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.
Jordan Hall of the Neurohacker Collective on decentralized collective intelligence. Sounds a lot how our group works, our collaborations creating something greater than our individual contributions, even though the latter are part and parcel of the process. What happens when we node thyself.
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.
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.
Here‘s the link to their lineup of new free e-books. Maybe we could even use one of them for a book discussion? I’m inclined toward the one titled Insight and Intuition: Two Sides of the Same Coin? Their blurb for the latter:
In the field of intuition it is widely accepted that problem solving proceeds in a more or less graded fashion from problem formulation to problem solution as previously encoded information is activated by clues to coherence. The resulting pattern of activation differentially sensitizes a person to new information that is pertinent for the solution. Eventually, the continuous (and rapid) build-up of coherent information is sufficient to cross a threshold of awareness or noticing. Accordingly, implicitly acquired knowledge and experience play an important role because their content is assumed to be non-consciously and gradually activated in memory from clues in the environment that initiate an automatic spreading of activation. These assumptions are summarized in what has been known as the continuity model of intuition.
On the contrary, the current literature on insight problem solving favors a discontinuity model. Particularly, insight is linked to processes that restructure the mental representation of a problem. It is assumed that prior knowledge and inappropriate assumptions result in self-imposed constraints that establish a biased representation of the problem and thus prevent a solution. Consequently, a discontinuity model suggests the first intuitive apprehension of the problem to lead to an impasse and has to be overcome by relaxing these constraints to find a solution.
Until now, there has neither been theoretical discussion nor empirical investigation on the continuity/discontinuity distinction. Our open research questions include the following:
1. Are continuity/discontinuity different sides of the same coin distinguishing different stages within a continuous solution process, or do they stand for mutual exclusive processes?
2. If intuition is seen as “coherence building mechanism”, is it conceivable to describe the different stages within insight problem solving as coherence changing processes?
3. What are the underlying neuro-cognitive mechanisms that allow the search for coherence, respectively the change of coherence (representational change)? Both processes might go beyond a simple spreading activation account.
4. How does re-combination and the generation of new and novel solutions fit into the intuitive framework?
5. Could the application of Darwinian principles help to inform us about the underlying principles of both?
I’ve found some thought-provoking answers on the Q&A social media site, Quora. Follow the link to a perceptive and helpful answer to, “Can a person be able to objectively identify exactly when and how their thinking processes are being affected by cognitive biases?”
The author provides some practical (if exhausting) recommendations that, if even partly followed by a third-to-half of people (my guestimate), would possibly collapse the adversarial culture in our country.
In this 20-minute video Jeremy Lent gives a brief introduction into his system of liology, his response to substance dualism. Conventional science maintains this dualism, so it is up to the ecological science of dynamical systems theory to correct it. He finds a precursor of systems science in Chinese Neo-Confucianism, which seems a bit of romantic retro-fitting to me, given their own environmental degradation which he minimalizes in his book The Patterning Instinct. That aside, he’s right about the emerging paradigm of systems science as a necessary metaphoric shift if we are to have any chance of curtailing climate change and implementing a sustainable and humane future.
This very rich, conversational thought piece asks if we, as participant designers within a complex adaptive ecology, can envision and act on a better paradigm than the ones that propel us toward mono-currency and monoculture.
We should learn from our history of applying over-reductionist science to society and try to, as Wiener says, “cease to kiss the whip that lashes us.” While it is one of the key drivers of science—to elegantly explain the complex and reduce confusion to understanding—we must also remember what Albert Einstein said, “Everything should be made as simple as possible, but no simpler.” We need to embrace the unknowability—the irreducibility—of the real world that artists, biologists and those who work in the messy world of liberal arts and humanities are familiar with.
In order to effectively respond to the significant scientific challenges of our times, I believe we must view the world as many interconnected, complex, self-adaptive systems across scales and dimensions that are unknowable and largely inseparable from the observer and the designer. In other words, we are participants in multiple evolutionary systems with different fitness landscapes at different scales, from our microbes to our individual identities to society and our species. Individuals themselves are systems composed of systems of systems, such as the cells in our bodies that behave more like system-level designers than we do.