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Langche Zeng - One of the best experts on this subject based on the ideXlab platform.

  • Theory and Evidence in International Conflict: A Response to De Marchi, Gelpi, and Grynaviski
    American Political Science Review, 2004
    Co-Authors: Nathaniel Beck, Gary King, Langche Zeng
    Abstract:

    We thank Scott de Marchi, Christopher Gelpi, and Jeffrey Grynaviski (2003; hereinafter dGG) for their careful attention to our work (Beck, King, and Zeng, 2000; hereinafter BKZ) and for raising some important methodological issues that we agree deserve readers' attention. We are pleased that dGG's analyses are consistent with the theoretical conjecture about International Conflict put forward in BKZ - The causes of Conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but they are large stable and replicable whenever the ex ante probability of Conflict is large (BKZ, p.21) - and that dGG agree with our main methodological point that out-of-sample forecasting performance should always be one of the standards used to judge studies of International Conflict, and indeed most other areas of political science. However, dGG frequently err when they draw methodological conclusions. Their central claim involves the superiority of logit over neural network models for International Conflict data, as judged by forecasting performance and other properties such as ease of use and interpretation (neural networks hold few unambiguous advantages . . . and carry significant costs relative to logit; dGG, p. 14). We show here that this claim, which would be regarded as stunning in any of the diverse fields in which both methods are more commonly used, is false. We also show that dGG's methodological errors and the restrictive model they favor cause them to miss and mischaracterize crucial patterns in the causes of International Conflict. We begin in the next section by summarizing the growing support for our conjecture about International Conflict. The second section discusses the theoretical reasons why neural networks dominate logistic regression, correcting a number of methodological errors. The third section then demonstrates empirically, in the same data as used in BKZ and dGG, that neural networks substantially outperform dGG's logit model. We show that neural networks improve on the forecasts from logit as much as logit improves on a model with no theoretical variables. We also show how dGG's logit analysis assumed, rather than estimated, the answer to the central question about the literature's most important finding, the effect of democracy on war. Since this and other substantive assumptions underlying their logit model are wrong, their substantive conclusion about the democratic peace is also wrong. The neural network models we used in BKZ not only avoid these difficulties, but they, or one of the other methods available that do not make highly restrictive assumptions about the exact functional form, are just what is called for to study the observable implications of our conjecture.

  • improving quantitative studies of International Conflict a conjecture
    American Political Science Review, 2000
    Co-Authors: Nathaniel Beck, Gary King, Langche Zeng
    Abstract:

    nonexistent. In this article we offer a conjecture about one source of this problem: The causes of Conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but they are large, stable, and replicable wherever the ex ante probability of Conflict is large. This simple idea has an unexpectedly rich array of observable implications, all consistent with the literature. We directly test our conjecture by formulating a statistical model that includes its critical features. Our approach, a version of a "neural network" model, uncovers some interesting structural features of International Conflict and, as one evaluative measure, forecasts substantially better than any previous effort. Moreover, this im~rovement comes at little cost, and it is easv to evaluate whether the model is a statistical improvement over the simpler models commonly used.

Edward D Mansfield - One of the best experts on this subject based on the ideXlab platform.

  • economic interdependence and International Conflict new perspectives on an enduring debate
    Foreign Affairs, 2003
    Co-Authors: Edward D Mansfield, Brian M Pollins
    Abstract:

    The claim that open trade promotes peace has sparked heated debate among scholars and policymakers for centuries. Until recently, however, this claim remained untested and largely unexplored. Economic Interdependence and International Conflict clarifies the state of current knowledge about the effects of foreign commerce on political-military relations and identifies the avenues of new research needed to improve our understanding of this relationship. The contributions to this volume offer crucial insights into the political economy of national security, the causes of war, and the politics of global economic relations.Edward D. Mansfield is Hum Rosen Professor of Political Science and Co-Director of the Christopher H. Browne Center for International Politics at the University of Pennsylvania.Brian M. Pollins is Associate Professor of Political Science at Ohio State University and a Research Fellow at the Mershon Center.

