scholarly journals The implementation of the random survival forests in conflict management data: An examination of power sharing and third party mediation in post-conflict countries

2020 ◽  
Author(s):  
Andrew Whetten ◽  
John R Stevens ◽  
Damon Cann
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Political Science ◽  
Selection Process ◽  
Power Sharing ◽  
Duration Models ◽  
Time To Event ◽  
Peace Agreement ◽  
Peace Agreements ◽  
Random Survival Forest

Time-to-event analysis is a common occurrence in political science. In recent years, there has been an increased usage of machine learning methods in quantitative political science research. This article advocates for the implementation of machine learning duration models to assist in a sound model selection process. We provide a brief introduction to the random survival forest (RSF) algorithm and contrast it to a popular predecessor, the Cox proportional hazards model. We implement both methods for simulated time-to-event data and the Power-Sharing Event Dataset (PSED) to assist researchers in evaluating the merits of machine learning duration models. We provide evidence of significantly higher survival probabilities for peace agreements with 3rd party mediated design and implementation. We also detect increased survival probabilities for peace agreements that incorporate territorial power-sharing and avoid multiple rebel party signatories. Further, the RSF provides a novel approach for ranking of peace agreement criteria importance in predicting peace agreement duration. Our findings justify the robust interpretability and competitive performance of the random survival forest algorithm in numerous circumstances, in addition to promoting a diverse, holistic model-selection process for time-to-event political science data.

scholarly journals The implementation of random survival forests in conflict management data: An examination of power sharing and third party mediation in post-conflict countries

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250963
Author(s):  
Andrew B. Whetten ◽  
John R. Stevens ◽  
Damon Cann
Keyword(s):  
Machine Learning ◽  
Political Science ◽  
Selection Process ◽  
Power Sharing ◽  
Event Data ◽  
Time To Event ◽  
Peace Agreement ◽  
Time To Event Data ◽  
Peace Agreements ◽  
Random Survival Forest

Time-to-event analysis is a common occurrence in political science. In recent years, there has been an increased usage of machine learning methods in quantitative political science research. This article advocates for the implementation of machine learning duration models to assist in a sound model selection process. We provide a brief tutorial introduction to the random survival forest (RSF) algorithm and contrast it to a popular predecessor, the Cox proportional hazards model, with emphasis on methodological utility for political science researchers. We implement both methods for simulated time-to-event data and the Power-Sharing Event Dataset (PSED) to assist researchers in evaluating the merits of machine learning duration models. We provide evidence of significantly higher survival probabilities for peace agreements with 3rd party mediated design and implementation. We also detect increased survival probabilities for peace agreements that incorporate territorial power-sharing and avoid multiple rebel party signatories. Further, the RSF, a previously under-used method for analyzing political science time-to event data, provides a novel approach for ranking of peace agreement criteria importance in predicting peace agreement duration. Our findings demonstrate a scenario exhibiting the interpretability and performance of RSF for political science time-to-event data. These findings justify the robust interpretability and competitive performance of the random survival forest algorithm in numerous circumstances, in addition to promoting a diverse, holistic model-selection process for time-to-event political science data.

scholarly journals The Comprehensive Peace Agreement and the Dynamics of Post-Conflict Political Partnership in Sudan

2009 ◽  
Vol 44 (3) ◽  
pp. 133-147 ◽  
Author(s):  
Einas Ahmed
Keyword(s):  
Power Sharing ◽  
Transitional Period ◽  
Political Transformation ◽  
Peace Agreement ◽  
The North ◽  
Post Conflict ◽  
Peace Agreements ◽  
Political Forces ◽  
The Government ◽  
Democratic Transformation

Most of the researches on peace agreements conclude that power-sharing arrangements included in these are mostly to the detriment of long-term democratic transformation. The basic argument of these studies is that peace deals consolidate mainly the power of the signatories to the detriment of other major political forces. This article illustrates that, in contrast to many cases, the Comprehensive Peace Agreement (CPA), which was signed in 2005 between the government of Sudan represented by the ruling party, the National Congress Party (NCP) and the Sudan People's Liberation Movement/Army (SPLM/A), has led to an important political transformation in state structure as well as in power relations. Although the CPA enhanced the legitimacy of the SPLM and the NCP and consolidated their political domination, it, nevertheless, contributed to a significant political opening for other political forces in the North and in the South. The CPA put an end to the historically exclusive political hegemony of the North. This article focuses on the dynamics of relations between the SPLM and the NCP during the transitional period and illustrates how these dynamics have impacted upon the process of political transformation.

