UC Riverside
Cliodynamics
Title
A History of Possible Futures: Multipath Forecasting of Social
Breakdown, Recovery, and Resilience

Permalink
https://escholarship.org/uc/item/0g05k07v

JournalCliodynamics, 9(2)Authors
Turchin, Peter
Witoszek, Nina
Thurner, Stefan
et al.

Publication Date
2018

DOI
10.21237/C7clio9242078

Copyright Information
Copyright 2018 by the author(s).This work is made available under theterms of a Creative Commons Attribution License, available athttps://creativecommons.org/licenses/by/4.0/

eScholarship.org

Powered by the California Digital Library
University of California

Cliodynamics: The Journal of Quantitative History and Cultural Evolution

A History of Possible Futures: Multipath Forecastingof Social Breakdown, Recovery, and Resilience
Peter Turchin1,2,3, Nina Witoszek4, Stefan Thurner1,5,6, David
Garcia1,5, Roger Griffin7, Daniel Hoyer3,8, Atle Midttun9, James
Bennett10, Knut Myrum Næss4, Sergey Gavrilets11
1 Complexity Science Hub Vienna
2 University of Connecticut

3 Seshat: Global History Databank
4 University of Oslo
5 Medical University Vienna
6 Santa Fe Institute
7 Oxford Brookes University
8 George Brown College
9 BI Norwegian Business School
10 University of Washington
11 University of Tennessee, Knoxville

Abstract
Recent years have seen major political crises throughout the world,and foreign policy analysts nearly universally expect to see risingtensions within (and between) countries in the next 5–20 years.
Being able to predict future crises and to assess the resilience ofdifferent countries to various shocks is of foremost importance inaverting the potentially huge human costs of state collapse and civil
war. The premise of this paper is that a transdisciplinary approach toforecasting social breakdown, recovery, and resilience is entirely
feasible, as a result of recent breakthroughs in statistical analysis oflarge-scale historical data, the qualitative insights of historical andsemiotic investigations, and agent-based models that translate
between micro-dynamics of interacting individuals and the collective
macro-level events emerging from these interactions. Our goal is toconstruct a series of probabilistic scenarios of social breakdown andrecovery, based on historical crises and outcomes, which can aid theanalysis of potential outcomes of future crises. We call this
Corresponding author’s e-mail: peter.turchin@uconn.edu
Citation: Turchin, Peter, Nina Witoszek, Stefan Thurner, David Garcia, Roger Griffin, Daniel
Hoyer, Atle Midttun, James Bennett, Knut Myrum Næss, and Sergey Gavrilets. 2018. History
of Possible Futures: Multipath Forecasting of Social Breakdown, Recovery, and Resilience.
Cliodynamics 9: 124–139.
Konstanzer Online-Publikations-System (KOPS)
URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-2-izudyandim4u9

Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
approach—similar in spirit to ensemble forecasting in weather
prediction—multipath forecasting (MPF). This paper aims to set out
the methodological premises and basic stages envisaged to realize
this goal within a transdisciplinary research collaboration: first, thestatistical analysis of a massive database of past instances of crisis todetermine how actual outcomes (the severity of disruption andviolence, the speed of resolution) depend on inputs (economic,
political, and cultural factors); second, the encoding of theseanalytical insights into probabilistic, empirically informed
computational models of societal breakdown and recovery—the MPF
engine; third, testing the MPF engine to “predict” the trajectories andoutcomes of another set of past social upheavals, which were notused in building the model. This “historical retrodiction” is aninnovation that will allow us to further refine the MPF technology.
Ultimately our vision is to use MPF to help write what we call “ahistory of possible futures,” in which the near- and medium-term
paths of societies are probabilistically forecast.

Introduction
Can we model and forecast the dynamics of social breakdown and recovery, or
society’s resilience to internal and external shocks? The major research challenge
in answering this question is that social breakdown results from multiple
interacting factors: economic, political, cultural, and emotional. An approach that
ignores any of these dimensions is bound to fail. Furthermore, these collective
processes operate on multiple levels: from slow structural changes in economy andculture to faster-moving influences that affect the passions and actions ofindividuals.
We propose a new holistic approach to societal forecasting that builds uponrecent breakthroughs in statistical analysis of large-scale historical data, thequalitative insights of historical and semiotic investigations, and agent-based
computational models that translate between micro-dynamics of interacting
individuals and the collective macro-level events that emerge from theseinteractions, such as social crisis and breakdown. Our goal is to construct a seriesof probabilistic scenarios of social breakdown and recovery. We call this approach—similar in spirit to ensemble forecasting used in numerical weather prediction—
multipath forecasting (MPF).
We can develop MPF, first, by statistically analyzing past instances of crisis todetermine how actual outcomes (the severity of disruption and violence) depend
on input factors (economic, political, and cultural). The second stage is to encode
these analytical insights into a suite of probabilistic, empirically-informed
mathematical and computational models of societal breakdown, recovery, and125

Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
resilience—the MPF engine. Third, we can use the MPF engine to “predict” anotherset of past societies’ trajectories/outcomes, which were not used in building themodel. This “historical retrodiction” will allow us to further refine the MPF
technology. Ultimately, the vision of MPF is to help write what we call “a history of
possible futures,” in which the near- and medium-term paths of societies are
forecast probabilistically by utilizing a massive set of relevant observable
quantitative inputs (structural trends, environmental influences, and collective
decisions).
The study of the processes that undermine social resilience has long sufferedfrom the split between “the two cultures” (Snow 1959): humanities and science.
However, we are now in a position to bridge this gap by measuring not justdemographic and economic trends, but also public sentiments, such as moral
outrage, resentment, fear, hope, and enthusiasm which flourish—implicitly or
explicitly—in political visions, fill mainstream and social media, and fuel social
protest. We will draw on recent literature pointing to the importance of levels ofcooperation, trust, and feelings of (in)justice in explaining both social crisis andrenewal (Witoszek 2013; Turchin 2016; Witoszek 2018; Witoszek and Midttun2018). We can identify and study these narratives, images, and habits, which
influence broad-based social cooperation, or conversely, foster partisan orpolarizing agendas. In this sense, we propose a “holistic” approach tounderstanding and modeling the resilience/fragility of social systems, whichinvestigates economic trends, power dynamics, cultural influences, and individual
passions. Only a “disciplinary fusion” can lead to a better understanding of thelinkage between micro-behaviors and emergent macro-effects.
In essence, MPF uses a combination of qualitative and quantitative dataanalytic approaches, bridging “thick” historical and semiotic descriptions of pastsocieties with “big” data techniques applying cutting-edge statistical and dynamic
modeling procedures to large datasets of historical information, while explicitly
linking micro-level behavior of individuals with macro-level dynamics of societies
in distress (Thurner 2011). This will allow us to boost the understanding of thefactors undermining or strengthening social stability, and how they (incombination) determine the likelihood of breakdown. The long-term longitudinal
data will enable us to identify and empirically validate these factors, as well as their
possibly relevant combinations (Turchin 2018).

The Challenge of Societal Prediction
Obviously, the track record of prophets and thinkers, from Nostradamus to
Fukuyama, does not inspire confidence in the possibility of historical prediction.
Forecasting social upheavals, outbreaks of political violence, and revolutions isparticularly challenging (Cederman and Weidmann 2017). There are several
reasons why historians, economists, and social scientists have so far been unable126Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
to provide convincing, reliable foresight into global trajectories of social
development, and especially in predicting internal conflict.
Revolutions are complex, emergent, collective processes driven by a combinationof structural and transient forces alongside individual agency (Goldstone 2002).
There is a large body of recent research focusing on transient forces and changes
in individual behaviors that lead to mass protests and revolutions (Granovetter
1978; Kuran 1989; Macy 1991; Heckathorn 1993; Marwell and Oliver 1993; Oliver
1993; Lohmann 1994; 1995; Yin 1998; Centola 2013). This work on “microfoundations” and research on the structural causes of revolutions (Skocpol 1979;
Goldstone 1991; Turchin 2012) have developed largely independently of eachother. Furthermore, most approaches studying the micro-foundations of collective
behavior treat individuals as self-interested rational agents who choose the courseof action yielding the greatest material benefit (Becker 1978; Elster 1999). Most
predictive work, thus, overlooks how the internalized cultural norms and values ofdifferent groups within a society—known in sociology as habitus (Bourdieu andNice 1977)—shape interactions between different groups in society, especiallybetween elites and non-elites, and how a change or rupture of social norms
governing the political discourse often precedes the outbreaks of violence that
accompany societal breakdown.
There is a recent rich, but separate scholarly literature drawing attention to theimportance of “irrational” motives: the “geopolitics of emotions,” the “culture offear,” “cultures of resentment,” and “schools of hate” (Moisi 2010; Nussbaum 2016;
Mishra 2017; Davies 2018; Furedi 2018). Finally, while there is an abundance ofapproaches asking why and how societies slide into a revolution or civil war, theopposite dynamics—emergence from crisis and re-establishment of social orderand communal cohesion—has been relatively neglected (Goldstone 2002: Chapter
5).
Further, since the classic work (Epstein 2002) there has been impressive
computational progress in quantifying (Clauset et al. 2007) and modelling social
unrest (Braha 2012). However, many agent models focus on reproducing stylized
facts about social dynamics and not (yet) on systematic predictive understandingof the prerequisites for social unrest. So far agents have not yet been modeled withregard to realistic emotional/psychological features, nor have they been placed
within a sufficiently realistic sociocultural context.
Previous work, thus, has been conducted largely by political theorists, policy
analysts, sociologists, historians, and computational modelers who worked inisolation from each other with focused, domain-specific data sources. Separately,
they all offer intriguing insights and have produced important discoveries, butultimately each can provide only one piece of the puzzle. The critical next step,which has yet to be attempted in large-scale, systematic manner, is to fit thesedifferent disciplinary research strains together into a single, coherent, theoreticalframework (Turchin et al. 2017). Indeed, any successful tool/framework for127Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
estimating the risk of state breakdown needs to integrate the study of macro- andmicro-levels of social unrest. Similarly, understanding and formulating informed
responses to nascent crises should draw on an empirical knowledge of processes
that enable societies to recover from crises. Such integration should include
structural components, transient and emergent forces, and deeply entrenched
cultural traditions, as well as the complex web of interrelationships and network
interactions of individuals within social systems.

