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14 - Modeling the Dynamics of Socioenvironmental Transitions

from Part II

Published online by Cambridge University Press:  13 December 2019

Sander van der Leeuw
Affiliation:
Arizona State University

Summary

This chapter looks at the emergence of urban societies out of rural villages. In the main part of the chapter, this is described in terms of interactions between slower environmental dynamics and faster societal ones. Whereas at the start of this evolution the slower environmental dynamics dominate, towards the end more rapid societal ones control the overall system. In an appendix, this is translated into a mathematical model.

Type
Chapter
Information
Social Sustainability, Past and Future
Undoing Unintended Consequences for the Earth's Survival
, pp. 263 - 284
Publisher: Cambridge University Press
Print publication year: 2020

Models are opinions embedded in mathematics.”

(O’Neill Reference O’Neill2016: Weapons of Math Destruction)

Introduction

In Chapter 11 I presented the qualities and limitations of information processing in various kinds of societal configurations under, respectively, universal control, partial control, and no control, and used a very simple percolation model to summarize the overall evolution of societal systems as a spreading activation net. In the second part of that chapter I discussed various aspects of heterarchical systems and the ways in which hierarchical and distributed information processing networks interact. It concluded with an argument to the effect that in such heterarchical systems, diversification of activities contributes substantially to the stability of the system.

This chapter is devoted to the dynamics and processes that occur between rural and urban contexts, engendering the transitions between these system states. The increasing connectivity that involves more and more people in the spreading activation net has major consequences for the structure of the information-processing network involved, and we need to look at them. That argument will be based on a complex systems model applied to the dynamics of information processing. Although this chapter is therefore based on a rather technical construct formulated in mathematical terms, I will initially present the argument as far as possible in non-technical terms. To demonstrate the potential and the relevance of the modeling approach, for readers who might be interested in some of the details, I will restate important elements of the argument in mathematical terms in Appendix B. Those who are not interested in this aspect can follow the overall reasoning of the book without interruption.

Second-Order Dynamics

To begin, we can gain a glimpse of the complex dynamics involved in the emergence of urbanism by identifying the long-term change in change dynamics (what I have called the second-order change dynamic) occurring in that process. This can be done by looking at the rhythms of the various processes that are involved. Whatever the societal form of organization, the human and environmental dynamics in it are interlocked in mutually interacting ways.

In rural situations, the environmental dynamic is the more complex and multilayered of the two, and is thus the slower one to change. The human dynamic, on the other hand, consists of relatively few superimposed rhythms and can change relatively quickly because people can learn. As a result, a faster human dynamic is essentially locked onto a slower environmental (natural) dynamic: humans adapt to the environment, and because the environment is slow to change the combined socioenvironmental system is rather stable.

In urban situations the two kinds of dynamic reverse their rhythms: the societal dynamic becomes more and more complex, and therefore more and more difficult and slow to change, whereas the environmental dynamic, in so far as it directly relates to the societal system, is simplified because humans have locally reduced the environmental complexity and diversity of their environment. The environment can now be adapted according to the needs of the society. But as the more rapid dynamic has now become the dominant one, the socioenvironmental system as a whole has become less stable. As Naveh and Lieberman (Reference Naveh and Lieberman1984) put it, “the environment has become disturbance-dependent [on society].”

The above reversal is the fundamental one that has brought our societies to their current, unsustainable, situation, and it draws our attention to the fact that the temporal dimensions of the rhythms constituting socioenvironmental interaction are crucial in the coevolutionary transitions we are discussing here. I will come back to these later in this chapter in the form of models that show how these temporal differences affect urban–rural interaction.

Mobile and Early Sedentary Societies

Looking now at the first major organizational transition of society, that from mobile gatherer-hunter-fisher societies to sedentary ones (whether based on stable, naturally available resources such as salmon in the Pacific Northwest of the USA, or based on cultivation such as early farming communities in the Near East, East Asia, and the Valley of Mexico), from the perspective we are developing here we must emphasize a difference that has of course been noted but in my opinion not sufficiently emphasized: the change in the way resources are used. Mobile gatherer-hunter-fisher societies collected what nature had to offer – they had a multi-resource subsistence strategy in which they were wholly dependent on the rhythms of nature, and their only way to adapt to challenges was to move to other places with different natural rhythms. They harvested, but did not in any way invest in, their environment. Over the lifespan of individual gatherer-hunter (mobile) groups, once they had mastered sufficient knowledge of the dynamics of their environment they dealt effectively with change at daily and seasonal temporal scales by moving around from resource to resource. But they probably experienced very variable foraging success, and thus at that scale they experienced high levels of uncertainty, but hardly any risks because they had not substantially invested in the environment.

