Understanding and Predicting Societal Collapse

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* Article/Chapter: Theories and Models: Understanding and Predicting Societal Collapse. by Sabin Roman. In: The Era of Global Risk: An Introduction to Existential Risk Studies. Edited by SJ Beard, Martin Rees, Catherine Richards, and Clarissa Rios Rojas. CSER,

URL = https://books.openbookpublishers.com/10.11647/obp.0336.pdf


"The second chapter, ‘Theories and Models: Understanding and Predicting Societal Collapse’ by Sabin Roman, looks at what those who study global risks can learn from efforts to understand and model the process of societal and ecological collapse, which is a significant global risk in itself and also an example of the kind of extreme, non-linear, and potentially dangerous transition that is associated with extreme global risks more generally. Surveying the extensive and interdisciplinary literature on this subject, in some cases extending back several centuries, the chapter illustrates the ways in which many qualitative and quantitative modelling approaches can be applied to shed insight on the causes and nature of such collapses. Some of these approaches are primarily concerned with the exogenous causes of collapse, such as conflict or environmental catastrophes. However, other approaches view collapse as endogenous to societies themselves, originating in economic inequality or shifting societal dynamics, and it is argued that even in the presence of external causes we cannot fully understand collapse unless we take account of these endogenous effects that ultimately make societies vulnerable in the first place. Perhaps most promisingly, the chapter indicates how we can create constructive new approaches based around modelling a variety of feedback loops between different elements, and how these can be adapted to generate and test new hypotheses about social and ecological collapse (either past or future)."



Sabin Roman:

"We have reviewed the main theories put forward to explain how societies collapse, starting with qualitative theories with a focus on single exogenous factors, such as resource depletion, competition and conflict with other societies, catastrophes, and contingent events. Then, we considered theories with an endogenous explanation of collapse, which focus predominantly on class structures and elite mismanagement. In this regard, the theories of Khaldun, Gibbon, and Toynbee were highlighted as notable examples throughout the history of the field. The theories considered up to this point posit that collapse occurs in conditions that have been recurrent throughout the history of societies. Hence, taking any one given event (e.g. resource shortage, war, rebellion, etc.) as being the cause of the collapse means ignoring similar historical precedents and not accounting for how the society became susceptible to collapse. Then, we considered theories that can be formulated as feedback mechanisms operating within societies. Historically, Malthus’ theory provides an early example, upon which several refinements were made, notably by Boserup and Tainter, and later in structural demographic theory. These later theories provide frameworks compatible with the nature of a complex system (such as a society) and do not have the epistemological problems of previously mentioned theories, as they provide a mechanism and causal pathway for increased vulnerability to collapse. Furthermore, they provide a stepping stone to mathematical modelling of collapse, which moves beyond qualitative considerations and gives quantitative insight into the phenomenon. Quantitative models of societal dynamics and collapse broadly fall into the following categories: agent-based models, integrated world models, and low-dimensional dynamical models. Agent-based models offer a bottom-up approach to understanding a system’s structure and behaviour. The insight these models can provide is how basic building blocks of the system in question behave. The difficulty lies in matching underlying agent behaviour with large-scale features with the data, and discriminating between alternative assumptions regarding the agent’s characteristics. Integrated world models have a high degree of complexity (many variables, equations, mechanisms, and sub-models) that hinder understanding and communication. Nevertheless, due to their complexity and comprehensiveness they are also the most realistic models, and are used in policy-making. Low-dimensional dynamical systems models have been widely used to capture societal mechanisms from a top-down perspective.94 The different schools of thought on the methodology of developing these models can be divided into either economically or ecologically focused; each one has different emphases and strengths. The advantage of lowdimensional models is that they can capture a specific idea or theory on how societal evolution takes place. This, plus their smaller number of variables, allows for comparison with the archaeological record. The main shortcoming of these models is the potential over-simplifications they make in describing the systems under study. However, searching for models at a mesoscopic scale of intermediate complexity can be advantageous, as sufficient societal elements can be accounted for to reproduce known data, but the model can also be kept manageable, so that it can be understood analytically and communicated more easily. Still, the topic of societal collapse has generally been approached from a mostly qualitative perspective, which presents arguments in a narrative form without a mathematical understanding of the underlying dynamics. In some cases, there even appears to be an aversion to quantitative models.95 Tainter argues that quantitative models are inadequate to capture the full scope of societal complexity and the underlying drivers of its evolution. Turchin disagrees, and argues that “a discipline usually matures only after it has developed mathematical theory” especially if the discipline deals with dynamical quantities.

Informal verbal models are appropriate if the underlying mechanisms are sufficiently simple (acting in a linear and additive manner), but generally misleading if the system exhibits non-linear feedback and time lags Casting hypotheses into quantitative models can help in illuminating uncertainties regarding the system, expose prevailing wisdom as incompatible with available data, guide data collection, or uncover new questions. Mathematical models can thus be “indispensable when we wish to rigorously connect the set of assumptions about the system to predictions about its dynamics behavior”. However, as with Lindy’s Law and the doomsday argument, there remains the general difficulty (as with any mathematical model) of choosing and calibrating its parameters. In addition, given the overall complexity of any given society, any proposed qualitative or quantitative description (feedback mechanism, models, etc.) can only aim to provide a partial description of societal dynamics and collapse. Establishing cause-effect relationships requires both empirical support and validated modelling, and remains subject to much debate. Applying this methodology to modern society comes with additional difficulties due to the system’s highly interconnected nature, but progress is being made with regard to threats such as climate change. The use of quantitative models to test the validity of hypotheses has not been common in social sciences historically, and a new field called ‘cliodynamics’ has emerged to tackle this issue102, and provide historical and current insight into social processes and emerging instability. Cliodynamics has the potential to follow in the footsteps of theoretical physics and mathematical biology in providing a robust, reliable modelling framework. The framework would be applicable to societal dynamics for ancient and modern cases. While the validation of any given model is difficult and debatable, by building a significant number of models with diverse features, a mathematical dictionary can be constructed that allows diverse social phenomena to be translated into equations or computational models. While economics provides one possible framework for modelling human behaviour, the models can be overly limiting in their underlying assumptions, whereas cliodynamics is more open to a diversity of assumptions from either an ecological or historical perspective."