TY - JOUR
AU1 - Bosley, LT Chris C.
AB - Abstract ‘’The post–Cold War peace dividend has been spent; the perception of the global strategic environment is one based in uncertainty with multiple, simultaneous, and diffuse threats wherein “crisis” is constant, conflict is pervasive, and instability is contagious. In such a dynamic, complex, and volatile environment, it is necessary to develop a comprehensive risk assessment mechanism to focus attention, funnel resources, and drive activities prior to acute instability. By leveraging existing academic and DoD forecasting models with diverging strengths and weaknesses, this framework will map the social and structural factors that shape the character of instability to depict states in which there is a volatile environment; identify the destabilizing conditions and existing friction points that are likely to drive the development of instability; and detect specific triggering events and tipping processes that could aggravate those conditions and ignite acute instability. Perception Is Reality Max Weber’s classic definition of a state as “a human community that (successfully) claims the monopoly of legitimate use of physical force within a given territory” has become a bromide (Weber 1946, 78). While convenient, this conception fails to capture the nuance necessary to convey the complexity of social forces that act upon communities. Modern states channel these social forces not only through legitimate force but also through economic networks; behavioral norms; cultural values, principles, and beliefs; and political systems. The resulting functionally differentiated institutional structures interact to manifest societies “constituted of multiple overlapping and intersecting sociospatial networks of power” that are funneled through a central ordering system (Mann 1986, 1; 1984, 187–88). Thus, necessarily engaged in more than simply coercive enforcement, a state is the centrally administered and internationally recognized set of institutions within a given territory that is concerned with the maintenance of order among social forces across the spectrum of social power (Gellner 1983, 4). Conceived as such, stability is highly concomitant with the state—it corresponds to the overlap of formal and informal roles of the state (or any other level of ordering system or authority) and society. “When the formal roles and structures set by authority match those constructed by informal social interaction, an object is stable. Where either set of roles or structures change so they conflict, an object is unstable to some degree” (Margolis 2010, 332).1 The four resulting stabilizing dynamics between the state and society are: the state’s authority to enforce its set of roles on society; the state’s legitimacy through society recognizing and accepting its roles; the state’s effectiveness in executing its roles; and the state’s resilience by reforming its roles to match social expectations. (Margolis 2012, 16–17; Lijphart 1977, 4) When the state fails to perform a role—as perceived by society—in any of these dynamics, society can enforce its set of roles by attempting to replace or amend the system (Margolis 2012, 17). Stability, then, is in large part a function of good governance—an accountable political system that reflects social forces to channel public goods accordingly via robust institutions results in greater degrees of stability. Societies are not frozen, however, so ordering systems must also be adaptable; otherwise, the development of stabilizing dynamics over time can foster degrees of volatility, shaping potential events as highlighted in Table 1 (Margolis 2012, 17–18; Lijphart 1977, 1–52). More than an imbalance in these stabilizing dynamics is needed to precipitate acute instability out of an unstable environment, however; a catalyst is “needed to convert existing tension” into acute instability (Margolis 2012, 17).2 Table 1. Characterizing instability3 Stabilizing dynamic Character Acute result Legitimacy Society’s recognition and acceptance of its roles as defined by the system; popular-level dynamics Protests, revolutions, insurgencies Authority The state’s ability to enforce its roles on society; elite-level dynamics Coups d’etat, secession conflicts,civil wars Effectiveness The state’s ability to execute its roles as they are perceived by society; institutional-level dynamics State failure, policy failures/deadlocks, impotent disaster relief Resilience The state’s ability to adapt and reform its roles to reflect society and address group grievances; institutional-, popular-, and elite-level dynamics Mass killings, discrimination and repression, forced relocation, institutional coercion Stabilizing dynamic Character Acute result Legitimacy Society’s recognition and acceptance of its roles as defined by the system; popular-level dynamics Protests, revolutions, insurgencies Authority The state’s ability to enforce its roles on society; elite-level dynamics Coups d’etat, secession conflicts,civil wars Effectiveness The state’s ability to execute its roles as they are perceived by society; institutional-level dynamics State failure, policy failures/deadlocks, impotent disaster relief Resilience The state’s ability to adapt and reform its roles to reflect society and address group grievances; institutional-, popular-, and elite-level dynamics Mass killings, discrimination and repression, forced relocation, institutional coercion Sources: Margolis 2012; Lijphart 1977. Table 1. Characterizing instability3 Stabilizing dynamic Character Acute result Legitimacy Society’s recognition and acceptance of its roles as defined by the system; popular-level dynamics Protests, revolutions, insurgencies Authority The state’s ability to enforce its roles on society; elite-level dynamics Coups d’etat, secession conflicts,civil wars Effectiveness The state’s ability to execute its roles as they are perceived by society; institutional-level dynamics State failure, policy failures/deadlocks, impotent disaster relief Resilience The state’s ability to adapt and reform its roles to reflect society and address group grievances; institutional-, popular-, and elite-level dynamics Mass killings, discrimination and repression, forced relocation, institutional coercion Stabilizing dynamic Character Acute result Legitimacy Society’s recognition and acceptance of its roles as defined by the system; popular-level dynamics Protests, revolutions, insurgencies Authority The state’s ability to enforce its roles on society; elite-level dynamics Coups d’etat, secession conflicts,civil wars Effectiveness The state’s ability to execute its roles as they are perceived by society; institutional-level dynamics State failure, policy failures/deadlocks, impotent disaster relief Resilience The state’s ability to adapt and reform its roles to reflect society and address group grievances; institutional-, popular-, and elite-level dynamics Mass killings, discrimination and repression, forced relocation, institutional coercion Sources: Margolis 2012; Lijphart 1977. Table 2. Acute instability Environment Degree of volatility based on broad trends of social factors, political structures, and stabilizing dynamics Destabilizing conditions Discrete, existing tensions and friction points that drive the development of instability Triggers and tipping points A specific event or string of events that sparks acute instability Environment Degree of volatility based on broad trends of social factors, political structures, and stabilizing dynamics Destabilizing conditions Discrete, existing tensions and friction points that drive the development of instability Triggers and tipping points A specific event or string of events that sparks acute instability Sources: Schelling 1971; Kuran 1989; Margolis 2012; Brehm 2015a; Straus 2015. Table 2. Acute instability Environment Degree of volatility based on broad trends of social factors, political structures, and stabilizing dynamics Destabilizing conditions Discrete, existing tensions and friction points that drive the development of instability Triggers and tipping points A specific event or string of events that sparks acute instability Environment Degree of volatility based on broad trends of social factors, political structures, and stabilizing dynamics Destabilizing conditions Discrete, existing tensions and friction points that drive the development of instability Triggers and tipping points A specific event or string of events that sparks acute instability Sources: Schelling 1971; Kuran 1989; Margolis 2012; Brehm 2015a; Straus 2015. During the four decades that constituted the Cold War, processes were developed to respond to a threat-based strategic environment; that is, an environment wherein there is one primary adversary that represents a single, known, and predictable threat, which provides strategic focus and reduces the risk of misperception. The fall of the Soviet Union facilitated a shift to the perception of an uncertainty-based strategic environment, one in which salient threats are dynamic, multiple, and unpredictable. Whether this marked an actual fundamental transformation in global patterns of conflict and instability or a popularized misconception fueled by heightened sensitivity to incidental events, the sudden withdraw of one superpower from the bipolar strategic environment caused a diffusion of Washington policymakers’ attention. No longer concerned with a primary adversary in a dyadic