Finding the Fuel of the Arab Spring Fire: A Historical Data Analysis

Purpose: This paper aims to address the reasons behind the varying levels of volatile conflict and peace as seen during the Arab Spring of 2011 to 2015. During this time, higher rates of conflict transition occurred than normally observed in previous studies for certain Middle Eastern and North African countries. Design/Methodology/Approach: Previous prediction models decrease in accuracy during times of volatile conflict transition. Also, proper strategies for handling the Arab Spring have been highly debated. This paper identifies which countries were affected by the Arab Spring and then applies data analysis techniques to predict a country’s tendency to suffer from high-intensity, violent conflict. A large number of open-source variables are incorporated by implementing an imputation methodology useful to conflict prediction studies in the future. The imputed variables are implemented in four model building techniques: purposeful selection of covariates, logical selection of covariates, principal component regression and representative principal component regression resulting in modeling accuracies exceeding 90 per cent. Findings: Analysis of the models produced by the four techniques supports hypotheses which propose political opportunity and quality of life factors as causations for increased instability following the Arab Spring

Multicollinearity Applied Stepwise Stochastic Imputation: A Large Dataset Imputation through Correlation‑based Regression

This paper presents a stochastic imputation approach for large datasets using a correlation selection methodology when preferred commercial packages struggle to iterate due to numerical problems. A variable range-based guard rail modification is proposed that benefits the convergence rate of data elements while simultaneously providing increased confidence in the plausibility of the imputations. A large country conflict dataset motivates the search to impute missing values well over a common threshold of 20% missingness. The Multicollinearity Applied Stepwise Stochastic imputation methodology (MASS-impute) capitalizes on correlation between variables within the dataset and uses model residuals to estimate unknown values. Examination of the methodology provides insight toward choosing linear or nonlinear modeling terms. Tailorable tolerances exploit residual information to fit each data element. The methodology evaluation includes observing computation time, model fit, and the comparison of known values to replaced values created through imputation. Overall, the methodology provides useable and defendable results in imputing missing elements of a country conflict dataset

Forecasting Country Conflict Using Statistical Learning Methods

Purpose — This paper aims to examine whether changing the clustering of countries within a United States Combatant Command (COCOM) area of responsibility promotes improved forecasting of conflict. Design/methodology/approach — In this paper statistical learning methods are used to create new country clusters that are then used in a comparative analysis of model-based conflict prediction. Findings — In this study a reorganization of the countries assigned to specific areas of responsibility are shown to provide improvements in the ability of models to predict conflict. Research limitations/implications — The study is based on actual historical data and is purely data driven. Practical implications — The study demonstrates the utility of the analytical methodology but carries not implementation recommendations. Originality/value — This is the first study to use the statistical methods employed to not only investigate the re-clustering of countries but more importantly the impact of that change on analytical predictions

Automated Software Testing in the DoD: Current Practices and Opportunities for Improvement

The concept of automating the testing of software-intensive systems has been around for decades, but the practice of automating testing is scarce in many industries, especially in the government defense sector. A one-year project initiated by the Office of the Secretary of Defense (OSD), Scientific Test and Analysis Techniques Center of Excellence (STAT COE) and sponsored by Navy OPNAV N94 set out to: study the degree to which the Department of Defense (DoD) has adopted automated software testing (AST); share the best software practices used by industry; and develop and distribute an AST implementation guide intended for program management and novice DoD software test automators. The Current State of Automated Software Testing in the Department of Defense, AST Practices and Pitfalls Guide, and the AST Implementation Guide are available at www.afit.edu/stat

Models of Nation-Building via Systems of Differential Equations

Nation-building modeling is an important field of research given the increasing number of candidate nations and the limited resources available. A modeling methodology and a system of differential equations model are presented to investigate the dynamics of nation-building. The methodology is based upon parameter identification techniques applied to a system of differential equations, to evaluate nation-building operations. Data from Operation Iraqi Freedom (OIF) and Afghanistan are used to demonstrate the validity of different models as well as the comparison of models

A Survey of Decision Making and Optimization under Uncertainty

Recent advances in decision making have incorporated both risk and ambiguity in decision theory and optimization methods. These methods implement a variety of uncertainty representations from probabilistic and non-probabilistic foundations, including traditional probability theory, sets of probability measures, uncertainty sets, ambiguity sets, possibility theory, evidence theory, fuzzy measures, and imprecise probability. The choice of uncertainty representation impacts the expressiveness and tractability of the decision models. We survey recent approaches for representing uncertainty in both decision making and optimization to clarify the trade-offs among the alternative representations. Robust and distributionally robust optimization are surveyed, with particular attention to standard form ambiguity sets. Applications of uncertainty and decision models are also reviewed, with a focus on recent optimization applications. These applications highlight common practices and potential research gaps. The intersection of behavioral decision making and robust optimization is a promising area for future research and there is also opportunity for further advances in distributionally robust optimization in sequential and multi-agent settings

