Predicting the costs of war

2019 ◽  
Vol 17 (3) ◽  
pp. 285-308
Author(s):  
Phil Henrickson
Keyword(s):  
Random Forest ◽  
International Relations ◽  
International System ◽  
Null Model ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Out Of Sample ◽  
Battle Deaths ◽  
Cost Of War ◽  
Bargaining Model Of War

The expected cost of war is a foundational concept in the study of international conflict. However, the field currently lacks a measure of the expected costs of war, and thereby any measure of the bargaining range. In this paper, I develop a proxy for the expected costs of war by focusing on one aspect of war costs – battle deaths. I train a variety of machine learning algorithms on battle deaths for all countries participating in fatal military disputes and interstate wars between 1816 and 2007 in order to maximize out-of-sample predictive performance. The best performing model (random forest) improves performance over that of a null model by 25% and a linear model with all predictors by 9%. I apply the random forest to all interstate dyads in the Correlates of War dataverse from 1816 to 2007 in order to produce an estimate of the expected costs of war for all existing country pairs in the international system. The resulting measure, which I refer to as Dispute Casualty Expectations, can be used to fully explore the implications of the bargaining model of war, as well as allow applied researchers to develop and test new theories in the study of international relations.

scholarly journals Corporate distress prediction using random forest and tree net for india

2020 ◽  
Vol 10 (1) ◽  
pp. 1-11
Author(s):  
Arvind Shrivastava ◽  
Nitin Kumar ◽  
Kuldeep Kumar ◽  
Sanjeev Gupta
Keyword(s):  
Random Forest ◽  
Learning Algorithm ◽  
Predictive Performance ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Data Mining Tool ◽  
Distress Prediction ◽  
Out Of Sample ◽  
Mining Tool ◽  
Corporate Distress

The paper deals with the Random Forest, a popular classification machine learning algorithm to predict bankruptcy (distress) for Indian firms. Random Forest orders firms according to their propensity to default or their likelihood to become distressed. This is also useful to explain the association between the tendency of firm failure and its features. The results are analyzed vis-à-vis Tree Net. Both in-sample and out of sample estimations have been performed to compare Random Forest with Tree Net, which is a cutting edge data mining tool known to provide satisfactory estimation results. An exhaustive data set comprising companies from varied sectors have been included in the analysis. It is found that Tree Net procedure provides improved classification and predictive performance vis-à-vis Random Forest methodology consistently that may be utilized further by industry analysts and researchers alike for predictive purposes.

Major powers and geopolitical transformations in the Middle East: competing visions for managing international and regional balances

2019 ◽  
pp. 301
Author(s):  
Marwan Awni Kamil
Keyword(s):  
Middle East ◽  
International Relations ◽  
International System ◽  
Great Powers ◽  
New Realism ◽  
Major Powers

This study attempts to give a description and analysis derived from the new realism school in the international relations of the visions of the great powers of the geopolitical changes witnessed in the Middle East after 2011 and the corresponding effects at the level of the international system. It also examines the alliances of the major powers in the region and its policies, with a fixed and variable statement to produce a reading that is based on a certain degree of comprehensiveness and objectivity.

The position of the State in the international system from the perspective of major schools of thought in international relations

2019 ◽  
pp. 79
Author(s):  
Salah Hassan Mohammed ◽  
Mahaa Ahmed Al-Mawla
Keyword(s):  
International Relations ◽  
International Politics ◽  
International System ◽  
International Order ◽  
The State ◽  
Point Of View ◽  
The Status ◽  
Future Vision

The Study is based on the state as one of the main pillars in international politics. In additions, it tackles its position in the international order from the major schools perspectives in international relations, Especially, these schools differ in the status and priorities of the state according to its priorities, also, each scholar has a different point of view. The research is dedicated to providing a future vision of the state's position in the international order in which based on the vision of the major schools in international relations.

