Defining tipping points for social-ecological systems scholarship—an interdisciplinary literature review

The increasing popularity of the tipping point concept in both scholarship (Russill and Nyssa 2009) and political and social discourse (Russill and Lavin 2012), combined with increasingly diverse conceptualizations of tipping points across multiple disciplines (Kopp et al 2016), currently limit its scholarly utility. Research on social-ecological systems (SES) has contributed to the growth and diversity of the use of the term, treating tipping points as features of SES. Two trends are important to distinguish. First, SES scholars increasingly use the term tipping point to describe the well-known phenomenon of regime shifts in ecological systems. Second, a small number of more recent publications, not only in SES, have begun to refer to non-linear forms of change in social systems, such as economies or resource governance institutions, as social tipping points (STPs), social-ecological tipping points (SETPs), but also social-ecological regime shifts. Some of these publications frame STPs as desirable or necessary to address a range of sustainability challenges (e.g. Westley et al 2011) and seek to understand the conditions for creating tipping points in social systems. These trends raise important questions concerning the meaning and appropriate use of the term tipping point in SES research.

Being located at the intersection between the social and natural sciences, SES researchers need to tread carefully when borrowing concepts from other disciplines. Such a move often involves the crossing of ontological boundaries, where the metaphorical use of a concept can mask important differences between two objects of study. The two phenomena included in the analogy should be similar in the sense that they can be characterized by common laws or principles. The success of the analogy depends on whether attributes of tipping points in the target domain can be tested and assessed similar to the one in the source domain (Daniel 1955, Gentner 1983).

However, SES research pays little attention to whether the presumed observation of tipping behavior in a social system is conceptually equal or (partly) different than tipping processes in an ecological system. It remains unknown whether tipping points in natural systems, such as a lake or the climate, display the same underlying mechanisms as tipping points in social systems, such as in financial markets or political institutions. Hence the question whether the social tipping point is not only metaphorically powerful, but also appropriate to describe the phenomenon in question—is it a distinct type of social change? The analytic challenge is confounded by the fact that SES are inherently linked systems, with multiple feedbacks and interdependencies between the ecological and the social system components. Given this fundamentally important framework of linked SES, a separation of the ecological and the social system is generally perceived as counterproductive to the advance of scientific understanding in the SES community. Such a separation contradicts a core tenet of the discipline.

Whilst scholars might perceive the distinction we make here as artificial and counterproductive—questioning whether there could be fundamental, ontological differences between ecological and social systems—this paper aims to create the foundation for a discussion within the SES research community about the appropriate use of the term tipping point, especially the relatively novel use of the terms social tipping point and social-ecological tipping point. We do not ask whether certain types of social system change should be understood as a distinct class of tipping points—that is a matter for future research. Instead we analyze how scholars across multiple disciplines have defined tipping points in general, and how they use the term social tipping points in particular.

For this purpose, we seek to create a fuller understanding of the status quo of the scholarly use of the term tipping point and similar concepts by reviewing existing literature across all scientific spheres. Using a bibliometric approach, we examine temporal trends in terminology, identify different definitions and their components, and explore the corresponding phenomena these definitions address. Based on this review, we discuss the most appropriate use of the concept for the study of SES. To the extent that differences exist between tipping point definitions across disciplines, we identify key definitional features relevant for SES research. We hope to sensitize scholars to potential differences between the phenomena described with the term tipping point, and to encourage a more critical and consistent use of the term in the future. Given that the use of STP and SETP language is at an early stage, this is an opportune time for this discussion.

The tipping point concept traces its origins back to scientific papers in chemistry (Hoadley 1884) and mathematics (Poincaré 1885), which refer to a qualitative change in a system described mathematically as a bifurcation. Bifurcation theory is still used today in mathematics, physics, complex systems science, and related fields.

In the social sciences, tipping points originated much later to address neighborhood dynamics of racial segregation in political science (Grodzins 1957), sociology/urban planning (Wolf 1963), and economics (Schelling 1978). Social scientists began to develop similar concepts of social change without the tipping point language. For example, sociologist Mark Granovetter (1978) uses the term threshold to understand the differences in individuals' decisions to engage in a collective behavior, such as rioting. Another example in social theory is punctuated equilibrium, especially historical institutionalism (Gould and Eldredge 1993). These theories emerged in the 1990s, drawing on evolutionary biology to explain long periods of policy stability that are interspersed with dramatic moments of change (Baumgartner and Jones 1993). Instead of using the typical formalization used in bifurcation theory, social scientists either developed different mathematical models or used the concept metaphorically.