relationship, the United States now attuned itself to a host of more peripheral, less predictable actors in a complex interactional web. In essence, the focus of US national security policy shifted from deterring an existential threat to deterring individual threats against US citizens and interests anywhere in the world, requiring both a wider aperture and sharper depth of field. Such an imperative necessitated a sudden understanding of stabilizing dynamics and of the global, regional, and national contexts in which they occur. After describing the nature of the contemporary twenty-first-century strategic environment, this article will explore the strengths and weaknesses of the primary quantitative and qualitative prisms through which instability estimation in such an environment is currently conducted. This discussion will motivate the development of a phased instability risk assessment framework that leverages and aggregates positivist and empirical models into an approach sensitized to mirror the uncertainty-based strategic environment to measure the environment, destabilizing conditions, and triggers and tipping points as depicted in Table 2 (Schelling 1971, 181-186; Kuran 1989, 45-51; Margolis 2012, 18-20; Straus 2015, 7-10). Phase 1 depicts the global instability environment. Quantitative models, interrogated by theoretical paradigms, measure social and structural factors that produce latent instability to highlight states that are highly volatile and inherently unstable, illuminating the stabilizing dynamics that are likely to shape the character of instability. Phase 2 identifies the dry tinder. With a focus on the states identified in Phase 1, Phase 2 pinpoints the specific destabilizing conditions and existing friction points that are likely to drive the development of instability. Finally, Phase 3 detects the spark. Interactional pattern analysis programs are utilized to chart trends in social interactions among a given set of actors to map the tone, intensity, and trajectory of a relationship. By correlating a spike of hostile interactions in a deteriorating or volatile relationship to the destabilizing conditions identified in Phase 2, this analysis will isolate triggers that are likely to mobilize social forces and catalyze existing latent instability into acute instability. Against the backdrop of the genesis of the Syrian civil war in 2011, this article will conclude with a concrete example of where and how this framework could have forecasted the descent into civil war when existing models failed to, mitigating surprise, focusing intelligence attention, and enabling resources to be funneled in such a way that anticipated the needs of key decision-makers. The New, New, New Thing4 After the mid-1980s, armed global conflict declined by 60 percent until 2011 (Marshall and Cole 2011, 3–4). Coincident with democracy’s third wave, stability was the norm despite quantitative forecasting models predicting instability that did not immediately manifest.5 The implication was that (1) the tenets of the international system had fundamentally changed and the democratic peace had been realized, or (2) a wave of instability was overdue and an outbreak of crises was imminent. Now, this wave of instability seems to have begun, the post–Cold War peace dividend appears to be largely spent, and the consequences of a threat-based posture in an uncertainty-based strategic environment—real or perceived—have become apparent (Fearon 2011, 9). The deficit of such a discontinuity is most evident in the inability to effectively respond to the regional and intrastate wars that have permeated through regions consisting of postcolonial fragile states since the advent of the Arab Spring in January 2011, the apparently abortive fourth wave of democratization (Marshall and Cole 2011, 29).6 Historically, democratization has been accompanied by corresponding instability as “the agony of liberalization” in emerging democracies inherently breeds conflict among a mosaic of social forces vying for political power (Merquior 1992, 340).7 Democracy requires a foundational consensus based on deliberative and negotiated dispute resolution; it cannot manage anarchy, and is quickly overwhelmed by the dissonant cacophony of demands stimulated by survival imperatives … Whereas the collective decision to adopt democratic practice is often momentous, the institutionalization and consolidation of democratic process requires considerable time, even under the most favorable conditions. The myth of democratic revolution creates a “revolutionary” expectation of immediate “off-the-shelf” democracy and universal welfare in lesser-developed societies. (Marshall 2011, 101) Modern nation-states are organized around the “like over like” principle that “each people should be self-ruled” (Wimmer 2013a, 2, 4). The implicit ideal behind the nation-state is that each nation deserves its own state—popular sovereignty and self-determination are derived from the idea that “like should rule over likes” and that the state and the nation should overlap (Wimmer 2013b, 90). Independence, however, is not a causal mechanism that automatically and spontaneously shifts an individual’s primary conception of identity—often heretofore defined along kinship, religious, regional, tribal, ethnic, sectarian, or racial lines—from traditional ties that often constitute a mosaic within the state to modern national ones that correspond neatly with the state (Geertz 1973, 270). The result is often a multi-phased conflict whereby violence erupts between a perceived despotic power and a subjugated social group over state control or the creation of a new nation-state. When the reins of the oppressive power are thrown off, however, internal divisions that were previously bridged by a common cause can become exposed, resulting in a subsequent sectarian struggle over which group should wield power (Wimmer 2013a, 23–24). In short, the imperial or authoritarian power is a unifying factor for social groups in a struggle for independence. After independence, however, the unifying factor is removed, political parties can factionalize around fixed-identity markers, and “new incentives to protest and rebel … are created” (Wimmer 2013a, 141).8 The simple existence of a pluralistic society consisting of several fixed-identity groups is not inherently a powder keg; rather, when such groups become politically salient—when “like,” self-rule, and political factions are defined along fixed-identity markers—they become pillared, political power becomes existentially threatening, and the social system crystalizes into a brittle zero-sum game. Fragile state institutions and weak territorial control—legacies of colonial rule—foster conditions of easy social mobilization susceptible to manipulation by rebellious non-state actors and opportunistic elites (Fearon and Laitin 2000, 853–55). The result is often a democratic reversal whereby the political system constitutes a rigid ruling regime that builds governance institutions along routinized clientele or patronage networks that channel interactions through pillared identities and exclude social outgroups from a stake in the state. Indeed, the formation of a nation-state is inherently a two-sided coin and an exclusionary process, as a state formed around a defined identity by default defines and excludes outgroups (Mann 2005, 68–69). “Resources, which can be distributed along [fixed-identity] lines and thus used to consolidate … clienteles, provide further fuel for the dynamics of [identity-driven] competition and conflict” (Wimmer 2013a, 141). Thus, political parties become inflexible and identity becomes existential, outgroup factions rebel to change the system, the ruling regime and its support base resort to despotism to repress the revolutionary social forces, and conflict becomes endemic. This zero-sum dynamic is fueling the identity-driven conflicts pervading regions that consist of fragile postcolonial states wherein borders were arbitrarily—or worse, manipulatively—drawn to pool and divide social identity groups within and across states in such a way that cohesive political identities could not form to challenge colonial authority or subsequently develop effective administrative orders. Thus, poorly organized civil and political society, exclusive development of institutions and infrastructure that precludes the formation of cross-cutting cleavages, and partial or incomplete democratic reforms are highly vulnerable to changes in local conditions and dynamics—particularly factionalism—that amplify rather than bridge complex identity divisions and rivalries (Marshall and Cole 2014, 29). The result has been an increase in volatility, conflict, and violence “concentrating in the center: the Arab Middle East and Africa”—the new, new, new thing (Marshall and Cole 2014, 15; Gurr 2000, 55). This instability is occurring against a backdrop of complex transnational interactions that have blurred the distinction between domestic and international sectors and interstate and intrastate conflict. Thus, “regional effects loom large and the negative influences, or ‘diffusion of insecurity,’ from neighboring states turn tensions into a deeper and more volatile form of insecurity” (Marshall and Cole 2014, 3). The result of such instability in regions cross-cut with myriad identities fragmented across and aggregated within state borders is a complex environment wherein acute instability occurs almost