Building the MPF Engine
A central process that we aim to measure/quantify and model is the dynamics ofsocial cooperation; that is, the level of cooperative social practices and values in asociety opposed to the dominance of partisan, or polarizing agendas. Important
contributors to this dynamic are changes in the perception of justice/equality andgeneralized trust(andits
opposite,
distrust).
The
study
of
cooperation/equality/trust dynamics crosscuts all the methodological approaches
in MPF: (1) in the historical-semiotic exploration of specific case-studies; (2) in thehistorical database of social collapse and recovery; and (3) in computational agentbased models that will investigate factors that trigger changes in emotions andperceptions, e.g. from a phase of general trust toward a phase of trust evaporation,and vice versa. Here is a more detailed explanation of the three approaches.
In-depth
Comparative
Case-studies
Investigating
EmotiveandNarrative/Mythic Drivers of Social Breakdown and Cohesion
The first objective is to forge our analytical framework by investigating theanatomy of collapse and recovery in a small subset of eight case-studies. This phaseof the project will examine key factors highlighted by Structural-Demographic
Theory (Goldstone 1991, Turchin 2003), among others, focusing on macro-level
demographic and economic trends. But we are particularly interested in how theseprocesses interact with habitus—cultural dispositions, values and practices that
affect social cooperation, trust, and sense of justice. Habitus (Bourdieu and Nice
1977) is neither a sole result of free will, nor determined by structures, but iscreated by the interplay between the two. In the language of complexity science, it
is an emergent property. The concept of habitus is similar to the special definitionof culture used in the new discipline of Cultural Evolution (Brewer et al. 2017):
socially transmitted information that influences human behavior. In addition to therole of economic factors provoking social disruption we will highlight often underresearched cultural accelerators of social unrest and renewal.
Witoszek’s pioneering historical semiotics, which has yielded high-impact
comparative studies of Ireland, Poland, Sweden, Norway and Germany, offers agood starting point and sheds new light on the role of cooperation in forging
sustainable modernity in the Nordic countries (Witoszek et al. 1990; Witoszek andTrägårdh 2003; Witoszek 2018; Witoszek and Midttun 2018). Notably, Witoszek’s
128Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
method allows for identifying a “cultural program” in a given society and thus
prefiguring social responses to new challenges. Our primary focus is on European
and North American societies (Germany, Austria, Italy, England, Poland, Ireland,
Norway, and the US) and on two historical eras (mid-19th-century revolutions andcivil wars and the turbulent 1910s–1930s). We shall define the elite-driven“vocabulary of crisis and renewal,” i.e. rhetorical ploys, narratives, and behavioral
patterns, which influence cooperation or sectarianism, trust or distrust, and thus
fuel conflict or forge sustainable recovery. This approach will enable us to answer
questions such as: why did the stresses of the 1920s and 1930s result in the rise offascism in some countries (Germany and Italy most famously), but lead to very
different outcomes in others (Norway and the USA)? Each case-study will comprisea century-long period with the focal episode of social turbulence—a revolutionary
situation—at its mid-point. This long-term, historical approach will enable us toinvestigate how each society entered into crisis and then emerged from it, and how
a sustainable “normality” returned (if it did). This will be, as far as we know, thefirst attempt to formally model habitus together with demographic and economic
structures in a single theoretical framework. This theoretical advance, we believe,
will be for predictive and retrodictive social science what the development of
unified climate science was for comparable advances in weather forecasting.
A Massive Database of Social Collapse and Recovery
Building on the insights from the first phase, we propose to collect a massive
amount of information on 200 past societies over the past five centuries that facedsome societal crisis. In each case we will trace the long-term dynamics of both entry
into a crisis and its consequences (recovery, upheaval, complete breakdown, see
Figure 1). Data collection will focus on the key variables or parameters identified
through the case-study analysis, including demographic, economic, institutional,
and habitus data (entrenched cultural narratives, practices and values forging acooperative or sectarian ethos). We will take a statistically sound sampling
approach, encompassing both a global and long-term (longitudinal) view. To
accomplish this phase, our team will leverage the considerable experience andmethodology developed through work with Seshat: Global History Databank(Turchin et al. 2015), one of the most comprehensive history databases on asocietal scale. Although the Seshat project addressed a different set of questions(how large-scale complex societies arose in human history), it pioneered the use oftime-resolved (longitudinal) historical data for empirically testing social andhistorical hypotheses (Mullins et al. 2018; Turchin et al. 2018; Whitehouse et al.
2018). In MPF, we will use Seshat methodology to establish a novel type ofhistorical database, of hitherto unknown scale and comprehensiveness, which will
enable us to systematically test hypotheses explaining social resilience andbreakdown.
129Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)