Whether or not it can be attributed to Malcolm Gladwell's book The Tipping Point (2000), starting around 2005, the term was widely adopted among climate scientists (Russill and Nyssa 2009, Kopp et al 2016) to describe rapid, non-linear change in parts of the climate system. Previously this phenomenon had been referred to with different terminology, such as critical points, but now climate scientists embraced tipping point language, with three papers using tipping point terminology to focus on ice sheet dynamics in the Arctic (Holland et al 2006, Lindsay and Zhang 2005, Winton 2006). A 2008 paper introduced the idea of tipping elements in the climate system, defined as subsystems of the climate system that can experience abrupt change, 'triggering a transition to a new state' (Lenton et al 2008, p. 1786).

The historical account of the movement of the concept from its origins in mathematics and chemistry to the social sciences, popular discourse and back to mathematical modeling in the climate sciences raises important scientific questions, as summarized by Russill and Nyssa (2009, 337):

'One difficulty in assessing the appropriateness of tipping point warnings is the frequent slippage from physical to biological to social referents,.... Is the notion appropriate as a description of the way physical components of the climate system change, or as a means of understanding social behavior, or both? Is it intended as a scientific concept, or as a metaphor?'

The increasingly frequent use of the concept of tipping points in both the natural and social sciences could be scientifically questionable: sociological and political tipping points might be very different phenomena than climatic tipping points, even if both natural and social systems may be subject to rapid qualitative change. If institutional tipping and ecosystem tipping are different 'things in nature'—different ontological entities—scientific language should not treat them as the same. Scientific language should clarify rather than veil potential differences between tipping points in different fields.

This is a challenge SES scholars will have to take up if they want to successfully grapple with dynamics of change in SES and the implications of linkages between natural system components and social system components, which are subject to different modeling challenges. The math applied to one might not transfer easily to the other.

SES scholarship has traditionally used two distinct terms to describe tipping point phenomena: regime shifts (also critical transitions (Scheffer 2009)) and transformations. Regime shifts are large, persistent changes in the structure and function of social-ecological systems (Folke et al 2004). Transformations are fundamental reorganizations of a system (Gunderson and Holling 2002, Olsson et al 2014) that might involve rapid, non-linear change or not. However, both regime shifts and transformations are the result of a system reaching and passing a tipping point rather than being synonymous with a tipping point. Hence, the use of the term tipping point to refer to these larger processes of change is imprecise.

Currently, regime shifts are generally thought of as undesirable processes that could and often should be prevented (Scheffer and Carpenter 2003), while (deliberate) transformations have primarily been framed as desirable and often guided change processes that communities and societies pursue in the face of sustainability challenges (Moore et al 2014). Westley et al (2011) have explicitly linked the idea of deliberate transformation to the tipping point concept, which has been picked up by policy makers and diplomats dealing with climate change and sustainable development. Against this background we aim to map and interpret the use of the term tipping point.

We deployed two complementary data collection and analysis protocols in order to create a synthesis of the scholarly use of tipping points from a quantitative and qualitative perspective, using a bibliometric approach. We developed the approach for both the quantitative and qualitative analyses through an expert elicitation process (Ayyub 2001, Swor and Canter 2011). Four of the authors issued a call for scholars with an interest in social tipping points in a network of young resilience scholars. We repeated our invitation to the audience (n = 33) of a session on 'Concepts, Methods, and Measurements of Social Tipping Points,' which we co-chaired during the Complex Systems Conference 2015 at Arizona State University. The call targeted a very specific audience: early career scholars from various disciplines, primarily conducting research on SES and resilience. The respondents joined the lead authors to form the group of experts, whose perspectives guided this analysis (see table S1 for details).

4.1. Methodology

4.2. Results

In total, the 20 initial search terms resulted in 10 354 publications for the time period analyzed. Removing duplicates (n = 852), the final quantitative dataset included 9476 unique publications. Table 1 displays the ten search terms generating the largest number of results and the results for each term after adding 'AND social' to the search string (see supplementary material for a complete list—table S2 available at stacks.iop.org/ERL/13/033005/mmedia). Full records for all publications were downloaded into a dataset, and labelled as per the related search term.

