20 May Methods of the Real Economy Lab
Ishan Shapiro is co-founder of Notthisbody and Metamaps.cc. His roles within the Real Economy Lab include creative direction, methodology design + analysis.
In this post we’d like to introduce a little bit deeper our methodology and approach towards mapping the new economy ecosystem and landscape. We’ve made an effort to design from the ground up an approach that holds paramount a holistic, systems perspective towards mapping the new economy landscape and the complex dynamics in and around it.
Firstly, the Real Economy Lab initiative is framed as a laboratory for observation and inquiry, with several basic components. Starting with a broad definition of the subject matter domain, we’ve outlined our working assumptions and hypotheses based on a review of current work and thought leadership in the field. Next, we’ve put together a set of tools and methods which we’re using to help us navigate the complexity and assist us in synthesizing insights from the data.
The first major component of our methodology is modeling an ‘actor‘ within the new economy ecosystem. An actor can be a non-profit, an association, a network, an enterprise – they can take different organizational forms. Defining what attributes these actors have within the context of our ecosystem view of new paradigm economics was our first step in the process. We use Metamaps.cc, an open-source, collaborative modeling platform and knowledge commons, to assist us in conceptualizing the taxonomy for the survey, as well as linking each taxonomy component to further research, questions, definitions and perspectives. What we end up with is a well-rounded model of an actor within the new economy ecosystem. Here’s a few of the dimensions that we arrived at:
After going through several iterations of this model, incorporating different perspectives into the taxonomy and testing them based on desk research, we settled on a basic foundation to build the second component – a survey to be sent out to actors within the new economic landscape.
When doing an ecosystems mapping such as this, the inquiry must always come back to – what is the context in which these questions are asked and answered? Taxonomy design is significant in that it creates an implicit and sometimes explicit contextual frame that shapes our analysis. Make it too rigid, and the results may be rather biased towards a particular perspective. Too loose, and it might as well be apples and oranges and sweet potatoes all in the same basket. We aimed for a middle ground – within our survey, there are some predefined responses (choose from list) and some which are open ended, while others request a scalar metric (where are you on the scale from 1-5).
We store these responses in a central database, then represent them graphically using Kumu, a graph visualization and analysis platform. Representing the data as a graph network brings us far beyond the capabilities of spreadsheets or as a mind-map, in that it allows each node to have attributes, each relationship to have metadata, and further, it makes it all computable and dynamic, so that anyone can begin querying the graph to draw out insights about the network itself.
Each actor is represented by a node (element) with its respective attributes (also nodes, color-coded) from the survey responses. Representing each actor and attribute as a node within a graph network has a few useful qualities to it. At a simple level it provides a way to interact and navigate the actors and their associated attributes. Within a glance, one can see what attributes are more or less common between the different groups – what are the attributes with the most connectivity within the ecosystem? What are the outliers? Where are there gaps, and opportunities? Can we identify complementarities/mutualities? There are certain metrics that you’re able to extract from computing networks – these are things like centrality, betweenness and closeness. Each element ends up with a score that can reveal an insight about behaviors within the larger system. We’ll go into more detail on these metrics in a later post, and how the data can be interpreted using them.
This, in a sense, is the groundwork that we’d like to lay for the next phase of the Real Economy Lab. Besides being an initiative to map the movement, we’re looking to identify key outcomes, themes and archetypes within the new economy ecosystem. What we’re most curious about are the stories that we can draw out of the data. What are the actionable insights that the Real Economy Lab can provide to the new economy ecosystem? How can we use these insights towards actively building coalitions and aligning resources around the most high-leverage inflection points for changemaking?
The next stage of the project is to progress further with our sense-making process, towards objectives like resolving shared understanding and common ground, developing collective strategic roadmaps and shared intent, and processes that can help address key issues of theory and practice amidst diverse parties.
This is just the beginning of the Real Economy Lab. Look for upcoming posts as we go into greater depth with analysis of the data we’re collecting and more details about our collaborative sense-making process. Do you have questions, ideas or insights that you’d like to put forward to the lab? Would you like to get involved in the sense-making process we’re developing? If so, send us a note to team [at] realeconomylab [dot] org.