Sedentary societies, on the other hand, developed a reciprocal, interactive, relationship with their environment in which they invested in the latter by clearing spaces, working the soil, sowing, and waiting to harvest. In the process, they reduced the range of resources exploited by focusing much effort on one or more specific ones. They tried to some – very limited – extent to control some aspects of their environment, and their investment carried some risk with it. This was clearly a dynamic in which humans engaged with their environment, but remained essentially beholden to many of the vagaries of the latter, in the form of climate, soil, vegetation, etc. Herding societies also developed an interactive relationship with their environment, managing the natural dynamics of herd reproduction yet (as far as we know) not investing in a particular place, instead following the environmental rhythms of herds and their resources.

Though the information processing in all these cases was essentially under universal control (hunter-gatherer-fisher societies, early agricultural village societies, and herding societies were and are mostly egalitarian), the transition was the beginning of a shift from societies dominated by natural and slow (environmental) rhythms to environments that are being modified by more rapid human societal rhythms. Initially, because human groups were small and their technologies relatively unsophisticated, the human impact on these natural rhythms was limited, and the complex environmental dynamics ensured long-term overall stability of this mode of social organization and information processing.

But once human dynamic rhythms were introduced into the system alongside environmental ones, because people could adapt more quickly the former rhythms grew in importance in step with the growth of the population involved and the consequent growth in complexity and technological capability of societal systems. Ultimately, they took over so much of the Earth system that we now speak of the Anthropocene as the period in the Earth’s history in which humans control (most of) the overall socioenvironmental dynamic on Earth. In the following sections, I will roughly outline how that process followed its course, ultimately leading to the rapid expansion of urban societies that we have seen over the last 150 years.

The Emergence of Hierarchies

How did hierarchies emerge in such societies? An example that I observed in Wiobo village in the Eastern Highlands of Papua New Guinea in 1990 can serve as an illustration. This is a highly isolated area, one of the last areas of Papua New Guinea to be opened up to western observation, this taking place in the 1950s. The society is a horticultural one, in which subsistence is provided locally by exploiting small gardens in which food is grown. When a dwelling for a new couple was being collectively built, a large part of the village came together around a meal prepared in a Polynesian oven. Suddenly an argument broke out between several males, concerning responsibility for a particular task in the village: keeping the landing strip alongside it in a serviceable state (cutting the grass, etc.). After a while, in which different contenders offered different solutions to the challenge, a consensus emerged that one person’s suggestion was the best one, and he was elected to be what one could call the keeper of the landing strip.

From an information processing perspective, what was happening cut two ways. On the one hand this process selected a particular channel that favored a specific set of signals over many others referring to the same topic, relegating the others to the status of noise, and on the other hand the group created a degree of vertical integration by according one person control over a specific part of the information flow in the society, and thereby according that person a degree of responsibility and prestige, as well as the capacity to mobilize others for the task concerned. Both aspects of this action clearly rendered the fulfilling of this specific task more efficient by aligning the information processing of the people involved in it.

By thus “electing” candidates who offered what were considered to be the best solutions to challenges faced by the group, a group could create a number of domain-specific (short) hierarchies that improved the group’s information processing substantively. Ultimately, of course, coordination between a growing number of such hierarchies, and thus between a number of job holders, would be necessary and would in all probability lead to a kind of coordinator function for which another individual was chosen. It is important to note that in the early stages of this development, these responsibilities were assigned ad hominem, were not heritable, and could also be revoked during a person’s tenure.

