I am trying to combine the line of logic which I developed in the proof-of-concept for the idea I labelled ‘Energy Ponds’ AKA ‘Project Aqueduct’ with the research on collective intelligence in human societies. I am currently doing serious review of literature as regards the theory of complex systems, as it looks like just next door to my own conceptual framework. The general idea is to use the theory of complex systems – within the general realm of which the theory of cellular automata looks the most promising, for the moment – to simulate the emergence and absorption of a new technology in the social structure.
I started to sketch the big lines of that picture in my last update in French, namely in ‘L’automate cellulaire respectable’. I assume that any new technology burgeons inside something like a social cell, i.e. a group of people connected by common goals and interests, together with some kind of institutional vehicle, e.g. a company, a foundation etc. It is interesting to notice that new technologies develop through the multiplication of such social cells rather than through linear growth of just one cell. Up to a point this is just one cell growing, something like the lone wolf of Netflix in the streaming business, and then ideas start breeding and having babies with other people.
I found an interesting quote in the book which is my roadmap through the theory of complex systems, namely in ‘What Is a Complex System?’ by James Landyman and Caroline Wiesner (Yale University Press 2020, ISBN 978-0-300-25110-4). On page 56 (Kindle Edition), Landyman and Wiesner write something interesting about the collective intelligence in colonies of ants: ‘What determines a colony’s survival is its ability to grow quickly, because individual workers need to bump into other workers often to be stimulated to carry out their tasks, and this will happen only if the colony is large. Army ants, for example, are known for their huge swarm raids in pursuit of prey. With up to 200 000 virtually blind foragers, they form trail systems that are up to 20 metres wide and 100 metres long (Franks et al. 1991). An army of this size harvests prey of 40 grams and more each day. But if a small group of a few hundred ants accidentally gets isolated, it will go round in a circle until the ants die from starvation […]’.
Interesting. Should nascent technologies have an ant-like edge to them, their survival should be linked to them reaching some sort of critical size, which allows the formation of social interactions in the amount which, in turn, an assure proper orientation in all the social cells involved. Well, looks like nascent technologies really are akin to ant colonies because this is exactly what happens. When we want to push a technology from its age of early infancy into the phase of development, a critical size of the social network is required. Customers, investors, creditors, business partners… all that lot is necessary, once again in a threshold amount, to give a new technology the salutary kick in the ass, sending it into the orbit of big business.
I like jumping quickly between ideas and readings, with conceptual coherence being an excuse just as frequently as it is a true guidance, and here comes an article on urban growth, by Yu et al. (2021[1]). The authors develop a model of urban growth, based on the empirical data on two British cities: Oxford and Swindon. The general theoretical idea here is that strictly speaking urban areas are surrounded by places which are sort of in two minds whether they like being city or countryside. These places can be represented as spatial cells, and their local communities are cellular automatons which move cautiously, step by step, into alternative states of being more urban or more rural. Each such i-th cellular automaton displays a transition potential Ni, which is a local balance between the benefits of urban agglomeration Ni(U), as opposed to the benefits Ni(N) of conserving scarce non-urban resources. The story wouldn’t be complete without the shit-happens component Ri of randomness, and the whole story can be summarized as: Ni = Ni(U) – Ni(N) + Ri.
Yu et al. (2021 op. cit.) add an interesting edge to the basic theory of cellular automata, such as presented e.g. in Bandini, Mauri & Serra (2001[2]), namely the component of different spatial scales. A spatial cell in a peri-urban area can be attracted to many spatial aspects of being definitely urban. Those people may consider the possible benefits of sharing the same budget for local schools in a perimeter of 5 kilometres, as well as the possible benefits of connecting to a big hospital 20 km away. Starting from there, it looks a bit gravitational. Each urban cell has a power of attraction for non-urban cells, however that power decays exponentially with physical distance.
I generalize. There are many technologies spreading across the social space, and each of them is like a city. I mean, it does not necessarily have a mayor, but it has dense social interactions inside, and those interactions create something like a gravitational force for external social cells. When a new technology gains new adherents, like new investors, new engineers, new business entities, it becomes sort of seen and known. I see two phases in the development of a nascent technology. Before it gains enough traction in order to exert significant gravitational force on the temporarily non-affiliated social cells, a technology grows through random interactions of the initially involved social cells. If those random interactions exceed a critical threshold, thus if there are enough forager ants in the game, their simple interactions create an emergence, which starts coagulating them into a new industry.
I return to cities and their growth, for a moment. I return to the story which Yu et al. (2021[3]) are telling. In my own story on a similar topic, namely in my draft paper ‘The Puzzle of Urban Density And Energy Consumption’, I noticed an amazing fact: whilst individual cities grow, others decay or even disappear, and the overall surface of urban areas on Earth seems to be amazingly stationary over many decades. It looks as if the total mass, and hence the total gravitational attraction of all the cities on Earth was a constant over at least one human generation (20 – 25 years). Is it the same with technologies? I mean, is there some sort of constant total mass that all technologies on Earth have, within the lifespan of one human generation, and there are just specific technologies getting sucked into that mass whilst others drop out and become moons (i.e. cold, dry places with not much to do and hardly any air to breathe).
