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This article provides a discussion on agent-based modeling. Two examples that show the ability of computational methods to extend game-theoretic results are presented. It then discusses modeling agents, modeling agent interactions, and system behaviour. In addition, it describes how agent-based models differ from and complement mathematical models and concludes with some suggestions for how one might best leverage the strengths of agent-based models to advance political science. Most mathematical analyses of game-theoretic models do not look into the stability and attainability of their equilibria and would be made richer by complementing them with agent-based models that explored those properties. The ability of computational models to test the robustness of formal results would be reason alone to add them to tool kits. As a methodology, agent-based modeling should be considered as in its infancy, its enormous potential limited only by the scientific and creative talents of its practitioners.

From an anthropological perspective, political leadership is a system of social relationships involving authority, charisma and other forms of personal or institutional power, whose rules are specific to, and embedded within, particular cultural contexts. More specifically, it is the art of controlling followers through the strategic mobilization of morality, rituals, and symbols. This article critically reviews anthropology’s contribution to the study of political leadership from the 1960s to the present. The article is in four parts. The first considers what political leadership is and why it matters. The second assesses pioneering works on leadership from 1960–1980 and the implications of the shift from small-scale, third-world communities towards more complex societies. The third considers studies since 1980, including seminal work on the relationship between political leadership, ritual and power, drawing on examples from Madagascar, Europe and the USA. The author also shows how post-1980, anthropological studies of leadership were subsumed within broader debates over ideology, hegemony, resistance, nation and state-formation, post-colonialism, and performance. Finally, the author considers some promising recent work that indicates new analytical directions. Anthropology’s key contribution lies in its attention to local social/cultural contexts, its understandings of how power is practised, and its concern with understanding the meanings of political leadership rather than simply its forms.

Network analyses of global and regional arms flows (including small arms and light weapons, major conventional weapons, and weapons of mass destruction) and related international insecurity and criminality have so far been limited. Yet the literature contains hypotheses that could be explored or tested using network analysis. This chapter discusses supply and demand effects, structural tradeoffs between security and efficiency, pressures to become more or less centralized, and the effects of geography and other network layers. It concludes by reviewing existing data sets and analyses and gauges the potential for network analysis to inform the study of arms transfer networks. Given the general import of these networks for both security studies and policy, there should be a renaissance in the study of arms supply and proliferation networks.

This article surveys modern Bayesian methods of estimating statistical models. It first provides an introduction to the Bayesian approach for statistical inference, contrasting it with more conventional approaches. It then explains the Monte Carlo principle and reviews commonly used Markov Chain Monte Carlo (MCMC) methods. This is followed by a practical justification for the use of Bayesian methods in the social sciences, and a number of examples from the literature where Bayesian methods have proven useful are shown. The article finally provides a review of modern software for Bayesian inference, and a discussion of the future of Bayesian methods in political science. One area ripe for research is the use of prior information in statistical analyses. Mixture models and those with discrete parameters (such as change point models in the time-series context) are completely underutilized in political science.

This article presents some guidance by cataloging nine different techniques for case selection: typical, diverse, extreme, deviant, influential, crucial, pathway, most similar, and most different. It also indicates that if the researcher is starting from a quantitative database, then methods for finding influential outliers can be used. In particular, the article clarifies the general principles that might guide the process of case selection in case-study research. Cases are more or less representative of some broader phenomenon and, on that score, may be considered better or worse subjects for intensive analysis. The article then draws attention to two ambiguities in case-selection strategies in case-study research. The first concerns the admixture of several case-selection strategies. The second concerns the changing status of a case as a study proceeds. Some case studies follow only one strategy of case selection.

This article presents a reconstructed definition of the case study approach to research. This definition emphasizes comparative politics, which has been closely linked to this method since its creation. The article uses this definition as a basis to explore a series of contrasts between cross-case study and case study research. This article attempts to provide better understanding of this persisting methodological debate as a matter of tradeoffs, which may also contribute to destroying the boundaries that have separated these rival genres within the subfield of comparative politics.

