The core observations this article is based on were written during and immediately after the CES2017, but the article has been written almost a year later after a lot of thinking, some reading of literature and producing a simulation model on object based learning. Many of the points I make might be due to just this one particular conference, with its accidental composition, the sessions I went to etc, and reflect my ignorance rather than the state of the field.
I consider myself a complex systems engineer / industrial ecologist that has a fair but high-level understanding of social sciences, policy analysis and management, without being an expert in them. The work I do with decision makers at companies or governments are directly aimed at influencing their actions towards a more sophisticated understanding of the systems they are part of and responsible for. Ultimately, the decisions these people take lead to multi-billion investments in industrial facilities, infrastructure systems and market & governance designs. My goal is to decrease the unsustainability of systems they are deciding upon, and help them to avoid the "obviously and no-so-obviously stupid" policies, decisions and actions, based on insights from complex adaptive systems, evolutionary thinking and based on models and simulation.
During my interactions with these stakeholders, I see all the time that group-based social learning takes place during meeting, workshops and daily practice. It is shaped and influenced by the physical state of the environment and the involved assets, various external (geo)political, economic and institutional developments, by power structures within and between companies, and even the gender composition of teams involved in the discussion. While I do not have a deep grasp of CE subtleties, I do observe many issues that I learned about from the CES 2017 and reading afterwards.
I will attempt to structure my reflection and thoughts about opportunities this along the lines of :
I hope this is useful to those active in the CE field, by providing a necessarily subjective and partial perspective from the outside and that these ideas will promote interdisciplinary collaboration.
In general, I had a great and mind-blowing time at the CES 2017. Not just the organization, venue and the city. The contents and the community were great, witnessed by the fact that I submitted 3 abstracts, one of which made it, and am coming back for more!
I found a open minded, relatively diverse and very friendly community, fantastic gender balance, awareness of other cultures, background and needs of less privileged people. This really puts all other communities I take part in to shame.
Empirical and quantitative
The amount of experimental and empirical work going on is impressive. I am thoroughly cured of my "bunch of social scientists talking to each other" prejudice. The SESHAT database, language phylogenies and databases are really impressive both technically as in scope and ambition, as are experiments in how learning happens in human and animal groups.
We seem to have a shared understanding of the roles of institutions and norms, even though we treat them a bit more mechanistically. This makes me very happy, as it confirms that we are really onto something with MAIA and the way we systematically model institutions.
Quite some models and simulations, Agent Based, statistical and various new modeling techniques, as well as lots of geeky statistical analysis.
Presence of a clear call to action, through The Evolution institute and the shared sense of urgency to fix the planet. This greatly resonates with me, and is in line with developments in fields like Industrial Ecology, Transition Management and many others
A number of things really surprised me, and probably says more about my expectations than the field, but anyway.
What is learning, and what is learned?
The most surprising observation is the degree to which it seems to be uncertainty and inconsistency about what and how is learned, given that various forms of learning seems to be the key mechanism behind Cultural Evolution. After some reading, it is clear to me just how complex this issues is, and that there is indeed a wide array of understandings on what and how learning happens. Yet, as an engineer, I am used to more precise and delineated problem definitions.
No memetics, Universal Darwinism
I came to CES looking for a informational basis of socio-technical evolution, but i found mainly talk about learning. Coming from an engineering background, I have identified memetics / generalized Darwinism / Universal Darwinism as relevant fields and topics, but have not found a singe talk about it. Is is simply not a thing, is it completely wrong or is it so fundamental/basic that nobody bothers talking about it? From informal conversations I understood that these views are considered obsolete and wrong. This is a new thing for me and made me reconsider how I approach modelling the transitions / evolution in and of industrial systems
No phylogeny of technology
It seems that nobody has created a proper phylogeny of technology, which do exist for languages and linguistic evolution. I believe such a phylogeny would greatly aid the discussions on how things are learned over time and between cultures. In one talk someone presented a puzzle of how south asian island communities adopted a particular roofing material in a particular order (if I recall correctly), and to me it seemed obvious that it had to do with the order at which certain technologies were invented, as some technology has to be perfected, before the next level of tech becomes possible. Something along the lines of you can not really use materials that have to be nailed into place, if you are unable to produce metal nails. I do understand that some of these material, technological dependencies are obvious to an engineer, but are unknown to a linguist or anthropologist.
KISS focus in modelling
Modelling that I have seen is focused on "simple", elegant, minimal (often statistical) or mathematical models. Most, if not all what I had seen follows the KISS (Keep it simple) approach, rather the KIDS (keep it descriptive) approaches that is default in my world. Somehow, give the tremendous richness of empirical evidence, narratives and mental models of how things work, I was expecting all of those details in simulations. Whether this comes from potentially limited software engineering skills, or from a methodological focus to reduce things to the essential core, I can not tell.
The "I never knew it existed!"