What if a new technology spreads like Tik-Tok, i.e. like a wildfire? There is science for everything, and there is some science about fires in peri-urban areas as well. That science is based on the same theory of cellular automata. Jiang et al. (2021[4]) present a model, where territories prone to wildfires are mapped into grids of square cells. Each cell presents a potential to catch fire, through its local properties: vegetation, landscape, local climate. The spread of a wildfire from a given cell R0 is always based on the properties of the cells surrounding the fire.
Cirillo, Nardi & Spitoni (2021[5]) present an interesting mathematical study of what happens when, in a population of cellular automata, each local automaton updates itself into a state which is a function of the preceding state in the same cell, as well as of the preceding states in the two neighbouring cells. It means, among other things, that if we add the dimension of time to any finite space Zd where cellular automata dwell, the immediately future state of a cell is a component of the available neighbourhood for the present state of that cell. Cirillo, Nardi & Spitoni (2021) demonstrate, as well, that if we know the number and the characteristics of the possible states which one cellular automaton can take, like (-1, 0, 1), we can compute the total number of states that automaton can take in a finite number of moves. If we make many such cellular automatons move in the same space Zd , a probabilistic chain of complex states emerge.
As I wrote in ‘L’automate cellulaire respectable’, I see a social cell built around a new technology, e.g. ‘Energy Ponds’, moving, in the first place, along two completely clear dimensions: physical size of installations and financial size of the balance sheet. Movements along these two axes are subject to the influence happening along some foggy, unclear dimensions connected to preferences and behaviour: expected return on investment, expected future value of the firm, risk aversion as opposed to risk affinity etc. That makes me think, somehow, about a theory next door to that of cellular automata, namely the theory of swarms. This is a theory which explains complex changes in complex systems through changes in strength of correlation between individual movements. According to the swarm theory, a complex set which behaves like a swarm can adapt to external stressors by making the moves of individual members more or less correlated with each other. A swarm in routine action has its members couple their individual behaviour rigidly, like marching in step. A swarm alerted by a new stressor can loosen it a little, and allow individual members some play in their behaviour, like ‘If I do A, you do B or C or D, anyway one out of these three’. A swarm in mayhem loses it completely and there is no behavioural coupling whatsoever between members.
When it comes to the development and societal absorption of a new technology, the central idea behind the swarm-theoretic approach is that in order to do something new, the social swarm has to shake it off a bit. Social entities need to loosen their mutual behavioural coupling so as to allow some of them to do something else than just ritually respond to the behaviour of others. I found an article which I can use to transition nicely from the theory of cellular automata to the swarm theory: Puzicha & Buchholz (2021[6]). The paper is essentially applicable to the behaviour of robots, yet it is about a swarm of 60 distributed autonomous mobile robots which need to coordinate through a communication network with low reliability and restricted capacity. In other words, sometimes those robots can communicate with each other, and sometimes they don’t. When some robots out of the 60 are having a chat, they can jam the restricted capacity of the network and thus bar the remaining robots from communicating. Incidentally, this is how innovative industries work. When a few companies, let’s say the calibre of unicorns, are developing a new technology. They absorb the attention of investors, governments, potential business partners and potential employees. They jam the restricted field of attention available in the markets of, respectively, labour and capital.
Another paper from the same symposium ‘Intelligent Systems’, namely Serov, Voronov & Kozlov (2021[7]), leads in a slightly different direction. Whilst directly derived from the functioning of communication systems, mostly the satellite-based ones, the paper suggests a path of learning in a network, where the capacity for communication is restricted, and the baseline method of balancing the whole thing is so burdensome for the network that it jams communication even further. You can compare it to a group of people who are all so vocal about the best way to allow each other to speak that they have no time and energy left for speaking their mind and listening to others. I have found another paper, which is closer to explaining the behaviour of those individual agents when they coordinate just sort of. It is Gupta & Srivastava (2020[8]), who compare two versions of swarm intelligence: particle swarm and ant colony. The former (particle swarm) generalises a problem applicable to birds. Simple, isn’t it? A group of birds will randomly search for food. Birds don’t know where exactly the food is, so they follow the bird which is nearest to the food. The latter emulates the use of pheromones in a colony of ants. Ants selectively spread pheromones as they move around, and they find the right way of moving by following earlier deposits of pheromones. As many ants walk many times a given path, the residual pheromones densify and become even more attractive. Ants find the optimal path by following maximum pheromone deposition.
Gupta & Srivastava (2020) demonstrate that the model of ant colony, thus systems endowed with a medium of communication which acts by simple concentration in space and time are more efficient for quick optimization than the bird-particle model, based solely on observing each other’s moves. From my point of view, i.e. from that of new technologies, those results reach deeper than it could seem at the first sight. Financial capital is like a pheromone. One investor-ant drops some financial deeds at a project, and it can hopefully attract further deposits of capital etc. Still, ant colonies need to reach a critical size in order for that whole pheromone business to work. There needs to be a sufficient number of ants per unit of available space, in order to create those pheromonal paths. Below the critical size, no path becomes salient enough to create coordination and ants starve to death fault of communicating efficiently. Incidentally, the same is true for capital markets. Some 11 years ago, right after the global financial crisis, a fashion came to create small, relatively informal stock markets, called ‘alternative capital markets’. Some of them were created by the operators of big stock markets (e.g. the AIM market organized by the London Stock Exchange), some others were completely independent ventures. Now, a decade after that fashion exploded, the conclusion is similar to ant colonies: fault of reaching a critical size, those alternative capital markets just don’t work as smoothly as the big ones.