The most popular search term from our honed list of disciplines was 'regime shift^⁎', with 3428 publications, followed by 'critical transition^⁎' with only half as many publications (1824) and 'tipping point^⁎' ranking third with 1718. However, 'tipping point^*' publications were much more likely to have a focus on social systems: there were 218 records for the search string containing 'AND social' (12.7% of 1718) compared to 223 'regime shift^*' papers (6.6%). The dominant disciplines using the term 'regime shift^*' were Ecology (784 papers) and Oceanography (666); 'critical transition^*' was dominated by various sub-fields of physics. The majority of 'tipping-point^*' publications were in the WoS categories Environmental Science (203) and Ecology (163).

All strings including tipping (tipping point, tip point, tipping element) appeared in 2342 unique records (25%). Specific uses of the term such as 'social tipping point^⁎' and 'climate tipping point^⁎' were found very few times—four and 34 respectively. The combination of 'climate change' and 'tipping point^*' was used in 357 publications.

4.2.1. Temporal developments of terminology

Figure 1 shows how the prevalence of the concept tipping point began to increase in the early 2000s, growing rapidly over the last decade.

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Figure 2 demonstrates the evolution of the five most popular search terms over time. The terms 'critical transition^*' and 'punctuated equilibrium' emerged earlier than the others. Publication counts for 'punctuated equilibrium' have stabilized around 40/year, while critical transitions work continues to expand with 202 publications in 2016. 'Regime shift^*' and 'alternative stable state^*' became established terms in the 1990s; the former has experienced much more dramatic growth than the latter. 'Tipping point^⁎' begins to spread after the year 2000, rapidly increasing after 2004. Since 2011, its annual publication count has been higher than that for 'critical transition^*'. Given its late emergence—about a decade after 'critical transition^*' and 'regime shift^*', its high rank could indicate that its popularity is higher than that of other terms.

5.1. Methodology

5.2. Results

Both the quantitative and qualitative analyses have highlighted similar temporal patterns in the scholarly use of the term tipping points since 1960, especially the term's late re-entry into scholarly discourse compared to similar terms, and a major popularity boost after 2008 in all scientific spheres. In research post-2000, the tipping point is not used to describe a unique phenomenon in nature, but added on to previously existing research with well-established terminology, such as regime shifts and critical transitions. There are weak signals that after a period of 'conceptual companionship' the tipping point becomes the preferred terminology, leading to a decline of the previously dominant term. This pattern is not unique in the history of science; examples include the use of terms ecosystem (Tansley 1935), boundary object (Leigh Star 2010) and resilience (Brand and Jax 2007, Baggio et al 2015). While the excitement around new concepts might have positive effects, it also involves risks. Most importantly, the application of the same term to diverse phenomena that were previously studied with diverse terminology might create the appearance of similarity where there is significant difference. Existing differentiation and empirical diversity in the objects of study might be concealed, and diversity in the ways of thinking about non-linear change might be lost. The popularity of a term might lead to homogenization that is detrimental to scientific progress.

Despite its late emergence compared to other terms, and the tendency to combine multiple terms, tipping point was the most popular concept overall in the qualitative data set and ranked third (close to critical transitions) in the quantitative data set. While other terms do not seem to be in decline, their growth might be slowing due to the growing popularity of the tipping point as a synonym.

The analysis revealed substantial definitional diversity and a broad array of themes within the working definitions of tipping points in our dataset. A number of these themes overlap, are related to each other, or are synonymous; in other words, these are not 23 distinct features of tipping points, but different terms or phrases used by scholars to describe a smaller set of tipping-point characteristics. For example, the theme Structural Reconfiguration is different than, but closely related to, the idea of Multiple Stable States—the latter implies and requires the former. Another example of related themes concerns causality: External Causes, Multiple Causes and Erosion of Current Attractor form a thematic cluster. Further, some themes appear to be more important for establishing the existence of a tipping point, while others refer to aspects of tipping points that are subject to a specific disciplinary interest (e.g. governance implications in Political Science). Given these relationships between different themes and the potential for clustering, redundancy and hierarchy among them, not all 23 themes are necessary to define tipping points.