Development of Prediction Models Using Machine Learning Algorithms for Girls with Suspected Central Precocious Puberty: Retrospective Study (Preprint)

2018 ◽  
Author(s):  
Liyan Pan ◽  
Guangjian Liu ◽  
Xiaojian Mao ◽  
Huixian Li ◽  
Jiexin Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Retrospective Study ◽  
Random Forest ◽  
Precocious Puberty ◽  
Prediction Models ◽  
Central Precocious Puberty ◽  
Machine Learning Algorithms ◽  
Stimulation Test ◽  
Gnrh Analogue ◽  
Prediction Probability

BACKGROUND Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling. OBJECTIVE We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test. METHODS In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models. RESULTS Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability. CONCLUSIONS The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.

Sovereignty at the Paris Peace Conference of 1919

2018 ◽  
Author(s):  
Leonard V. Smith
Keyword(s):  
World War Ii ◽  
International Relations ◽  
International System ◽  
International Relations Theory ◽  
League Of Nations ◽  
Great Power ◽  
World War ◽  
Treaty Of Versailles ◽  
The World ◽  
Paris Peace Conference

We have long known that the Paris Peace Conference of 1919 “failed” in the sense that it did not prevent the outbreak of World War II. This book investigates not whether the conference succeeded or failed, but the historically specific international system it created. It explores the rules under which that system operated, and the kinds of states and empires that inhabited it. Deepening the dialogue between history and international relations theory makes it possible to think about sovereignty at the conference in new ways. Sovereignty in 1919 was about remaking “the world”—not just determining of answers demarcating the international system, but also the questions. Most histories of the Paris Peace Conference stop with the signing of the Treaty of Versailles with Germany on June 28, 1919. This book considers all five treaties produced by the conference as well as the Treaty of Lausanne with Turkey in 1923. It is organized not chronologically or geographically, but according to specific problems of sovereignty. A peace based on “justice” produced a criminalized Great Power in Germany, and a template problematically applied in the other treaties. The conference as sovereign sought to “unmix” lands and peoples in the defeated multinational empires by drawing boundaries and defining ethnicities. It sought less to oppose revolution than to instrumentalize it. The League of Nations, so often taken as the supreme symbol of the conference’s failure, is better considered as a continuation of the laboratory of sovereignty established in Paris.

scholarly journals Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

2020 ◽  
Vol 13 (1) ◽  
pp. 10
Author(s):  
Andrea Sulova ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Google Earth ◽  
Summer Season ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

The Regional Path to Peaceful Change: What the Asian and European Experiences Tell Us

2020 ◽  
Vol 34 (4) ◽  
pp. 535-545
Author(s):  
Mark Beeson
Keyword(s):  
International Relations ◽  
Comparative Politics ◽  
Western Europe ◽  
International System ◽  
Historical Analysis ◽  
The European Union ◽  
Security Challenges ◽  
Comparative Historical Analysis ◽  
State Of Affairs ◽  
The Eu

AbstractOne of the more striking, surprising, and optimism-inducing features of the contemporary international system has been the decline of interstate war. The key question for students of international relations and comparative politics is how this happy state of affairs came about. In short, was this a universal phenomenon or did some regions play a more important and pioneering role in bringing about peaceful change? As part of the roundtable “International Institutions and Peaceful Change,” this essay suggests that Western Europe generally and the European Union in particular played pivotal roles in transforming the international system and the behavior of policymakers. This helped to create the material and ideational conditions in which other parts of the world could replicate this experience, making war less likely and peaceful change more feasible. This argument is developed by comparing the experiences of the EU and the Association of Southeast Asian Nations and their respective institutional offshoots. The essay uses this comparative historical analysis to assess both regions’ capacity to cope with new security challenges, particularly the declining confidence in institutionalized cooperation.