Figure 1. Idealized illustration of the entry into crisis and multipath trajectories
following crisis, which range from the most negative (collapse) to most positive(rapid recovery). Black curves trace the dynamics of collective well-being, and thered curve indicates the rise of social pressures preceding crisis. A “revolutionary
situation” arises when the state loses its resilience to internal and external shocks
and becomes vulnerable to outbreaks of political violence. It is characterized by the
proliferation of radical movements and extremist groups and a general climate ofviolence, but needs a trigger, or a set of triggers, to become an actual revolution, or
civil war.
A Socio-computational Approach to Societal Collapse and Recovery
We will use insights about key processes and estimates of parameter values fromthe empirical approaches (phases 1 and 2) to construct computational agent-basedmodels (ABMs) of individuals; as well as mathematical models of crisis andrecovery dynamics operating on a more coarse-grained societal level. Taking
advantage of the availability of computing power and progress in statistical andcomputational methods, it is now, for the first time, possible to build and analyze
models of millions of interacting individuals. Pioneering work by Thurner andcolleagues (Thurner 2011; Poledna et al. 2018) has demonstrated that thesemodels can be used effectively to assess the complex dynamic interrelationships
between individuals and their groups at all different scales. We have previously
demonstrated in the context of the financial system that modeling at the individual
scale (and taking individual interactions into account) is necessary to understand130Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
system-wide collapse (Thurner and Fuchs 2015; Poledna and Thurner 2016).
Using insights into the social mechanisms of crisis and recovery from the empiricalanalyses, we will construct a series of mathematical models that are able todescribe the crisis dynamics and outcomes in the eight in-depth case-studies. We
use a multilevel approach, combining micro- and macro-level models. The micromodels will take the form of agent-based simulations involving large numbers ofinteracting agents and their organizations/institutions. Macro-models operate oncoarse-grained variables (averaged and aggregated quantities) that can be directlycompared with the data. Typically, the macro-models will take the form ofphenomenological non-linear differential equations that link the essential
variables (Turchin 2005). The goal is to make the micro-models compatible withthe macro-models. In other words, the aggregated results from the micro-model
should predict the dynamics of the macro-variables. We will select an extra set offour cases outside Europe that will serve as an “out-of-sample” performance testof the MPF engine.

Figure 2. Synergistic interconnections between the various research strands in the
MPF project.

131

Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
Although we have divided the basic goals and approaches of this research initiative
into three phases, this was done primarily for strategic convenience and clarity ofpresentation. As depicted in Figure 2, there is an intimate connection between thethree main approaches (boxes), two cross-cutting approaches (ellipses), and thecentral research objective: multipath forecasting (MPF). The figure illustrates the
thick web of feed-forward and feed-back loops between the different
methodologies that integrates them into a seamless whole. The two linking
approaches, Text/Emotional Analysis (automated analysis of large-scale textual
data to quantify the expression of affect, emotional tone, optimism, etc) and MacroModels (which aggregate micro-interactions into coarse-grained variables that canbe compared to data), serve as explicit connectors between the three main ones.
The MPF engine will combine methodology from complexity science (agentbased models, coarse-grained aggregated dynamics, network science, big data
analytics) with the insights from the human sciences (history, anthropology,
cultural studies). Using an extended set of demographic, economic, political, andsemiotic-historical data collected by our interdisciplinary team, we will quantify
probabilistically the fragility of social systems and their vulnerability to potential
triggers of social collapse. Iteratively working back and forth between models andempirical results, we will systematically develop the MPF engine by testing theassumptions and parameters in our models by “retrodicting” a set of historicalcases in our database, using errors or failures to refine the models, and repeating
until the predicted high-probability trajectories match the historical data.