The First Bifurcation

The next transition is one that sees the expansion of these small, sedentary (or herding) groups. They are still dependent on locally available energy and resources, and their information processing networks are hierarchical within the community. These hierarchies may at this point become more stable, giving rise to so-called great men and big men positions (Godelier Reference Godelier1982; van der Leeuw Reference van der Leeuw, van Bakel, Hagesteijn and van de Velde1986) that ultimately may even become heritable. As the groups grow, the partial control of the different functional hierarchical information-processing networks creates inhomogeneities in the information pool. Those in control of a hierarchy process more information than others, which makes them leaders, but also leads to misunderstandings and potentially to conflicts. One way to deal with this is for the group to institute occasional or periodic group meetings to reduce communication distances between all members, and thus serve to rehomogenize the information pool and readjust it to changing circumstances, whether caused in the environment by human exploitation or by externally triggered fluctuations in the social or natural environment. One would expect these resets to occur more frequently as maladaptations between the state of the environment and the state of environmental information processing grow.

From a dynamic model perspective on information processing, one could characterize such systems as oscillating around a fixed-point attractor. Stability based on a fully shared information pool dominates. But the societal system is subject to an oscillation between an accelerating/structuring phase and a decelerating/destructuring phase. In the former, the system is more deterministic, in the latter more stochastic. In more tangible everyday terms, people alternate between strengthening their system around a core set of ideas, customs, and institutions, and the opposite, widening the range of ideas and behaviors.

As contacts intensify, non-hierarchical distributed connections within groups are strengthened by family relationships maintained through networks of marriages. Owing to the combination of hierarchical and distributed information processing networks, information spreads very quickly, correcting imbalances in the information pool. But these societies are still slow to adapt as they are heavily constrained by slow environmental dynamic rhythms and have very few decision-makers (of limited diversity).

The Second Bifurcation

As societies grow in size, the hierarchical aspect of information processing also grows in depth and size, involving more and more people. As we saw at the end of Chapter 11, it also becomes more and more specific by losing a number of its branches as it focuses more sharply on tasks at hand, and thus becomes less adaptive. The distributed information processing network in the society, being more adaptive, gains in importance. We can thus imagine that at some point there could emerge a second bifurcation between hierarchical and distributed communication modes, in which they are separated spatially. This could for example occur when in some locations a faster adaptation of the socioenvironmental system is required than in others because the system is more dependent on the human dynamic than on the environmental one, whereas in other locations it is the reverse. Poorer environments, or environments that are more likely to be handicapped by certain environmental dynamics (climate, water, erosion) might trigger such more rapid adaptations, and favor distributed information processing.

Initially, this bifurcation might simply be enacted by certain people in a settlement who begin to specialize in communicating with others, for example in terms of exchanges or even trade, while others continue to be focused on immediate subsistence activities and to be linked to a hierarchical information-processing system. This would be one way to look at the prestige goods economy (e.g., Frankenstein & Rowlands Reference Frankenstein and Rowlands1978), which is in some places contemporaneous with emergent proto-urban centers and locally generates a settlement size hierarchy. Physically, this requires a point of connection between the distributed communications network and the hierarchical one. Because it is the point of introduction of new ideas and values, it quickly becomes the apex of the local hierarchy.

Over time, as the community of people linked into a distributed communication network grows, this may lead to the emergence of specialized periodic trading centers, such as the early medieval Northern European trading emporia, examples being Hedeby and Dorestat. These were located in geographical locations that were particularly suitable for communication, such as along rivers (at fords or branching points), along the coast, or at points where other conditions favor them.

In modeling terms, this is an information-processing system in which more permanent and spatially wider-spread communication corridors based on distributed information processing emerge between spatially separated hierarchical islands, structured as stochastic information webs wherever structured and unstructured oscillations form a pattern of interferences (Chernikov et al. Reference Chernikov, Sagdeev, Usikov, Zakharov and Zaslavsky1987). Qualitatively, these webs involve information brokers between different hierarchically organized villages, such as ambulant tradesmen and others who are independent from the village hierarchies. In pre-classical Greece, one could also interpret priests in liminally placed sanctuaries, such as Delphi, as examples of such brokers. Currently, one finds them in very many places in the developing world.

The Third Bifurcation

The third bifurcation could be called preurban smouldering – a situation in which, at a regional level, limited-term and more complex structuring occurs here and there, after a while petering out, then rekindling elsewhere. The existence of long-distance distributed processing corridors that are relatively stable over a period, and sufficiently frequently used to have a sufficient channel capacity (bandwidth) to maintain the information flows involved, permit certain groups of hierarchically organized societies to integrate into a larger system. This has a locally destabilizing effect because the symbiotic, hierarchical systems’ connectivity is enhanced through spatial extension (see White Reference White, Lane, Pumain, van der Leeuw and West2009). Dealing with this requires increased reliance on distributed information processing and energy obtained from elsewhere, and has probably led to instabilities in these systems, as I argue in Appendix B by constructing a set of dynamic models of these interactions.

