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The interdisciplinary field of migration studies is broadly interested in the causes, patterns, and consequences of migration. Much of this work, united under the umbrella of the “new economics of migration” research program, argues that personal networks within and across households drive a wide variety of migration-related actions. Findings from this micro-level research have been extremely valuable, but it has struggled to develop generalizable lessons and aggregate into macro-level and meso-level insights. In addition, at group, region, and country levels, existing work is often limited by only considering migration total inflows and/or total outflows. This focus misses many critical features of migration. Using location networks, network measures such as preferential attachment, preferential disattachment, transitivity, betweenness centrality, and homophily provide valuable information about migration cascades and transit migration. Some insights from migration research tidily aggregate from personal networks up to location networks, whereas other insights uniquely originate from examining location networks.

Article

Ryan Scott and Branda Nowell

Managing complexity requires appropriate governance structures and effective coordination, communication, and action within the incident response network. Governance structures serve as a framework to understand the interrelated relationships that exist during a crisis. Governance structures can be classified as either hierarchical and managed, autonomous and networked, or a hybrid of hierarchies and networks, and represent a continuum of crisis response systems. As such, effective crisis management is first a function of a leader’s ability to leverage hierarchical, hybrid, and network forms of crisis management governance to manage complex disasters. Second, it hinges on the proficiency of the disaster response network in managing distributed information, coordinating operations, and collaborating among jurisdictions. Combining these two points results in high-performing disaster response networks that operate fluidly between governing structures and across jurisdictions, thus increasing our national capacity to manage complex disasters.

Article

Network analysis has been one of the fastest-growing approaches to the study of politics in general and the study of international politics in particular. Network analysis relies on several key assumptions: (a) relations are interdependent, (b) complex relations give rise to emergent and unintended structures, (c) agents’ choices affect structure and structure affects agents’ choices, and (d) once we understand the emergent properties of a system and the interrelations between agents and structure, we can generalize across levels of analysis. These assumptions parallel many of the key features of international relations. Key contributions of network analysis helps shed light on important puzzles in the study and research of international relations. Specifically, (a) network analytic studies helped refine many key concepts and measures of various aspects of international politics; (b) network analysis helped unpack structures of interdependence, uncovering endogenous network effects that have caused biased inferences of dyadic behavior; (c) network analytic studies have shed light on important aspects of emergent structures and previously unrealized units of analysis (e.g., endogenous groups); and (d) network analytic studies helped resolve multiple puzzles, wherein results found at one level of analysis contradicted those found at other levels of analysis.

Article

An improved understanding of foreign policy learning necessitates a clarification of what foreign policy learning is, who learns, and how such learning occurs. Cognitive and social psychologists, sociologists, and political scientists situated in a variety of subfields have contributed to the understanding of foreign policy learning, a multidisciplinary area of inquiry. Learning theorists seek to show how a change in an actor’s beliefs due to experience or observation can lead to changes at other units, such as organizations and within the government. This cognitive dimension is important because actors may pursue a new course of action for politically expedient reasons rather than having genuinely “learned”—a distinction referred to as “complex” vs. “simple” learning. Foreign policy learning can be internal or external. The former type of learning entails what individuals, governments, or organizations learn from their prior experience. Learning theorists who focus on the individual level of analysis borrow insights from political psychology in an effort to shed light on the personal characteristics, the belief structures, and the cognitive psychological mechanisms of political actors that can better inform policymaking. Leaders whose cognitive structures are described as relatively open and complex—like Soviet leader Mikhail Gorbachev, whose learning brought about the dramatic changes that ultimately led to the demise of the Soviet Union—are more likely to alter their beliefs than their cognitively closed and simple counterparts. Yet external learning occurs as well. Policy diffusion studies show that learning can result from demonstration effects. Foreign policy learning via diffusion is not instrumental, but instead occurs through osmosis. Privatization in the former communist states, China’s Foreign Direct Investment liberalization, and the diffusion of environmental norms in the European Union are examples of learning that is contagious, not chosen. A more conscious mode of learning than diffusion is policy transfer, which entails policymakers’ transferring ideas from one country and implementing them in another. Technological innovations, unlike lessons that involve political ideology, are generally easier lessons to transfer—for example, Japan’s success in applying lessons from the West to modernize its army in the second half of the 19th century. The constraints to foreign policy learning are formidable. Decision makers are not always open to reconsidering views that challenge their beliefs. Leaders tend to resort to, and misuse, analogies that prevent learning. Even a change in a decision maker’s beliefs may not lead to foreign policy change, given the myriad political pressures, bureaucratic hurdles, and economic realities that often get in the way of implementing new ideas. Indeed, foreign policy learning and foreign policy change are not synonymous. Scholars face significant obstacles in studying foreign policy learning. There is no consensus on the definition of learning, on what constitutes learning, on how actors learn, when they learn, or on how to assess whether learning has taken place. Despite attempts to make sense of the confusion, scholars face the daunting challenge of improving understanding of how learning is shaped and funneled through the interaction of agents and the structures in which they are situated, as well as the relationship between learning and foreign policy change.

