Text Analytics Tools for Deliberative Democracy

In our last post, we spoke about various control mechanisms that can be implemented to support direct democracy (which we  interpreted to mean the control of the allocation of common resources by the people who pooled in).

We also examined how these controls could be used to curtail man-in-the-middle corruption.

In this article, we examine a more sophisticated form of direct democracy called a deliberative democracy.

In a deliberative democracy, in addition to the control mechanisms prescribed for direct democracy, there need to be mechanisms to allow deliberation (discussion) before a referendum or any other action is taken.

I quote from the Wikipedia article on deliberative democracy:

Deliberative democracy holds that, for a democratic decision to be legitimate, it must be preceded by authentic deliberation, not merely the aggregation of preferences that occurs in voting.

In elitist deliberative democracy, principles of deliberative democracy apply to elite societal decision-making bodies, such as legislatures and courts; in populist deliberative democracy, principles of deliberative democracy apply to groups of lay citizens who are empowered to make decisions.

The article on direct democracy had the following to say:

Democratic theorists have identified a trilemma due to the presence of three desirable characteristics of an ideal system of direct democracy, which are challenging to deliver all at once. These three characteristics are participation – widespread participation in the decision making process by the people affected; deliberation – a rational discussion where all major points of view are weighted according to evidence; and equality – all members of the population on whose behalf decisions are taken have an equal chance of having their views taken into account.

(Aside to computer scientists: doesn’t this trilemma remind you of the CAP theorem that applies to database systems? Here’s a simple explanation of the CAP theorem: http://ksat.me/a-plain-english-introduction-to-cap-theorem/).

So, for example, representative democracy satisfies the requirement for deliberation and equality but sacrifices participation.

Participatory democracy allows inclusive participation and deliberation but sacrifices equality.

And then there is direct democracy which supports participation and equality, but not deliberation.

The problem seems to be that when a large number of people are invited to participate in a deliberation (and given that deliberations take time), it will not be possible to compensate them all for their time. Consequently, only those more interested in the issue being debated (or more likely to benefit from one position or the other) are more likely to participate, biasing the sample in their favour (all sections of the population are no longer equally represented in the discussion/decision).

So, it seems that all the three properties desired in an ideal democratic system – participation, equality and deliberation – cannot be present at the same time in a real democratic system.

But then, a while ago, we began wondering if this trilemma is merely a result of the lack of suitable technology and not really a fundamental property of democracy.  So, we proposed a design for (though we have not yet realized it) a tool that can support the participation of a large number of people in deliberations.  We call it the MCT (Mass Communication Tool).

It could be used as a method to enable direct democracies to support deliberations in which all citizens can participate, ahead of a vote on any subject.

It uses text clustering algorithms to solve the problems of volume as well as numeric asymmetry in the flow of communications between the deliberating participants and the moderators of the communications.

There’s a brief overview of the system in our lab profile.

MCTs are bound to have a huge impact on our experience of representative government.  A typical use case would involve a public figure, (say President Obama), sounding out the electorate before introducing legislation on say healthcare reform.

 

By first discussing the competing proposals with large numbers of people, it might be possible for the initiator of the discussion to get a sense of what might or might not work and what the response to the legislation was likely to be.

 

An MCT would have to be capable of supporting a live dialog involving a large number of people.

It would use natural language processing and machine learning to enable a few moderators (for example, the CEO of a company) to interact with a large number of people (for example, all the employees of the company) in real time (for example, during a virtual all-hands meeting), get a synopsis of a large number of concurrent discussions in real time, and participate in a significant fraction of the discussions as they are taking place.

The system would consist of:

  1. an aggregator of messages (built from natural language processing components) that groups together messages and discussions with identical semantic content;
  2. a hierarchical clustering system (built from natural language processing components) that assigns aggregated messages their place in a hierarchy by specificity with more general messages closer to the root of the hierarchy and more specific messages closer to the leaves of the hierarchy;
  3. a summarization system (built from natural language processing components) that creates a summary of the aggregate of all messages in a sub-tree; and
  4. a reply routing system (built from natural language processing components) that routes replies from cluster to cluster based on their relevance to the discussion threads.
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