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Kenneth A Schultz - One of the best experts on this subject based on the ideXlab platform.
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the enforcement problem in coercive bargaining Interstate Conflict over rebel support in civil wars
International Organization, 2010Co-Authors: Kenneth A SchultzAbstract:This article explores the strategic problems that arise when a state seeks to use military force to compel changes in another state's policies. Although the costs associated with military action mean that there generally exist compromises that both sides prefer to Conflict, bargaining may fail if such deals are not enforceable in the face of temptations to renege on policy concessions. This study develops a model that shows that inefficient Conflict can occur when states bargain over policies that one of them can change unilaterally and covertly. I then show that this theory is useful for understanding a common source of international Conflict: Conflicts that arise when one state supports rebel groups engaged in a civil war with another state. Episodes of rebel support are associated with a substantial increase in the risk of Interstate militarized disputes, the lethality of these disputes, and the likelihood of repeated violence. Agreements to limit rebel support are unlikely to reduce Interstate violence unless they are coupled with concessions by the target state and/or monitoring mechanisms, both of which are shown theoretically to mitigate the enforcement problem.
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the enforcement problem in coercive bargaining Interstate Conflict over rebel support in civil wars
Social Science Research Network, 2007Co-Authors: Kenneth A SchultzAbstract:This paper explores the strategic problems that arise when a state seeks to use military force to compel changes in another state's policies. Although the costs associated with military action mean that there generally exist compromises that both sides prefer to Conflict, bargaining may fail if such deals are not enforceable in the face of temptations to renege on policy concessions. I develop a model which shows that inefficient Conflict can occur when states bargain over policies that one of them can change unilaterally and covertly. I then show that this theory is useful for understanding a common, but under-appreciated, source of international Conflict: Conflicts that arise when one state supports rebel groups engaged in a civil war with another state. Indeed, episodes of rebel support are associated with a substantial increase in the risk of militarized Conflict, comparable to the risk associated with territorial disputes. I show that agreements to limit rebel support are most likely to reduce Interstate violence if they are coupled with concessions by the target state and/or monitoring mechanisms, both of which are shown theoretically to mitigate the enforcement problem.
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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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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multi layer perceptron and radial basis function for modeling Interstate Conflict
2011Co-Authors: Tshilidzi Marwala, Monica LagazioAbstract:This chapter introduces and then compares the multi-layer perceptron neural network to the radial basis function neural network to help understand and predict Interstate Conflict. These two techniques are described in detail and justified with a review of relevant literature and they are implemented to Interstate Conflict. The results obtained from the implementation of these techniques demonstrate that the multi-layer perceptron neural network is better at predicting Interstate Conflict than the radial basis function network. This is mainly due to the cross-coupled chartacteristics of the multi-layer perceptron’s network compared to the radial basis function network.
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automatic relevance determination for identifying Interstate Conflict
2011Co-Authors: Tshilidzi Marwala, Monica LagazioAbstract:This chapter introduces the Bayesian and the evidence frameworks to construct an automatic relevance determination method. These techniques are described in detail, relevant literature reviews were conducted and their use is justified. The automatic relevance determination technique was then applied to determine the relevance of Interstate variables that are essential for modeling Interstate Conflict. Conclusions are drawn and explained within the context of political science.
Tshilidzi Marwala - One of the best experts on this subject based on the ideXlab platform.
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Flexibly-Bounded Rationality in Interstate Conflict
Artificial Intelligence Techniques for Rational Decision Making, 2014Co-Authors: Tshilidzi MarwalaAbstract:This chapter applies the theory of flexibly bounded rationality to Interstate Conflict. Flexibly bounded rationality is a theory that states that the bounds prescribed by Herbert Simon in his theory of bounded rationality are flexible. On contextualizing the theory of flexibly bounded rationality, inference, the theory of rational expectation, the theory of rational choice and the theory of rational conterfactuals are described. The theory of flexibly bounded rationality is applied for decision making process. This is done by using a multi-layer perceptron network and particle swarm optimization.
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rational counterfactuals and decision making application to Interstate Conflict
2014Co-Authors: Tshilidzi MarwalaAbstract:This chapter introduces the concept of rational counterfactuals which is an idea of identifying a counterfactual from the factual (whether perceived or real), and knowledge of the laws that govern the relationships between the antecedent and the consequent, that maximizes the attainment of the desired consequent. In counterfactual thinking factual statements like: ‘Saddam Hussein invaded Kuwait and consequently George Bush declared war on Iraq’ and with its counterfactual being: ‘If Saddam Hussein did not invade Kuwait then George Bush would not have declared war on Iraq’. In this chapter in order to build rational counterfactuals neuro-fuzzy model and genetic algorithm are applied. The theory of rational counterfactuals is applied to identify the antecedent that gives the desired consequent necessary for rational decision making. The rational counterfactual theory is applied to identify the values of variables Allies, Contingency, Distance, Major Power, Capability, Democracy, as well as Economic Interdependency that give the desired consequent Peace.