continuously and often diffuses rapidly across borders. Conflict is pervasive as regional states intercede on behalf of a kindred identity caught in conflict, violence diffuses as social identity groups span state borders, copy-cat revolutions cascade successively as social forces perceive situational similarities to revolutions occurring elsewhere, transnational threats such as terrorist groups disregard state boundaries, resource scarcity encourages a scramble for access, alliances and regional power structures cause sequential involvements, global and regional powers intervene to protect perceived national interests, and displaced social groups seek viable escape routes and settlements elsewhere in the region and in neighboring regions. In today’s interdependent world facilitated by the communications and information revolution, “the complexities and densities of interactions, interconnection, and networks among the myriad actors” foster a development-security-governance-stability nexus wherein “any change in one direction will have consequences for each of the other dimensions” (Marshall and Cole 2008, 4). Such an environment requires a posture that is dynamic and able to rapidly focus attention and respond to these events as they reverberate regionally and globally. This is an environment of constant crises; thus, a construct based on “crisis”—which implies an exception to the norm and a singular, stand-alone event that requires augmented and specialized attention—is unwieldy and does not accurately reflect the dynamics of today’s uncertainty-based strategic environment. Despite the denser interaction capacities that marshaled increased opportunities for inter- and intrastate conflict, infectiousness of violence across borders, and attunement to events in peripheral regions, international security analysis since the Cold War has continued to rely primarily on individual or dyadic systems focused on relatively static structural conditions or interactions between two states (Marshall and Cole 2008, 3). False Veils and Fat Tails Individual forecasting models tend to work better in theory than in practice. They are limited in what specifics they can predict until something obvious occurs. While they can provide a broad picture, the complexity of social dynamics and the fat tails of singular models make them ineffective for pinpoint forecast.9 Neither quantitative nor qualitative approaches alone will afford such comprehensive insight in today’s complex strategic environment. Quantitative approaches present instability as a function of measurable variables (Goldstone 2008, 3). Computational analysis enables the management of information overload and recognizes “patterns and commonalities of behavior across diverse societies,” thus identifying common indicators that function as signposts to unstable states (Marshall 2011, 96). While quantitative approaches—such as the Political Instability Task Force, the Integrated Crisis Early Warning System’s iCast program, the Index of State Weakness, the State Fragility Matrix, and the Fragile States Index—can approach 80 percent success rates in predicting acute instability and enable indices that rank countries based on their degrees of latent instability, they fail to deliver context or to consider the “shape, scale, or pace” of developing instability (Margolis 2012, 14). They reproduce the entire gamut of social dynamics and thus reflect extraneous noise that lends “a veil of false precision” (Marshall 2011, 96). Quantitative models “privilege uniform scholarship over a tailored case-specific” approach, which removes the human bias in favor of logical and mathematical equations, but they are prone to miscalculation because some countries are more (or less) resilient than the mathematical model can account for due to heuristic factors or unobserved heterogeneity (Margolis 2012, 14). Indeed, similar precipitating factors—even those shown to be statistically significant—can yield wildly divergent outcomes across a range of societies (Ward, Greenhill, and Blake 2010, 365). Although random-effects models are designed to account for state-specific covariates not included or detected in the model by assuming a given set of parameters can yield a range of outcomes rather than a single fixed effect, they present their own set of challenges (Renshon and Spirling 2015, 217). Like other political science regression analyses based upon social interaction and human actors, random-effects models suffer from the fat-tail limitation. While they offer an array of possible effects, they discount the possibility of outcomes that are on the extreme edges or even outside the highlighted continuum. Such fat tails are amplified in random-effects models because the range of outcomes is often determined by the use of random deviations rather than specifically identified deviations based on empirical analysis of specific states (Renshon and Spirling 2015, 218). Thus, random-effects models do not take social contexts into account but rather hedge their predictions by offering a set of possible outcomes that bracket the model’s result—these models remain ineffective for pinpoint forecast. So, while parsimonious models enable ease of communication to policymakers, they devalue the impact of context and state- and region-specific conditions and return results derived from a few artificially deterministic factors. As such, statistical analysis can provide supporting strength but not insight or judgment in a vacuum. Instability assessment has traditionally relied on qualitative analysis by experts. Qualitative approaches—such as the International Crisis Group’s CrisisWatch, the Council on Foreign Relations’ Preventive Priorities Survey, and a majority of intelligence indication-and-warning constructs—are “based on identifying key similarities across countries … What is important here is how well an analyst can identify meaningful similarities, as opposed to coincidental or superficial similarities” (Goldstone 2008, 5). These models are intuitive and inherently consider case-specific dynamics, affording them an adaptability that quantitative analysis alone cannot offer and enabling a closer exploration that may uncover variables not accounted for by data-based analysis. Qualitative analysis, however, is loath to reveal surprises due to its tendency to focus on historical patterns to inform emerging trends (Margolis 2012, 15). Moreover, by focusing on a specific country, qualitative analysis is likely to assess that country in a vacuum, tempting tunnel vision that cheapens the influence or importance of other countries, organizations, or groups. Qualitative models are based upon human beings, not computers, and are saddled with all the intricacies of human psychological dynamics thereof. Thus, the accuracy of such assessments is wholly dependent upon the knowledge, thoroughness, and biases of the analyst, all of which are subject to whims. Not only does good assessment require a good analyst, but it requires a good analyst who is having a good day. Qualitative models tend to favor stability over instability by focusing on historical patterns and projecting the present into the future; however, quantitative models are not void of their own biases based upon the static theoretical assumptions inherent in the calculations programed into the model. Thus, the combination of qualitative assessments with their attunement to country-specific and heuristic dynamics and quantitative models with their ability to aggregate massive amounts of data “provides a far superior method of screening for instability” (Goldstone 2008, 13). Wider and Sharper: Instability Risk Assessment Framework Multi-Phased: Depict, Identify, and Detect Estimating instability goes beyond simply warning. Leaders “do not need complex models to recognize the fragility of Somalia, Iraq, or Burma” (Margolis 2012, 14). It requires a structured, multilevel analysis of global, regional, national, and subnational social environments. The key question is how to transform model results into usable indicators to provide instability risk assessments, not unreliable predictions of acute instability. Acute instability is a function of environment—broad patterns of stabilizing dynamics and structural factors that foster degrees of volatility and shape the character of instability within a given social system; destabilizing conditions—discrete, existing tensions and friction points that drive the development of instability; and triggers or tipping points—specific events or strings of events that are likely to exacerbate and catalyze the destabilizing conditions in an unstable society to galvanize emotive content, mobilize social forces, and spark acute instability (Margolis 2012, 18). Thus, risk assessment models necessarily require analysis along multiple levels that aggregate into a three-phase model, including social and structural preconditions, escalatory factors and existing tensions, and the proximal events that precipitate acute instability (Brehm 2015a, 1). Phase 1: Depicting the Environment Quantitative The Political Instability Task Force (PITF) model is a parsimonious, quantitative one that forecasts the outbreak of acute instability with 80 percent accuracy over two years, and it provides a suitable surrogate for the exploration of Phase 1 analysis (Goldstone etal. 2010, 191).10 A macro-structural forecasting model, it analyzes the “relationship between country instability