictability goes down.

That, in its turn, is an argument for openness. If data held by a government, say, is released freely, anyone can explore its implications and then be able to modify their actions based on them, and thus escape being a statistical part of the predictability that would otherwise be implied by remaining in ignorance. As Lundblad writes:

If there is a conclusion here it seems to be to explore the amazing value of data under the imperative of openness to the the full extent possible to ensure that our societies gain from this new, fantastic age of data innovation, discovery and exploration that we are entering into, but never compromise on that openness in the pursuit of macro-social predictability.

Follow me @glynmoody on Twitter or, and on Google+

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Comments on “Another Reason We Need Open Government Data: To Avoid Information Asymmetries”

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Lawrence D'Oliveiro says:

Raw Data Won?t Be Enough

Having the data is one thing, processing it in the right way is another. What if someone discovers a secret algorithm that gives them greater predictive power over society than anybody else? And what if they won?t share that secret? Can they be forced to give up what some might see as an unfair advantage?

Bengie says:

Re: Re: Raw Data Won?t Be Enough

If that person shares it. You don’t have to share anything, you can keep it a secret and never copyright it.

Luckily, most anything that one person discovers is usually discovered by others. Only a few exceptions to this.

The other good thing to know is most people who discover truly wonderful things also tend to have the personality to find recognition reward enough.

PW (profile) says:

Wouldn’t that lead to just a different set of eventually predictable behaviors (how people react to knowing certain info)? Separately, the idea of people reviewing the data presumes that they all know how to read it the same way and come to the same or similar conclusions. That intuitively feels unlikely, much the same way as stock market models lead different investors to different conclusions. The predictability concept also fails to inspire since it’s always about what data is being analyzed and while there may be times that it correlates well, short of having perfect data sets for everything, it’s unlikely that predicability will hold over the long term. Again, I hold hedge funds and other stock investors as demonstrations of this sort of systemic failure.

Anonymous Coward says:

No Economic Benefit

Information asymmetry is always bad for the economy. This is what the laws against insider trading are supposed to be about. When an inside trader makes a profit because he has learnt about some company action before the rest of the market, then that profit comes at the expense of other players in the market. Information asymmetry benefits a few, and disadvantages the many. Thus the many find it unprofitable to invest, which then causes lower aggregate economic activity, which then causes unemployment and poverty.

The global financial crisis was caused by information asymmetry. The misled buyers of CDOs thought they were buying AAA securities. Meanwhile, the sellers knew perfectly well that they were selling junk. When the information asymmetry went away, the price of the securities went down to zero. Vast losses were incurred by the many. The few walked away with vast profits. We are still living through the economic damage so caused.

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