The analysis also suggests that different disciplines focus on different combinations of themes, but we were not able to identify discipline-specific definitions (i.e. distinct patterns of thematic combinations). However, the frequency (popularity) analysis we presented above offers a first indication of what might be a minimum set of necessary conditions to identify a tipping point. The eight most frequently used themes across all disciplines—without redundancies—can be reduced to four necessary (and potentially sufficient) conditions: (1) Multiple Stable States (implying a certain Magnitude of Change and a structural reconfiguration of the system), (2) Abruptness (also Non-linearity or Disproportionality between Cause and Effect), (3) Feedbacks as system-internal drivers of change between the two system states as well as state stabilizers, and (4) Irreversibility. The fourth condition needs to be weakened in the sense that limited reversibility (Hysteresis) and Irreversibility on a timescale relevant for human societies are sufficient to fulfill that condition. The remaining eighth popular theme—Predictive Challenges—is not so much a characteristic of tipping points themselves as a characteristic of tipping point research given current methodological and data gathering challenges. Figure 3 illustrates this differentiation between necessary conditions in the center of the concentric circles, and sets of additional themes grouped around these necessary conditions. The reasons for grouping certain themes and their presumed distance from the circle's center all raise questions for future research.

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Based on these four minimum conditions, we suggest that tipping points in general can be defined as the point or threshold at which small quantitative changes in the system trigger a non-linear change process that is driven by system-internal feedback mechanisms and inevitably leads to a qualitatively different state of the system, which is often irreversible. This new state can be distinguished from the original by its fundamentally altered (positive and negative) state-stabilizing feedbacks.

Applying this general definition to social tipping phenomena in SES requires a number of additional specifications. Importantly, the tipping point has to take place in a social system, but be linked in some meaningful way to an ecological system change. That does not preclude social drivers or causes. Whilst a social tipping point with purely social system drivers and outcomes is of course possible (e.g. widespread opinion changes on same-sex marriage), this kind of STP would not necessarily be of interest to SES scholars. Therefore, a social tipping point can be defined as a point within an SES at which a small quantitative change inevitably triggers a non-linear change in the social component of the SES, driven by a self-reinforcing positive feedback mechanisms, that inevitably and often irreversibly lead to a qualitatively different state of the social system. Due to the interconnectedness between social and ecological system components, crossing a social (or ecological) tipping point leads to a qualitatively different SES, which is characterized by a different set of stabilizing positive and negative feedbacks.

This proposed definition offers an opportunity to narrow the focus of future SES research on STPs. Our analysis revealed that SES scholarship uses all of the 15 most popular themes with low convergence across papers. Advancing knowledge on STPs and coherence within SES research would benefit from a more clearly defined set of necessary and sufficient conditions, as we attempt to present above. These conditions would create much needed clarity by allowing researchers to distinguish cases of change that qualify as tipping points from those that do not. A focus on the four characteristics we emphasize above could be complemented by two or three additional ones that are of special interest to SES scholars. These additional features could include Magnitude of Change (as well as Impact Severity), which is important to distinguish exceptional, infrequent but high-impact events, delineating tipping point change from other forms of change, and drawing policy attention. Work on Predictive Challenges is already progressing in SES with regard to early warning signals for regime shifts in ecosystems (Scheffer et al 2009, Dakos et al 2012, Bauch et al 2016) and could be expanded to STPs.

However, even if we assume that STPs are ontologically similar to climate tipping points or ecological regime shifts, and hence can be defined as we have proposed, it is important to note that STPs require a different set of methodological approaches than natural science research on tipping points. Social systems have a number of characteristics that do not exist in an ecological system, decreasing their regularity compared to ecological systems, limiting comparability across locations and hence their predictability. These characteristics include phenomena such as power and inequality, agency, reflexivity, decision-making and strategic behavior at individual and collective system scales. The continuous collection of time-series data in a social system that would enable the observation and measurement of non-linear change, feedbacks and different stable states is challenging, if not impossible given currently available social scientific methods and approaches. If the relevant variables can be identified, they are often hard to quantify and measure over time. Even if they can be measured, continuous data collection for a specific system over years or decades is rare and would be extremely expensive. Some of the relevant social system variables include cognitive and emotional states of actors or institutional decision-making processes subject to political contestation, value judgements and other social processes that present significant data collection problems. Social scientific methods, such as interviews, surveys, process tracing, ethnography, or document analysis tend to be spotty, incomplete and not able to offer a sufficient, continuous database for quantitative tipping point analysis.