This article investigates the tradition of case-oriented configurational research, focusing specifically on qualitative comparative analysis (QCA) as a tool for causal inference. It first presents two analytic procedures commonly used by comparative researchers. A short description of the state-of-the-art of QCA applications is offered, in terms of discipline, types of cases, models, combinations with other methods, and software development. It then reviews different uses of QCA, as well as generic ‘best practices’. Some key recent evolutions are illustrated: on the one hand the development, beyond dichotomous ‘crisp set’ QCA (csQCA), of multi-value QCA (mvQCA), fuzzy sets, and fuzzy-set QCA (fsQCA), and on the other hand technical advances and refinements in the use of the techniques. Finally, the article gives some concluding reflections as to expected developments, upcoming innovations, remaining challenges, expansion of fields of application, and cross-fertilization with other approaches.

Though scholars have developed an increasingly rich set of research findings regarding the structure of political networks, identifying causal associations between these networks and political outcomes of interest presents a variety of challenges. Addressing these challenges is especially important given the prominence of networks in theories of individual and collective behavior. This chapter uses the framework of the Neyman-Rubin causal model (potential outcomes framework) to discuss challenges to identification researchers face when studying how networks affect political outcomes. It then describes a set of strategies researchers can employ to address these challenges, including suggestions for best practices in the context of both observational and experimental research designs.

This article discusses methods that combine survey weighting and propensity score matching to estimate population average treatment effects. Beginning with an overview of causal inference techniques that incorporate data from complex surveys and the usefulness of survey weights, it then considers approaches for incorporating survey weights into three matching algorithms, along with their respective methodologies: nearest-neighbor matching, subclassification matching, and propensity score weighting. It also presents the results of a Monte Carlo simulation study that illustrates the benefits of incorporating survey weights into propensity score matching procedures, as well as the problems that arise when survey weights are ignored. Finally, it explores the differences between population-based inferences and sample-based inferences using real-world data from the 2012 panel of The American Panel Survey (TAPS). The article highlights the impact of social media usage on political participation, when such impact is not actually apparent in the target population.

This chapter explores the dual concern with causality and time in historical-institutionalism using a graphical approach. Conceptualizing causes as filters, the chapter analyses three concepts that are central to this field: critical junctures, gradual change, and path dependence. The analysis makes explicit and formal the logic underlying studies that use these “causal-temporal” concepts. The chapter shows visually how causality and temporality are linked to one another in varying ways depending on the particular pattern of change. Through this unifying visual grammar, the chapter also outlines an approach that can accommodate and reconcile both models of critical junctures and gradual change. The chapter provides new tools for describing and understanding change in historical institutional analyses.

This article provides an overview of causal thinking by characterizing four approaches to causal inference. It also describes the INUS model. It specifically presents a user-friendly synopsis of philosophical and statistical musings about causation. The four approaches to causality include neo-Humean regularity, counterfactual, manipulation and mechanisms, and capacities. A counterfactual is a statement, typically in the subjunctive mood, in which a false or ‘counter to fact’ premise is followed by some assertion about what would have happened if the premise were true. Three basic questions about causality are then addressed. Moreover, the article gives a review of four approaches of what causality might be. It pays attention on a counterfactual definition, mostly amounting to a recipe that is now widely used in statistics. It ends with a discussion of the limitations of the recipe and how far it goes toward solving the epistemological and ontological problems.

This chapter explains the method of policy network (PN) analysis and its benefits (and limits) for cross-national comparative analysis. The purpose of the PN approach is to understand how the structure of relationships among organizations engaged in a policy domain affects the content of policy and outcomes. The chapter illustrates the use of the PN method with reference to the ongoing cross-national project Comparing Climate Change Policy Networks (Compon). Global climate change constitutes an (un)naturally occurring quasi-experiment; in the face of a common threat, the various societies have exhibited divergent responses to reducing the cause, carbon emissions. This research project and network method can provide knowledge helpful to global negotiations as well as open up new vistas on thorny theoretical questions about the behavior and outputs of political systems.

