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The Past and Future of the Scientific Study of International Processes

Summary and Keywords

The scientific study of international processes (SSIP) has made substantial progress over the past twenty years, establishing itself as the mainstream research community in the field of international relations (IR) and attracting more and more attention from other disciplines. This was due to the convergence of several revolutions that have taken place in the field, including the data revolution, the formal modeling revolution, the methods revolution, the substantive revolution, and the epistemological revolution. In addition to the dramatic increase in the number of the community of scholars who use scientific logic, systematic methods, and empirical data to study IR, there was a significant improvement in the quality of research. This research has yielded important contributions to our understanding of international processes. Some of these contributions went far beyond the field; they have attracted the attention of policy makers as well as quite a few scholars from other disciplines. Some of the key findings that emerged from this research have become—correctly or incorrectly—a key component of the discourse of political leaders. Growing data availability, increased methodological sophistication, and greater scientific discipline within the profession have converged to open new research frontiers, but important challenges remain, such as the disconnect between theory and empirical tests that exists in many cases, and the almost exclusive reliance on the dyadic level of analysis. It is important to make our understanding of international processes translated into broader policy implications.

Keywords: scientific study of international processes, international relations, data revolution, formal modeling revolution, methods revolution, substantive revolution, epistemological revolution, international processes


The past 20 years have witnessed a remarkable evolution of the scientific study of international processes. This was due to the convergence of several revolutions that have taken place in the field. The numerical growth of the community of scholars who use scientific logic, systematic methods, and empirical data to study international relations was matched by a huge improvement in the quality of research. This research has yielded important contributions to our understanding of international processes. Some of these contributions went far beyond the field; they have attracted the attention of policy makers as well as quite a few scholars from other disciplines. At the same time, the field still faces important challenges. Some of the key questions that have preoccupied scholars for the past 20 years are wide open. More important, new questions and new conceptions have arisen which require a significant broadening, and perhaps a major reconceptualization of our discipline. In this chapter, I will lay out my perspective of the achievements of the field in the past 30 years or so. I will then discuss what I consider to be the principal challenges of the scientific study of international politics. I will also identify new research frontiers and approaches that might help confront these challenges.

The reader must be warned that this is one person's – possibly biased – view of a very diverse, and dynamic field of scholarship. If another person had written this chapter, the reader would have been presented with a different picture. Readers are invited to react to, and comment on the ideas presented below. The more we discuss these issues, the better we understand the challenges that lie ahead. And with improved understanding comes better research.

This chapter is organized as follows. I start by outlining the key revolutions of SSIP over the past 30 years. I do not discuss the history of the field; others have done that (Kadera and Zinnes, this volume). Rather, my focus is conceptual; I review the major trends in the field and evaluate the key accomplishments. In the third section I focus on the key shortcomings of these trends. I discuss the major problems and deficiencies that have plagued research, the lacunae of knowledge, and the criticisms leveled at the SSIP community over the years. The fourth section outlines some of the key challenges: the open questions, the need to reconsider and reconceptualize some of the key foci of this research, and the methodological and logical issues that require a more refined attention and scholarship. I conclude with a brief discussion of some concepts, approaches, and methods that can help us in this future journey.

Five Revolutions

As Kadera and Zinnes point out, after a relatively short period of early spring in the 1960s and 1970s, behavioral approaches retreated to the background of the field. The study of international politics was dominated by qualitative approaches. With few exceptions – e.g., the work of strategists such as Thomas Schelling (Schelling 1960; 1963) – research using quantitative methods or formal models in IR remained largely in the margins. The major figures – people whose ideas and concepts continue to influence much of the scholarship in the field – not only shied away from scientific approaches; they were outright hostile to them. The attitude of many scholars to the use of scientific approaches and quantitative data was eloquently expressed by Hadley Bull: “that which is important cannot be quantified; that which is quantifiable, is not important.”

This reality has changed dramatically. Despite its small size, the SSIP community has become the mainstream research community in international relations: its members dominate the high-impact publications; the citations of their works have soared over the past three decades. Some of the key findings that emerged from this research have become – correctly or incorrectly – a part and parcel of the discourse of political leaders. While qualitative approaches continue to be highly popular in the field, even journals such as World Politics, International Organization, and International Security that had systematically rejected scientifically oriented research in the past, have begun increasingly to publish articles using quantitative methods and/or formal models. The impact factors of the more scientifically oriented journals – Journal of Conflict Resolution, International Studies Quarterly, and Journal of Peace Research, to name just a few – has been among the top in political science and international relations. Departments that had consistently emphasized scientific approaches have risen in the national and international rankings. Departments that traditionally had been the bastions of qualitative research began recruiting scientifically oriented faculty members. Those departments that maintained their strictly qualitative orientation have declined in the rankings.

This dramatic rise in the influence of SSIP research is due to five major revolutions that characterize research over the past three decades. Not all of these “revolutions” involved dramatic transformation of the ways we do research; some of these reflect dramatic changes in magnitude – essentially much more of the same – but all of these trends had a tremendous impact on modern IR research.

