Krakauer is the new President of the Santa Fe Institute. Here is his interview on the above topic. The blurb follows. There’s also a transcript at the link.
“For 300 years, the dream of science was to understand the world by chopping it up into pieces. But boiling everything down to basic parts does not tell us about the way those parts behave together. Physicists found the atom, then the quark, and yet these great discoveries don’t answer age-old questions about life, intelligence, or language, innovation, ecosystems, or economies.
“So people learned a new trick – not just taking things apart but studying how things organize themselves, without a plan, in ways that cannot be predicted. A new field, complex systems science, sprang up to explain and navigate a world beyond control.
“At the same time, improvements in computer processing enabled yet another method for exploring irreducible complexity: we learned to instrumentalize the evolutionary process, forging machine intelligences that can correlate unthinkable amounts of data. And the Internet’s explosive growth empowered science at scale, in networks and with resources we could not have imagined in the 1900s. Now there are different kinds of science, for different kinds of problems, and none of them give us the kind of easy answers we were hoping for.
“This is a daring new adventure of discovery for anyone prepared to jettison the comfortable categories that served us for so long. Our biggest questions and most wicked problems call for a unique and planet-wide community of thinkers, willing to work on massive and synthetic puzzles at the intersection of biology and economics, chemistry and social science, physics and cognitive neuroscience.”
And the way forward. Granted it’s not full-blown collaborative commons but more like a healthy social democracy of the kind Sanders promotes and Scandinavia has. But I think it’s a necessary stepping stone on that road. The blurb:
“Rising inequality and growing political instability are the direct result of decades of bad economic theory, says entrepreneur Nick Hanauer. In a visionary talk, he dismantles the mantra that ‘greed is good’ — an idea he describes as not only morally corrosive, but also scientifically wrong — and lays out a new theory of economics powered by reciprocity and cooperation.”
We’ve seen quite a few descriptions of an emerging paradigm known as the collaborative commons (CC). But a problem arises when we take another step by extrapolating from that data and then try to prescribe what we need to do in order to create a CC. I.e., we form a model of what the CC should be, and top down we try to implement it. Whereas the technology that enables the CC to grow organically has no apparent need of this top down imposition. To the contrary, it seems more of a capitalistic holdover instead of the middle out way the CC is naturally evolving.
Bonnita Roy has noted that “In a world as diverse in people and rich in meanings as ours, big change might come from small acts by everyone operating everywhere in the contexts that already present themselves in their ordinary lives.” It is quite the contrast from the enlightened heroes figuring it all out from their complex ivory towers which supposedly and hopefully ‘trickles down’ to the rest of us. This seems much more how the CC works in practice. Political and social revolution arises from the external socioeconomic system, the mode of production. Development is accomplished not by having a ‘higher’ model to which one must conform, but by the actual practice of operating within the emerging socioeconomic system.
Jennifer Gidley noted a similar phenomenon in that there is a difference between research that identifies postformal operations from those who enact those operations. And much of that research identifying it has itself “been framed and presented from a formal, mental-rational mode.” Plus those enacting postformal operations don’t “necessarily conceptualize it as such.” So are those that identify postformality via formal methodology really just a formal interpretation of what it might be? Especially since those enacting it disagree with some of the very premises of those identifying them?
The online discussions I engage with on meta-models is representative of this difference. It seems the abstract modeling of the development of the CC is what is operating to create it in a top-down manner. Not only that, what appears to be happening in all cases is that not only does each individual have their own thoughts and opinions on the topic, which is to be expected in diverse groups, we all end up justifying our own take over others. We all seem to be so attached to our own discoveries that we build an edifice and seek out and find supporting evidence to justify it. When confronted with different perspectives or evidence, our first inclination is to see how it fits into our own model or worldview, how we can twist and manipulate it to support our biases. What is there in common that holds us together if we are so closed to taking in new information from other perspectives, allowing them to sit in their own right, their own space, instead of trying to fit them into our own predispositions?
I’m reminded of what Said Dawlabani said, that the distributed network of the collaborative commons follows no ideologies. That it is open source, highly networked and depends on the wisdom of the crowd. I’m guessing that equally applies to our models on trying to create the CC, as we tend to idealize and attach to them. Is our ownership of our ideas more indicative of capitalism that the CC? It also seems that those who are enacting this new paradigm are doing so without need of any explicit theory or model about it. So is arguing about the correct theory even a necessary part of its enactment, as if like capitalism it too needs a top down elite model to implement it? Are our models just getting in the way and actually counter-productive to its natural evolution?
