The pre-solicitation phase of Government R and D contracting

Decision environment during pre-solicitation phase of procurement cycle in government agency contractin

Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings

Foreign policy analysis has been struggling to find ways to measure policy preferences and paradigm shifts in international political systems. This paper presents a novel, potential solution to this challenge, through the application of a neural word embedding (Word2vec) model on a dataset featuring speeches by heads of state or government in the United Nations General Debate. The paper provides three key contributions based on the output of the Word2vec model. First, it presents a set of policy attention indices, synthesizing the semantic proximity of political speeches to specific policy themes. Second, it introduces country-specific semantic centrality indices, based on topological analyses of countries' semantic positions with respect to each other. Third, it tests the hypothesis that there exists a statistical relation between the semantic content of political speeches and UN voting behavior, falsifying it and suggesting that political speeches contain information of different nature then the one behind voting outcomes. The paper concludes with a discussion of the practical use of its results and consequences for foreign policy analysis, public accountability, and transparency

A Customer Value Assessment Process (CVAP) for Ballistic Missile Defense

A systematic customer value assessment process (CVAP) was developed to give system engineering teams the capability to qualitatively and quantitatively assess customer values. It also provides processes and techniques used to create and identify alternatives, evaluate alternatives in terms of effectiveness, cost, and risk. The ultimate goal is to provide customers (or decision makers) with objective and traceable procurement recommendations. The creation of CVAP was driven by an industry need to provide ballistic missile defense (BMD) customers with a value proposition of contractors’ BMD systems. The information that outputs from CVAP can be used to guide BMD contractors in formulating a value proposition, which is used to steer customers to procure their BMD system(s) instead of competing system(s). The outputs from CVAP also illuminate areas where systems can be improved to stay relevant with customer values by identifying capability gaps. CVAP incorporates proven approaches and techniques appropriate for military applications. However, CVAP is adaptable and may be applied to business, engineering, and even personal every-day decision problems and opportunities. CVAP is based on the systems decision process (SDP) developed by Gregory S. Parnell and other systems engineering faculty at the Unites States Military Academy (USMA). SDP combines Value-Focused Thinking (VFT) decision analysis philosophy with Multi-Objective Decision Analysis (MODA) quantitative analysis of alternatives. CVAP improves SDP’s qualitative value model by implementing Quality Function Deployment (QFD), solution design implements creative problem solving techniques, and the qualitative value model by adding cost analysis and risk assessment processes practiced by the U.S DoD and industry. CVAP and SDP fundamentally differ from other decision making approaches, like the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), by distinctly separating the value/utility function assessment process with the ranking of alternatives. This explicit value assessment allows for straightforward traceability of the specific factors that influence decisions, which illuminates the tradeoffs involved in making decisions with multiple objectives. CVAP is intended to be a decision support tool with the ultimate purpose of helping decision makers attain the best solution and understanding the differences between the alternatives. CVAP does not include any processes for implementation of the alternative that the customer selects. CVAP is applied to ballistic missile defense (BMD) to give contractors ideas on how to use it. An introduction of BMD, unique BMD challenges, and how CVAP can improve the BMD decision making process is presented. Each phase of CVAP is applied to the BMD decision environment. CVAP is applied to a fictitious BMD example

Conspiracy in the Time of Corona: Automatic detection of Emerging Covid-19 Conspiracy Theories in Social Media and the News

Abstract Rumors and conspiracy theories thrive in environments of low confi- dence and low trust. Consequently, it is not surprising that ones related to the Covid-19 pandemic are proliferating given the lack of scientific consensus on the virus’s spread and containment, or on the long term social and economic ramifications of the pandemic. Among the stories currently circulating are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frame- works supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real-time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread

An Integrated Multicriteria Decision-Making Approach for Evaluating Nuclear Fuel Cycle Systems for Long-term Sustainability on the Basis of an Equilibrium Model: Technique for Order of Preference by Similarity to Ideal Solution, Preference Ranking Organization Method for Enrichment Evaluation, and Multiattribute Utility Theory Combined with Analytic Hierarchy Process

The focus on the issues surrounding spent nuclear fuel and lifetime extension of old nuclear power plants continues to grow nowadays. A transparent decision-making process to identify the best suitable nuclear fuel cycle (NFC) is considered to be the key task in the current situation. Through this study, an attempt is made to develop an equilibrium model for the NFC to calculate the material flows based on 1 TWh of electricity production, and to perform integrated multicriteria decision-making method analyses via the analytic hierarchy process technique for order of preference by similarity to ideal solution, preference ranking organization method for enrichment evaluation, and multiattribute utility theory methods. This comparative study is aimed at screening and ranking the three selected NFC options against five aspects: sustainability, environmental friendliness, economics, proliferation resistance, and technical feasibility. The selected fuel cycle options include pressurized water reactor (PWR) once-through cycle, PWR mixed oxide cycle, or pyroprocessing sodium-cooled fast reactor cycle. A sensitivity analysis was performed to prove the robustness of the results and explore the influence of criteria on the obtained ranking. As a result of the comparative analysis, the pyroprocessing sodium-cooled fast reactor cycle is determined to be the most competitive option among the NFC scenarios.ope

Visualization, Feature Selection, Machine Learning: Identifying the Responsible Group for Extreme Acts of Violence

The toll of human casualties and psychological impacts on societies make any study on violent extremism worthwhile, let alone attempting to detect patterns among them. This paper is an effort to predict which violent extremist organization (VEO), among 14 currently active ones throughout the world, is responsible for a violent act based on 14 features, including its human and structural tolls, its target type and value, intelligence, and weapons utilized in the attack. Three main steps in our paper include: 1) the visualization of the violent acts through linear and non-linear dimensionality reduction techniques; 2) sequential forward feature selection based on the generalization accuracy of three machine learning models–decision tree, and linear and nonlinear SVM; and 3) employing multilayer perceptron to predict the VEO based on the selected features of a violent act. Top-ranked selected features were related to the target type and plan and the multilayer perceptron achieved up to 40% test accuracy