The Data Revolution

Kadera and Zinnes point out that the process of systematic collection of quantitative data on major international phenomena – principally conflict and war – started out in the mid-twentieth century by Lewis Fry Richardson and Quincy Wright, and was continued by the Correlates of War Project, the WEIS and COPDAB events-data projects, and to a lesser extent by the DON project. The focus in these projects was on international conflict. Yet, the past 30 years have witnessed an explosion of data collection on political and economic variables that has little or no parallel in other fields of political science. A significant number of conflict data sets were added – primarily the MID/war and the ICB data sets – that became the focal point of research in international relations. The principal variables of such data sets featured prominently on the left-hand side of many equations estimated over the years. In addition several data sets have emerged (or have been updated and expanded) that focused on possible predictors of conflict and cooperation. These included data on alliances, international organizations, international trade, diplomatic representation, arms transfers, material capabilities (which included a number of economic, demographic, and military indicators), regime and polity types. More specialized data sets on specific forms of conflict (including enduring rivalries, terrorism, insurgency, and different types of warfare) have emerged more recently.

Virtually all of these and many other data sets are in the public domain. In the past, students and researchers who wished to engage in data-based research had to spend months and years collecting and managing data. In many cases, they were forced to rely on their own resources because funding agencies had been reluctant to support research that entailed large data collection efforts. At present, researchers are able to download multiple data sets on major aspects of international relations, with a click of a mouse.

One of the most important engines of the data revolution was the Data Development in International Research (DDIR) enterprise – headed by Dina Zinnes and Richard Merritt – an NSF funded project that enabled multiple data projects to expand, revise, and update existing data sets as well as to collect new data. Other NSF-funded projects enabled expansion and update of the MID data set, and the Canada Council has provided important support for the ICB project. The data revolution – at least the public domain aspect thereof – was significantly aided by the replication policy of the leading journals in the field. This policy required researchers to post the data used for their research online as a condition for publication. The result of this policy was not only that people could replicate published data-based studies. It was that scholars who wished to publish in leading journals could no longer monopolize the use of the data they had collected, as had been the case in the past.

The reason I describe this trend as revolutionary is threefold. First, the magnitude and spatial-temporal scope of most of these data sets is impressive. Most of the major data sets in the field cover all states in the system over an extended time-span. Some go as far back as 1800 (e.g., Polity) or 1816 (most COW data sets, ATOP), others go to the second half of the nineteenth century (trade data sets), some to the beginning of the twentieth century (e.g., ICB), and many more cover the second half of the twentieth century and the first decade of the twenty-first century. This scope allows analyses of the entire population of states, dyads, and systems over an extended empirical domain. Very few other social science disciplines and only a handful of political data sets are comparable in terms of spatial-temporal scope.

Second, these data encompass several levels of analysis, thus enabling scholars to conduct research with the nation-year, dyad-year, region-year, or system-year level of analysis. They enable the use of sophisticated methods that overcome some of the problems that have plagued research in the past – more on that below.

Third, recent data-mining and data-management technologies enabled machine coding and machine-management of data, thus significantly facilitating data collection and the management of multiple and complex data sets. Two examples that had important influence on quantitative IR research include the EUGene project and the KEDS/CAMEO project that evolved into IDEA. EUGene is a large-scale data depository that contains an engine allowing researchers to build their own data set at various levels of analysis, selecting variables, and a spatial-temporal domain. The KEDS/CAMEO project is an events-based data set, largely building on WEIS in which data are machine-coded via the use of Boolean search techniques and AI methods. The reliability of this method has yet to be proven,1 but if and when it catches on, it will greatly facilitate data collection.

Parallel to the new and improved data sets that are available to researchers due to the efforts of SSIP members, a large number of data sets are becoming available via the efforts of individuals and international organizations. These individuals or organizations collect data on matters that help us redefine the field – various aspects of human security, gender equality, public health, and other aspects of political and social life. These will become increasingly important foci of research as we expand our definitions of what is included in the study of international relations.

In sum, the data revolution creates significant opportunities for systematic analysis at levels that are fundamentally broader and more sophisticated than had existed in the past. This revolution is still evolving as new data sets emerge and become public. Yet, data – even the most detailed and extensive ones – do not improve knowledge by themselves. It takes theory to guide research, and sophisticated methods need to be employed in analyzing these data. Fortunately, both types of revolutions transpired in parallel to the data revolution.

The Formal Modeling Revolution

Here too, some of the pioneers in developing models of international interactions based on formal mathematical logic were not political scientists. Starting with the arms race model of Richardson, economists such as Thomas Schelling and Daniel Ellsberg, and biologists such as Anatol Rapoport first turned our attention to the value of developing rather simple models of interstate interaction based on game theory. This too, was largely at the margins of the discipline. The models were also quite restricted in their application – they primarily offered very useful metaphors of strategic interaction. The testing and empirical implications of these models were not evident. Nor was it possible to test most of the propositions that could be deduced from these models, because they entailed counterfactuals – such as nuclear escalation or limited nuclear war. The “science” part of these early studies was largely a science of ideas; it was difficult to tell how and to what extent these ideas were indeed helpful in explaining empirical facts.

