The Experts below are selected from a list of 1443 Experts worldwide ranked by ideXlab platform
Bruce Russett - One of the best experts on this subject based on the ideXlab platform.
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the effects of the international security environment on national military expenditures a multicountry study
International Organization, 2012Co-Authors: William D. Nordhaus, John R. Oneal, Bruce RussettAbstract:We consider the influence of countries' external security environments on their military spending. We first estimate the ex ante probability that a country will become involved in a fatal militarized Interstate Dispute using a model of dyadic conflict that incorporates key elements of liberal and realist theories of international relations. We then estimate military spending as a function of the threat of armed Interstate conflict and other influences: arms races, the defense expenditures of friendly countries, actual military conflict, democracy, civil war, and national economic output. In a panel of 165 countries, 1950 to 2000, we find our prospectively generated estimate of the external threat to be a powerful variable in explaining military spending. A 1 percentage point increase in the aggregate probability of a fatal militarized Dispute, as predicted by our liberal-realist model, leads to a 3 percent increase in a country's military expenditures.
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The Effects of the Security Environment on Military Expenditures: Pooled Analyses of 165 Countries, 1950-2000
2009Co-Authors: William D. Nordhaus, John R. Oneal, Bruce RussettAbstract:Countries' military expenditures differ greatly across both space and time. This study examines the determinants of military spending, with particular reference to the importance of the external security environment. Using the liberal-realist model of international relations, we first estimate the probability that two countries will be involved in a fatal militarized Interstate Dispute. We then aggregate these ex ante estimates of the likelihood of dyadic conflict, calculating the annual joint probability that a country will be involved in a fatal Dispute. This is our measure of the external threat. We then estimate the level of military spending by country and year as a function of the security environment, arms races with foes and the defense expenditures of friendly countries, states' involvement in actual military conflict, economic output, and various other political variables. In analyses of a panel of 165 countries, 1950 to 2000, we find that the security environment is a powerful determinant of military spending. Indeed, our prospectively measured estimate of the external threat is more influential than any of several influences known only ex post. Our best estimate is that a one percentage point rise in the probability of a fatal Dispute leads to a 3 percent increase in military spending.
William D. Nordhaus - One of the best experts on this subject based on the ideXlab platform.
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the effects of the international security environment on national military expenditures a multicountry study
International Organization, 2012Co-Authors: William D. Nordhaus, John R. Oneal, Bruce RussettAbstract:We consider the influence of countries' external security environments on their military spending. We first estimate the ex ante probability that a country will become involved in a fatal militarized Interstate Dispute using a model of dyadic conflict that incorporates key elements of liberal and realist theories of international relations. We then estimate military spending as a function of the threat of armed Interstate conflict and other influences: arms races, the defense expenditures of friendly countries, actual military conflict, democracy, civil war, and national economic output. In a panel of 165 countries, 1950 to 2000, we find our prospectively generated estimate of the external threat to be a powerful variable in explaining military spending. A 1 percentage point increase in the aggregate probability of a fatal militarized Dispute, as predicted by our liberal-realist model, leads to a 3 percent increase in a country's military expenditures.
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The Effects of the Security Environment on Military Expenditures: Pooled Analyses of 165 Countries, 1950-2000
2009Co-Authors: William D. Nordhaus, John R. Oneal, Bruce RussettAbstract:Countries' military expenditures differ greatly across both space and time. This study examines the determinants of military spending, with particular reference to the importance of the external security environment. Using the liberal-realist model of international relations, we first estimate the probability that two countries will be involved in a fatal militarized Interstate Dispute. We then aggregate these ex ante estimates of the likelihood of dyadic conflict, calculating the annual joint probability that a country will be involved in a fatal Dispute. This is our measure of the external threat. We then estimate the level of military spending by country and year as a function of the security environment, arms races with foes and the defense expenditures of friendly countries, states' involvement in actual military conflict, economic output, and various other political variables. In analyses of a panel of 165 countries, 1950 to 2000, we find that the security environment is a powerful determinant of military spending. Indeed, our prospectively measured estimate of the external threat is more influential than any of several influences known only ex post. Our best estimate is that a one percentage point rise in the probability of a fatal Dispute leads to a 3 percent increase in military spending.
Erin K. Little - One of the best experts on this subject based on the ideXlab platform.
