24 Jun Preliminary Analysis and Synthesis
Real Economy Lab is building an online a hub to support the coming together of a global movement on the new economy, with a focus on finding commonalities and reaching consensus to advance the shared aims of constituents in the field. To that end the Lab takes a whole systems approach, modeling the subject matter and data to link disparate activity and ideology under a unifying taxonomy and narrative framework.
The central feature of the online lab is a “mind-map” or network visualization: a complex web of data points and relationships spanning the field of inquiry. This web of data has meaningful (technically speaking, it is semantic) structure which allows viewers to readily scan, filter, evaluate, and otherwise sort through the contents to derive valuable insight.
Our present data structure is designed to accommodate different types of analysis. The Kumu.io graph tool used here is outfitted with a number of features which can be used for customized queries and plots from the latest data.
Having completed the first round of data collection and surveying, it’s time for a closer look at ensuing analysis and synthesis. So far, we’ve developed an initial taxonomy, or semantic categorization. We’ve collected survey data using this taxonomy as a set of initial lenses, incorporating new perspectives and attributes that arise in the process. We’ve set up and run trial calculations on the survey results that reveal common threads and groupings.
Starting from a basic statistical rundown, we’ll add a couple of layers to reach deeper understanding. Below, we see the five leading responses in each of four question areas, shown as a percentage of total respondents (plots created on infogr.am):
The next layer of the sense-making process makes use of a simple narrative framework. Narrative provides a familiar cognitive foundation on which to base higher-order analysis and inquiry. We use narrative structure to correlate data types – i.e. Who is doing What, Where, How – to see where these coincide, and how the ecosystem tends to behave as a whole.
Two sets of patterns we’re exploring in this way are archetypes and themes. Archetypes represent a recurring role or mode of action, characterized by distinctive traits and other cues we can discern from data about how an organization operates. Themes are common rallying points or shared value systems borne out in the stated aims and outcomes of participants. Both provide significant new basis for convergence and mutuality.
The story lens helps us to reduce abstract principles and arguments to more familiar, tangible context-cause-effect mechanisms. An ‘actor’ (take ourselves, for instance, Real Economy Lab) aims for a particular result (output or outcome) based on a set of explicit motives and means (and perhaps some implicit or emergent ones as well). This setup allows us to identify and link the trajectories of individual and collective behavior, affording a degree of foresight and synthesis at the systems level by surfacing the root story lines within underlying cultural and psychological substrates. As the threads are drawn out in accessible, visual, nonlinear form, clear patterns show up to inform subsequent action by all parties.
As we continue to design, test, and deploy the online collaborative infrastructure to enable all this, several possibilities arise when we consider the different ways people may wish to work with and contribute to the data set. Our intent is that the cumulative resources of the lab will serve a range of research interests and methodologies.
Real Economy Lab is creating an online hub stocked with tools and information for anyone interested in re-authoring economic systems in light of the collective wisdom and experience of peers. Next, we’ll start putting hypotheses to the test. Do you have a belief, hunch, wish, or helpful anecdote that could lead to a combinatorial effort amongst the “tribes” and a more effective movement for economic systems change? We’d love to help put it to the test.