Arguably, one of the most important upgrades of formal models of international relations was the work of Bruce Bueno de Mesquita on war initiation (Bueno de Mesquita, 1981). The rational choice model of war initiation developed therein was used to deduce propositions about the origins and outcomes of wars; these propositions were generally supported by rigorous empirical tests. This work was criticized by many, but at the same time it attracted a great deal of attention. More importantly, it helped build a community of scholars applying rational choice theories to the central questions in the field: the origins of war, the conditions of peace, international political economy, the resort to terrorism and anti-terrorist strategies, or the design and operation of international institutions, to name just a few.

The formal modeling group within the IR community can be broadly divided into two categories: pure formal modelers and empirically oriented formal modelers. The former category consists of scholars who are primarily interested in developing formal, logically grounded, mathematical models of international processes. Many of the studies of members of this group have helped reshape our thinking about international relations. For example, the literature on war as a (failed) bargaining problem (Fearon 1995; Wagner 2000; Powell 2004), helped spark a large number of models as well as empirical investigations. A growing literature evolved around the issue of the rational choice of terrorism and counterterrorism (Sandler et al. 1983; Acre and Sandler 2005; Bueno de Mesquita 2007; Enders and Su 2007), or mediation in international and domestic conflicts (Kydd 2006; Maoz and Terris 2006; Rauchhaus 2006). The issue of selection effects underlying such questions as the design of international institutions was also an important consequence of rational choice models (Koremenos et al. 2004; Koremenos 2005; Chapman 2007). More broadly, these formal models have had an important impact on empirical investigations of international problems. They allowed for a wealth of empirically testable propositions. Quite a few of these propositions have been subjected to empirical testing, but typically by other people than the originators of these models.

The latter group is interested – in addition to specifying formal models of international politics – in actually testing the propositions derived from these models on empirical data. Bruce Bueno de Mesquita and his students/collaborators have made consistently continuous contributions to our knowledge in these areas (e.g., Bueno de Mesquita and Lalman 1992, Bueno de Mesquita et al. 2003). They have also demonstrated that these models have good predictive power, and can help policy makers make better decisions (e.g., Bueno de Mesquita 2010). Others have focused on refining and testing some of the key propositions of bargaining models of conflict (e.g., Werner 1998; Filson and Werner 2002; 2004; Slantchev 2003; 2004), mediation (Terris and Maoz 2005; Maoz and Terris 2006). Finally, formal models of international institutions helped shape empirical research on the rise, and effects of international institutions (e.g., Koremenos 2005; Von Stein 2005; Rathburn 2011).

The rational choice literature has dominated formal models of international problems. Of less visibility – but probably no less importance – was the literature on dynamic models of international processes. These models were based on differential equation models of conflict (Zinnes and Muncaster 1984; Kadera 1998; 2001), arms races (Zinnes et al. 1977; McGinnis and Williams 1989), and system dynamics (Lee et al. 1994). They offered an alternative approach to model strategic interaction – with the principal benefit being that such interaction was viewed in dynamic terms, in which relations evolve over time, and the past featuring as an important aspect of present and future interactions. Here too, some of the studies focused on pure modeling. However, the bulk of this literature examined empirical processes dynamically, with some interesting implications. Some of these included raising important problems rather than resolving them. For example, this research has highlighted the fact that international interactions, in general, and of arms races, in particular, suggest that states are more likely to emulate their past behavior than to react to one another. The best predictor of a given state's military expenditures at a given point in time is the same state's expenditures at a previous point in time, rather than the opponent's military expenditures (Zinnes 1980). More sophisticated models of arms race or strategic interactions (e.g., Ward 1984; Goldstein and Freeman 1991) revealed interesting combinations of strategic interactions of a dyadic or triadic nature.

The contribution of the formal modeling revolution is fairly obvious: it offers rigorous models of international processes, deeply grounded in scientific logic – with an emphasis on explicit statement of assumptions and deduction of testable propositions via mathematical logic. The tests of these propositions suggest not only an ability to scientifically analyze such processes, but to provide empirical support for the entire modeling process that gave birth to these propositions.

The Methods Revolution

Initial research in the field was largely correlational. Major examples here include the original studies on the relations between domestic and international conflict (e.g., Rummel 1963; Tanter 1966, Wilkenfeld 1968)2 and some systemic analysis on the relationship between alliances and war (e.g., Singer and Small 1968). The results were largely negative, or mixed at best. Even when some positive results emerged, the correlations tended to be very low.

The 1950s and 1960s saw also a wave of simulation studies – many of them relying on role-playing by experts (Guetzkow et al. 1963). However, gradually the simulation field started moving increasingly towards computer models of international processes (e.g., Bremer 1977; Cusack and Stoll 1990). Here too, some interesting ideas emerged regarding a wide variety of international processes and foreign policy behavior. However, little validation took place.

