Analyzing Responses from Likert Surveys and Risk-Adjusted Ranking: A Data Analytics Perspective

We broadly consider the topic of ranking entities from surveys/opinions. Often, numerous ranks from different respondents are available for the same entity, e.g., a candidate from a pool, and yet an averaging of those ranks may not serve the purpose of identifying a consensus candidate. We first consider a risk-adjusted paradigm for ranking, where the rank is defined as the average (mean) rank plus a scalar times the risk in the rank; we use standard deviation as a risk metric. In case of a candidate being ranked either on the basis of opinions of a selection committee\u27s members or on social interactions in a social network such as Facebook, risk-adjusted ranking can result in selecting a consensus candidate who/which does not secure the best average rank, but is acceptable to a large number of the opinion providers. Second, we present an approach to develop the margin of error in Likert surveys, which are increasingly being used in data analytics, where the responses are on a five-point scale, but one is interested in a binary response, e.g., yes-no, agree-disagree. Computing the margin of error in Likert surveys is an open problem

On Step Sizes, Stochastic Shortest Paths, and Survival Probabilities in Reinforcement Learning

Reinforcement learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the problems have a very large number of states. We present an empirical study of (i) the effect of step-sizes (learning rules) in the convergence of RL algorithms, (ii) stochastic shortest paths in solving average reward problems via RL, and (iii) the notion of survival probabilities (downside risk) in RL. We also study the impact of step sizes when function approximation is combined with RL. Our experiments yield some interesting insights that will be useful in practice when RL algorithms are implemented within simulators

Attitudes towards Face-To-Face Meetings in Virtual Engineering Teams: Perceptions from a Survey of Defense Projects

Modes of communication used in virtual defense projects have changed dramatically over the years with tools such as email and video-conferencing dominating face-to-face (FTF) meetings. We conducted a survey at a defense firm with an aim to test current attitudes towards FTF meetings – with respect to significant problems faced, project success, transfer of technical requirements, preference for FTF vis-à-vis virtual meetings, differences between virtual and co-located environments, criticality of various forms of communication, and whether FTF meetings were scheduled as often as desired. Our survey participants, about one hundred in number, were experienced engineers, technicians, and program managers – working in a virtual product development team at a defense firm. The results suggest that despite significant advances in virtual communication technologies, FTF meetings remain critical and cannot be eliminated from defense firms. Further, it is also clear that FTF meetings can play a significant role in reducing chances of miscommunication

A Markov Chain Approach for Forecasting Progression of Opioid Addiction

The U.S. is currently facing an opioid crisis. Naltrexone is a common treatment for drug addiction; it reduces the desire to take opiates. However, addicts often stop treatment or continue to use opioids while in treatment. This results in increased fatalities and associated costs. A Markov-chain model is presented to analyze the progression of opioid addiction to assist the medical community in developing appropriate treatments. The model includes patients who continue opiate use while on naltrexone (blocked patients) and those who use opiates after missing naltrexone doses (unblocked patients). The other types of patients are abstinent (the best-case scenario) and dropout (the worst-case scenario). The Markov-chain model is built on probability estimates of transitions from one stage to another; the model predicts the proportion of patients in the different stages for a given rate of intervention on dropouts. Many factors, including psychological, environmental, sociodemographic, and access-to-healthcare, impact transition probabilities and thereby the observational data used for constructing the Markov-chain model. Markov chains have been used successfully in predicting the progression of HIV (Human Immunodeficiency Virus) and other diseases. Modeling statistically provides an offline method, based on existing data, to develop successful strategies for addressing this public-health crisis

Deep Reinforcement Learning for Approximate Policy Iteration: Convergence Analysis and a Post-Earthquake Disaster Response Case Study

Approximate Policy Iteration (API) is a Class of Reinforcement Learning (RL) Algorithms that Seek to Solve the Long-Run Discounted Reward Markov Decision Process (MDP), Via the Policy Iteration Paradigm, Without Learning the Transition Model in the Underlying Bellman Equation. Unfortunately, These Algorithms Suffer from a Defect Known as Chattering in Which the Solution (Policy) Delivered in Each Iteration of the Algorithm Oscillates between Improved and Worsened Policies, Leading to Sub-Optimal Behavior. Two Causes for This that Have Been Traced to the Crucial Policy Improvement Step Are: (I) the Inaccuracies in the Policy Improvement Function and (Ii) the Exploration/exploitation Tradeoff Integral to This Step, Which Generates Variability in Performance. Both of These Defects Are Amplified by Simulation Noise. Deep RL Belongs to a Newer Class of Algorithms in Which the Resolution of the Learning Process is Refined Via Mechanisms Such as Experience Replay And/or Deep Neural Networks for Improved Performance. in This Paper, a New Deep Learning Approach is Developed for API Which Employs a More Accurate Policy Improvement Function, Via an Enhanced Resolution Bellman Equation, Thereby Reducing Chattering and Eliminating the Need for Exploration in the Policy Improvement Step. Versions of the New Algorithm for Both the Long-Run Discounted MDP and Semi-MDP Are Presented. Convergence Properties of the New Algorithm Are Studied Mathematically, and a Post-Earthquake Disaster Response Case Study is Employed to Demonstrate Numerically the Algorithm\u27s Efficacy

