A COMPLEX CONVERSATION OF COMPLEX STRUCTURES FOR COMPLEX SOLVING: notes from Manuel Lima's A Visual History of Human Knowledge TED talk
It's a fine mist of weather and quiet afternoon music here in Nashville, and I'm enjoying the obligatory milk tea and TED talk deconstruction while letting the dark feel its way across the floorboards.
Today's listen has been Manuel Lima's A Visual History of Human Knowledge from the March 2015 TED. I highly recommend watching the video if only for Lima's fantastically gathered visual assets and obvious enthusiasm, but my text-based notes are below anyway, largely for the rampant archivist in me who only fully processes information by re-articulating it into my own words, the forecaster in me who can't help but to holistically explore connections, and for the artist in me who just can't shake away from the concepts quite yet.
I've tried to make clear where I diverge from Lima's direct quotes and summaries into my own phrasing and thoughts through an "(IMOW:)" or "In My Own Words:" indicator; any misunderstandings or mispresentations of Lima's ideas in these areas are purely my own verbal missteps as I walk along the path of these concepts and pull in other sources or intellectual tangents.
Lima starts by covering a short introduction to the history of traditional visual mapping and how humans have interpreted their belief systems of the natural order of knowledge. Lima credits Aristotle and crew with the earliest forms of visual mapping through the "Great Chain of Being" (or Scala Naturae in Latin, both phrases of which delight the poet in me to no foreseeable end), where (IMOW:) everything had its (deity-of-choice decreed) place in a very hierarchical, typically linear structure whose categories are defined by opposing characteristics. Everything imaginable is intended to fit into the scala naturae, giving order and meaning to the universe through (to quote dear Thom Yorke) everything in its right place.
Lima then moves to introduce the illustrative shift to the Porphyrian Tree, or Tree of Knowledge, model, which, while still often having a starting, or 'root' point, begins (IMOW:) to embrace the concept that ideas or categories can fractal off from the starting point and create/engage new ideas or categories that, while technically related back to the original idea, are more closely tied to their branch point than to the main concept. In many ways, the tree model accepts causal fractalling as a fundamental part of how to organize and make sense of information. Because of its flexibility in expressing systems in clear ways, Lima says that the Porphyrian Tree became THE visual metaphor for mapping specific systems of knowledge (like scientific explorations), ideas (like law and morality), or social concepts (like consanguinity or community structures), and Lima credits Ramon Llull with really owning this metaphor in 1296 with literal illustrations of "Trees of Science" and discussing "branches" of study. (Given my own love of the extended metaphor as literary and educational tool, I suspect Llull and I would have had a grand old time talking over beers and fleshing out stuff like this.)
What makes me so so excited by Lima is his acknowledgement that the old linear models don't work for us anymore when it comes to the complex problems we now investigate as a species. Lima posits that the model of a Network - a nonlinear deconstruction of the interconnectivity between information elements, one that could be best expressed as an interdependent web - is and will continue to take the place of the old scala and tree models. (IMOW:) Information classifications, rather than being limited solely to hierarchies of importance or "known knowledge" fractalling, begins to resemble more the egalitarian and complex systems of neurons and synapses found in the human brain, where a multitude of separate inputs and factors come into action at various levels and times in order to achieve a particular expressive outcome. In it's most complex and thorough (and verbose) form, Lima points to Wikipedia as "the largest rhizomatic structure created by man," as (IMOW:) the ultimate decentralized example of being able to start anywhere on the map and move into other areas of seemingly disparate information through a series of solid connections (a macrocosmic game of "Six Degrees of Kevin Bacon," if you will).
In their simpler forms, Network models employ a pretty sophisticated visual syntax that explores a more holistic view of how things relate. Lima states that Network models have five main characteristics that define their structure: nonlinearity, decentralization, interconnectivity, interdependence, and multiplicity. These characteristics give the model its strength, allowing the viewer to look at the complex factors that create and influence a scenario, and how those results create new ripples of influence and effect.