The methods revolution that started out in the early to mid-1970s had several phases. Each phase was dominated by a specific set of aspects. The first phase entailed two related processes. The first was a shift from a set of simple bivariate analyses to multivariate analyses using regression models – mostly simple ordinary least squares (OLS) models. The second was a growing emphasis on time-related violations of OLS assumptions, and thus a shift from OLS to time-series models with correction for autocorrelation, especially in the studies that analyzed dynamic models of arms races and international interactions. This shift was due to scholars' focus on more complex models – in part trying to resolve the kind of puzzles that emerged in empirical analyses that relied on simpler models (e.g., the arms race puzzle). In part, such analyses were due to both substantive and methodological criticisms of the more simple models of the previous era. Some of these studies also entailed thinking about international processes in more complex terms.

Many of the quantitative analyses of international problems in the 1970s and early 1980s focused on the system level of analysis. And as some analysts pointed out (e.g., Bueno de Mesquita and Lalman 1988; Vasquez 1983 [1998]) the results were largely disappointing: most of the propositions deduced from structural realist models received little or no support.

Parallel to the development of refined and more complex data sets, an alternative set of models evolved around the dyad as the unit of analysis. Early dyadic analyses of the effects of arms races on war and dispute outcomes (Wallace 1982; Diehl 1983; 1985) opened up new controversies, and suffered from a host of methodological problems (selection bias, violation of time-series and spatial-dependence assumptions, absence of controls). But they helped highlight the value of studying the behavior of dyads over time. Other studies (e.g., Rummel 1983; 1986; Weede 1984; Maoz and Abdolali 1989) focusing on the relationship between democracies and peace helped bring to the fore some “positive” findings, suggesting that we can actually gain knowledge about things that actually happen in international politics, rather than on disproving ideas and propositions of extant theories.

Subsequent dyadic analyses started using increasingly more sophisticated models and estimation methods. Bremer (1992; 1993) and Maoz and Russett (1992; 1993) started employing negative binomial models for event-count data and logit models, respectively, to test propositions about the factors that affect the probability or frequency of MIDs using the dyad-year as the unit of analysis. The latter study used also a critical test approach to evaluate competing explanations of the democratic peace phenomenon. These studies had not only substantive impact on the field, by virtue of their empirical findings. They highlighted the value of using complex models – that include control variables – to test hypotheses about international relations, in general, and about international conflict, in particular.

The key methodological challenges to the simple dyad-year analyses of the early 1990s were threefold. First, it was argued that such models failed to account for time-dependence (Beck et al. 1998). Second, most of the dependent variables used in such analyses were rare events; the probability of an event – e.g., conflict, alliance – was several orders of magnitude lower than the probability of a non-event (King and Zeng 2001). The third criticism had to do with the use of control variables – in particular, how and how many controls should be used in quantitative analyses of conflict (Achen 2005; Ray 2005). Several relatively simple fixes were offered, and replication of several of these studies led to substantive conclusions that were not far off those of the original studies. In fact, these “fixes” in many cases helped corroborate the previous results; this was in itself a rare event in the history of the field; more important, they helped articulate some important conditions when these results hold more or less (e.g., Beck et al. 2000; Oneal and Russett 2005).

Recent methodological challenges involve two key arguments. The first has to do with in-sample versus out-of-sample tests. The challenge here is that the fit of the models with large samples in terms of their ability to predict the distribution of the dependent variable is quite poor (Ward et al. 2007). As well, there is significant dependency among dyads (Cranmer and Desmarais 2011). Here too, several solutions were offered, the most significant of which was to apply network analytic models (such as Exponential Random Graph Models – ERGMs) to control for complex dependencies in the data. A more general critique of the use of simple regression and regression-like models comes from the more recent generation of studies that emphasizes network analytic concepts and methods. Two central points emerge in this critique. First, states in the international system are affected by their environment but the complexity of these effects is hardly modeled by the current methodologies used to estimate models of international relations. Second, the abandonment of systemic studies of conflict was premature; the failure to find significant results at the system level was largely due to the treatment of systemic patterns as an aggregation across lower levels of analysis; in fact systemic structures are emergent in complex ways. Here too, network analysis was offered as a potential advancement (Maoz 2006; 2010).

Other approaches allowing the mixing of multiple levels (such as multilevel models) offer important improvement. Here too, the combination of constant improvement of the methods we are using with better data offers a great deal of hope for the future.

The Substantive Revolution

The substantive revolution is a by-product of other revolutions that have transformed the discipline. The first and most important one is the collapse of the Berlin Wall of international relations – the breakdown of the artificial wall that separated international relations from comparative politics. The Berlin Wall of international relations was constructed in the 1970s and 1980s by structural realists who argued that the condition of international anarchy that defines international politics calls for a theoretical logic that is fundamentally different from the logic of domestic politics, which is “the authoritative allocation of values for society” (Easton 1965). Largely influenced by Waltz's (1979) work, the dominant idea was that national security decisions are influenced by rational reasoning of raison d’état. States pursue power and aim for either dominance (if you are an offensive realist), or security and balancing (if you are a defensive realist). This also called for a systemic conception of international politics with a top-down logic suggesting that foreign policy is influenced largely by factors and processes residing outside the state's borders and by the structure of the international system. Consequently, domestic political factors and processes that operate states have little or no impact on how they behave.