and broad trends in political, social, economic, and demographic factors … to describe an environment that makes a country more or less susceptible to various forms of instability” (O’Brien 2010, 92). The PITF is coded to predict three types of events: (1) large-scale violent conflicts, (2) adverse regime changes, and (3) genocides and politicides (Goldstone etal. 2010, 191–92).11 Using data compiled “to construct a cross-national time-series data set covering the period from 1955 through 2003 for all countries with a population over 500,000,” the PITF identified the “combination of factors that could predict the onset of violent conflict … [and] dropped from the analysis variables that bore a statistically insignificant relationship with subsequent instability, as well as those that proved statistically significant but whose inclusion made no appreciable impact upon the model’s ability to distinguish stable from unstable cases” (Goldstone etal. 2010, 191, 194). From this analysis, four independent variables were found to have a significant impact on the onset of acute instability. Regime type was found to be, by far, the most powerful factor. Derived from the Polity IV dataset, regime type is plotted on a scale from fully institutionalized autocracy to fully institutionalized democracy (Marshall, Gurr, and Jaggers 2014, 14–17).12 While democracy and autocracy are fundamentally divergent forms of governance, they are the two most stable and effective at maintaining social order. Rather, intermediary regime types have been shown to be prone to instability (Marshall and Cole 2008, 4). Partial autocracies are disposed to breakdowns in authority stabilizing dynamics such as civil war and adverse regime change, and partial democracies to failures of legitimacy dynamics like adverse regime change (Goldstone etal. 2010, 195–96). Partial democracies with factionalism have been shown to be exceptionally unstable, susceptible to collapses in both authority and legitimacy stabilizing dynamics (Goldstone etal. 2010, 196–97). While the regime function of the PITF conditions this model to highlight states wherein the structural conditions may reflect imbalances in authority and legitimacy stabilizing dynamics, it likely does not sensitize the model to effectiveness or resilience dynamics that could further undermine a state’s authority or legitimacy in a feedback loop. In addition to regime type, high infant mortality rates, bad neighborhoods, and state-led discrimination practices are also variables that the PITF codes to correspond with instability (Goldstone etal. 2010, 197). The bad neighborhood effect is of particular importance when the geopolitical landscape is conceived as a societal system. As such, acute instability becomes contagious and clusters in space and time. This infectious, non-random clustering increases the likelihood of additional onsets of acute instability in states proximate to an original outbreak. This PITF variable is flawed, however, as it flags only those countries that are bordered by four or more states experiencing major armed civil or ethnic conflict; thus, it is biased to predict acute instability in large, landlocked countries (Goldstone etal. 2010, 194). The programmed variable, then, does not accurately reflect transnational dynamics and is likely to underestimate volatility in many smaller states with fewer neighbors, such as Tunisia or Burundi. The infant mortality rate variable, intuitively, roughly corresponds with effectiveness stabilizing dynamics, but it ignores other measures of this dynamic—such as the prevalence of non-state actors performing traditional state functions or even sparse or nonexistent reporting on fundamental economic or demographic data, indicating a lack of institutional presence—and is ineffective at predicting acute instability fostered by other stabilizing dynamics (Simpson 2016, 2). Finally, using state-led discrimination as a variable to measure the resilience dynamic puts the proverbial cart before the horse. This is not a structural condition at all, but rather the result of a malfunction in this dynamic—it measures an effect, not an enabling condition. Thus, while the PITF may hone itself to characterizing as volatile the environment of states wherein structural and social conditions predispose it to a failure of authority and legitimacy dynamics, it is less attuned to measuring effectiveness and resilience dynamics. As such, it is vital to engage in qualitative analysis to provide context and heuristics for not only the country of interest but also its larger geopolitical region (Ward, Greenhill, and Blake 2010, 364). Qualitative PITF predictions should be interrogated by qualitative analysis that enables tailored, theoretically informed, country-specific assessments, which quantitative algorithms alone cannot replicate. Thus, qualitative models are vital to constructing a comprehensive understanding of the dynamics that shape instability. Remote analysts and experts offer qualitative assessments through comprehensive research globally, and fieldwork can provide real-time, firsthand awareness to deliver empirical observations. Given the advantage of qualitative assessments’ ability to consider case-specific dynamics, analysts’ main task is to find out what is happening and why: “They identify the … immediate causes of tension. They find the people who matter and discover what or who influences them” (Goldstone 2008, 5). Moreover, qualitative analysis can consider sub-annual and subnational data to illuminate local hotspots of crime and violence, indicating social pressures and strains that could diffuse nationally or regionally (Brehm 2015a, 5). Indeed, studies have shown that contemporary events of acute instability often originate locally rather than nationally, whereas most quantitative models use only national-level data as inputs (Ward, Greenhill, and Blake 2010, 373). Perhaps the most important role of qualitative analysis is the application of a theoretical lens to assess instability. For too long, scholarly discourse has resided high in the ivory tower, policymaking has occurred deep in the domain of labyrinthine bureaucratic hallways, and rarely have the two met. The introduction of academic theory and decision-making, the integration of intellectual frameworks and practical experience, is vital to understand the complex modes and trends of instability that characterize the contemporary strategic environment. While such dialogue is diverse, so too are societies—analysts can cherry-pick from among theoretical prisms with assumptions and dynamics that correspond with the conditions and contexts of a society. While infinitely more complex and nuanced than can be captured in this short space, the literature on political instability can be divided into several interacting categories: uneven development, institutionalism, identity conflict, bad neighborhoods, grievance, greed, and opportunistic elites (Chenoweth and Ulfelder 2015, 4–12). While traditional modernization theory holds that the host of social transformations that occur as a society modernizes—industrialization, urbanization, education, and economic expansion—are mutually reinforcing by fostering social mobilization and functional differentiation of political structures, when these pillars develop too rapidly or at different rates, shocks and gaps appear that instead generate a breakdown of legitimacy stabilizing dynamics in modernizing societies (Lipset 1963, 46–76; Deutsch 1961, 505). Institutionalist theories take the implications of modernization gaps a step further by suggesting that the complexity of social forces unleased by modernization must be managed by coherent, adaptable, complex, autonomous, and inclusive state institutions that build state capacity before restraining those institutions with rule of law and norms of democratic accountability (Huntington 1968, 12–24; Wimmer 2013b, 214; Fukuyama 2015, 12, 17). Highly centralized states can aggregate and articulate interests in such a way that more liberal governance systems cannot, enabling the nation-state to speak with a single voice. In plural societies based upon politically salient pillared identity cleavages, as opposed to fluid cross-cutting cleavages, social identity groups can conflict as the nation-state develops around a constructed national identity—such conflict manifests as the state exercises its coercive nature to consolidate this identity to correspond with the state’s territorial borders (Lijphart 1977, 1–52). Through such “pathological homogenization,” outgroups are sorted out, oftentimes resulting in dysfunctional legitimacy stabilizing dynamics as minority groups lose stake in the state and act to revise the system, resilience stabilizing dynamics such as mass displacements and cleansings as the ruling regime attempts to repress the popular uprising, and authority stabilizing dynamics as elites scramble to manipulate the conflicting identity groups (Rae 2002, 4). Effectiveness stabilizing dynamics amplify these as the state builds infrastructure to provide public goods and channel the benefits of modernization to reinforce clientele and patronage relationships or along routinized networks that include geographically concentrated ingroups and exclude geographically isolated outgroups (Geertz 1973, 254–60; Snyder 2000, 45–69; Wimmer 2002, 2–5). Such conflict can diffuse geographically to proximate states where there are cross-border