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an analysis of the militarized Interstate Dispute mid dataset 1816 2001
International Studies Quarterly, 2016Co-Authors: Douglas M. Gibler, Steven V. Miller, Erin K. LittleAbstract:This research note discusses a five-year examination of the original coding work of the Militarized Interstate Dispute (MID) project. After strictly applying MID coding rules, we recommend dropping 251 cases (or over 10% of the dataset), as either we were unable to find a militarized incident in the historical record or the Dispute appeared elsewhere in the data. We found evidence linking 75 Disputes to other cases, and we could not identify 19 cases in the historical record. Among the remaining Disputes, we recommend major changes (changes in Dispute year, fatality level, and participants) in 234 Disputes and minor changes in 1,009 Disputes. We use this article to examine the potential impact of our suggestions on existing studies. Though we identified several systematic problems with the original coding effort, we also find that these problems do not affect current understandings of what predicts the onset of Interstate conflict. However, estimates in our replications of three recent studies of Dispute escalation, Dispute duration, and Dispute reciprocation all witness substantial changes when using corrected data—to the point of reversing previous conclusions in some cases.
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Heterogeneity in the Militarized Interstate Disputes (MIDs), 1816–2001: What Fatal MIDs Cannot Fix
Political Science Research and Methods, 2016Co-Authors: Douglas M. Gibler, Erin K. LittleAbstract:We examine a major source of heterogeneity across cases in the Correlates of War Militarized Interstate Dispute Dataset, 1816–2001, and demonstrate that this variation across cases biases most analyses of conflict. Disputes are coded using two logics—the familiar state-to-state militarized action represents one case while the second relies on sponsor governments to protest state targeting of private citizens. We show that the latter introduces additional measurement bias and does not match well the original conceptualization of what constituted a Dispute. The protest-dependent cases are caused by different processes, and omitting them from analyses provides truer estimates of the effects of most conflict predictors. We find that previous controls for heterogeneity in the Dispute data—such as using fatal militarized Interstate Disputes only—substantially underestimates the dangerous effects of contiguity and the pacifying effects of regime similarity. We also show that governments are seldom willing to risk militarized conflict for private citizens during these unique cases. We provide a list of the protest-dependent cases for future conflict analyses.
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An Analysis of the Militarized Interstate Dispute (MID) Dataset, 1816–2001
International Studies Quarterly, 2016Co-Authors: Douglas M. Gibler, Steven V. Miller, Erin K. LittleAbstract:This research note discusses a five-year examination of the original coding work of the Militarized Interstate Dispute (MID) project. After strictly applying MID coding rules, we recommend dropping 251 cases (or over 10% of the dataset), as either we were unable to find a militarized incident in the historical record or the Dispute appeared elsewhere in the data. We found evidence linking 75 Disputes to other cases, and we could not identify 19 cases in the historical record. Among the remaining Disputes, we recommend major changes (changes in Dispute year, fatality level, and participants) in 234 Disputes and minor changes in 1,009 Disputes. We use this article to examine the potential impact of our suggestions on existing studies. Though we identified several systematic problems with the original coding effort, we also find that these problems do not affect current understandings of what predicts the onset of Interstate conflict. However, estimates in our replications of three recent studies of Dispute escalation, Dispute duration, and Dispute reciprocation all witness substantial changes when using corrected data—to the point of reversing previous conclusions in some cases.
Tshilidzi Marwala - One of the best experts on this subject based on the ideXlab platform.
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Causal Function for Rational Decision Making: Application to Militarized Interstate Dispute
Artificial Intelligence Techniques for Rational Decision Making, 2014Co-Authors: Tshilidzi MarwalaAbstract:This chapter describes and defines a causal function within the context of rational decision making. This is implemented using rough sets to build the causal machines. The rough sets were successfully used to identify the causal relationship between the militarized Interstate Dispute variables (causes) and conflict status effects.
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Support Vector Machines for Modeling Interstate Conflict
Advanced Information and Knowledge Processing, 2011Co-Authors: Tshilidzi Marwala, Monica LagazioAbstract:Militarized conflict is one of the risks that have a significant impact on society. Militarized Interstate Dispute is defined as an outcome of Interstate interactions, which result either in peace or conflict. The effective prediction of the possibility of conflict between states is an important decision support tool for policy makers. In previous chapters, neural networks were implemented to predict militarized Interstate Disputes. Support vector machines have proved to be excellent predictors and hence are introduced in this chapter for the prediction of militarized Interstate Disputes and then compared with the hybrid Monte Carlo trained multi-layer perceptron neural networks. The results demonstrated that support vector machines predict militarized Interstate Dispute better than neural networks, while neural networks give a more consistent and easy to interpret sensitivity analysis than do support vector machines.
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Conclusions and Emerging Topics
Advanced Information and Knowledge Processing, 2011Co-Authors: Tshilidzi Marwala, Monica LagazioAbstract:The capability to scientifically comprehend the causes of militarized Interstate Disputes and then to apply this knowledge to build and spread peace in the international context is unquestionably a vital endeavor. Recent advances in the conflict literature have underlined the importance of handling international conflicts as complex phenomena, exhibiting non-linear and complex interactions amongst the relevant militarized Interstate Dispute variables.
