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