Qualitative Political Methodology

Agent-based computational modeling (ABM, for short) is a formal and supplementary methodological approach used in international relations (IR) theory and research, based on the general ABM paradigm and computational methodology as applied to IR phenomena. ABM of such phenomena varies according to three fundamental dimensions: scale of organization—spanning foreign policy, international relations, regional systems, and global politics—as well as by geospatial and temporal scales. ABM is part of the broader complexity science paradigm, although ABMs can also be applied without complexity concepts. There have been scores of peer-reviewed publications using ABM to develop IR theory in recent years, based on earlier pioneering work in computational IR that originated in the 1960s that was pre-agent based. Main areas of theory and research using ABM in IR theory include dynamics of polity formation (politogenesis), foreign policy decision making, conflict dynamics, transnational terrorism, and environment impacts such as climate change. Enduring challenges for ABM in IR theory include learning the applicable ABM methodology itself, publishing sufficiently complete models, accumulation of knowledge, evolving new standards and methodology, and the special demands of interdisciplinary research, among others. Besides further development of main themes identified thus far, future research directions include ABM applied to IR in political interaction domains of space and cyber; new integrated models of IR dynamics across domains of land, sea, air, space, and cyber; and world order and long-range models.

Civil wars typically have been terminated by a variety of means, including military victories, negotiated settlements and ceasefires, and “draws.” Three very different historical trends in the means by which civil wars have ended can be identified for the post–World War II period. A number of explanations have been developed to account for those trends, some of which focus on international factors and others on national or actor-level variables. Efforts to explain why civil wars end as they do are considered important because one of the most contested issues among political scientists who study civil wars is how “best” to end a civil war if the goal is to achieve a stable peace. Several factors have contributed to this debate, among them conflicting results produced by various studies on this topic as well as different understandings of the concepts war termination, civil war resolution, peace-building, and stable peace.

Comparative public policy (CPP) is a multidisciplinary enterprise aimed at policy learning through lesson drawing and theory building or testing. We argue that CPP faces the challenge of conceptual and analytical standardization if it is to make a significant contribution to the explanation of policy decision-making. This argument is developed in three sections based on the following questions: What is CPP? What is it for? How should it be done? We begin with a presentation of the historical evolution of the field, its conceptual heterogeneity, and the persistence of two distinct bodies of literature made of basic and applied studies. We proceed with a discussion of the logics operating in CPP, their approaches to causality and causation, and their contribution to middle-range theory. Next, we explain the fundamental problems of the comparative method, starting with a synthesis of the main methodological pitfalls and the problems of case selection and then revising the main protocols in use. We conclude with a reflection on the contribution of CPP to policy design and policy analysis.

Counterfactuals seek to alter some feature or event of the pass and by means of a chain of causal logic show how the present might, or would, be different. Counterfactual inquiry—or control of counterfactual situations—is essential to any causal claim. More importantly, counterfactual thought experiments are essential, to the construction of analytical frameworks. Policymakers routinely use then by to identify problems, work their way through problems, and select responses. Good foreign-policy analysis must accordingly engage and employ counterfactuals.

There are two generic types of counterfactuals: minimal-rewrite counterfactuals and miracle counterfactuals. They have relevance when formulating propositions and probing contingency and causation. There is also a set of protocols for using both kinds of counterfactuals toward these ends, and it illustrates the uses and protocols with historical examples. Policymakers invoke counterfactuals frequently, especially with regard to foreign policy, to both choose policies and defend them to key constituencies. They use counterfactuals in a haphazard and unscientific manner, and it is important to learn more about how they think about and employ counterfactuals to understand foreign policy.

Deterrence theory is slowly beginning to emerge from a long sleep after the Cold War, and from its theoretical origins over half a century ago. New realities have led to a diversification of deterrence in practice, as well as to new avenues for its study and empirical analysis. Three major categories of changes in the international system—new actors, new means of warfare, and new contexts—have led to corresponding changes in the way that deterrence is theorized and studied. First, the field of deterrence has broadened to include nonstate and nonnuclear actors, which has challenged scholars with new types of theories and tests. Second, cyberthreats, terrorism, and diverse nuclear force structures have led scholars to consider means in new ways. Third, the likelihood of an international crisis has shifted as a result of physical, economic, and normative changes in the costs of crisis, which had led scholars to more closely address the crisis context itself. The assumptions of classical deterrence are breaking down, in research as well as in reality. However, more work needs to be done in understanding these international changes and building successful deterrence policy. A better understanding of new modes of deterrence will aid policymakers in managing today’s threats and in preventing future deterrence failures, even as it prompts the so-called virtuous cycle of new theory and additional empirical testing.

Qualitative Comparative Analysis (QCA) is a method, developed by the American social scientist Charles C. Ragin since the 1980s, which has had since then great and ever-increasing success in research applications in various political science subdisciplines and teaching programs. It counts as a broadly recognized addition to the methodological spectrum of political science. QCA is based on set theory. Set theory models “if … then” hypotheses in a way that they can be interpreted as sufficient or necessary conditions. QCA differentiates between crisp sets in which cases can only be full members or not, while fuzzy sets allow for degrees of membership. With fuzzy sets it is, for example, possible to distinguish highly developed democracies from less developed democracies that, nevertheless, are rather democracies than not. This means that fuzzy sets account for differences in degree without giving up the differences in kind. In the end, QCA produces configurational statements that acknowledge that conditions usually appear in conjunction and that there can be more than one conjunction that implies an outcome (equifinality). There is a strong emphasis on a case-oriented perspective. QCA is usually (but not exclusively) applied in y-centered research designs. A standardized algorithm has been developed and implemented in various software packages that takes into account the complexity of the social world surrounding us, also acknowledging the fact that not every theoretically possible variation of explanatory factors also exists empirically. Parameters of fit, such as consistency and coverage, help to evaluate how well the chosen explanatory factors account for the outcome to be explained. There is also a range of graphical tools that help to illustrate the results of a QCA. Set theory goes well beyond an application in QCA, but QCA is certainly its most prominent variant.

There is a very lively QCA community that currently deals with the following aspects: the establishment of a code of standards for QCA applications; QCA as part of mixed-methods designs, such as combinations of QCA and statistical analyses, or a sequence of QCA and (comparative) case studies (via, e.g., process tracing); the inclusion of time aspects into QCA; Coincidence Analysis (CNA, where an a priori decision on which is the explanatory factor and which the condition is not taken) as an alternative to the use of the Quine-McCluskey algorithm; the stability of results; the software development; and the more general question whether QCA development activities should rather target research design or technical issues. From this, a methodological agenda can be derived that asks for the relationship between QCA and quantitative techniques, case study methods, and interpretive methods, but also for increased efforts in reaching a shared understanding of the mission of QCA.