I am very happy to have learned about Multi Level Group Selection, and the degree how those ideas are developed. For the first time I learned that "Industrial/Organisational anthropology" is a thing and that is exceptionally useful for my modelling efforts. Talking to people involved with it further reinforced my belief that this is indeed a very specific skill and mindset and I must involve experts pro-actively.
There are a number of aspects that I do not understand about the field, or at least the conversations that were taking place.
There seems to be a case of wonderfully overlapping vocabularies between the CE and engineering / environmental sciences. It took me almost two days and several conversations to realise what "affordances" were and that "ecology" means the technical /physical context, such as infrastructures, technology, and is not specific to the the bio-geo-chemical environment. I am also sure that a lot of the vocabulary "we" use are equally confusing.
Limited future orientation
All the work seemed to be focused on either the past, or the now, with basically no discussions about possible future directions society might take, the boundary conditions and societal rules of engagement that are likely, desirable or undesirable. Given that a number of prominent researchers loudly and actively advocate rising to the sustainability challenge, this is very surprising.
It seems that forecasting / back-casting thinking, adaptive transition pathways, common in my areas is absent. Is simulating cultural evolution identify potential futures and explore impacts of various interventions a not done thing, or is just nobody doing it yet? I am not sure if my models on futures would be considered CE, as they do not have explicit informational basis for changes (ala memetics) but they do often containing learning and adaptive agents. Would our endogenous emergent policy or endogenous emergent institutional simulations be considered CE? Does it matter?
Where to publish work about modeling, industrial systems, social learning in models and practice, so that I can reach this community? I have seen mentions of evolutionary biology, anthropology, psychology, archeology etc journals, but they seem way too far off this topic.
There is a great opportunity for combining insight from CE and engineering and environmental fields. Technology and the physical domain were not an active part of conversations at CES2017, as far as I could see. Whether this is a problem depends of course entirely of who is looking, and what you want to achieve. In my view, if we want CE to become a practical guideline for "saving the civilization" from the sustainability crisis, we must explicitly consider these. But I can imagine that many scholars in the field may not subscribe to this perception of the field.
As CE is focused on learning, it perceives the world from an informational perspective. Information does not follow conservation laws, as mass and energy does, since it can be created and destructed, copied forever, mutated etc. Explicitly building in the relation between a non-conserving, weakly spatially bound information, and strictly conserving and very local mass and energy in CE models must surely lead to better science in all involved domains.
The physical world, such as ecosystems, resources, etc provide very real limitations to CE. I expected that talks on "material culture" would deal with these things, but that turned out to be yet another vocabulary confusions. Culture, in its broad and narrow sense, directly impacts the physical world through individual and collective consumption. These impacts can be direct and provide fast feedback, such as habitat loss or arable land erosion around a rural community. Sometimes, they take very long time to materialize, as is the case with climate change. But both slow and fast feedback loops affect the speed and direction of CE, but in very different ways, and may be very related to the technical, and physical environmental context.
Furthermore, and probably as a consequence of the previous, it seems that discussions on the role of technology are limited. I have heard talks about technology as informational traits, which I fully agree with, but they seemed to neglect that tech is very often a physical thing, bound my mass, energy, limited by availability of other technology.
Also, I have not seen any talk about industrial systems or infrastructures, while these are essential physical contexts for CE. Technical systems form a coupled fitness landscapes with the rest of the culture, limit to what is can do, and give a lot of path dependency to society.
Finally, as this is my engineering bias speaking, I have seen limited orientation on action, such as actively influencing and shaping CE, focus being on analysis. While this is the classical science vs engineering debate, I do feel that the CE community has tremendous amount of actionable insight to offer.
I believe strongly that in science "one mans garbage is other mans gold" and that the CE community is not completely aware of its potential in application. I believe many of, what would be considered basic or trivial insights would be mind-blowing in other fields. Case in point was a young PhD student talking about her work on how religion influences choice, and demonstrating empirical experimental evidence on how this influence works. She remarked towards the end of her talk "Oh, this is not applied or useful at all, my knowledge is fun but useless" In the meantime, in the back of the room my mind is reeling with possibilities of using these, to me new and incredibly powerful insights, in shaping the evolution of industrial systems during the energy transition.
Things I would love to see at and after CES2018 is the start of a deep conversation between this field, the (systems) engineers and environmental scientists. I believe we engineers are the hands and feet of CE, but are generally oblivious of the evolutionary context and the social systems subtleties. Also, I would like to see a much deeper integration of environmental sciences, linking the social practices, culture etc to the (changes of) environmental impacts/ global cycles. I believe modelling and simulation can act as a bridge. But that will be my next post.
Finally, I would like to acknowledge the insights and advice of Taylor Davis, who has helped me navigate and make sense of a completely new field.
I very much welcome feedback on this piece, and hope to see you all at CES2018!