The Berlin Wall of IR did not collapse at once; this was a process. The first cracks emerged and expanded due to repeated failures to develop any empirical support for most systemic propositions of structural realism (Mansbach and Vasquez 1981; Vasquez 1983 [1998]; Bueno de Mesquita and Lalman 1988; Vasquez 1997). In some cases, the empirical evidence strongly suggested that in fact many propositions of political realism need to be inverted – for example, repeated evidence emerged suggesting that power parity substantially increases the probability of conflict (Organski and Kugler 1980; Bremer 1992; Geller 1993). This was the opposite of the expectation of balancing ideas of realists.

Initially, structural realists chose to ignore the rigorous evidence of non-relationship between variables deduced from their theories, or to develop alternative theories connecting structural factors to state behavior (Vasquez 1997). Ironically, SSIP scholars who engaged in rigorous tests of the main propositions of structural realism contributed significantly to the citation count of structural realists. Yet, the latter tended to completely ignore the published work of the former. The real debate between structural realists and alternative paradigms was between the former and the less scientifically inclined scholars who advanced a liberal agenda (e.g., Keohane 1986; Keohane and Nye 1987). In this debate there were a lot of interesting arguments, but very little empirical evidence and virtually no use of quantitative methods or formal logic.

Perhaps the major blow to the Berlin Wall of IR was the emerging evidence on the democratic peace (Rummel 1983; Weede 1984; Maoz and Abdolali 1989; Bremer 1992; 1993; Maoz and Russett 1992; 1993; Dixon 1993; 1994). The democratic peace proposition was broadened to a more general liberal peace agenda (Russett and Oneal 2001), which emphasized the “Kantian Tripod,” the idea that joint democracy, economic interdependence, and joint participation in international organizations have significantly dampened the probability of dyadic conflict. The idea that democratic states behave in significantly different ways (even if only towards each other) than other states meant that domestic politics affect international behavior in fundamental ways. The explanations of democratic peace – the institutional (Morgan and Swebach 1992), normative (Maoz and Russett 1993), and political survival (Bueno de Mesquita et al. 2003) models – challenged the notion that all states behave alike under anarchy. Even more troubling to realists – who had also held the near-monopoly on policy-relevant speculations – was the fact that political leaders picked up on this finding and internalized it into their rhetoric and policies (Maoz 1998; 2004).

The realist reactions to the democratic peace came along several fronts. The first involved dismissal of the empirical evidence on the grounds that there were very few democracies so that the a priori probability of conflict between democracies was extremely low (Mearsheimer 1990; Spiro 1994). An alternative reaction entailed a number of different arguments about the spuriousness or temporal limitation of this phenomenon (e.g., Cohen 1994; Farber and Gowa 1995; Gowa 1999). Third, some argued that while mature democracies indeed do not fight each other, democratizing states are far more likely to fight than either states with stable regimes or states that are autocratizing (Mansfield and Snyder 1995; 2006). Finally, other elements of the realist rearguard action using case studies of conflicts between democracies tried to demolish the various explanations of the democratic peace (Elman and Elman-Fendius 1997; Rosato 2003).

The substantive upshot of the debate between realists and liberal scholars was twofold. First, by and large, the democratic peace proposition survived most of the criticisms (Maoz 1997; 1998; Thompson and Tucker 1997; Ray 1998; 1995; Dafoe 2011). Second, even staunch structural realists finally awoke to the reality of a powerful interrelationship between domestic factors and processes and international relations. For example, in 1992, Steven Walt realized that revolutions increase the probability of war (Walt 1992; 1996); Mansfield and Snyder's account of the effect of democratization on war propensity invokes the need of political elites to mobilize support for state building and regime stabilization by externalizing conflict (Mansfield and Snyder 2006). Finally, Walt and Mearsheimer's study of the Israel lobby policy entails a huge revelation: interest groups have a powerful effect on US foreign policy (Mearsheimer and Walt 2007). The wall collapsed.

The second aspect of the substantive revolution concerned a paradoxical implication of the democratic peace debate. The empirical analyses that have been conducted in the context of this debate provided more support for some realist propositions than decades of qualitative (and systemic) research. For example, it was shown that – controlling for other factors – alliances have a dampening effect on the probability of dyadic conflict. Likewise (and in a twisted way) it was shown that power differentials – in particular power preponderance – also has a significant dampening effect while rapid changes in relative power have a significant positive impact on the probability of dyadic war. In addition, using a more limited laboratory for theory testing – the enduring (or strategic) rivalry population – scholars were able to develop more refined tests of the key propositions of deterrence theory (Huth 1988; Huth and Russett 1984; 1993).

The third aspect of the substantive revolution concerned the shift from systemic approaches to dyadic-based studies. The previous dominance of top-down conceptions of international politics yielded very little evidence that withstood replication and robustness checks. Consequently, evidence that had been shown to support a particular hypothesis in one study was quickly dismissed as questionable or very limited in scope given a new set of studies. The shift to dyadic analyses of conflict and cooperation generated knowledge that withstood quite a few refutation attempts and was shown to be relatively robust with respect to model specification, data, and estimation methods.