affinities or continuities among identity groups in conflict. Pathological homogenization and exclusive institutional infrastructure can result in grievance-based instability, which is predicated upon “injustices, such as the unequal distribution of power or wealth in a society” (Chenoweth and Ulfelder 2015, 4). Such perceived injustices have been correlated with fragile legitimacy stabilizing dynamics, especially rebellion and civil wars (Gurr 1970, 143–44; Fearon and Laitin 2003, 88–89). In transnationalized dependent societies wherein economic and technocratic elites seek to subordinate institutions to engineer a capitalist economy by deactivating legitimate means of popular articulation, economic tensions often result in dichotomous and volatile swings between populist and authoritarian governance, generating collapses in legitimacy, authority, and resilience dynamics (O’Donnell 1973, 53–114; 1988, 1–33). Grievances can contribute to the bad neighborhood phenomenon as conflict may diffuse to other states wherein social forces perceive similarities in the grievances and circumstances of a social group in conflict elsewhere, resulting in demonstration effects. Grievances and exclusion alone, however, rarely precipitate acute instability without the potential for social mobilization. Such broad participation can be unleashed by urbanization that “concentrates the growing population in ways that may facilitate coordination and cooperation among disgruntled” people (Chenoweth and Ulfelder 2015, 8). Additionally, bulges of unemployed youth; states wherein a robust autonomous civil society yields high organizational capacity; and the presence of primary commodities that geographically favor certain segments, provide opportunities to finance warfare, and tempt exploitation for material benefit may increase the potential for entrepreneurial agents to manipulate social mobilization for political opportunity, increasing the risk of weak legitimacy and authority stabilizing dynamics (Kaplan 2010, 355–56; Collier and Hoeffler 2004, 565–70). The intersections and departures among these theories of instability demonstrate why quantitative analysis alone is insufficient to characterize and forecast instability in a complex strategic environment. While positivist models may leverage theory as a foundation to determine which variables and indicators to program, social systems are such that the causes, endurance, and nature of instability may migrate among different theoretical paradigms depending upon a society’s developmental phase, trajectory, and characteristics. Thus, the tailored analysis afforded by qualitative methods that incorporate regional and local context, sub-annual factors, and theoretical prisms that correspond to dynamic social structural factors can be used to sanity check and augment the algorithmic index provided by the PITF. A Mixed-Methods Approach Whereas qualitative assessments are loath to predict deviations from the norm, the PITF favors greater degrees of instability for intermediate regime types while overvaluing the stability of fully institutionalized, low-income democracies and autocracies. Moreover, the PITF is unable to dynamically apply different theoretical lenses or predict the magnitude of acute instability. Thus, the combination of such divergent models relying on different data and different theoretical assumptions will deliver a greater ability to assess the likelihood and character of acute instability. The additional triangulating power of a second model adds to the identification, even if the second model is less powerful than the first … Having at least two independent approaches to assessing instability, if they point in the same direction, greatly increases the confidence of predictions … If we consider the possibility of using a qualitative forecasting model … in conjunction with the PITF quantitative model, in neither case do we have a “magic die” in which we know which predictions are correct … [If both models] are 80 percent accurate, then by using one model alone, we can never get more than 80 percent of predictions … correct. However, if we use both models, and they both predict a country to be unstable, our confidence in that prediction is much greater. (Goldstone 2008, 7–8) Should the PITF and heuristic analysis of societies yield conflicting assessments, intensive qualitative analysis should be used to interrogate the PITF through a theoretical prism. States experiencing modernization impulses—such as urbanization and increased interactional capacities, improved literacy and education rates, structural and functional differentiation, and economic development—should be examined for imbalances in the development among and within those pillars that could foster discontent in segments of the population, precipitating an environment vulnerable to a failure in legitimacy stabilizing dynamics. When such legitimacy dynamics result in multiple ongoing mass civil resistance movements, resilience dynamics can break down, and the likelihood of those movements remaining nonviolent significantly decreases (Chenoweth and Perkoski 2015). States with weak infrastructural capacity unable or unwilling to provide an equitable distribution of public goods and services may amplify the existing vulnerabilities with imbalanced effectiveness dynamics. In plural societies wherein pillared identities have become existential, ideological, and politically salient, poor resilience stabilizing dynamics are likely to manifest. When these identity groups are organized such that charismatic or empowered elite agents can manipulate social mobilization, a society may become vulnerable to a collapse of authority stabilizing dynamics. Such dynamics are especially present in many fragile postcolonial states in transition from authoritarian rule, especially those experiencing tensions between more populist elites and conservative technocratic elites (O’Donnell 1973, 53–114). Dictatorships are most at risk when they are engaging in reforms designed to “loosen” or “open up” the regimes … Warning bells should always ring when dictatorships change course. Unfortunately, such stirrings of reform do not appear in the PITF data in real time … Conversely … full democracies may be vulnerable to creeping corruption … In these cases, the qualitative analysis should be used to interrogate the PITF results. (Goldstone 2008, 12–13) Measuring social and structural conditions is a vital component of identifying states wherein an unstable environment is likely to generate acute instability. These factors are relatively static features within a social system. While they can evolve, they are resistant to rapid change absent a monumental catalyst. Thus, they are receptive to positivist analysis and can be measured with quantitative indices, interrogated with the theoretical prism of contextual qualitative assessments, to depict the global stability environment and illuminate states that are inherently volatile and prone to acute instability given escalatory tensions and a trigger (Brehm 2015a, 2–4). It is this static nature of societal structures, however, that highlights the necessity of a subsequent phase of analysis to provide accurate risk assessments of acute instability. Phase 2: Identifying Destabilizing Conditions Acute instability does not occur in a vacuum; while specific manifestations are unpredictable and difficult to pinpoint, they are not random. Broad social dynamics and structural factors can influence and shape the character of acute instability, but they do not drive when it will occur (Brehm 2015b). Rather, discrete destabilizing conditions, existing geopolitical strains, and extant social pressures—such as planned power shifts, resource competition, upcoming diplomatic initiatives, anti-state movements, intragroup cleavages, political or economic reforms, political or social repression or marginalization, cross-border disputes, security dilemmas, and so forth—are the fuels that exacerbate instability (Brehm 2015a, 4–6). These drive the development of acute instability; they are the dry tinder. But these simmering tensions alone cannot predict when and to what extent acute instability will erupt. While they precipitate conflict, they do not spontaneously do so. Dry tinder, even when lying in a volatile environment, does not simply erupt into a fire—a spark is needed. “Though the dynamics may differ in each case, all of these conflicts stem from social, economic, and political pressures that have not been managed by professional, legitimate, and [inclusive] state institutions”—this is the essence of a fragile state (Haken etal. 2014, 9). The analysis of destabilizing conditions and the ability of state structures to manage them—evaluated by social and structural factors in Phase 1—can deliver an estimation of instability. Aggregating Phases 1 and 2 into a terrain map that measures environment and includes friction-point analysis yields a comprehensive depiction of countries wherein a triggering event could aggravate destabilizing conditions into acute instability and assesses whether that instability will likely manifest through authority, legitimacy, resilience, or effectiveness dynamics. Rather than produce a precise, pinpoint prediction, the terrain map will highlight a range of countries wherein an unstable environment is primed and destabilizing conditions exist for a trigger event to spark acute instability. Maps are static snapshots