Angela C. Dalton

Why Split? Organizational Splits among Ethnopolitical Organizations in the Middle East:

Why do political organizations split? Drawing insight from organizational theory and social movement literature, this article explores the effect of organizational factors on group schism. Using a new data set of 112 ethnopolitical organizations in the Middle East, the article examines to what extent organizational factors such as leadership structure, organizational legality, and tactical intensity, as well as contextual variables such as state violence and external support for the organization, influence group schism. Findings show that organizations with a factional or competing leadership structure and those that use violence as a tactic are at a greater risk to split. Contrary to research on political parties, which highlight the importance of factional leadership structure in relation to the maintenance and growth of the party organization, findings suggest that competing leadership structure, along with the employment of tactical violence, precipitates ethnopolitical organizational fission and eventual splintering.

Is It Ideology or Desperation: Why Do Organizations Deploy Women in Violent Terrorist Attacks?

Why do some terrorist organizations deploy women on the front lines and in violent attacks? This study explores the social conditions, economic factors, and organizational characteristics that might explain womens participation in violent terrorist activity. With a new data set of 395 terrorist organizations, womens participation in terrorist attacks was quantified and coded. The logistic regression analysis results suggest that womens educational attainment, social rights, terrorist organizations age and size, and the level of a countrys economic development are important predictors of the deployment of women in terrorist violence while a terrorist groups ideological or religious orientation and the level of democracy do not significantly influence the likelihood of womens participation.

Parameterizing bayesian network representations of social-behavioral models by expert elicitation

Bayesian networks provide a general framework with which to model many natural phenomena. The mathematical nature of Bayesian networks enables a plethora of model validation and calibration techniques: e.g. parameter learning, structure learning, goodness of fit tests, and diagnostic checking of the model assumptions. However, they are not free of shortcomings. With regard to parameter learning, in practice it is not uncommon to find oneself lacking adequate data to reliably estimate all model parameters. In this paper we present the early development of a novel application of conjoint analysis as a method for eliciting and modeling expert opinions and for using the results in a methodology for calibrating the parameters of a Bayesian network.

Risk Analysis and Decision Making FY 2013 Milestone Report

Risk analysis and decision making is one of the critical objectives of CCSI, which seeks to use information from science-based models with quantified uncertainty to inform decision makers who are making large capital investments. The goal of this task is to develop tools and capabilities to facilitate the development of risk models tailored for carbon capture technologies, quantify the uncertainty of model predictions, and estimate the technical and financial risks associated with the system. This effort aims to reduce costs by identifying smarter demonstrations, which could accelerate development and deployment of the technology by several years.

Framework for Evaluating the Impact of Environmental Conditions on Manual Actions

Ensuring successful protection from and mitigation of external floods at nuclear power plants (NPPs) has received increasing attention in the wake of the Fukushima nuclear accident. Following the incident, the U.S. Nuclear Regulatory Commission (NRC) required all operating U.S. NPPs to identify nonconforming conditions and to verify the adequacy of monitoring and response procedures. Additional NRC initiatives aim to ensure that manual actions, i.e. actions taken outside of the main control room for flood protection and mitigation, are both feasible and reliable. We developed a framework to identify the key components and relationships required for an analytical approach or model to assess the impacts of environmental conditions (ECs) on the ability of individuals to perform flood protection and mitigation manual actions.

TRL Computer System User’s Guide

We have developed a wiki-based graphical user-interface system that implements our technology readiness level (TRL) uncertainty models. This document contains the instructions for using this wiki-based system.

CCSI Technology Readiness Levels Likelihood Model (TRL-LM) User’s Guide

This is the manual for the Carbon Capture Simulation Initiative (CCSI) Technology Readiness Level Likelihood model based on PNNL velo.

Technosocial predictive analytics for illicit nuclear trafficking

Illicit nuclear trafficking networks are a national security threat. These networks can directly lead to nuclear proliferation, as state or nonstate actors attempt to identify and acquire nuclear weapons-related expertise, technologies, components, and materials. The ability to characterize and anticipate the key nodes, transit routes, and exchange mechanisms associated with these networks is essential to influence, disrupt, interdict or destroy the function of the networks and their processes. The complexities inherent to the characterization and anticipation of illicit nuclear trafficking networks requires that a variety of modeling and knowledge technologies be jointly harnessed to construct an effective analytical and decision making workflow in which specific case studies can be built in reasonable time and with realistic effort. In this paper, we explore a solution to this challenge that integrates evidentiary and dynamic modeling with knowledge management and analytical gaming, and demonstrate its application to a geopolitical region at risk.

Utility of Social Modeling in Assessment of a State’s Propensity for Nuclear Proliferation

This report is the third and final report out of a set of three reports documenting research for the U.S. Department of Energy (DOE) National Security Administration (NASA) Office of Nonproliferation Research and Development NA-22 Simulations, Algorithms, and Modeling program that investigates how social modeling can be used to improve proliferation assessment for informing nuclear security, policy, safeguards, design of nuclear systems and research decisions. Social modeling has not to have been used to any significant extent in a proliferation studies. This report focuses on the utility of social modeling as applied to the assessment of a States propensity to develop a nuclear weapons program.