  • Trade Blocs, Trade Flows, and International Conflict
    International Organization, 2000
    Co-Authors: Edward D Mansfield, Jon C. Pevehouse
    Abstract:

    The relationship between foreign trade and political Conflict has been a persistent source of controversy among scholars of International relations. Existing empirical studies of this topic have focused on the effects of trade flows on Conflict, but they have largely ignored the institutional context in which trade is conducted. In this article we present some initial quantitative results pertaining to the influence on military disputes of preferential trading arrangements (PTAs), a broad class of commercial institutions that includes free trade areas, common markets, and customs unions. We argue that parties to the same PTA are less prone to disputes than other states and that hostilities between PTA members are less likely to occur as trade flows rise between them. Moreover, we maintain that heightened commerce is more likely to inhibit Conflict between states that belong to the same preferential grouping than between states that do not. Our results accord with this argument. Based on an analysis of the period since World War II, we find that trade flows have relatively little effect on the likelihood of disputes between states that do not participate in the same PTA. Within PTAs, however, there is a strong, inverse relationship between commerce and Conflict. Parties to such an arrangement are less likely to engage in hostilities than other states, and the likelihood of a military dispute dips markedly as trade increases between them.

Nathaniel Beck - One of the best experts on this subject based on the ideXlab platform.

  • Theory and Evidence in International Conflict: A Response to De Marchi, Gelpi, and Grynaviski
    American Political Science Review, 2004
    Co-Authors: Nathaniel Beck, Gary King, Langche Zeng
    Abstract:

    We thank Scott de Marchi, Christopher Gelpi, and Jeffrey Grynaviski (2003; hereinafter dGG) for their careful attention to our work (Beck, King, and Zeng, 2000; hereinafter BKZ) and for raising some important methodological issues that we agree deserve readers' attention. We are pleased that dGG's analyses are consistent with the theoretical conjecture about International Conflict put forward in BKZ - The causes of Conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but they are large stable and replicable whenever the ex ante probability of Conflict is large (BKZ, p.21) - and that dGG agree with our main methodological point that out-of-sample forecasting performance should always be one of the standards used to judge studies of International Conflict, and indeed most other areas of political science. However, dGG frequently err when they draw methodological conclusions. Their central claim involves the superiority of logit over neural network models for International Conflict data, as judged by forecasting performance and other properties such as ease of use and interpretation (neural networks hold few unambiguous advantages . . . and carry significant costs relative to logit; dGG, p. 14). We show here that this claim, which would be regarded as stunning in any of the diverse fields in which both methods are more commonly used, is false. We also show that dGG's methodological errors and the restrictive model they favor cause them to miss and mischaracterize crucial patterns in the causes of International Conflict. We begin in the next section by summarizing the growing support for our conjecture about International Conflict. The second section discusses the theoretical reasons why neural networks dominate logistic regression, correcting a number of methodological errors. The third section then demonstrates empirically, in the same data as used in BKZ and dGG, that neural networks substantially outperform dGG's logit model. We show that neural networks improve on the forecasts from logit as much as logit improves on a model with no theoretical variables. We also show how dGG's logit analysis assumed, rather than estimated, the answer to the central question about the literature's most important finding, the effect of democracy on war. Since this and other substantive assumptions underlying their logit model are wrong, their substantive conclusion about the democratic peace is also wrong. The neural network models we used in BKZ not only avoid these difficulties, but they, or one of the other methods available that do not make highly restrictive assumptions about the exact functional form, are just what is called for to study the observable implications of our conjecture.

  • improving quantitative studies of International Conflict a conjecture
    American Political Science Review, 2000
    Co-Authors: Nathaniel Beck, Gary King, Langche Zeng
    Abstract:

    nonexistent. In this article we offer a conjecture about one source of this problem: The causes of Conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but they are large, stable, and replicable wherever the ex ante probability of Conflict is large. This simple idea has an unexpectedly rich array of observable implications, all consistent with the literature. We directly test our conjecture by formulating a statistical model that includes its critical features. Our approach, a version of a "neural network" model, uncovers some interesting structural features of International Conflict and, as one evaluative measure, forecasts substantially better than any previous effort. Moreover, this im~rovement comes at little cost, and it is easv to evaluate whether the model is a statistical improvement over the simpler models commonly used.

Gary King - One of the best experts on this subject based on the ideXlab platform.