The upshot of this is that today we have better evidence on the factors that increase the probability of dyadic conflict (e.g., territorial disputes, political instability, power parity) or factors that reduce the probability of dyadic conflict (e.g., joint democracy, alliance, power preponderance, economic interdependence). There are still quite a few factors that have no clear cut effect on the probability of conflict, but this is a normal state of affairs in any domain of empirical knowledge.

Another consequence of the substantive revolution in IR is that other disciplines started paying much closer attention to the substantive models, the methods, and the variables that SSIP scholars have been using. Economists, sociologists, and even natural scientists have started to cite our studies at rates that were as high as or higher than the interdisciplinary cites of more traditional scholars.3 This has been largely due to both the substantive and methodological contributions of the SSIP community.

The Epistemological Revolution

There are three aspects to the epistemological revolution in SSIP over the past 20 years. The first concerns the shift from a “revolutionary science” epistemology to a “normal science” practice. A “revolutionary science” epistemology (Kuhn 1970) reflects a struggle within a scientific community among competing paradigms. One characteristic of revolutionary science is that most studies attempt to build a new paradigm or to discredit others. Consequently, very little replication happens. Much of the research in the 1960s, 1970s, and 1980s was characterized by this tendency. Even within the SSIP community, relatively few studies used the theoretical frameworks, variables, measures, data, and even methods of previous scholars. In contrast, perhaps the key characteristic of scholarship over the past 20 years is its normal science mode – a tendency to work within a relatively well-defined theoretical and methodological framework. Much of the research in the last period focused on replication of previous knowledge, even when the goal was to build a new paradigm or demolish an older one.

The normal science form of extant SSIP research is reflected in:

  1. (a) data sharing and usage – including near standardization of certain central data sets;

  2. (b) focus on the dyadic level of analysis;

  3. (c) the dominant use of a number of variables either as key independent variables or as control variables (e.g., joint democracy, alliance, economic interdependence, joint IGO membership, capability ratios, contiguity or distance);

  4. (d) increasing attention to questions of selection effects and selection bias; and

  5. (e) replication, replication, replication.

The second aspect of the epistemological revolution concerned the co-evolution of logics and empirics. Both the formalization of existing theories and approaches in mathematical models, and the specification of new theories via formal modeling went side-by-side with the systematic derivation of testable propositions from these models and their rigorous testing. A growing understanding emerged in the SSIP community regarding two principles. First, formal models – no matter how neat and general they are – cannot remain in the realm of mathematical propositions; to have meaningful value, these propositions must be tested empirically.

Second, empirical regularities such as the democratic peace – no matter how robust they turn out to be due to rigorous statistical tests – do not mean much unless there is a good logical explanation for why they persist. The upshot of this recognition is a growing convergence between the formal modeling and the empiricist communities of SSPI. The Empirical Implications of Theoretical Models (EITM) movement in political science is an outgrowth of these two principles.

The third aspect of the epistemological revolution is that the evolution of the traditions outlined above had essentially put to the rest – at least in the American IR community – the heated debate between quantitative and qualitative methodologies. Even traditional qualitative scholars realized that qualitative methods require reliance on principles of logical inference that are transparent and replicable.4 The attention to research design within the qualitative research community and even the establishment of an annual workshop on qualitative research are in fact evidence of the influence of the demand for rigor and systematic analysis even when scholars do not want or cannot use quantitative methods. In this sense, the current debate between quantitative and qualitative scholars is no longer of the form of which method best suits political research; rather it is about which method is best fitted to analysis of a given set of substantive questions. More importantly, the growing use of multi-method approaches is as good evidence as any that quantitative and qualitative methods are not mutually exclusive, but compatible approaches.


The convergence of these five revolutions has resulted in better data, better models, better methods, more refined and verified knowledge, and an improved understanding on how research is to be done in international relations. It has also resulted in recognition that the key paradigms of the field are not necessarily incompatible; rather, each one has certain valid aspects and each one has fundamental limitations. Despite these impressive accomplishments, research in the past several years reflects some persistent problems. The problem areas of SSIP research cover theoretical, substantive, and methodological issues. These categories of problems are interrelated, so I discuss them no particular order. The key problem of SSIP research concerns a disconnect between theory and empirical tests that exists in many cases. Many theoretical frameworks which guided research articulated dynamic processes. For example, the “steps to war” model (Vasquez 1993) stipulates a process consisting of a series of “steps.” The emergence of a territorial dispute leads – in some cases – to the rise of rivalries, which – in some cases – militarize into intensive arms races. These rivalries and arms races increase the security dilemma between rivals. To cope with security requirements some of them seek allies. Alliances may serve as deterrent, but in some cases they intensify threat perception, culminating in militarized disputes. These disputes tend to be wider and are more likely to escalate to full-blown hostilities. This process is theoretically compelling, but attempts to test it empirically were mostly partial – picking up one step of the process and attempting to connect it to the probability of war outbreak (e.g., the literature on arms races and war, rivalries, and war, alliances and war, etc.). Alternatively (Senese and Vasquez 2008), all variables in the process are bunched together on the right-hand side of a single equation (with war outbreak as the dependent variable). These tests establish whether – in isolation, or controlling for other variables that are included in the steps-to-war framework – a specific factor or a specific set of factors posited by this framework is correlated with dyadic war outbreak. They tell us very little about the validity of the dynamics stipulated by this framework.

