The recent United Nation Secretary General's report on sustaining peace speaks to an urgent crisis of complexity in global affairs, where a wide assortment of nonstate actors wields more political power than ever before. In this context, the international community's traditional ways of forecasting, planning, policymaking, and assessing impact are becoming rapidly obsolete. In response, policymakers are calling for more holistic or systemic approaches to peace and development. Unfortunately, these proposed changes are merely ‘systems light’, essentially a metaphorical characterization of peace systems where their component parts are seen as interconnected and complicated. This form of systems thinking is insufficiently informed by more sophisticated methods from complexity science. This article will illustrate how two methods derived from complexity science, causal loop diagramming and mathematical modeling, can help us understand the properties and dynamics of intervention in complex peace systems. Causal loop diagrams help us to identify the peace factors and the connections between them. Mathematical modeling helps us determine the quantitative results of the interactions between all the peace factors. Using these methods together can lead to new insights for peacebuilding and for mitigating the unintended consequences of well intended policies.
Relevance for Complex Systems Knowledge
This article looks at an increasing complexity in peace and development studies. It illustrates the use of two methods derived from complexity science, causal loop diagramming and.mathematical modeling. Causal loop diagrams help us to identify the peace factors and the connections between
them. Mathematical modeling helps us determine the quantitative results of the interactions between all the peace factors. Systems thinking to understand peace is requested from various UN bodies. Unfortunately, these proposed changes are merely variations of ‘systems light’: metaphorical characterizations of peace and development systems where their component parts are seen as connected and complicated. This form of
systems thinking is not wrong, but it is insufficient.
Focus of the international community has been on identifying the factors that contribute to peace: the primary conditions and policies for promoting peace, and the target goals and indices for measuring and evaluating sustainably peaceful nations. These include an overarching social identity that unites groups across their
differences by ‘expanding the Us to include the Them’; interconnections among subgroups whether they be through trade, intermarriage, or shared ceremonies among social units such as lineages, sports teams, schools, workplaces, and social clubs that bring together members of different groups to peacefully live, work, learn, and play together;cooperative forms of interdependence or shared goals resources, or fates due to mutual ecological or economic dependencies or common security interests; socialization of non warring values and taboos against violence in homes, schools and communities; symbols and ceremonies that celebrate and reinforce peacefulness; functional superordinate institutions that promote intergroup integration; fair and constructive conflict management mechanisms that help manage disputes between members of different groups when they arise; and visionary leadership that offers a sense of the positive potential for peace and how to achieve it. Research adds the physical security of women, demonstrating that the level of violence against women in society is a better predictor of state peacefulness, both internally and internationally,than levels of democracy, wealth, or prevalence of Islamic religion.
Findings indicate that the manner in which different peace factors interact with one another over time to affect cultures of peace is as important to understanding peace sustainability as any of the factors themselves. To predict the responses to a new policy intervention requires a sufficient understanding of the broader system and how the whole system will respond to a given intervention overtime.
Causal loop diagramming (CLD) offers a facile, agile, and surprisingly powerful approach to operationalizing systems thinking in peacebuilding by enabling collaborative visioning and planning exercises. Bringing stakeholders together from different segments of society (civil society, academia, business, government, and so on) to physically draw how different conditions and factors in communities affect one another in complex ways. Such processes can foster more nuanced forms of systemic thinking, engender meaningful
dialogues between stakeholders, and offer new insights and opportunities. These visualization methods
can also help to generate new questions and hypotheses for data gathering, organize available knowledge in more integrative ways, and act as a diagnostic tool to help identify potential gaps in current policy approaches. CLDs are limited in that they make it hard to trace through the effects of a change in one factor to the many other factors that it influences, and then even further from those influenced factors to the additional peace that they influence. In addition, the factors included in most CLDs usually do not have quantitative values assigned to them so their relative importance in the whole system is often hard to determine. Thus, the real value of this approach to policy making around peace is threefold: heuristic, exploratory, and descriptive.
the CLD approach needs to be augmented with the diagnostic, analytic, and prescriptive
value that additional modeling can provideto help move beyond a metaphorical understanding of complexity, and begin to integrate the insights, models and methods from the study of complex networks, emergence processes, attractors dynamics, and other areas of nonlinear dynamics.
Developing mathematical models of sustainably peaceful communities suggests that there is no single
leverage point, no magic single action that leads to peace. Rather it depends on collective actions of a large number of independent peace factors. Mathematical models can be used to determine how microscale individual interactions between parts of a system produce the macroscale system properties of the
entire system. The mathematical model is only useful to social scientists, practitioners, and policy makers if they understand its behavior and are able to vary the parameters of the model to explore consequences of those interventions. The value of mathematical models to policymakers is greatly enhanced by employing a graphical user interface, so that interventions can be put into the system through point and click actions. Policymakers can then use the interface to explore the system-wide effects and the time course resulting from different interventions. In this way, different hypothetical scenarios can be tried, evaluated, and judged against one another.
A complexity informed analytical approach eschews the faux certainty of conventional methods and embraces uncertainty as a core, inevitable component of social and political systems.