Cognitive maps of political issues
Life will be better in that fine not-so-far-off day when Science has devised a representation for political issues that is able to capture their complex structure and inter-relationships. Even when discussed on the news and in political debates, issues are usually treated as isolated concepts, indexed and identified via keywords. This approach to issues misses a lot. Advances in natural language processing will allow us to create better representations of issues, and these will help us improve the supporting technologies for politics.
At present, the most widely deployed technologies treat political issues as categorical variables. Each issue (“climate change”, “tax reform”, “jobs”, etc) is an isolated entity. As human beings who understand words, we know that these words are connected: jobs are related to climate change, for example. Still, it is common to treat each issue separately. It’s easier. The typical approach is to associate keywords and phrases with issues and then use those associations to determine which category a block of text is best matched to. Tools that support this type of analysis, such as WordStat, NVivo, and Word2Vec are critical for status quo analysis of “unstructured” (text) data.
If you want to see the limits of isolated word analysis, try writing to your Senators. The most popular software on the Hill for processing incoming letters appears to be Quorum. Quorum will match you letter to a bank of keywords and associated responses. A staffer will review the automatically assembled response before being sending it to you. If your letter does not easily match the current issue, and mismatches like yours are occurring frequently enough, then the software may trigger creation of a new issue. In this case, staff will use built-in workflows to write an automated response for the “issue”, which in this context means a new collection of keywords.
The deep structure is of issues is important to political discourse. One group believes that climate change and jobs are related because there will be more green jobs in the future. Another group believes that climate change and jobs are related because fighting climate change will require the loss of jobs in mining and oil. Those underlying relationships between political issues and concepts are where the work of political discourse resides; not in the simple, siloed way we structure issues at present.
Political issues, like other human concepts, are supported and/or implemented psychologically by associative networks. The neuronal mechanisms behind concepts remain unknown. However, the inter-relationships between concepts can be probed experimentally, as Freud famously did through free association. There are many such experimental methods. For example, reaction time differences provide repeatable metrics on associations between concepts with a well-studied effect called priming. When you see “horse” your time to recognize “knight” is reduced. Having seen “night” you are quicker to recognize “moon”. Priming spreads through our conceptual network. “Horse” activates “knight” which activates “armor” — so that you will be quicker to recognize that word — but “horse” does not activate “moon”.
The application of cognitive science to political science has produced maps but no central theory or systematic mapping of the concepts and associations that define political issues. Political scientists seem mostly to be concerned with the meaning of political concepts within political theories (“what does equality mean in a democracy”). The cognitive scientists appear mostly concerned with understanding how our insights into decision-making apply to voting behavior. Working political scientists, like Frank Luntz and George Lakoff, are able to identify how subtle changes in wording can influence our thinking, but I’ve not seen these techniques applied to produce a compendium of distinct political psychologies. It would be helpful to understand the degree and extent to which liberals and conservatives are fundamentally speaking different languages while superficially talking about the same thing.
Assuming it is possible to aggregate cognitive maps across individuals, it will be interesting to compare the results of different methods. For example, concepts can be mapped by priming studies of individuals; alternatively, we could develop a map by applying Word2Vec to large corpuses of topically diverse but ideologically consistent political text (ie DailyKos or Breitbart); alternatively, we could conceivably construct a map from the overlap of the donor and/or membership lists of issue advocacy groups (in essence, this would allow some vendor to supply us with collaborative filtering on political purchases).
Improved science around the cognitive maps that we use to understand politics should lead to improved tools for conducting politics. Media, parties, candidates, governments, professional associations, interest groups, corporations, and individuals will all benefit to some extent from improvement in tracking the national dialogue. Improvements in message refinement could help us reach durable compromises, just as we have seen these technologies used to deepen existing schisms. Perhaps with better automation for sensing and comprehending the political environment, we may come to understand the world in more sophisticated terms than liberal vs conservative. Maybe the Democratic Party could even recognize how it is falling out of step with the voters without first having to lose its place in all branches of government!
Mapping cognitive networks can contribute to advances in many domains besides politics. Job referral sites and HR departments would work better with a better model of the complex interrelationship between skills. In these cases, the application would be to map the associations of experts to provide guidance to the non-experts who need to match resumes to jobs. Collaborative filtering interfaces (for movies, products, prospective first dates) may also be improved. In these cases, the filters are domain-limited cognitive maps that can perhaps be inferred rapidly from broader cognitive maps.
An understanding of the cognitive maps of political concepts for individuals or ideologies will help us construct better tools for (eg) processing official correspondence, tracking issue debates, and providing consumers with news and opinion content they are likely to find interesting. There are many possibilities for advances in political science, political reporting, and political organizing.