Theories and Models: Understanding and Predicting Societal Collapse

This is a chapter by Sabin Roman in the new book, The Era of Global Risk: An Introduction to Existential Risk Studies, which can be downloaded at no charge. Existential risk is now a well-researched academic subject and is certainly worthy of attention. I may comment on other chapters, but this one attracted me first. 

Generally, societal collapse is associated with decreasing complexity. The chapter breaks down societal collapse into types. Exogenous factors and one-time events are one type. These include resource depletion, such as the deforestation of Easter Island as described by Jared Diamond, and soil degradation for the Maya civilization. They also include competition with other societies. And one-time events include volcanic eruptions, asteroid impacts and solar flares. Roman sums up:

Overall, arguments based on competition with other societies, intruders, or catastrophes neglect the fact that these types of events have previously been encountered by a given society but no collapse occurred, e.g. earthquakes in Minoan civilization, barbarian attacks on the Roman front, or competition between the Mayan centres. In addition, these theories have the added difficulty of placing the drivers of the collapse outside of the society in question, which is incomplete from an epistemological perspective without accounting for changes in social structures and dynamics.

Another type is social structure and class conflict. This has been written about since 1377, by Ibn Khaldun, Edward Gibbon in the 18th century, and Arnold Toynbee in 1961. Many of the theories in this group include class conflict. Roman summarizes the limitations of this approach as follows:

The main difficulty in the explanations above is that they force the cause to be considered a single factor and posit the causal mechanism as a direct, linear process. Given the complexity of the systems involved, collapse is often a multi-faceted process that requires accounting for multiple interrelated factors. Simply listing the different contributing phenomena is insufficient to give us additional insight....

This leads to the third type of approach: feedback mechanisms. These were pioneered by Thomas Malthus in 1798, with his description of the relationship between agricultural productivity and population growth. More recently, this theory has been refined to argue that growing societies tend to reach a period of diminishing returns that may precipitate their collapse, and the increasing complexity of a society may eventually make it unmanageable. Roman writes:

A theory of collapse built on feedback mechanisms describing social dynamics is consistent with the nature of a complex system, wherein multiple interacting factors are present, the evolution is non-linear, and causality cannot be assigned to singular aspects of the system.

The fourth and final approach starts with the third and is based on quantitative models, which are broken down into two classes:

(a) agent-based models (ABMs), which represent individuals (or communities) as agents with set attributes and behavioural rules, such that a realistic rendering of relevant behaviour is desired with the aim of obtaining larger scale emergent phenomena. Often, they also explicitly model the spatially extended features, such as terrain; and 

(b) integrated world models, which employ a wide variety of modelling techniques (system dynamics, econometrics, etc.) and aim for an accurate, detailed representation of the system under study. They are complex models that use a large number of variables and parameters.

Several different quantitative models have been developed in recent years. The 'Limits to Growth' study by Donella Meadows focused on three 200-year scenarios from 1900 to 2100: one fitted to historical data, one to environmental sustainability and one to technology and industry:

The first and third scenarios led to a peak industrial output in the 21st century and a subsequent decline in economic activity and demographic levels. The sustainable case manages to reach a steady state with little loss of life, but it requires parameter choices that, in the real world, would require drastic action to curtail pollution and population growth.

Roman says that the economic-based models are hampered partly because "the fundamental assumption of rational human behavior is not justified empirically." So far, the quantitative models are not widely accepted by sociologists. He concludes:

If a common set of historical mechanisms can be found throughout multiple time periods and a modelling framework with a toolkit of methodologies adaptive to different scenarios can be built, then the science-fiction discipline of psychodynamics that Asimov imagined would be within reach.

I am glad that people like Sabin Roman are doing this work, because we are already seeing multiple examples of environmental decline, climate-related catastrophes, geopolitical tensions and poorly-informed populist movements. I agree with Martin Rees, who says in the preface that action must start with voters, because politicians have little incentive to address events that will play out after they have left office. Unfortunately, most voters don't respond to theories and models, and, more often than not, are scientifically illiterate. That is why I often conclude my posts with the hope that AI will soon come to the rescue and replace both capitalism and democracy. Though people may respond as conditions get worse, it is important to remember that capitalism brought us to where we are now and that democracy is not a foolproof system for addressing complex issues.