scholarly journals Does Peace Trickle Down? Micro-Level Evidence from Africa

2017 ◽  
Author(s):  
Felix Haass ◽  
Martin Ottmann
Keyword(s):  
Fixed Effects ◽  
Political Power ◽  
Well Being ◽  
Power Sharing ◽  
Peace Agreement ◽  
Peace Agreements ◽  
Wide Range ◽  
Micro Level ◽  
Post War ◽  
The Impact

Do peace agreements generate socio-economic peace dividends for citizens in post-war countries? While much research has focused on the elite level implications of peace agreements for the survival of peace, little is known about the micro-level, redistributive effects of peace agreements. We investigate the impact of peace agreement provisions and their implementation---specifically power-sharing arrangements---on individually reported measures of well-being. Building on a political economy theory of post-war politics, we conceptualize rebel organizations as political organizations that engage in distributive politics after conflict. As a result of such politically motivated redistribution, we expect an uneven manifestation of peace dividends on the micro-level that accumulates over the long-term. Specifically, we hypothesize that individuals with ethnic ties to rebel organizations that secure political power through a peace agreement perceive their well-being better than individuals without these links. To test this argument, we link data from recent Afrobarometer surveys to information on individuals' ethnic ties to rebel organizations in power-sharing arrangements in four African post-war countries. Controlling for a battery of factors that might simultaneously predict an ethnic group's propensity to gain political power and their members' well-being, results from a wide range of fixed effects specifications indicate support for our hypothesis. Peace trickles down, but not to everyone equally.

scholarly journals Novel Meta-Features for Automated Machine Learning Model Selection in Anomaly Detection

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Milos Kotlar ◽  
Marija Punt ◽  
Zaharije Radivojevic ◽  
Milos Cvetanovic ◽  
Veljko Milutinovic
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Anomaly Detection ◽  
Learning Model ◽  
Machine Learning Model ◽  
Automated Machine Learning

scholarly journals Making and Keeping Promises: Regime Type and Power-Sharing Pacts in Peace Agreements

2018 ◽  
Vol 43 (2) ◽  
pp. 178-204
Author(s):  
Anna Jarstad ◽  
Desirée Nilsson
Keyword(s):  
Power Sharing ◽  
Regime Type ◽  
Peace Agreements

scholarly journals Fragmented Ethnopolitical Social Representations of a Territorial Peace Agreement: The Mindanao Peace Talks

2012 ◽  
Vol 6 (2) ◽  
pp. 37-47 ◽  
Author(s):  
Cristina Jayme Montiel ◽  
Judith M. de Guzman ◽  
Ma. Elizabeth J. Macapagal
Keyword(s):  
Ethnic Groups ◽  
Social Representations ◽  
Peace Agreement ◽  
Territorial Conflict ◽  
Research Results ◽  
Peace Agreements ◽  
The Social ◽  
Agreement Study ◽  
Social Landscapes

This article examines fractures in the social representations of a contested peace agreement in the longstanding territorial conflict of Mindanao. We compared representational structures and discourses about the peace talks among Muslims and Christians. Study One used an open-ended survey of 420 Christians and Muslims from two Mindanao cities identified with different Islamised tribes, and employed the hierarchical evocation method to provide representational structures of the peace agreement. Study Two contrasted discourses about the Memorandum of Agreement between two Muslim liberation fronts identified with separate Islamised tribes in Mindanao. Findings show unified Christians’ social representations about the peace agreement. However, Muslims’ social representations diverge along the faultlines of the Islamised ethnic groups. Findings are examined in the light of ethnopolitical divides that emerge among apparently united nonmigrant groups, as peace agreements address territorial solutions. Research results are likewise discussed in relation to other tribally contoured social landscapes that carry hidden, yet fractured ethnic narratives embedded in a larger war storyline.

A systematic review of machine learning-based missing value imputation techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tressy Thomas ◽  
Enayat Rajabi
Keyword(s):  
Machine Learning ◽  
Selection Process ◽  
Evaluation Metrics ◽  
Correct Prediction ◽  
Data Sets ◽  
Data Set ◽  
Missing Value ◽  
Content Type ◽  
Missing Value Imputation ◽  
Literature Reviews

PurposeThe primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?Design/methodology/approachThe review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.FindingsThis study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.Originality/valueIt is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.

scholarly journals Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Process ◽  
Information Criterion ◽  
Small Sample ◽  
Information Criteria ◽  
Path Model ◽  
Sample Sizes ◽  
Bounds Test

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

Sign in / Sign up

Export Citation Format

Share Document