From this piece:
“There is not one, but at least four, hereditary systems recognized by biologists today. Eva Jablonka and Marion Lamb lay this out clearly in their 2006 book, Evolution in Four Dimensions, as they walk through the research literature on genetics, epigenetics, behavioral repertoires, and symbolic culture as four distinct pathways where traits are ‘heritable’ in appropriately defined fashion.”
“Specifically, I am thinking of three areas where significant progress has been made during the last forty years: the birth of complexity science in the early 1980’s, developments in the study of human conceptualization and cognitive linguistics since the mid-70’s, and the explosion of digital media in the age of personal computers and later via the internet.”
“Applied to meme theory, this body of tools and techniques [cognitive linguistics] demonstrates that researchers across many fields have found value in the perspective that culture can be studied as information patterns that arise in a variety of social settings routinely and with modular elements that are readily discernible in each new instance. The claim that information patterns do not replicate is contradicted by the evidence for image-schematic structures.”
New draft paper by me. Update: Published here. The abstract:
A ‘power law’ refers specifically to a statistical relationship between quantities, such that a change in one quantity has a proportional change in another. One property of this law is scale invariance, otherwise known as ‘scale-free,’ meaning the same proportion repeats at every scale in a self-similar pattern. Mathematical fractals are an example of such a power law. Power laws are taken as universal and have been applied to any and all phenomena to prove the universality of this law.
However, a recent study (Broido and Clauset, 2019) claims that “scale free networks are rare.” They conducted an extensive review of one thousand social, biological, technological and information networks using state of the art statistical methods and concluded what the title of their article states. To the contrary, “log-normal distributions fit the data as well or better than power laws.” And that scale-free structure is “not an empirically universal pattern.” Hence it should not be used to model and analyze real world structures.
In his new book, Range: Why Generalists Triumph in a Specialized World, David J. Epstein investigates the significant advantages of generalized cognitive skills for success in a complex world. We’ve heard and read many praises for narrow expertise in both humans and AIs (Watson, Alpha Go, etc.). In both humans and AIs, however, narrow+deep expertise does not translate to adaptiveness when reality presents novel challenges, as it does constantly.
As you ingest this highly readable, non-technical book, please add your observations to the comments below.
This TED talk discusses how tech innovation is driven by those with diverse experience that syntegrate a variety of genres instead of specialists that are limited to a few. They call it ‘lateral’ thinking but that term sets up a dichotomy with hierarchical thinking, which the syntegral approach is certainly much more than. The hierarchical complexity approach would limit that way of thinking merely to what it calls horizontal complexity, again missing the boat entirely of the sort of cross-paradigmatic thinking involved in syntegration, aka hier(an)archical synplexity.
Here’s an interesting infographic of the main concepts and thinkers in complexity science across time. Notice S. Kauffman is slated in the 1980s column, suggesting the graphic depicts when influential thinkers first make their marks.
Neuroscientist Larry Cahill takes issue with a Feb 2019 Nature favorable book review of Gina Rippon’s The Gendered Brain: The New Neuroscience That Shatters The Myth Of The Female Brain.
Cahill’s response prompted an interview by Medium Neuroscience writer Meghan Daum.
Scientific findings have a way of upsetting apple carts, especially when we consider our oft-demonstrated human capacity to bend science to advantage some power-coveting groups over others.
Valid research amply shows there are real differences in male and female neuroanatomy and functions. Honest science must follow the evidence where it leads. Clearly, any discovered differences cannot be allowed to justify unequal social or economic opportunities or treatment. Cahill compares the situation to genetics. That people differ genetically in a vast number of ways cannot be taken as cause to misstate scientific findings or preclude further learning about genetics.
There are times and circumstances in which certain research approaches must be blocked for humane or other reasons but that is a different argument than denying the findings of a body of research because they are uncomfortable or inconvenient.
If you are familiar with complex systems theorist Dr. Stuart Kauffman’s ideas you know he covers a broad range of disciplines and concepts, many in considerable depth, and with a keen eye for isomorphic and integrative principles. If you peruse some of his writings and other communications, please share with us how you see Kauffman’s ideas informing our focal interests: brain, mind, intelligence (organic and inorganic), and self-aware consciousness.
Do you find Kauffman’s ideas well supported by empirical research? Which are more scientific and which, if any, more philosophical? What intrigues, provokes, or inspires you? Do any of his perspectives or claims help you better orient or understand your own interests in our focal topics?
Following are a few reference links to get the conversation going. Please add your own in the comments to this post. If you are a member and have a lot to say on a related topic, please create a new post, tag it with ‘Stuart Kauffman,’ and create a link to your post in the comments to this post.