Epistemic Networks and Idea Exchange

Earlier this week, I gave a brief lightning talk as part of the fall welcome event for Northeastern’s Digital Scholarship Group and NULab for Texts, Maps, and Data. In my talk, I gave a high-level introduction to the motivation and concept behind a research project I’m in the early stages of formulating with my advisor Nick Beauchamp and my Tufts colleague Peter Levine.

I didn’t write out my remarks and my slides don’t contain much text, but I thought it would be helpful to try to recreate those remarks here:

I am interested broadly in the topic of political dialogue and deliberation. When I use the term “political” here, I’m not referring exclusively to debate between elected officials. Indeed, I am much more interested politics as associated living; I am interested in the conversations between every-day people just trying to figure out how we live in this world together. These conversations may be structured or unstructured.

With this group of participants in mind, the next question is to explore how ideas spread. There is a great model borrowed from epistemology that looks at spreading on networks. Considering social networks, for example, you can imagine tracking the spread of a meme across Facebook as people share it with their friends, who then share it with friend of friends, and so on.

This model is not ideal in the context of dialogue. Take the interaction between two people, for example. If my friend shares a meme, there’s some probability that I will see it in my feed and there is some probability that I won’t see it in my feed. But those are basically the only two options: either I see it or I don’t see it.

With dialogue, I may understand you, I may not understanding you, I may think I understand you…etc. Furthermore, dialogue is a back and forth process. And while a meme is either shared or not shared, in the back and forth of dialogue, there is no certainty that an idea is actually exchanged to that a comment had a predictable effect.

This raises the challenging question of how to model dialogue as a process at the local level. This initial work considers an individual’s epistemic network – a network of ideas and beliefs which models an given individual’s reasoning process. The act of dialogue then, is no longer an exchange between two (or more) individuals, it is an exchange between two (or more) epistemic networks.

There are, of course, a lot of methodological challenges and questions to this approach. Most fundamentally, how do you model a person’s epistemic network? There are multiple, divergent way to do this from which you can imagine getting very different – but equally valid results.

The first method – which has been piloted several times by Peter Levine – is a guided reflection process in which individuals respond to a series of prompts in order to self-identify the nodes and links of their epistemic network. The second method involves the automatic extraction of a semantic network from a written reflection or discussion transcript.

I am interested in exploring both of these methods – ideally with the same people, in order to compare both construction models. Additionally, once epistemic networks are constructed, through either approach, you can evaluate and compare their change over time.

There are a number of other research questions I am interested in exploring, such as what network topology is conducive to “good” dialogue and what interactions and conditions lead to opinion change.

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One thought on “Epistemic Networks and Idea Exchange

  1. Kevin Dye

    This is an interesting area of research.

    Take a look at http://www.futureworlds.eu/wiki/SDDPs_organized_by_Future_Worlds_Center for examples of structured dialogue. It might be easier to start with watching how a particular idea emerges in importance during a dialogue in dialogues that have already been tracked in some way. In the reports on this website the order ideas came into a conversation, the individual clarification of ideas, the collective assessment of their comparative importance, the clustering of ideas by similar meaning, and the collective assessment of the influence of ideas on other ideas is done the same way for every case. There a like a hundred cases – rich data set.

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