All that science I have quoted makes my mind wander, and it starts walking down the path of hilarious and absurd. I return, just for a moment, to another book: ‘1177 B.C. THE YEAR CIVILIZATION COLLAPSED. REVISED AND UPDATED’ by Eric H. Cline (Turning Points in Ancient History, Princeton University Press, 2021, ISBN 9780691208022). The book gives in-depth an account of the painful, catastrophic end of a whole civilisation, namely that of the Late Bronze Age, in the Mediterranean and the Levant. The interesting thing is that we know that whole network of empires – Egypt, Hittites, Mycenae, Ugarit and whatnot – collapsed at approximately the same moment, around 1200 – 1150 B.C., we know they collapsed violently, and yet we don’t know exactly how they collapsed.
Alternative history comes to my mind. I imagine the transition from Bronze Age to the Iron Age similarly to what we do presently. The pharaoh-queen VanhderLeyenh comes up with the idea of iron. Well, she doesn’t, someone she pays does. The idea is so seducing that she comes, by herself this time, with another one, namely tax on bronze. ‘C’mon, Mr Brurumph, don’t tell me you can’t transition to iron within the next year. How many appliances in bronze do you have? Five? A shovel, two swords, and two knives. Yes, we checked. What about your rights? We are going through a deep technological change, Mr Brurumph, this is not a moment to talk about rights. Anyway, this is not even the new era yet, and there is no such thing as individual rights. So, Mr Brurumph, a one-year notice for passing from bronze to iron is more than enough. Later, you pay the bronze tax on each bronze appliance we find. Still, there is a workaround. If you officially identify as a non-Bronze person, and you put the corresponding sign over your door, you have a century-long prolongation on that tax’.
Mr Brurumph gets pissed off. Others do too. They feel lost in a hostile social environment. They start figuring s**t out, starting from the first principles of their logic. They become cellular automata. They focus on nailing down the next immediate move to make. Errors are costly. Swarm behaviour forms. Fights break out. Cities get destroyed. Not being liable to pay the tax on bronze becomes a thing. It gets support and gravitational attraction. It becomes tempting to join the wandering hordes of ‘Tax Free People’ who just don’t care and go. The whole idea of iron gets postponed like by three centuries.
[1] Yu, J., Hagen-Zanker, A., Santitissadeekorn, N., & Hughes, S. (2021). Calibration of cellular automata urban growth models from urban genesis onwards-a novel application of Markov chain Monte Carlo approximate Bayesian computation. Computers, environment and urban systems, 90, 101689. https://doi.org/10.1016/j.compenvurbsys.2021.101689
[2] Bandini, S., Mauri, G., & Serra, R. (2001). Cellular automata: From a theoretical parallel computational model to its application to complex systems. Parallel Computing, 27(5), 539-553. https://doi.org/10.1016/S0167-8191(00)00076-4
[3] Yu, J., Hagen-Zanker, A., Santitissadeekorn, N., & Hughes, S. (2021). Calibration of cellular automata urban growth models from urban genesis onwards-a novel application of Markov chain Monte Carlo approximate Bayesian computation. Computers, environment and urban systems, 90, 101689. https://doi.org/10.1016/j.compenvurbsys.2021.101689
[4] Jiang, W., Wang, F., Fang, L., Zheng, X., Qiao, X., Li, Z., & Meng, Q. (2021). Modelling of wildland-urban interface fire spread with the heterogeneous cellular automata model. Environmental Modelling & Software, 135, 104895. https://doi.org/10.1016/j.envsoft.2020.104895
[5] Cirillo, E. N., Nardi, F. R., & Spitoni, C. (2021). Phase transitions in random mixtures of elementary cellular automata. Physica A: Statistical Mechanics and its Applications, 573, 125942. https://doi.org/10.1016/j.physa.2021.125942
[6] Puzicha, A., & Buchholz, P. (2021). Decentralized model predictive control for autonomous robot swarms with restricted communication skills in unknown environments. Procedia Computer Science, 186, 555-562. https://doi.org/10.1016/j.procs.2021.04.176
[7] Serov, V. A., Voronov, E. M., & Kozlov, D. A. (2021). A neuro-evolutionary synthesis of coordinated stable-effective compromises in hierarchical systems under conflict and uncertainty. Procedia Computer Science, 186, 257-268. https://doi.org/10.1016/j.procs.2021.04.145
[8] Gupta, A., & Srivastava, S. (2020). Comparative analysis of ant colony and particle swarm optimization algorithms for distance optimization. Procedia Computer Science, 173, 245-253. https://doi.org/10.1016/j.procs.2020.06.029