scholarly journals Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales

2021 ◽  
Vol 13 (11) ◽  
pp. 2074
Author(s):  
Ryan R. Reisinger ◽  
Ari S. Friedlaender ◽  
Alexandre N. Zerbini ◽  
Daniel M. Palacios ◽  
Virginia Andrews-Goff ◽  
...  
Keyword(s):  
Habitat Selection ◽  
Predictive Models ◽  
Regional Variation ◽  
Large Scale ◽  
Predictive Performance ◽  
Humpback Whale ◽  
Machine Learning Algorithms ◽  
Humpback Whales ◽  
Environmental Covariates ◽  
Animal Habitat

Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between animal occurrences and habitat characteristics. For broadly distributed species, habitat selection often varies among populations and regions; thus, it would seem preferable to fit region- or population-specific models of habitat selection for more accurate inference and prediction, rather than fitting large-scale models using pooled data. However, where the aim is to make range-wide predictions, including areas for which there are no existing data or models of habitat selection, how can regional models best be combined? We propose that ensemble approaches commonly used to combine different algorithms for a single region can be reframed, treating regional habitat selection models as the candidate models. By doing so, we can incorporate regional variation when fitting predictive models of animal habitat selection across large ranges. We test this approach using satellite telemetry data from 168 humpback whales across five geographic regions in the Southern Ocean. Using random forests, we fitted a large-scale model relating humpback whale locations, versus background locations, to 10 environmental covariates, and made a circumpolar prediction of humpback whale habitat selection. We also fitted five regional models, the predictions of which we used as input features for four ensemble approaches: an unweighted ensemble, an ensemble weighted by environmental similarity in each cell, stacked generalization, and a hybrid approach wherein the environmental covariates and regional predictions were used as input features in a new model. We tested the predictive performance of these approaches on an independent validation dataset of humpback whale sightings and whaling catches. These multiregional ensemble approaches resulted in models with higher predictive performance than the circumpolar naive model. These approaches can be used to incorporate regional variation in animal habitat selection when fitting range-wide predictive models using machine learning algorithms. This can yield more accurate predictions across regions or populations of animals that may show variation in habitat selection.

scholarly journals A novel framework for designing a multi-DoF prosthetic wrist control using machine learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chinmay P. Swami ◽  
Nicholas Lenhard ◽  
Jiyeon Kang
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Upper Limb ◽  
Daily Living ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Random Forest Regression ◽  
Prosthetic Devices ◽  
Upper Limb Function ◽  
The Neural Network

AbstractProsthetic arms can significantly increase the upper limb function of individuals with upper limb loss, however despite the development of various multi-DoF prosthetic arms the rate of prosthesis abandonment is still high. One of the major challenges is to design a multi-DoF controller that has high precision, robustness, and intuitiveness for daily use. The present study demonstrates a novel framework for developing a controller leveraging machine learning algorithms and movement synergies to implement natural control of a 2-DoF prosthetic wrist for activities of daily living (ADL). The data was collected during ADL tasks of ten individuals with a wrist brace emulating the absence of wrist function. Using this data, the neural network classifies the movement and then random forest regression computes the desired velocity of the prosthetic wrist. The models were trained/tested with ADLs where their robustness was tested using cross-validation and holdout data sets. The proposed framework demonstrated high accuracy (F-1 score of 99% for the classifier and Pearson’s correlation of 0.98 for the regression). Additionally, the interpretable nature of random forest regression was used to verify the targeted movement synergies. The present work provides a novel and effective framework to develop an intuitive control for multi-DoF prosthetic devices.

scholarly journals PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

2021 ◽  
Vol 22 (5) ◽  
pp. 2704
Author(s):  
Andi Nur Nilamyani ◽  
Firda Nurul Auliah ◽  
Mohammad Ali Moni ◽  
Watshara Shoombuatong ◽  
Md Mehedi Hasan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Web Application ◽  
Computational Prediction ◽  
Vital Role ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Post Translational Modification ◽  
Multiple Sequence ◽  
Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

Sign in / Sign up

Export Citation Format

Share Document