Article

First-wave international political economy (IPE) was preoccupied with the “complex interdependencies” within a world system that (it believed) was rapidly devolving following the 1971 collapse of the Bretton Woods system of fixed exchange rates. The original IPE scholars were more dedicated to theorizing about the emergence and evolution of global systems than any strict methodology. As IPE developed, it began to emphasize the possibility that institutions could promote cooperation in an anarchic environment, so IPE scholarship increasingly studied the conditions under which these institutions might emerge. Second-wave IPE scholars began to focus on the domestic “level of analysis” for explanatory power, and in particular analyzed the role of domestic political institutions in promoting global economic cooperation (or conflict). They also employed a “second-image reversed” paradigm in which the international system was treated as an explanatory variable that influenced the domestic policymaking process. In opening up the “black box” of domestic politics, in particular as it pertained to foreign economic policy, the “American school” of IPE thoroughly explored the terrain with regression-based statistical models that assume observational independence. As a result, complex interdependencies in the global system were increasingly ignored. Over time the analytical focus progressively shifted to micro-level units—firms and individuals, whenever possible—using neoclassical economic theory as its logical underpinning (with complications for political factors). This third wave of IPE, “open economy politics,” has been criticized in the post-crisis period for its narrow focus, rigid methodology, and lack of systemic theory. Leading scholars have called modern IPE “boring,” “deplorable,” “myopic,” and “reductionist,” among other epithets. A “fourth-wave” of IPE must retain its strong commitment to empiricism while re-integrating systemic processes into its analysis. A new class of complex statistical models is capable of incorporating interdependencies as well as domestic- and individual-level processes into a common framework. This will allow scholars to model the global political economy as an interdependent system consisting of multiple strata.

Article

Kumail Wasif and Jeff Gill

Bayes’ theorem is a relatively simple equation but one of the most important mathematical principles discovered. It is a formalization of a basic cognitive process: updating expectations as new information is obtained. It was derived from the laws of conditional probability by Reverend Thomas Bayes and published posthumously in 1763. In the 21st century, it is used in academic fields ranging from computer science to social science. The theorem’s most prominent use is in statistical inference. In this regard, there are three essential tenets of Bayesian thought that distinguish it from standard approaches. First, any quantity that is not known as an absolute fact is treated probabilistically, meaning that a numerical probability or a probability distribution is assigned. Second, research questions and designs are based on prior knowledge and expressed as prior distributions. Finally, these prior distributions are updated by conditioning on new data through the use of Bayes’ theorem to create a posterior distribution that is a compromise between prior and data knowledge. This approach has a number of advantages, especially in social science. First, it gives researchers the probability of observing the parameter given the data, which is the inverse of the results from frequentist inference and more appropriate for social scientific data and parameters. Second, Bayesian approaches excel at estimating parameters for complex data structures and functional forms, and provide more information about these parameters compared to standard approaches. This is possible due to stochastic simulation techniques called Markov Chain Monte Carlo. Third, Bayesian approaches allow for the explicit incorporation of previous estimates through the use of the prior distribution. This provides a formal mechanism for incorporating previous estimates and a means of comparing potential results. Bayes’ theorem is also used in machine learning, which is a subset of computer science that focuses on algorithms that learn from data to make predictions. One such algorithm is the Naive Bayes Classifier, which uses Bayes’ theorem to classify objects such as documents based on prior relationships. Bayesian networks can be seen as a complicated version of the Naive Classifier that maps, estimates, and predicts relationships in a network. It is useful for more complicated prediction problems. Lastly, the theorem has even been used by qualitative social scientists as a formal mechanism for stating and evaluating beliefs and updating knowledge.