For instance, compare the possibility for observing an ecological or social tipping point in a community of resource extractors, such as a fishing village. Researchers could track variables related to the fishery, including fish stock and harvest size, water pollution, water temperature and state of the local reef, which would enable them to identify an ecological regime shift, leading to the potentially irreversible collapse of the fishery. An STP in the same village would be harder to determine because it would require measuring state variables such as identity or community values, but also somewhat easier-to-observe variables related to behavior. For instance, if fishermen cease to fish over time (maybe motivated by a decline in the fish stocks), and start taking up other income-generating activities, the community's identity as a fishing community may shift irreversibly to a different social state, altering the social-ecological feedbacks (e.g. loss of fishing knowledge, no investments in fishing assets etc.).

This methodological challenge might be related to a specific difference between tipping point research in the natural and social sciences we identified above. Natural scientists (especially in Earth system and climate science), complex systems scholars, and a small number of SES scholars (Lade et al 2013, Sugiarto et al 2015, Biggs et al 2009) apply bifurcation theory when studying tipping points, whilst research in the social sciences and the majority of SES work tends to use the term more loosely and metaphorically, lacking a formal or mathematical model. Of course, there are exceptions to this observation, both inside and outside of our limited dataset. For example, work in economics and sociology has developed different mathematical models to study tipping points (e.g. Sakamoto 2014). However, attempts to apply Poincare's math to aggregate social behavior in the social sciences and SES remain limited, leaving the question unanswered whether tipping points in natural and social systems are in fact similar, i.e. operating with the same underlying mechanisms of feedback-driven non-linear change.

We have explored the scholarly use of the tipping points concept across all spheres of science over the last five decades with the aim to discuss the appropriate use of the term in SES research, especially with regard to social tipping points. Our research found that the tipping point concept is applied to a vast array of change processes, ranging from ice sheet dynamics to societal transformations, which might mask ontological differences between these diverse phenomena. Concerned about the pattern of terminological replacement—the use of tipping point language instead of previously existing terms—and its potential effects on the quality of science, we encourage researchers to critically assess their terminological choices and avoid 'conceptual amnesia'.

Despite the historical roots of the concept in mathematics, very little work outside the natural sciences and SES uses mathematical models and bifurcation theory. SES scholarship could make a significant contribution to this debate, while addressing the lack of definitional focus and rigor, by applying existing mathematical models to the study of social tipping points. If one assumes that natural and social systems are ontologically similar enough to render the tipping point more than a conceptual metaphor, it should be possible to apply the same mathematical construct to study both, identifying slow and fast variables, underlying causal mechanisms and feedbacks, measuring and quantifying relevant variables. If one assumes STPs to be ontologically different, this could be explicitly formalized in the mathematical treatment of the specific phenomenon in question, for example, by incorporating agent based approaches to represent heterogeneity in agency and power dynamics, or unambiguously identifying the social mechanisms that are absent in the ecological counterparts of STPs (e.g. Alshams et al 2017). However, whether it is possible to apply these tools to social and social-ecological change phenomena remains unclear and is a subject that requires future research. Establishing such similarities would be welcome news for work in SES, which seeks to study linked natural-social systems as a single unit. More generally, SES researchers need to tend to the question whether there might be differences between tipping processes in ecological and social systems.

To conclude, we have proposed a unifying definition for tipping points, building on the most frequent themes identified in our analysis: a tipping point is a threshold at which small quantitative changes in the system trigger a non-linear change process that is driven by system-internal feedback mechanisms and inevitably leads to a qualitatively different state of the system, which is often irreversible. This definition establishes a minimum set of four constitutive features of tipping points that apply across disciplines (multiple stable states, non-linear change, feedbacks as driving mechanism, limited reversibility). If these four essential characteristics are given, the use of the term tipping point is justified. We have also proposed a definition for social tipping points specifically for use in SES scholarship. Future work in this area should include the application of these definitions to case studies of tipping points in the existing SES literature, and also within new research on tipping points in SES.

Research for this paper was not supported by any grants. We are grateful to the Department of Community Sustainability, Michigan State University; the Julie Ann Wrigley Global Institute of Sustainability, Arizona State University; and the Wageningen University & Research Investment theme Resilience (KB-29-006-003) for supporting the publication of this work.