The Innovation of MPF
All complex societies, including our own, experience recurrent (long-term) periodsof declining sociopolitical resilience and stability, often resulting in systemic social
breakdown (Turchin and Nefedov 2009). Previous research by Turchin andcoworkers (Turchin 2003, 2013; Turchin et al. 2018) has identified a number ofbroad “macro-historical patterns” in human history, namely that specificcombinations of factors tend to be present in the vast majority of known cases ofsocietal breakdown and crisis. Witoszek (2003, 2018; Witoszek and Midttun 2018)has found that semiotic-emotional clusters are powerful triggers of social
breakdown and recovery. Her research shows the potency of metaphors andcultures’ founding narratives that have historically influenced levels ofcooperation, trust, and perceived injustice. Drawing on her work, we shall ask how
culturally significant and emotionally charged stories and practices have been used
and abused by elites and politicians, and how they relate to national traditions.
What is their power to polarize or unite the community? We shall also build on
Witoszek’s (2018) research on the catalyzing role of cultural outliers andinnovators in the stabilization of chaos and stimulating social recovery. Thurnerand collaborators have successfully built a data-driven model with millions ofindividuals interacting in a modern economic system. The model demonstrated
132Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
explicitly the impact of a financial shock on a society’s economy (Klimek et al. 2015;
Poledna et al. 2018). To move beyond previous work by our team, we need tosynergistically combine these simulation methodologies with detailed knowledgeof how interactions of people lead to societal patterns, and with longitudinal
historical data on hundreds of cases of societal crisis and breakdown.
The novelty of our approach is both methodological and conceptual, and lies in
the new way it couples qualitative and quantitative empirical information on pastsocieties. It combines thick description of cultural norms and values that influencesocial crisis and resilience with big data analytics to detect their actual dynamics
in a multitude of cultures. Further, it links micro-social behavior of individuals withcollective macro-level societal dynamics. We explicitly incorporate into ourmathematical and computational models the mechanisms drawn from sociological,
anthropological, and semiotic research about cultural expectations, norms, andvalues, as well as under which conditions and how these aspects of habitus break
down. Typically, predictive societal models incorporate only readily quantifiable
information concerning financial activity, economic and institutional structures,
demography, climate, and environmental or ecological trends (Goldstone et al.
2010; Cumming and Peterson 2017; Messner et al. 2018). However, such a limiting
view fails to account for the role played by cultural values and perceptions of(in)justice and social cooperation, even if these traits may be more elusive tomeasure. Indeed, the often-unwritten expectations that characterize a society arechallenged during shocks, and guide—if not constrain or even force—the possible
responses of individuals (and factions) to a crisis. As these norms are challenged,
the kinds of disruptive collective actions groups undertake escalate, often withincreased levels of political violence. Encoding the habitus of different socialgroups into the MPF engine will be key to understanding the multifacetedmechanisms of social breakdown and allowing the emergence of macro-historicalpatterns from our fine-grained models. This enables our probabilistic multipath
forecasting approach to explicitly track the trajectories of groups and coalitions
within the society with respect to collective harmonization or collective
antipathies in response to various shocks.
A second major innovation of our project is to combine theoretical approaches
and massive empirical data sources pitched at multiple scales. Rather than relying
on a single modeling approach, as much of previous work, we will develop amultilevel modeling framework encompassing two types of models: (1) finegrained, micro-level (agent-based) models of individual behavior and decisionsand (2) more coarse-grained, macro-level models that aggregate individual
behaviors into broader economic, social, and cultural variables (incomes, social
mobility rates, and moral sentiments held by different factions within the
population). Rather than focusing on one or two main causes of social crisis andrecovery, our approach emphasizes a systematic application of a comprehensive
set of literally dozens of accessible factors and potential causes to determine the133Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
probabilities of different paths the societies might take depending on initial andboundary conditions.

The Feasibility of the Approach
The prediction of possible future trajectories of sociopolitical instability is a very
ambitious goal. It is therefore legitimate to ask whether such a goal is achievable,
especially given that such predictability has eluded seers, social scientists, andeven well-funded government agencies in the past. Karl Popper (1957) argued thata science of history is impossible and, under the influence of poststructuralism,
historians have largely abandoned “grand theory” be it in the guise of Marxism,
Social Darwinism, or Postmodernism (Darnton 1999).
However, such “grand” qualitative theory is not what we intend to do. Neither
do we aim to predict detailed future paths of history, or unique events. Instead, wewant to understand which factors (economic, social, cultural, emotional,
psychological, group-dynamical), and which combinations of them, create
environments where social breakdown is highly likely. Many of these factors
change gradually and can be observed with the present availability of data. Data on
many of the factors are also available for historical societies, so that the intended“retrodiction” with our computational models should be feasible. Thus, weemphasize the foresight in our conception of forecast, because the focus is on
scenario exploration rather than on “hard” prediction. Our MPF is an entirely
different beast from the data-poor and speculative qualitative and teleologicalhistorical narratives that Popper derided as “historicist.” We have entered a newresearch era, characterized by previously unimaginable computational power,
readily available or “obtainable” data, and, most importantly, novel conceptual andtheoretical tools with which to study complex systems.
Agent-based simulations give us a tool for detailed understanding of why
certain conditions (factor combinations) lead to disruptive events. Again, we are
not interested in predicting when and how specific events occur, but in the(possibly abrupt) increase in their likelihood, given that certain combinations offactors are realized. We know from our previous work on financial and economiccrisis and recovery (Thurner et al. 2012; Klimek et al. 2015) that ABMs play a
crucial role in the detailed understanding of the collective mechanisms that emerge
from individual behavior in certain environments. We have already demonstrated
that we’ve mastered these modeling techniques on massive scales. The challenge
now is to enrich economic agents with realistic emotional and psychologicalcomponents, which is why the input from cultural historians and semioticians is
crucial. This will enable our models to predict collective dynamics of violenceoutbreaks, from urban riots to popular insurrections. Thurner has used this fact innumerous previous ABMs of collective human behavior: opinion formation(Lambiotte et al. 2007; Klimek et al. 2008), financial investments (Thurner et al.
2012, 2013), organization of human groups (Szell et al. 2010; Fuchs et al. 2014,134

Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)2014; Klimek et al. 2015), emergence of cooperation (Thurner et al. 2012), voting
behavior (Klimek et al. 2012), and rice farming in Bali (Lansing et al. 2017).
A century ago, the idea of reliable weather and climate forecasting seemed
outlandish. Yet today it is a reality—with certain caveats well known to complexity
scientists. Agent-based models, coupled with modern data approaches, arepowerful tools for visualizing possible futures, and the probability of these futures.
Through MPF we can investigate not only the paths illuminated by hard data but
also the intended, as well as the unintended, consequences of human actions.
We think that achieving a level of multipath forecasting as envisioned here isanalogous to the current predictive ability of earth sciences such as meteorology,
climatology, and geology. Combining modern analytic tools with the mutuallyenriching conversation between the humanities and the natural sciences, the ideaof building a forecasting engine for societal breakdown is too tempting to resist.
We are convinced that as a result of our attempt to refine sociopolitical forecastingby drawing on deep history through MPF, this project will not only allow us torethink the discipline of history but will show that it is indeed possible to link thehumanities with complexity science to create something radically new: a way ofunderstanding the patterns underlying the dynamics of social conflict andresilience whose recognition can make better sense of the past while informing
policy decisions and political choices influencing the future.