A similar case can be made with respect to the democratic peace explanations. The three key explanations of this phenomenon emphasize processes linking the evolution of institutions or norms in democracies to their foreign policy behavior. Some of the critiques of the democratic peace literature – primarily those that claim that democracy is a result, not a cause of peace (e.g., Thompson 1996) or those that claim that settlement of territorial disputes affect democratization (e.g., Gibler and Tir 2010) – also emphasize a relationship between process and outcomes. However, here too, processes are converted into single-equation models rather than more dynamic tests.

One of the unfortunate consequences of this disconnect is that some persisting theoretical and empirical debates (e.g., the debate about the relationship between arms races and war, or the debate on whether peace is a cause of democracy rather than vice versa) remain unresolved even with improved data and better methods. The road to resolving these debates must go through a better connection between the theoretical logic underlying the models and dynamic methodologies that enable capturing causality within processes.

Computational models are one of the more promising methodologies for tapping causal dynamics in international models (e.g., Cederman 2001; 2003). However, their empirical validation remains wanting. Another approach that has been less commonly used to model stage-based processes is that of structural equation models. Here too, too few studies have specified dynamic processes in a manner that enables the use of two-or three-stage least squares models. We should be doing more of that in order to form a better connection between our theories and our empirical estimation procedures.

A second problem with much of SSIP research is due to the almost exclusive reliance on the dyadic level of analysis. This focus has indeed helped generate important insights into international processes. However, dyadic analysis has significant limitations. First and foremost, most dyadic analyses assume that dyads are independent of each other. This assumption is tenuous (Ward et al. 2007; Cranmer and Desmarais 2011; Ward et al. 2011). The choice of a partner for conflict or to a given type of cooperation (e.g., alliance, trade, IGO membership) may depend not only on the attributes of the focal actor and the would-be partner; it may (and usually does) depend on the relationship that members of the dyads have with third parties. More importantly, complex dependencies in international networks may have important effects on both individual and on pairs of actors. There is good evidence to suggest that much of the predictive power of dyadic models can be improved by models that explicitly incorporate such complex dependencies. However, the solution is not only in the methodological realm; models of the nature of complex dependencies and on the effect of such dependencies on dyadic interactions are necessary.

Another consequence of the near-exclusive focus on dyads is the neglect of other levels of analysis. While the study of systemic processes was not completely neglected, the impact of system effects has been discounted in recent SSIP scholarship. For example, consider the effect of significant perturbations in system structure on dyadic behavior. Major wars have typically resulted in imperial collapse; empire collapse typically led to the widespread state formation. The Napoleonic Wars ended the Spanish empire in Latin America; World War I led to the collapse of the Austro-Hungarian and Ottoman empires; World War II led to the collapse of the British and French empires in Asia, the Middle East, and Africa. The collapse of the Soviet hold on Eastern Europe also resulted in the formation of new states in central Asia and Eastern Europe. All these rapid state formations not only created new dyads – they created new territorial disputes, and thereby affected the magnitude and nature of the kind of processes spelled out by such theories as the “steps to war.”

Systemwide processes – such as major shocks – may have important implications for international relations on all levels of analysis, but this has not received sufficient attention. Moreover, in the past, measures of systemic attributes were treated as some sort of aggregation of unit-based or dyadic attributes. However, most characteristics of the international system that are of interest to international relations scholars (e.g., polarization, interdependence, reciprocity) are emergent – they are a result of complex interactions among units and often do not reflect a linear (or even a simple non-linear) function of individual properties (Maoz 2010: 335–6). Here too, network analytic approaches offer significant insights into the relationships between individual attributes and behaviors, and emergent systemic processes. They are also useful in helping understand how systemic structures induce individual and dyadic effects.5

The near-exclusive focus on dyads has induced undue overconfidence in some empirical findings. However, generalizing such findings to other levels of analysis yields paradoxical results. For example, the democratic peace proposition is, in fact, a three-way puzzle. Democracies are unlikely to fight each other, but they are equally (or more) likely to fight non-democratic states. Moreover, there is either no relationship or a positive relationship between the proportion of democracies in the international system and the level of systemic conflict (Maoz 2010: 251–3). A similar result applies to the relationship between alliances and war (Maoz 2000; 2002). The failure to generalize across levels of analysis is a problematic feature of much of our research; it also sends misleading messages to policy makers about what we do know and what we do not (Maoz 2010: ch. 8). For example, some political leaders have taken the dyadic democratic peace and freely generalized to a systemic level arguing that the more democratic the system, the less conflictual it would be (c.f., Maoz 2005). However, we know that this is not necessarily the case. This requires explanations that resolve these paradoxes. Some examples suggest that this may be possible (Maoz 2010: chs 8–9).