Lima lists the usual examples of knowledge benefiting from a more holistic Network model (especially as our understanding of the how the world functions continues to expand), but he also points out the usefulness of the Network model in making sense of social ties and structure, and how in today's world, representing this information usually doesn't benefit from a linear scala or tree-based approach. (IMOW:) For example, digital connections complicate traditional ideas and formations of friendship, intimacy, and connectivity; a changing job market paired with fluid, contractor-based workforces create a need for nontraditional administrative and organizational structures; collaborative environments are no longer limited to geographical location, fields of study, or depths of expertise.
(IMOW:) In particular, Network models are especially interesting when employed in fields (such as social sciences, longitudinal medical studies, and psychological examinations, to list a few) where "hard" facts (such as age or city of residence, things that can be easily defined) are either supplemented by or supplanted by "soft" correlations (such as lifestyle habits, social circles, online connections: the kinds of factors that are related to the issue but much harder to pin down as concrete and consistent influences on the data except through a study over time). In a base form, Network models can show how these things relate in some way, allowing the viewer to make sense of data sets that are seemingly disparate, inconsistent, or unrelated, but are still a part of the information to review, and, if viewed through time-based factors, can reveal how these play a larger role in the overall system. (I may taking a very birds-eye view of influence here, but given the proliferation of these sorts of exploratory studies by entities like OKCupid and Facebook - or those who use data from these sources - I think it's a relevant perspective for a forecaster to take.)
Network models embrace a new way of thinking about the world around us, and Lima (in his most excited) introduces the idea of 'Networkism' as a cultural meme: that in their efforts to better portray and express the ideas being Network models, data visualists have looked to the arts as a source of inspiration and in turn have highly influenced the world of art. (IMOW:) As these Network models are essentially structures that could best exist as 3D and 4D expressions, and that the intent of visualizers is to seek the most accurate and clear translation of the data and the data's relevance to the viewer, it's not surprising to see the two fields begin to share concepts and approaches. The flexibility of thinking artistically can sometimes better express complex concepts by translating them, often into installation-based objects or environments whose spatial interactivity better exemplifies an understanding of the whole. Lima lists Emma McNally's graphite drawings, Tomas Saraceno's installation works, Chiharu Siota's filament-based pieces, and Sharon Molloy's network-derived paintings as varying levels of how artists translate information into works with conceptual and resonant depth.
(IMOW:) As an artist, what I love most about the installation-based Networkist pieces is that through existing in a physical, interactive way, these art-based Network models embrace the concept that we as the observer are also an influence on and part of the system being observed (such as similar effects of the 'quantum observer' in physics, the Hawthorne Effect in social science's observational research studies or, obvious and relevant to this thoughtline, in navigating the role of the audience within the arts itself as expressed through this artist's research paper).
(IMOW:) By creating spatial engagement with the ideas presented in a Networkist piece, we are also creating space for the viewer to inhabit a place and a role within those ideas: either through directly engaging the art piece in a physical way (where the individual directly touches or changes the artpiece by having physically or digitally interacted with it); through an tangential expression of becoming part of the network experience solely by existing within the physical or digital space for that period of time (digital footprints that show how many visits to the page exist, or actual footprints such as museum ticket sales or audience-posted reactions to the work through social media sharing, exhibition reviews, etc); and/or through the passive dispersal of the idea meme itself through the audience member having had the opportunity to absorb and digest the information on some personal level. Thus, the work extends the network web into and through the individual, sending ripples of influence out in a physical, real-world manner.
Most importantly, Lima believes that this new way of looking at the world - as an interconnected, interdependent, nonhierarchical system - is vital to being able to solve the difficult problems and understand the complex universe that we have ahead of us as a species. Whether on the smallest scale of knowledge of a mouse brain or on the largest scale of knowledge of galactic drift, Lima reinforces Bruce Mau's belief from Massive Change that "when everything is connected to everything else, for better or for worse, everything matters."
And as a simple, basic model for understanding and interacting with the world? At the end of the day, for me, that's something that makes a whole lot of sense no matter how you depict it.