Acknowkledgements
PT and DH were supported by a John Templeton Foundation grant to the Evolution
Institute, entitled "Axial-Age Religions and the Z-Curve of Human Egalitarianism,"a Tricoastal Foundation grant to the Evolution Institute, entitled "The Deep Rootsof the Modern World: The Cultural Evolution of Economic Growth and Political
Stability," an ESRC Large Grant to the University of Oxford, entitled "Ritual,
Community, and Conflict" (REF RES-060-25-0085), a grant from the European
Union Horizon 2020 research and innovation programme (grant agreement No
644055 [ALIGNED, www.aligned-project.eu]), and an European Research Council
Advanced Grant to the University of Oxford, entitled “Ritual Modes: Divergent
modes of ritual, social cohesion, prosociality, and conflict.”
SG was supported by the U. S. Army Research Office grants W911NF-14-1-0637
and W911NF-17-1-0150, the Office of Naval Research grant W911NF-18-1-0138,
the National Institute for Mathematical and Biological Synthesis through NSF
Award #EF-0830858.

References
Becker, G. S. 1978. The Economic Approach to Human Behavior. Chicago: Universityof Chicago Press.
Bourdieu, P., and R. Nice. 1977. Outline of a Theory of Practice. Cambridge:
Cambridge University Press. doi: 10.1017/cbo9789511812507.
135

Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
Braha, D. 2012. “Global Civil Unrest: Contagion, Self-organization, and Prediction.”
PLOS ONE 7: e48596. doi: 10.1371/journal.pone.0048596.
Brewer, J., M. Gelfand, J. C. Jackson, I. F. MacDonald, P. N. Peregrine, P. J. Richerson,
P. Turchin, H. Whitehouse, and D. S. Wilson. 2017. “Grand Challenges for the
Study of Cultural Evolution.” Nature Ecology & Evolution 1 (3): 70. doi: 10.1038/s41559-017-0070.
Cederman, L.-E., and N. B. Weidmann. 2017. “Predicting armed conflict: Time toadjust our expectations?” Science 355: 474–476. doi: 10.1126/science.
aal4483.
Centola, D. M. 2013. “Homophily, networks, and critical mass.” Rationality andSociety 25. doi: 10.1177/1043463112473734.
Clauset, A., M. Young, and K. S. Gleditsch. 2007. “On the Frequency of Severe
Terrorist Events.” Journal of Conflict Resolution 51: 58–87. doi: 10.1177/
0022002706296157.
Cumming, G. S., and G. D. Peterson. 2017. “Unifying Research on Social–Ecological
Resilience and Collapse.” Trends in Ecology & Evolution 32: 695-713. doi: 10.
1016/j.tree.2017.06.014.
Darnton, R. 1999. “President's Column: History Lessons.” Perspectives on History
37 (6): 1–3.
Davies, W. 2018. Nervous States: How Feeling Took Over the World. London:
Random House.
Elster, J. 1999. Alchemies of the Mind: Rationality and the Emotions. Cambridge:
Cambridge University Press.
Epstein, J. M. 2002. “Modeling civil violence: An agent-based computational
approach.” Proceedings of the National Academy of Sciences 99: 7243-7250. doi:
10.1073/pnas.092080199.
Fuchs, B., D. Sornette, and S. J. S. r. Thurner. 2014. “Fractal multi-level organisationof human groups in a virtual world.” Scientific Reports 4: 6526. doi: 10.1038/
srep06526.
Fuchs, B., and S. J. Thurner. 2014. “Behavioral and network origins of wealthinequality: Insights from a virtual world.” PLOS ONE 9: e103503. doi:
10.1371/journal.pone.0103503.
Furedi, F. 2018. How Fear Works: Culture of Fear in the Twenty-First Century.
London: Bloomsbury Publishing.
Goldstone, J. A. 1991. Revolution and Rebellion in the Early Modern World. Berkeley,
CA: University of California Press. doi: 10.4324/9781912282654.
Goldstone, J. A. 2002. Revolution: Theoretical, Comparative, and Historical Studies,
3rd ed. Fort Worth: Wadsworth.
Goldstone, J. A., R. H. Bates, D. L. Epstein, T. R. Gurr, M. B. Lustick, M. G. Marshal, J.
Ulfelder, and M. Woodward. 2010. “A Global Model for Forecasting Political
Instability.” American Journal of Political Science 54: 190–208. doi: 10.1111/j.
136Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
1540-5907.2009.00426.x.
Granovetter, M. 1978. “Threshold Models of Collective Behavior.” American Journalof Sociology 83: 1420–1443. doi: 10.1086/226707.
Heckathorn, D. D. 1993. “Collective Action and Group Heterogeneity: Voluntary
Provision Versus Selective Incentives.” American Sociological Review 58: 329–
350. doi: 10.2307/2095904.
Klimek, P., R. Lambiotte, and S. Thurner. 2008. “Opinion formation in laggard
societies.” EPL (Europhysics Letters) 82: 28008. doi: 10.1209/0295-5075/82/
28008.
Klimek, P., S. Poledna, J. D. Farmer, S. Thurner. 2015. “To bail-out or to bail-in?
Answers from an agent-based model.” Journal of Economic Dynamics andControl 50: 144–154. doi: 10.1016/j.jedc.2014.08.020.
Klimek, P., Y. Yegorov, R. Hanel, and S. Thurner. 2012. “Statistical detection ofsystematic election irregularities.” Proceedings of the National Academy of
Sciences 109: 16469–16473. doi: 10.1073/pnas.1210722109.
Kuran, T. 1989. “Sparks and prairie fires: A theory of unanticipated politicalrevolution.” Public Choice 61: 41–74. doi: 10.1007/bf00116762.
Kuran, T. 1995. Private Truths, Public Lies. Cambridge, MA: Harvard University
Press.
Lambiotte, R., S. Thurner, and R. Hanel. 2007. “Unanimity rule on networks.”
Physical Review E 76: 046101. doi: 10.1103/physreve.76.046101.
Lansing, J. S., S. Thurner, N. N. Chung, A. Coudurier-Curveur, Ç. Karakaş, K. A.
Fesenmyer, and L. Y. Chew. 2017. “Adaptive self-organization of Bali’s ancient
rice terraces.” Proc Natl Acad Sci USA 114: 6504–6509. doi:
10.1073/pnas.1605369114.
Lohmann, S. 1994. “The Dynamics of Informational Cascades: The Monday
Demonstrations in Leipzig, East Germany, 1989–91.” World Politics 47: 42–
101. doi: 10.2307/2950679.
Macy, M. W. 1991. “Chains of cooperation: threshold effects in collective action.”
American Sociological Review 56: 730–747. doi: 10.2307/2096252.
Marwell, G., and P. Oliver. 1993. The critical mass in collective action. Cambridge,
MA: Cambridge University Press. doi: 10.1017/cbo9780511663765.
Messner, J. J., N. Haken, P. Taft, I. Onyekwere, H. Blyth, C. Fiertz, C. Murphy, A. Quinn,
and M. Horwitz. 2018. “Fragile States Index 2018 – Annual Report.” The Fundfor Peace, Washington, DC.
Mishra, P. 2017. Age of Anger: A History of the Present. Macmillan.
Moisi, D. 2010. The Geopolitics of Emotion: How Cultures of Fear, Humiliation, andHope are Reshaping the World. New York: Anchor Books.
Mullins, D. A., D. Hoyer, C. Collins, T. Currie, K. Feeney, P. François, P. E. Savage, H.
Whitehouse, and P. Turchin. 2018. “A Systematic Assessment of 'Axial Age'
Proposals using Global Comparative Historical Evidence.” American Sociology
Review 83 (3): 596–626. doi: 10.1177/0003122418772567.
137

Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
Nussbaum, M. C. 2016. Anger and Forgiveness: Resentment, Generosity, Justice.
Oxford, Oxford University Press.
Oliver, P. E. 1993. “Formal Models of Collective Action.” Annual Reviews of Sociology
19: 271–300. doi: 10.1146/annurev.soc.19.1.271.
Poledna, S., S. Hochrainer-Stigler, M. G. Miess, P. Klimek, S. Schmelzer, J. Sorger, E.
Shchekinova, E. Rovenskaya, J. Linnerooth-Bayer, U. J. Dieckmann, and S.
Thurner. 2018. “When does a disaster become a systemic event? Estimating
indirect economic losses from natural disasters.” arXiv: 1801.09740. https://
arxiv.org/abs/1801.09740.
Poledna, S., and S. Thurner. 2016. “Elimination of systemic risk in financial
networks by means of a systemic risk transaction tax.” Quantitative Finance 16:
1599–1613. doi: 10.1080/14697688.2016.1156146.
Popper, K. R. 1957. The Poverty of Historicism. London: Routledge and Kegan Paul.
doi: 10.4324/9780203538012.
Skocpol, T. 1979. States and social revolutions: a comparative analysis of France,
Russia, and China. Cambridge, UK: Cambridge University Press. doi: 10.1017/
cbo9780511815805.
Snow, C. P. 1959. The Two Cultures. London: Cambridge University Press.
Szell, M., R. Lambiotte, and S. Thurner. 2010. “Multirelational organization of largescale social networks in an online world.” PNAS 107: 13636–13641. doi: 10.
1073/pnas.1004008107.
Thurner, S. 2011. “Systemic financial risk: agent based models to understand theleverage cycle on national scales and its consequences.” IFP/FGS Working
Paper 14.
Thurner, S., and B. J. Fuchs. 2015. “Physical forces between humans and how
humans attract and repel each other based on their social interactions in an
online world.” PLOS ONE 10: e0133185. doi: 10.1371/journal.pone.0133185.
Thurner, S., and S. J. Poledna. 2013. “DebtRank-transparency: Controlling systemicrisk in financial networks.” Scientific Reports 3: 1888. doi: 10.1038/srep01888.
Thurner, S., M. Szell, and R. J. Sinatra. 2012. “Emergence of Good Conduct, Scaling
and Zipf Laws in Human Behavioral Sequences in an Online World.” PLOS ONE
7: e29796. doi: 10.1371.journal.pone.0029796.
Turchin, P. 2003. Historical Dynamics: Why States Rise and Fall. Princeton, NJ:
Princeton University Press. doi: 10.2307/j.cttwf4e45.
Turchin, P. 2005. “Dynamical feedbacks between population growth andsociopolitical instability in agrarian states.” Structure and Dynamics 1(1):Article 3.
Turchin, P. 2012. “Dynamics of Political Instability in the United States, 1780–
2010.” Journal of Peace Research 4: 577–591. doi: 10.1177/
0022343312442078.
Turchin, P. 2013. “Modeling Social Pressures Toward Political Instability.”
Cliodynamics 4: 241–280. doi: 10.21237/c7clio4221333.
138Turchin et al.: Multipath Forecasting. Cliodynamics 9:2 (2018)
Turchin, P. 2016. Ages of Discord: A Structural-Demographic Analysis of American
History. Chaplin, CT: Beresta Books.
Turchin, P. 2018. “Fitting Dynamic Regression Models to Seshat Data.”
Cliodynamics 9: 25–58. doi: 10.21237/c7clio9137696.
Turchin, P., R. Brennan, T. Currie, K. Feeney, P. Francois, D. Hoyer, J. Manning, A.
Marciniak, D. Mullins, A. Palmisano, P. Peregrine, E. A. L. Turner, and H.
Whitehouse. 2015. “Seshat: The Global History Databank.” Cliodynamics 6: 77–
107. doi: 10.21237/c7clio6127917.
Turchin, P., T. E. Currie, H. Whitehouse, P. Francois, K. Feeney, D. Mullins, D. Hoyer,
C. Collins, S. Grohmann, P. Savage, G. Mendel-Gleason, E. Turner, A. Dupeyron,
E. Cioni, J. Reddish, J. Levine, G. Jordan, E. Brandl, A. Williams, R. Cesaretti, M.
Krueger, A. Ceccarelli, J. Figliulo-Rosswurm, P.-J. Tuan, P. Peregrine, A.
Marciniak, J. Preiser-Kapeller, N. Kradin, A. Korotayev, A. Palmisano, D. Baker,
J. Bidmead, P. Bol, D. Christian, C. Cook, A. Covey, G. Feinman, A. D. Juliusson, A.
Kristinsson, J. Miksic, R. Mostern, C. Petrie, P. Rudiak-Gould, B. Ter Haar, V.
Wallace, V. Mair, L. Xie, J. Baines, E. Bridges, J. Manning, B. Lockhart, A. Bogaard,
and C. Spencer. 2018. “Quantitative historical analysis uncovers a single
dimension of complexity that structures global variation in human socialorganization.” Proc Natl Acad Sci U S A 115: e144–e151. doi: 10.1073/pnas.
1807312115.
Turchin, P., S. Gavrilets, and J. A. Goldstone. 2017. “Linking ‘Micro’ to ‘Macro’
Models of State Breakdown to Improve Methods for Political Forecasting.”
Cliodynamics 8: 159–181. doi: 10.21237/c7clio8237429.
Turchin, P., and S. Nefedov. 2009. Secular Cycles. Princeton, NJ: Princeton
University Press.
Whitehouse, H., P. François, P. E. Savage, T. E. Currie, K. C. Feeney, E. Cioni, R.
Purcell, R. M. Ross, J. Larson, J. Baines, B. t. Haar, A. Covey, and P. Turchin. 2018.
“Complex societies precede moralizing gods throughout world history.”
Preprint.
Witoszek, N. 2013. The Origins of the Regime of Goodness: Remapping the
Norwegian Cultural History. Oslo: Norwegian University Press.
Witoszek, N. 2018. The Origins of Anti-Authoritarianism. London: Routledge. doi:
10.4324/9781315164540.
Witoszek, N., and A. Midttun. 2018. Sustainable Modernity: The Nordic Model and
Beyond. New York: Routledge. doi: 10.4324/9781315195964.
Witoszek, N. and P. Sheeran. 1990. The Irish Funerary Traditions. Galway: Social
Sciences Research Centre, University College Galway.
Witoszek, N., and L. Trägårdh. 2003. Culture and Crisis: Sweden and Germany inComparison. Oxford: Berghahn.
Yin, C.-C. 1998. “Equilibria of collective action in different distribution of protest
thresholds.” Public Choice 97: 536–567. doi: 10.1023/a:10049221725560.
139