Such a fluid and essentially discontinuous process of structuring and restructuring is imperfectly captured by any single spatial, all-encompassing, geometric structure as an explanation of societal organization. For example, under the type of dynamic evolution postulated here, territoriality and the societal boundedness of societies must have been subject to constant redefinition; a political tug of war between competing, adjacent polities for control and supremacy in exchange relations, both within the transportation and communication network itself and outside it. Under such circumstances, preeminent societal control by any single social group is unlikely for other than short periods.

Such essentially unstable systems were not confined to the European La Tène period, on which our models have been based. In Europe we see them again after the collapse of the Roman Empire, in the seventh to eleventh centuries CE. But I would surmise that we see them also in the Preclassic Maya (900 BCE–300 CE) area before the hegemony of Tikal and Caracol, in certain phases of Chinese history (such as the period of the warring states, 475–221 BCE), in the Uruk phase in the Near East (c. 4000–3100 BCE), and elsewhere.

An important aspect of the emergence of these long-distance distributed communications is that they infuse local hierarchical systems with new values (materials, objects, technologies, ideas). This enables them to extend the set of values of the community involved, and over time it enables the alignment of more and more people in different local systems into one value system.1 I return to this aspect in Chapter 16.

The Fourth Bifurcation

In many parts of the world, the first real towns emerge as a network of small, more or less equivalent, city states in what has been called peer–polity interaction, invoking a kind of mutual bootstrapping (Renfrew & Cherry Reference Renfrew and Cherry1987, title). This phenomenon resembles in many ways that of convection and might be modeled as an example of Bénard-like convection (see Chapter 7; Nicolis & Prigogine Reference Nicolis and Prigogine1977; Prigogine & Stengers Reference Prigogine and Stengers1984). The peer polity/convection cell model is essentially one of increasing information flow in a local circuit, which has a differentiating and structuring effect on the inhabitants of the cell itself: center–periphery, town–hinterland. The regional and supraregional exchange that takes place is initially effectively stochastic (down the line).

As these cells grow, the cores come to interact more closely and boundary phenomena take over: neighboring cores begin to exchange information on a regular basis, i.e., no longer in a stochastic manner but directionally. In this intermediate phase, long distance exchange becomes hybrid, i.e., between cells it moves stochastically, but once it hits the periphery of a unit, it cannot but go to its center. This entails a major reduction in stochasticity of communication as well as the beginnings of opening up the cells. Once the flows are directional, the cells can become dependent on them; the time delays in communication are drastically reduced, and this enables them to play to each other’s needs.

As more and more individuals participate in the (now) heterarchical channels, long-distance communication becomes more and more directional, meets more and more needs, and eventually connects very large spaces to such a degree that the centers become dependent on their trade networks. Importantly, the way the individual centers developed is highly dependent on minimal differences in initial conditions and on the paths they took. Guérin-Pace (Reference Guérin-Pace1993) sketches this highly variable dynamic at the regional level within a full-grown urban structure. The crucial variable in the transition seems to be the degree of long-distance complementarity.

Eventually, the growth of these large heterarchical systems threatens stability and increases sluggishness in adapting to change. Some degree of separation of interactive spheres may be a response (city states?) as well as internal hierarchization (for example in the early development of Greek city states, in which oscillations took place between tyranny and democracy). The towns eventually become permanent heterarchical systems.

Summary and Conclusion

In this chapter I have tried to outline a trajectory from early egalitarian societies to heterarchical urban ones. In doing so, I have used a conceptual model to link known observations about intermediate stages of this development by assuming several important bifurcation points (transitions, tipping points) between the different states of the information processing system. But I have not discussed the last stage of this evolution, which has led to the current challenging sustainability predicament. That is dealt with in Chapters 1518. Altogether, it needs to be emphasized that this has no other purpose than to propose a different way to view social evolution from an a priori perspective rather than the existing a posteriori one. Whether such an approach will in the long run help us deal with a number of the issues involved remains to be seen.

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