This article indicates that comparative historical analysis is complementary to statistical analysis because it deals with ‘causes of effects’ rather than ‘effects of causes’. It also presents some ideas about how one can tackle the problems posed by engaging in comparative historical inquiry. In addition, the article argues that comparative-historical analysis and statistical analysis pursue different research goals, and that while they both face methodological challenges, they both play an essential role in generating knowledge in political science. Comparative-historical studies that use regression analysis in the course of process tracing are not necessarily more powerful than comparative-historical studies that do not use any statistical testing. There is nothing inherently wrong with conducting comparative-historical work that does not include a statistical component (and vice versa).

This article presents guidance on how to think about the concepts. It proposes to explore some issues in the assessment of concepts and quantitative measures. It presents the basic problem in its general outlines. It then offers a very short example, typically using published research. The structuring and aggregating concepts and measures indicates that one must first consider the theory embodied in the concept. Then one should survey plausible aggregation and structural relationships that could be applied in a quantitative measure. It is noted that one needs to ask about the existence or not of zero points. The gray zone needs to be explored independently of the two extremes. Homogeneity is another aspect of comparing within and between various concepts and measures of the same phenomenon. This article generally highlights that it is the lack of integration of theory and methodology which proves problematic.

Context matters for leadership. Contextual analysis in leadership studies tries to find out when and how contexts shape leadership possibilities, and reversely when and how leaders are able to mould (others’ understandings of) the contexts in which they operate. This article reviews the multidisciplinary body of scholarship devoted to these questions. It signals some key analytical and methodological issues facing studies of this kind. It concludes by suggesting priority areas for future research on the nexus between context and political leadership.

This article shows that counterfactuals can be used along with case studies to make inferences, although strong theories are needed for this. The article also argues that game theory is one approach that provides this kind of theory because a game explicitly models all of the actors' options including those possibilities that are not chosen. The article then indicates that any counterfactual argument requires a detailed and explicit description of the alternative antecedent which is plausible and involves a minimal rewrite of history, and suggests that one of the strengths of game theory is its explicitness about alternatives. The validity of counterfactual arguments is assessed in explaining cases or testing theoretical propositions. Counterfactuals should change as few aspects of the real world as possible in order to isolate their causal effects.

In the analysis of path-dependent institutions, the concept of critical juncture refers to situations of uncertainty in which decisions of important actors are causally decisive for the selection of one path of institutional development over other possible paths. The chapter parses the potentialities and the limitations of the concept in comparative-historical analysis, and proposes analytical tools for the comparative analysis of the smaller-scale and temporally proximate causes that shape decision-making on institutional innovation during critical junctures. In particular, the chapter discusses several patterns of short-term politics of institutional formation --innovative coalition-building for reform; “out-of-winset” outcomes; ideational battles; and near-missed institutional change—that can have a long-term impact on the development of policies and institutions.

This article reports the main methodological approaches to the statistical problem. It describes the fundamental indeterminacy of the problem. It also provides a framework that coherently binds the variety of approaches that have been proposed to address this problem. Then, an overview of these various approaches and their respective contributions are mentioned. The ecological inference problem within the literature of partial identification and the recent work generalizing the use of logical bounds on possible solutions as an identification region for the general r × c problem are explained. It finally covers some admonitions about this fascinating problem that has enthralled decades of scholars from varied disciplines. The analysis by Duncan and Davis made clear that aggregate data only partially reveal the structure of individual behaviour. However, their contribution has largely been viewed as limited and an appreciation for the idea of bounds or an identification region has yet to fully emerge.

This chapter examines the development of cross-national survey research in political science and the challenges that it brings. Cross-national surveys have proliferated across the globe and arguably now form one of the most important frontiers in the development of survey research in political science. Cross-national comparison allows researchers to investigate the importance of institutional and cultural contexts that shape public opinion and political behavior. The chapter traces the development of such instruments for the purposes of comparative analysis in political science, in the context of more general developments in polling and survey research. As an example, it focuses on the case of the Comparative Study of Electoral Systems (CSES), an international collaboration active since 1996, examining the development of the project and evaluating issues such as cross-cultural equivalence in questionnaire design, survey mode and response rates, and case selection.