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modeling Interstate Conflict the role of economic interdependency for maintaining peace
2013Co-Authors: Tshilidzi MarwalaAbstract:This chapter assumes that peace is a necessary condition for healthy economic activities. It explores the role of trade in maintaining peace and, therefore, healthy economic activities. This is done by constructing the relationship between independent variables Allies, Contingency, Distance, Major Power, Capability, Democracy, as well as Economic Interdependency and the dependant variable Interstate Conflict. The chapter applies artificial intelligence techniques to study the sensitivity of the variable Economic Interdependency on driving peace and thus a healthy economic environment.
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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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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.
Cullen S. Hendrix - One of the best experts on this subject based on the ideXlab platform.
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Oil prices and Interstate Conflict
SAGE Publications, 2017Co-Authors: Cullen S. HendrixAbstract:Anecdotal evidence suggests that high oil prices embolden oil-rich states to behave more aggressively. This article contends that arguments linking oil-exporter status to Interstate Conflict are implicitly price contingent, and tests this via a reanalysis of works by Colgan and Weeks. It finds a contingent effect of oil prices on Interstate disputes, with high oil prices associated with significant increases in dispute behavior in petrostates, for which oil exports constitute more than 10% of GDP, while having a null effect in non-petrostates. Directed-dyadic tests indicate that this is due to petrostates initiating disputes, rather than becoming more attractive targets for conquest or coercion.
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oil prices and Interstate Conflict behavior
Research Papers in Economics, 2014Co-Authors: Cullen S. HendrixAbstract:Anecdotal evidence suggests high oil prices embolden leaders in oil-rich states to pursue more aggressive foreign policies. This article tests the conjecture in a sample of 153 countries for the time period 1947–2001. It finds strong evidence of a contingent effect of oil prices on Interstate disputes, with high oil prices associated with signifi cant increases in dispute behavior among oil-exporting states, while having either a negative or null effect on dispute behavior in nonexporting states.
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Trends and triggers redux: Climate change, rainfall, and Interstate Conflict
Political Geography, 2014Co-Authors: Colleen Devlin, Cullen S. HendrixAbstract:Abstract Given freshwater is crucial to sustaining life and forecasted to decline in relative abundance under most climate change scenarios, there is concern changing precipitation patterns will be a cause of future Interstate Conflict. In theorizing the impact of climate change for Interstate Conflict, we distinguish between trends (long-term means) that may affect the baseline probability of Conflict, and triggers (short-term deviations) that may affect the probability of Conflict in the short run. We jointly model the effects of mean precipitation scarcity and variability (trends) and year-to-year changes in precipitation (triggers) on militarized Interstate disputes between states. We find higher long-run variability in precipitation and lower mean levels of precipitation in dyads are associated with the outbreak of militarized Interstate disputes (MIDs). Contra neo-Malthusian expectations, however, we find joint precipitation scarcity – defined as both countries experiencing below mean rainfall in the same year – has a Conflict-dampening effect. These findings push the literature in a direction that more closely aligns our modeling of human impacts with our understanding of the physical impacts of climate change.
Mark J.c. Crescenzi - One of the best experts on this subject based on the ideXlab platform.
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Reputation and Interstate Conflict
American Journal of Political Science, 2007Co-Authors: Mark J.c. CrescenziAbstract:In international politics, states learn from the behavior of other nations, including the reputations states form through their actions in the international system. This article presents a model of how states process this information and examines how this learning affects international Conflict. The model builds off of cognitive balance theory and foreign policy learning models and breaks new ground in its ability to provide a contextual assessment of reputation in world politics. The article then investigates whether a dyad is more likely to experience Conflict if at least one state has a reputation for hostility. This hypothesis is tested empirically across all dyads in the international system from 1817 to 2000. The results indicate that states do engage in this learning behavior and that the information generated by extra-dyadic interaction of states has a significant bearing upon the likelihood of dyadic Conflict.
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economic exit interdependence and Conflict
The Journal of Politics, 2003Co-Authors: Mark J.c. CrescenziAbstract:This article examines the question of whether economic interdependence constrains or motivates Interstate Conflict. The theoretical model predicts when and how interdependence influences Conflict, using exit costs to separate economic interdependence from less binding economic interaction. Analysis of the model suggests that when exit costs exceed an endurance threshold for at least one state, the threat of exit becomes a viable but limited bargaining tool. Exceeding this threshold increases low-level Conflict as states use economic and diplomatic tools to resolve demands, but it decreases high-level Conflict because states take advantage of more efficient means of dispute resolution. If the stakes are too high, however, exit costs fail to check Conflict, and the economic relationship becomes an ineffective bargaining arena. Empirical analysis provides support for the hypotheses derived from the model.