in time; however, the world is too dynamic to prepare for only one likely event. Thus, while parsimonious models deliver a simple easy-to-understand range of probabilities, they can belie the challenge to treat the world through a lens of complexity and dynamism, and they require a further phase of analysis to provide near-term and actionable risk assessments. Phase 3: Detecting Triggers and Tipping Points Though the specific events that can aggravate instability and trigger acute manifestations are unpredictable, the trajectory of social dynamics is measurable and can provide a valuable tool “to gauge … likelihood of threatening developments occurring” (Stares 2014, 3). A trigger or tipping point is like the spark that ignites the dry tinder. It is “an event or chain of events that initiate[s] a sharp escalation in … violence … and [is] causally related to violence”; a specific event that “shifts a country from being at risk” of acute instability to acute instability actually beginning by accelerating mobilization of social forces around a cause (Straus 2015, 5, 10).13 While there may be some overlap between destabilizing conditions and triggers as tensions evolve and inflame over time, such as swelling anti-state movements or spiraling security dilemmas, “a tipping point or a trigger is, by definition, a change” (Brehm 2015a, 4). Acute instability is caused by an upswell in emotive content that stimulates action. While structural and social conditions and tensions “may shape situations where [acute instability] is likely to unfold, such factors can rarely explain the onset of violence. Instead, more immediate factors affect the onset of” acute instability (Brehm 2015a, 4). A triggering event “actualizes tensions and emotions that are already present in a particular setting and in turn funnels that tension and emotion in a particular direction” (Straus 2015, 7). Triggers—such as irregular regime change, secession attempts, proximate violence, assassination attempts, legal changes, military attack, violent repression, and so forth—are the immediate events that take advantage of social dynamics, tension, and destabilizing conditions to mobilize social forces and stimulate acute instability; they ignite action. While “a single spark can start a prairie fire … given the right combination of social conditions,” intensification of instability will likely be short lived with only a single spark (Kuran 1989, 60). To escalate latent instability into sustained acute instability, strings of sparking events often must be held in the collective consciousness of a society until a tipping point is reached. “There is a period of tension, fear, polarization, and deterioration of relations that precedes significant violence … A precipitating incident can accelerate and crystalize those tensions and emotions, but the triggering incident cannot be separated from the deterioration and tension that preceded it” (Straus 2015, 9). Tipping is a process determined not by the frequency or distribution of sparks but rather by their aggregation into a constructed narrative that galvanizes social mobilization. Whereas triggers are specific events, the tipping point refers to the juncture or threshold at which a string of events building emotive content around a common narrative “disturbs the original equilibrium” and causes a violent reaction (Schelling 1971, 182). Social dynamics and structural conditions drive societal and institutional sensitivity, resiliency, and reaction to events; every society will respond differently to even similar sets of sparks. Thus, given their inconsistency in probability and impact, detecting a complex series that constitutes a narrative is difficult to map, and identifying a tipping point is near impossible, especially in an uncertainty-based strategic environment (Straus 2015, 11). Because of the complexity and emotive nature of social systems, neither triggers nor tipping points are like a lit fuse—triggers are not visible until something happens, and tipping points can be reached at seemingly arbitrary points along a string of sparks. Still, measuring the intensity and tone of social and political interactions can gauge the likelihood of a trigger being tripped or a tipping point being reached. While “context matters in their accounts … so do the events themselves. Events have independent causal power. They crystallize energy and emotion and present a new beginning to a situation” (Straus 2015, 8). The Conflict and Mediation Event Observations (CAMEO) coding typology—utilized by programs such as the Integrated Crisis Early Warning System (ICEWS) and Global Data on Events, Language, and Tone (GDELT)—“parse[s] and convert[s] digital news reports into structured indices that reflect the character and intensity of interactions” and enables trigger-event and tipping-point detection (O’Brien 2010, 92; Schrodt 2012, 1–87).14 An interactional pattern model, it assesses tone, intensity, and trends of interaction both between and within states to depict the character and trajectory of a relationship that could portend acute instability in the short term—it measures social mobilization and its nature (O’Brien 2010, 92). Because this model analyzes media reports, however, it has some inherent drawbacks. Many unstable states, especially authoritarian ones, exert strong influence or total control over the media. Thus, reports coming from these countries could be highly manipulated or even suppressed entirely, poorly reflecting actual social dynamics. Weak states with an inadequate communications infrastructure could also yield sparse or nonexistent media reporting—of course, such an observation of limited interactions, either as a result of infrastructural deficiencies or communications manipulation, could itself indicate dysfunctional effectiveness or authority stabilizing dynamics. Moreover, such automatic parsing of human interactions can result in incorrect coding of complex or nuanced reporting, thus misrepresenting the social dynamic actually portrayed. Still, a major strength of CAMEO-coded programs is the ability to distill millions of pieces of near-real-time information into a form that is relevant and easily digestible. This information is analyzed by complex algorithms to account for dozens of social dynamics, including demographic pressures, economic development, social grievances, migration effects, leadership traits, state legitimacy, public goods distribution, security sector effectiveness, and external influence variables. CAMEO-based systems aggregate dozens of agent-based analysis models, logistic-regression models, geospatial-network models, and Bayesian model averaging to graphically depict the tone and intensity of interactions among selected actors during a given range of time. Events ranging from –1 (make pessimistic comment) to –5 (demand) are characterized as hostile nonviolent, events ranging to –6 (threaten) to –10 (armed conflict) are categorized as hostile violent, and events ranging from 0 (make public statement) to 7 (provide aid) are considered cooperative. When events indicate conflict de-escalation, they are coded from 8 (sign formal agreement) to 10 (retreat or surrender militarily). Bollinger Bands tracing the deviation between hostile and cooperative intensity in temporally proximate events can graphically depict volatility; divergent lines indicate a volatile relationship.15 In such a relationship, a spike in the number of hostile events has a higher potential of reflecting a tripped trigger, and a steady negative trajectory of interactions may indicate that a social narrative is under construction and social mobilization is occurring that may lead to a tipping point. “People who come to dislike their government are apt to hide their desire for change” as long as such popular opposition appears weak or the government strong; a seemingly sedate relationship might actually represent a latent tipping process, and acute instability may result from even a small spark (Kuran 1989, 41). Thus, a spike that is followed by a cascading trend of hostile interaction can indicate such preference falsification, and the tipping point may be near. Disaggregating and comparing the nature of specific events that comprise interaction spikes and trends to the destabilizing conditions identified in Phase 2 enables a characterization of the sources fueling breakdowns in stabilizing dynamics and galvanizing social mobilization, thus facilitating appropriate responses to focus attention, funnel resources, and drive activities. The Instability Risk Assessment Framework is a multi-phased model. By measuring broad social and structural factors, it enables an assessment of the stabilizing dynamics extant in each state shaping the nature of stability or instability, highlighting volatile states wherein the environment is vulnerable to acute instability. The identification of specific destabilizing conditions in those states provides a tool to assess the concrete tensions that are likely to drive the development of instability. Analyzing the tone and trajectory of social interactions charts social mobilization by comparing the nature of specific sparking events in a deteriorating or volatile relationship with the nature of the destabilizing conditions therein. A spike in the intensity of hostile interactions that correlates with the destabilizing conditions may indicate that a trigger has been tripped, and a sustained negative trend