One of the persisting biases in the study of international conflict – regardless of the level of analysis focus of this research – was the failure to address the fact that most international conflicts were fought by a small fraction of all states. Most states in the international system were involved in few conflicts and virtually no wars throughout their history. This is what I called the “fightaholism” phenomenon (Maoz 2004; 2009). This phenomenon cries for a systematic explanation. Some speculations were offered in these studies, but we need a more general account of why some states tend to resort to force very often while others systematically shy away from fights.

Finally, the SSIP community invested a great deal of effort in the attempt to unpack the causes, courses, and implications of international (and now increasingly internal) conflict. However, relatively little systematic effort was invested in the study of international cooperation. While qualitative scholars have developed a number of interesting theories about the origins and causes of international cooperation (e.g., Keohane 1984; Axelrod and Keohane 1985; Wendt 1999; Koremenos et al. 2001), systematic studies of the origins and causes of cooperation at the system level of analysis are relatively rare.


Clearly, one of the key challenges that the SSIP community has been facing is the need to deal with the theoretical, substantive, and methodological problems – both those I have pointed out above and others that have not been mentioned. We are still in need of both substantive ideas and methodological innovations that would take us away from single-equation models of conflict and cooperation. We need to develop better tools and substantive ideas that systematically tap causal sequences. The greater emphasis on new (or newly discovered) approaches such as agent-based models and network analysis is welcome. But the validation problems associated with the former approach, and the difficulty of tapping dynamics in the latter are still issues that need to be resolved before we can move forward.

We need to do a better job in developing models that connect levels of analysis and allow greater generalization of results across different levels. Despite some recent advances in this area, there is much more to be done. Both substantive and methodological advances (e.g., multilevel models) enable more sophisticated thinking on persistent level-of-analysis puzzles in international research.

Much of SSIP research focused on basic science; many of our results have little or no practical applications. However, this is not a general rule. The appeal of some of the major traditional thinkers in our fields to attentive publics and policy communities was that they could package their ideas in terms that could be easily understood, and politically manipulated. This is the case even with ideas that lack any empirical foundation. Some people made an entire career out of being wrong. In contrast, it may take years of training in sophisticated methodology to understand what many of us are doing. So even if such sophisticated scholarship results in important findings, we often fail to deliver the substantive message to the outside world. What made the democratic peace appealing was the simple message: democracies do not fight each other. Some of the other results that may have equal substantive and practical import are left buried in prestigious academic journals that few outside of the professional community read. This is not meant to imply that we must stop doing what we are doing. It does mean that we can focus more on presenting our approaches, methods, and results in terms that are less technical and more practical.

Related to that is a need to make our understanding of international processes translated into broader policy implications. Here too, the challenge is not to do differently what we do, but rather it is to make a greater effort on translating our empirical results into ideas that can be used by policy makers. We must be careful, however, not to offer premature advice, on the one hand, and not to create the impression that our results – however, robust they might seem – are only probabilistic in nature. We must also be on the alert to convey just what our results imply, no more and no less – in particular, stressing the limits of our analysis is crucially important. Nevertheless, our field has sufficiently matured to enable us to get out of the box of basic research and ask questions about influencing policy.


The scientific study of international processes has made significant strides over the past 20 years. It has, in many respects, established itself as the mainstream research community in the field, and attracted growing attention from other disciplines. Some of the results of this work have also had some impact on policy; however the misuse of these results was almost as common as their correct interpretation. New research frontiers have opened with growing data availability, increased methodological sophistication, and greater scientific discipline within the profession – with more attention paid to replication and expansion of knowledge – instead of constant quibbling.

At the same time, important challenges remain down the road. There is no reason for complacency. But there is also hope; younger generations of scholars are broadening the substantive and methodological frontier of the discipline. I hope that future reviews of the field continue to praise the positive trajectory of this small but influential research community in international studies.


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                                                                                                                                                                                                                                                  (1.) A new update of the MID data set is based partially on this data mining technology.

                                                                                                                                                                                                                                                  (2.) These studies relied on factor analysis to generate clusters of indicators of domestic and international conflict, but the key tests of the linkage hypotheses were correlational in nature.

                                                                                                                                                                                                                                                  (3.) For example, I compared the non-political science/international relations cites of four of the most cited SSIP authors – Bruce Russett, Bruce Bueno de Mesquita, James Fearon, Gary King (only articles with IR data or content) to the number of non-political science/ international relations cites of four of the most cited authors from more traditional schools of thought – Kenneth Waltz, Robert Keohane, John Mearsheimer, and Alexander Wendt. The former authors were collectively cited 1615 times in non political science or IR journals, while the latter were cited 1445 times (with quite a few cites of King, Keohane, and Verba 1994 overlapping between these two sets). SSIP highly cited scholars were primarily cited by economists, sociologists, and non-social scientists. Traditional highly cited scholars were cited primarily by sociologists and historians.

                                                                                                                                                                                                                                                  (4.) Much of this was due to the seminal book of King et al. (1994) and the debate it invoked (e.g., George and Bennett 2005; Brady and Collier 2010).

                                                                                                                                                                                                                                                  (5.) Cf. Jervis (1999) for a more qualitative discussion of the nature and effects of systems.