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Simulated Annealing Optimized Rough Sets for Modeling Interstate Conflict
Advanced Information and Knowledge Processing, 2011Co-Authors: Tshilidzi Marwala, Monica LagazioAbstract:In this chapter, methods to optimally granulize rough set partition sizes using simulated annealing technique, are proposed. The proposed procedure is applied to model the militarized Interstate Dispute data. The suggested technique is then compared to the rough set partition method that is based on particle swarm optimization. The results obtained demonstrate that simulated annealing provides higher forecasting accuracies than particle swarm optimization method.
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Genetic Algorithm with Optimized Rough Sets for Modeling Interstate Conflict
Advanced Information and Knowledge Processing, 2011Co-Authors: Tshilidzi Marwala, Monica LagazioAbstract:This chapter presents methods to optimally granulize rough set partition sizes using a genetic algorithm. The procedure is applied to model the militarized Interstate Dispute data. The procedure is then compared to the rough set partition method that was based on simulated annealing. The results obtained showed that, for the data being analyzed, a genetic algorithm provides higher forecasting accuracy than does the process of simulated annealing.
Monica Lagazio - One of the best experts on this subject based on the ideXlab platform.
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Support Vector Machines for Modeling Interstate Conflict
Advanced Information and Knowledge Processing, 2011Co-Authors: Tshilidzi Marwala, Monica LagazioAbstract:Militarized conflict is one of the risks that have a significant impact on society. Militarized Interstate Dispute is defined as an outcome of Interstate interactions, which result either in peace or conflict. The effective prediction of the possibility of conflict between states is an important decision support tool for policy makers. In previous chapters, neural networks were implemented to predict militarized Interstate Disputes. Support vector machines have proved to be excellent predictors and hence are introduced in this chapter for the prediction of militarized Interstate Disputes and then compared with the hybrid Monte Carlo trained multi-layer perceptron neural networks. The results demonstrated that support vector machines predict militarized Interstate Dispute better than neural networks, while neural networks give a more consistent and easy to interpret sensitivity analysis than do support vector machines.
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Conclusions and Emerging Topics
Advanced Information and Knowledge Processing, 2011Co-Authors: Tshilidzi Marwala, Monica LagazioAbstract:The capability to scientifically comprehend the causes of militarized Interstate Disputes and then to apply this knowledge to build and spread peace in the international context is unquestionably a vital endeavor. Recent advances in the conflict literature have underlined the importance of handling international conflicts as complex phenomena, exhibiting non-linear and complex interactions amongst the relevant militarized Interstate Dispute variables.
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Simulated Annealing Optimized Rough Sets for Modeling Interstate Conflict
Advanced Information and Knowledge Processing, 2011Co-Authors: Tshilidzi Marwala, Monica LagazioAbstract:In this chapter, methods to optimally granulize rough set partition sizes using simulated annealing technique, are proposed. The proposed procedure is applied to model the militarized Interstate Dispute data. The suggested technique is then compared to the rough set partition method that is based on particle swarm optimization. The results obtained demonstrate that simulated annealing provides higher forecasting accuracies than particle swarm optimization method.
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Genetic Algorithm with Optimized Rough Sets for Modeling Interstate Conflict
Advanced Information and Knowledge Processing, 2011Co-Authors: Tshilidzi Marwala, Monica LagazioAbstract:This chapter presents methods to optimally granulize rough set partition sizes using a genetic algorithm. The procedure is applied to model the militarized Interstate Dispute data. The procedure is then compared to the rough set partition method that was based on simulated annealing. The results obtained showed that, for the data being analyzed, a genetic algorithm provides higher forecasting accuracy than does the process of simulated annealing.
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Assessing Different Bayesian Neural Network Models for Militarized Interstate Dispute Outcomes and Variable Influences
Social Science Computer Review, 2006Co-Authors: Monica Lagazio, Tshilidzi MarwalaAbstract:This article develops and compares two Bayesian neural network models, a more restrictive Bayesian framework using Gaussian approximation and a less restrictive one using a hybrid version of Markov Chain Monte Carlo method (HMC), for the prediction of militarized Interstate Disputes (MIDs). In addition, to compare and analyze different Bayesian models for international conflict, the authors introduce a new measurement to interpret the relative influence of the model variables on the MIDs. The results indicate that the Gaussian approximation and HMC models are not statistically different in their performance. However HMC correctly recognized a marginally higher number of militarized Disputes whose classification is important for policy purpose. On the variable effect, both models indicate similar patter of influences, where the two key liberal variables, democracy and economic interdependence, produce a strong dynamic feedback loop among each other, which greatly increases or decreases the probability of MIDs.