in social interactions can indicate a tipping process has further undermined the equilibrium of stabilizing dynamics in a highly volatile state. Instability risk assessment is not formulaic. In an environment of uncertainty across vastly disparate and complex societies, there is not a single or universal combination of measurable factors that can pinpoint specific manifestations of acute instability. There are, however, social dynamics that can be analyzed to reveal a realm of possibilities within which events are likely to occur. While neither specific triggering events nor exact tipping points can be predicted, it is possible to monitor the effect of specific events and the trajectory of social dynamics, which can indicate whether a trigger has been tripped or a tipping point is near. Disrupting the Flow Coincident with the end of the Cold War, the strategic environment shifted from a threat-based one—wherein the United States faced a single, predictable adversary—to an uncertainty-based one—wherein salient threats are diffuse, multiple, and simultaneous. Outside the dyadic polarity of the Cold War, the complexities of increased transnational interdependence and interaction capacity have demanded a wider aperture from Washington policymakers and a sharper depth-of-field from analysts in an environment wherein anything can happen at any time. The post–Cold War peace dividend has been spent, and the result is the perception of an unpredictable global strategic environment wherein “crisis” is constant and conflict is pervasive. In such a strategic environment, “assessments must go beyond specialized area knowledge, narrative case studies and anecdotal evidence to identify and grasp broad social trends” (Haken etal. 2014, 9). But large-N models that aggregate myriad distinct samples with vastly varying contexts into a single probabilistic model “can lead to misleading policy prescriptions” (Ward, Greenhill, and Blake 2010, 364). Thus, a mixed-methods, multi-phased approach of qualitative research and theoretical prisms, as well as quantitative methodologies and statistical computation, is required to identify social and structural conditions, ascertain existing tensions and friction points, establish patterns, monitor the trajectory and tone of social interactions, and deliver a comprehensive instability risk assessment. In 2011, most analysts were surprised as generally peaceful protests in Syria turned violent, precipitating a complex, multi-sided civil war that has lasted over five years. While hindsight provides an easy target, both quantitative models and qualitative analysis in isolation failed to predict Syria’s descent into civil war. For example, even after mass demonstrations began in 2011 and the regime began to crack down, the Peace and Conflict Instability Ledger continued to rank Syria’s risk of future instability as low (Hewitt 2012, 15; Backer and Huth 2014, 7). Likewise, separate quantitative models by the Brookings Institution, the Center for Systemic Peace, and the Fund for Peace from 2008 to 2011 assessed that the instability environment in Syria was only modestly volatile (Rice and Patrick 2008, 40; Marshall and Cole 2009, 27; Fund for Peace 2011, 15). When interrogated qualitatively, analysts generally agreed with the quantitative models due to the historical endurance of autocratic regimes in the region and the general standard of stability observed in consolidated autocracies (Backer and Huth 2014, 7). Assessments by the International Crisis Group did not forecast violent conflict in Syria before it began, and the Council on Foreign Relations failed to reflect the potential for instability in Syria in its 2011 Preventive Priorities Survey (International Crisis Group 2011; Council on Foreign Relations 2011). While aggregated indicators of instability run through computational algorithms may have produced a collective total that suggested the probability of stability, and qualitative assessments failed to anticipate significant upheaval based on historical patterns in the region, Syria was indeed experiencing high levels of structural and social conditions that correlated with a breakdown in legitimacy and resilience stabilizing dynamics, including mass displacements, group grievances, human rights violations, uneven economic development, and identity manipulation by political elites (Borshchevskaya 2010, 46–47; Fund for Peace 2011, 15). Through the prism of social network dynamics, then, the conclusions of the quantitative model should have warranted further exploration through a theoretical lens. Ruled by an authoritarian minority Alawite regime, Syria had developed its infrastructure and institutions around patronage networks to manipulate social identities and buttress the Alawite minority while excluding other identity groups from full participation in the state. The result of such patronage systems is the institutionalization of politically salient pillared identities and the failure of the state to channel social forces through institutions that are complex, accountable, inclusive, autonomous, or adaptable—structural conditions that put Syria at risk of failures in legitimacy and resilience stabilizing dynamics (Donate 2013, 36; Heydemann and Leenders 2013). Since taking power in 2000, Bashar al-Asad had attempted to expand his patronage network by renewing the regime’s entrepreneurial clientele through “selective liberalization” and modernization of the banking and financial sectors, aimed at encouraging higher levels of macroeconomic performance to enhance the regime’s access to capital used to capture the loyalty of a wider base of clientelist elites (Donate 2013, 36–40). These efforts had the effect of weakening the power of Syria’s traditional Ba’athist patronage base and eroding cohesion among the ruling elite, fostering high levels of dysfunctional authority dynamics (Sadowski 1987, 457; Donate 2013, 49). Moreover, the upshot of redistribution of infrastructure away from rural areas was uneven development between urban centers and the rural periphery, opening development gaps and introducing grievance-based stresses to legitimacy stabilizing dynamics (Hinnebusch 2012, 108). Such a potent combination of the mobilization potential of high levels of unemployed youth, pillared identity groups, and disaffected elites combined to create an environment wherein these social groups could be manipulated to place pressure on the Asad regime, amplifying the existing broken authority dynamics that accompanied selective liberalization and legitimacy dynamics of excluded social groups mobilizing around perceived grievances. Thus, there existed a volatile environment experiencing breakdowns in legitimacy dynamics that could precipitate protests or revolution; authority dynamics that could result in coups d’etat and civil wars; and potentially resilience dynamics that could result in state-led repression, mass displacements, and mass killings. The confirmation of powerful destabilizing dynamics would have escalated Syrian analysis to Phase 2: identifying destabilizing conditions. By February 2011, Arab Spring uprisings were occurring in at least five other countries—Tunisia, Egypt, Libya, Yemen, and Bahrain. Analogous protests, driven by perceived similarities between the circumstances of social segments in Syria and those of other Arab Spring countries, occurring in Syria amounted to a clear friction point that threatened to drive acute instability shaped by the dysfunctional authority and legitimacy dynamics identified in Phase 1. Rather than reforming its institutions to reflect these grievances, the regime responded with large-scale repression (Heydemann and Leenders 2013, 3). Destabilizing conditions, then, consisted of regime-threatening civil resistance movements, the potential for bad neighborhood demonstration effects, and increasing state-led repression. Through January and February, after Tunisian and Egyptian protesters succeeded in forcing a change in government, Phase 3 analysis—detecting triggers and tipping points—yielded CAMEO-coded models that registered a steep negative trajectory in the tone of interactions between the Syrian regime and the opposition, possibly representing a tipping process. The tipping point appears to have been breached in March, however, when NATO airstrikes in support of the Libyan opposition were followed by a significant spike in the intensity of hostile interactions in Syria. Given the strong possibility of international diffusion effects suggested by the timing of intensifications in the Syrian uprising, it is likely that the prospect of NATO military support triggered armed conflict in Syria as the stakes became more saliently existential for both the Asad regime and the opposition (Kuperman 2013, 131). While counterfactual analysis is far from definitive, there is a high likelihood that the use of this framework prior to the outbreak of mass violence in Syria could have anticipated the conflict and mitigated the surprise of Washington policy elites, opening decision space and better positioning policymakers to make key decisions regarding intelligence focus and timely resource allocation. “The history of humankind can be viewed as a narrative flow with myriad tributaries and estuaries that meander and converge into rivers and streams, and whose courses are punctuated periodically by monumental events. Monumental events occur as the result of the interplay between the dynamic stream and its structured landscape, and are driven by a ‘gravitational force.’ They alter the course of the narrative flow in ways that appear disruptive” (Marshall 2011, 95). Acute instability occurs as emotive responses to the complex and dynamic interplay between communal, national, regional, and global environments. “The emotive content of violence distinguishes it from all other forms of collective action,” and lends unpredictability to an interconnected world wherein instability is contagious (Marshall 2011, 100). While the specific manifestations of instability are unpredictable, environments and existing social dynamics shape the general character of them. Thus, a comprehensive, phased approach, depicted in Figure 1, that (1) analyzes social and structural factors that shape the character of instability to depict the global environment; (2) identifies contextually attuned social friction points and geopolitical tensions that comprise the destabilizing conditions driving instability; and (3) monitors the trends and trajectories of social dynamics and interactions to detect triggering events and tipping processes that could catalyze these conditions, mobilize social forces, galvanize emotive reactions, and spark acute instability. In this way, resources can be leveraged to quickly pivot attention as monumental events disrupt the flow of the river. Fig. 1. View largeDownload slide Instability risk assessment framework. Fig. 1. View largeDownload slide Instability risk assessment framework. Acknowledgements For their provocative insights and conversations, and for their invaluable comments, critiques, and suggestions on earlier drafts and multiple rounds of revisions that significantly strengthened this article, the author would like to graciously acknowledge and thank Hollie Brehm, Jack A. Goldstone, Monty G. Marshall, three anonymous reviewers, and the editorial staff at ISR. Disclaimer: The opinions and views expressed herein are solely those of the author and do not reflect or represent endorsement by the US Joint Staff, Department of the Navy, Department of Defense, or US government. 1 This article is primarily focused on state-level dynamics because intrastate conflicts and fragile states constitute the bulk of salient instability in the contemporary global environment; however, society exists at multiple levels—including subnational, regional, international, and global—each of which consists of social forces and some degree of ordering system or authority. Thus, the concepts developed here can be adapted to explain stabilizing dynamics and deliver risk assessments at any level of society. Further research, however, would be required to specifically operationalize the framework to each level. 2 Instability exists when there is a failure in one or more of the stabilizing dynamics in society; it can be latent or acute. Instability is latent where such dysfunctional stabilizing dynamics result in an unstable, volatile, or fragile environment wherein tensions and the potential for instability to become acute exist but have not yet become manifest. Acute instability occurs when social forces have mobilized as the result of a breakdown of stabilizing dynamics and such instability has become symptomatic, resulting in violent conflict, a salient risk of irregular regime change or threat to territorial integrity, or what Mann (2005, 12) referred to as “murderous cleansing,” which can be observed in extreme forms of institutional coercion, policed repression, callous policies, and premeditated mass killing. 3 These divisions are artificial because of complex social dynamics that are overlapping, intersecting, interdependent, and multifaceted; they should not form the foundation of any risk assessment but merely provide ideals and structure to frame analysis and a prism through which instability risk assessments can enable a rudimentary characterization of the likely nature of acute instability. 4 Gurr (2000, 55) dubbed the decline in ethnic warfare during the 1990s the “New New Thing,” juxtaposing it with the “new thing,” the peak in ethnic conflict from 1970 to 1993. 5 For quantitative forecasts and assumptions predicting increased instability after the Cold War, see Huntington (1991, 12); Puddington (2011, 1–2); and Marshall and Cole (2008, note 3). For empirical evidence of decreased conflict, see Gurr (2000, 53–54); Marshall and Gurr (2005, 11); Human Security Centre (2005, 148); Marshall and Cole (2008, 3,7); Goldstone etal. (2010, 192); Themnér and Wallensteen (2011, 525); Marshall (2011, 95); and Marshall and Cole (2014, 12). 6 For a description of the cascades of democratization, see Marshall and Cole (2014, 3). There have been four cascades of democratization since the end of World War II. The first cascade occurred in South America beginning in the late 1970s. The second cascade “became evident in the East European region in the late 1980s.” A third cascade “followed the collapse of communism and the ending of the Cold War” in the former Soviet states and in sub-Saharan Africa (Marshall and Cole 2014, 28–29). 7 See also Rabushka and Shepsle (1972, 202); Geertz (1973, 269–79); Lijphart (1977, 18); Linz and Stepan (1996, 24–33); Snyder (2000, 27–31); Wimmer (2002, 107–9); Paris (2004, 6–7); Mansfield and Snyder (2005, 39); and Miller (2013, 90). 8 Fixed-identity markers are what Geertz (1973, 259) referred to as “the assumed givens”—those features that determine attachments and communal bonds that are formed ipso facto and are not easily changed, such as religion, region, primary language, custom, assumed blood ties, race, tribe, ethnicity, physical attributes, and so on. 9 A fat tail describes the tails that bookend the distribution of probabilities on the left and right extremities of a statistical bell curve. When these tails balloon rather than nearly vanish, they are “fat,” indicating that events located along the fringes, perceived to be unlikely, are in actuality more likely to occur than one would expect. 10 The PITF divides its rankings into tiers, the highest tier constituting the most likely countries to experience acute instability. It measures its accuracy by the percentage of acute instability events that occurred in countries included in the top tier. 11 Goldstone etal. (2010, 191–92) define large-scale violent conflicts as those that result “in at least 1,000 total deaths from conflicts involving state forces, sustained at a rate of at least 100 deaths per year.” Adverse regime changes are “major, adverse shifts in political institutions that involve the sudden loss of authority of central state institutions and/or their replacement by a more radical or nondemocratic regime … [signaled by] a downward shift, within three years, of six or more points on the 20-point Polity IV autocracy-democracy scale. Also counted as an adverse regime change is the collapse of central state authority … the overthrow of a government by a radical revolutionary regime … and the contested dissolution of federated states or the secession of a substantial area of a state by extrajudicial means.” Genocides and Politicides are “sustained and purposive efforts by states or their agents to visit extreme violence and/or death upon a particular communal group or political group… . Genocides and politicides often result in fewer victims than civil wars.” 12 Marshall, Gurr, and Jaggers (2014, 4, 14–16) explain that: the Polity IV dataset encompasses 162 contemporary countries … including all countries where the 2006 population exceeds five hundred thousand … Democracy is conceived as three essential, interdependent elements. One is the presence of institutions and procedures through which citizens can express effective preferences about alternative policies and leaders. Second is the existence of institutionalized constraints on the exercise of power by the executive. Third is the guarantee of civil liberties to all citizens in their daily lives and in acts of political participation … The Democracy indicator is an additive eleven-point scale (0–10) … In mature form, autocracies sharply restrict or suppress competitive political participation. Their chief executives … exercise power with few institutional constrains … An eleven-point Autocracy scale is constructed additively … The Polity score is computed by subtracting the Autocracy score from the Democracy score; the resulting unified Polity scale ranges from +10 (strongly democratic) to –10 (strongly autocratic). 13 In empirical analysis of 16 cases of atrocity crimes since 1941, Straus (2015, 11) found that “the primary triggers for atrocity are 1) significant changes in the strategic environment; 2) takeovers of territory with populations perceived to be associated with the enemy; 3) crackdowns on protest; and 4) violations of symbolically significant situations.” 14 See Arva etal. (2013) and Ward etal. (2013) for discussions on the differences between GDELT and ICEWS. 15 Bollinger Bands were developed in the 1980s as a risk assessment tool to measure market price volatility. 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TI - The “New” Normal: Instability Risk Assessment in an Uncertainty-Based Strategic Environment
JF - International Studies Review
DO - 10.1093/isr/viw038
DA - 2016-12-16
UR - https://www.deepdyve.com/lp/oxford-university-press/the-new-normal-instability-risk-assessment-in-an-uncertainty-based-QuRH05WA95
SP - 1
EP - 227
VL - Advance Article
IS - 2
DP - DeepDyve
ER -