表紙、目次、投稿規定、奥付(Vol.49 No.3)

Proceedings of the 2006 Winter Simulation Conference, 1357-1364.SEED Center PaperHigh-resolution combat models have become so complex that the time necessary to create and analyze a scenario has become unacceptably long. A lower resolution approach to entity-level simulation can complement such models. This paper presents Dynamic Allocation of Fires and Sensors (DAFS), a low-resolution, constructive entity-level simula- tion framework, that can be rapidly configured and exe- cuted. Through the use of a loosely-coupled component ar- chitecture, DAFS is extremely flexible and configurable. DAFS allows an analyst to very quickly create a simulation model that captures the first-order effects of a scenario. Al- though the modeling of entities is done at a low-resolution, DAFS contains some sophisticated capabilities: within the model, commander entities can formulate and solve opti- mization problems dynamically. DAFS can be used to ex- plore large areas of the parameter space and identify inter- esting regions where high-resolution models can provide more detailed information

Optimal Multi-stage Allocation of Weapons to Targets Using Adaptive Dynamic Programming

We consider the optimal allocation of resources (weapons) to a collection of tasks (targets) with the objective of maximizing the reward for completing tasks (destroying targets). Tasks arrive in two stages, where the first stage tasks are known and the second stage task arrivals follow a random distribution. Given the distribution of these second stage task arrivals, simulation and mathematical programming are used within a dynamic programming framework to determine optimal allocation strategies. The special structure of the assignment problem is exploited to recursively update functional approximations representing future rewards using subgradient information. Through several theorems, optimality of the algorithm is proven for a two-stage Dynamic Weapon-Target Assignment Problem

PULMONARY FUNCTION AND RADIOLOGICAL ABNORMALITIES IN PATIENTS WITH SJOGREN'S SYNDROME : A CROSS-SECTIONAL AND LONGITUDINAL STUDY

In order to assess the pulmonary manifestations of Sjögren's syndrome (SS), pulmonary function and radiological findings were evaluated in 82 patients with primary SS (pSS), 14 patients with secondary SS and rheumatoid arthritis (SS+RA), and 26 patients with rheumatoid arthritis without SS (RA). The most common functional abnormalities were small-airway disturbances in all 3 groups : 54.9% in pSS, 28.6% in SS +RA, and 15.4% in RA. Restrictive and obstructive ventilatory disturbances appeared less frequently in all groups. The incidence of restrictive ventilatory disturbance was 6.1% in pSS, 7.1% in SS+RA, and 3.8% in RA, and that of obstructive ventilatory disturbance was 6.1% in pSS, 14.3% in SS+RA, and 15.4% in RA. The lung diffusion factor for carbon monoxide (DLco) was decreased in pSS (49%), in SS+RA (43%), and in RA (42%). The incidence of small-airway disturbances was signifiantly higher in SS than that in RA (p< 0.01). The incidence of radiographic changes in each group was as follows ; interstitial changes, 14.6% in pSS, 14.3% in SS+RA, and 42.3% in RA ; bronchiolitic changes, 11.0% in pSS, 14.3% in SS+RA, and 15.4% in RA ; pleurisy, 8.5% in pSS, 21.4% in SS+RA, and 15.4% in RA; and lymphnode lesions, 2.4% in pSS, 7.1% in SS+RA, and 3.8% in RA. The incidence of interstitial changes was significantly higher in RA than those in SS or SS+RA (p<0.01). Moreover, a 3-year longitudinal evaluation of pulmonary involvement was performed in 24 patients with pSS. %DLco decreased in 4 patients, and total lung capacity (TLC) and functional residual capacity (FRC) decreased in 2 patients during follow-up. In conclusion, small-airway disturbances were the most common functional abnormal- ities in pSS. Remarkable reductions in %DLco, TLC and FRC during follow-up suggest the progression in interstitial lesions in the course of pSS

Incorporating information networks into military simulations

Information superiority is considered a critical capability for future joint forces. As advances in technology continue to boost our ability to communicate in new and different ways, military forces are restructuring to incorporate these technologies. Yet we are still limited in our ability to measure the contributions made by information networks. We describe three recent studies at the Naval Postgraduate School that involve information networks. First, we examine a simulation model expanded from a two-person, zero-sum game to explore how information superiority contributes to battlefield results and how sensitive it is to information quality. Second, we examine how network-enabled communications affect the logistics operations in a centralized receiving and shipping point. The results are intended to provide operational insights for terminal node operations within a sustainment base. Third, we explore how social networks might be incorporated into agent-based models representing civilian populations in stability operations