  • Theory and Evidence in International Conflict: A Response to De Marchi, Gelpi, and Grynaviski
    American Political Science Review, 2004
    Co-Authors: Nathaniel Beck, Gary King, Langche Zeng
    Abstract:

    We thank Scott de Marchi, Christopher Gelpi, and Jeffrey Grynaviski (2003; hereinafter dGG) for their careful attention to our work (Beck, King, and Zeng, 2000; hereinafter BKZ) and for raising some important methodological issues that we agree deserve readers' attention. We are pleased that dGG's analyses are consistent with the theoretical conjecture about International Conflict put forward in BKZ - The causes of Conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but they are large stable and replicable whenever the ex ante probability of Conflict is large (BKZ, p.21) - and that dGG agree with our main methodological point that out-of-sample forecasting performance should always be one of the standards used to judge studies of International Conflict, and indeed most other areas of political science. However, dGG frequently err when they draw methodological conclusions. Their central claim involves the superiority of logit over neural network models for International Conflict data, as judged by forecasting performance and other properties such as ease of use and interpretation (neural networks hold few unambiguous advantages . . . and carry significant costs relative to logit; dGG, p. 14). We show here that this claim, which would be regarded as stunning in any of the diverse fields in which both methods are more commonly used, is false. We also show that dGG's methodological errors and the restrictive model they favor cause them to miss and mischaracterize crucial patterns in the causes of International Conflict. We begin in the next section by summarizing the growing support for our conjecture about International Conflict. The second section discusses the theoretical reasons why neural networks dominate logistic regression, correcting a number of methodological errors. The third section then demonstrates empirically, in the same data as used in BKZ and dGG, that neural networks substantially outperform dGG's logit model. We show that neural networks improve on the forecasts from logit as much as logit improves on a model with no theoretical variables. We also show how dGG's logit analysis assumed, rather than estimated, the answer to the central question about the literature's most important finding, the effect of democracy on war. Since this and other substantive assumptions underlying their logit model are wrong, their substantive conclusion about the democratic peace is also wrong. The neural network models we used in BKZ not only avoid these difficulties, but they, or one of the other methods available that do not make highly restrictive assumptions about the exact functional form, are just what is called for to study the observable implications of our conjecture.

  • an automated information extraction tool for International Conflict data with performance as good as human coders a rare events evaluation design
    International Organization, 2003
    Co-Authors: Gary King, Will Lowe
    Abstract:

    Despite widespread recognition that aggregated summary statistics on International Conflict and cooperation miss most of the complex interactions among nations, the vast majority of scholars continue to employ annual, quarterly, or (occasionally) monthly observations. Daily events data, coded from some of the huge volume of news stories produced by journalists, have not been used much for the past two decades. We offer some reason to change this practice, which we feel should lead to considerably increased use of these data. We address advances in event categorization schemes and software programs that automatically produce data by “reading” news stories without human coders. We design a method that makes it feasible, for the first time, to evaluate these programs when they are applied in areas with the particular characteristics of International Conflict and cooperation data, namely event categories with highly unequal prevalences, and where rare events (such as highly Conflictual actions) are of special interest. We use this rare events design to evaluate one existing program, and find it to be as good as trained human coders, but obviously far less expensive to use. For large-scale data collections, the program dominates human coding. Our new evaluative method should be of use in International relations, as well as more generally in the field of computational linguistics, for evaluating other automated information extraction tools. We believe that the data created by programs similar to the one we evaluated should see dramatically increased use in International relations research. To facilitate this process, we are releasing with this article data on 3.7 million International events, covering the entire world for the past decade.

  • improving quantitative studies of International Conflict a conjecture
    American Political Science Review, 2000
    Co-Authors: Nathaniel Beck, Gary King, Langche Zeng
    Abstract:

    nonexistent. In this article we offer a conjecture about one source of this problem: The causes of Conflict, theorized to be important but often found to be small or ephemeral, are indeed tiny for the vast majority of dyads, but they are large, stable, and replicable wherever the ex ante probability of Conflict is large. This simple idea has an unexpectedly rich array of observable implications, all consistent with the literature. We directly test our conjecture by formulating a statistical model that includes its critical features. Our approach, a version of a "neural network" model, uncovers some interesting structural features of International Conflict and, as one evaluative measure, forecasts substantially better than any previous effort. Moreover, this im~rovement comes at little cost, and it is easv to evaluate whether the model is a statistical improvement over the simpler models commonly used.

Roseanne W. Mcmanus - One of the best experts on this subject based on the ideXlab platform.