Strategies for Using Technology When Grading Problem-Based Classes

More and more work is being done today using technology. Email and digital drop boxes are useful tools for professors; however the challenge comes when one is teaching a quantitative class. The issue of using technology to manage work in a quantitative class is increasing as more engineering programs embrace distance education. In this paper we will review the advantages and disadvantages of several methods of collecting, grading, and returning homework assignments to students. The techniques considered include faxing, PDF grading using a Wacom Tablet, and various email approaches. Student survey results are also included in the paper

Flexible and Intelligent Learning Architectures for SOS (FILA-SoS)

Multi-faceted systems of the future will entail complex logic and reasoning with many levels of reasoning in intricate arrangement. The organization of these systems involves a web of connections and demonstrates self-driven adaptability. They are designed for autonomy and may exhibit emergent behavior that can be visualized. Our quest continues to handle complexities, design and operate these systems. The challenge in Complex Adaptive Systems design is to design an organized complexity that will allow a system to achieve its goals. This report attempts to push the boundaries of research in complexity, by identifying challenges and opportunities. Complex adaptive system-of-systems (CASoS) approach is developed to handle this huge uncertainty in socio-technical systems

An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model

A major challenge to the successful planning and evolution of an acknowledged System of Systems (SoS) is the current lack of understanding of the impact that the presence or absence of a set of constituent systems has on the overall SoS capability. Since the candidate elements of a SoS are fully functioning, stand-alone Systems in their own right, they have goals and objectives of their own to satisfy, some of which may compete with those of the overarching SoS. These system-level concerns drive decisions to participate (or not) in the SoS. Individual systems typically must be requested to join the SoS construct, and persuaded to interface and cooperate with other Systems to create the “new” capability of the proposed SoS. Current SoS evolution strategies lack a means for modeling the impact of decisions concerning participation or non-participation of any given set of systems on the overall capability of the SoS construct. Without this capability, it is difficult to optimize the SoS design. The goal of this research is to model the evolution of the architecture of an acknowledged SoS that accounts for the ability and willingness of constituent systems to support the SoS capability development. Since DoD Systems of Systems (SoS) development efforts do not typically follow the normal program acquisition process described in DoDI 5000.02, the Wave Model proposed by Dahmann and Rebovich is used as the basis for this research on SoS capability evolution. The Wave Process Model provides a framework for an agent-based modeling methodology, which is used to abstract the nonutopian behavioral aspects of the constituent systems and their interactions with the SoS. In particular, the research focuses on the impact of individual system behavior on the SoS capability and architecture evolution processes. A generic agent-based model (ABM) skeleton structure is developed to provide an Acknowledged SoS manager a decision making tool in negotiating of SOS architectures during the wave model cycles. The model provides an environment to plug in multiple SoS meta-architecture generation multiple criteria optimization models based on both gradient and non-gradient descent optimization procedures. Three types of individual system optimization models represent different behaviors of systems agents, namely; selfish, opportunistic and cooperative, are developed as plug in models. ABM has a plug in capability to incorporate domain-specific negotiation modes and a fuzzy associative memory (FAM) to evaluate candidate architectures for simulating SoS creation and evolution. The model evaluates the capability of the evolving SoS architecture with respect to four attributes: performance, affordability, flexibility and robustness. In the second phase of the project, the team will continue with the development of an evolutionary strategies-based multi-objective mathematical model for creating an initial SoS meta architecture to start the negotiation at each wave. A basic generic structure will be defined for the fuzzy assessor math model that will be used to evaluate SoS meta architectures and domain dependent parameters pertaining to system of systems analysis and architecting through Agent Based Modeling. The work will be conducted in consideration of the national priorities, funding and threat assessment being provided by the environment developed for delivery at end of December 2013

An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model

The goal of this research is to model the evolution of the architecture of an acknowledged SoS that accounts for the ability and willingness of constituent systems to support the SoS capability development. Since DoD Systems of Systems (SoS) development efforts do not typically follow the normal program acquisition process described in DoDI 5000.02, the Wave Model proposed by Dahmann and Rebovich is used as the basis for this research on SoS capability evolution. The Wave Process Model provides a framework for an agent-based modeling methodology, which is used to abstract the non- utopian behavioral aspects of the constituent systems and their interactions with the SoS. In particular, the research focuses on the impact of individual system behavior on the SoS capability and architecture evolution processes.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0029, RT 044).H98230-08-D-017