An Analytic Approach to Selecting a Nonprofit

Charity giving continues to be an important aspect of the economic and social fabric of the United States. The number and total assets of nonprofits registered with the Internal Revenue Service (IRS) under the section 501(c)(3) of the tax code have grown significantly over the past decade. Given the significant share of donations in supporting the activities of nonprofits, it is important for donors to have a better understanding of their operations and governance. As the number of nonprofits with similar objectives increases, it becomes overly complicated for donors to make a choice that is consistent with their own purpose for giving. The goal of this paper is to develop an analytic framework for selecting a nonprofit from among competing alternatives. Specifically, we propose a process in which consultants or financial advisors help donors evaluate nonprofits using a set of financial and governance criteria to generate a ranked short list of alternatives for further evaluation. Donors differ in their criteria for evaluating the performance of nonprofits. The methodology we use allows donors to incorporate their preferences for specific criteria to the selection of a nonprofit in a consistent manner.http://deepblue.lib.umich.edu/bitstream/2027.42/64420/1/wp951.pd

AN ANALYTIC APPROACH TO SELECTING A NONPROFIT

Charity giving continues to be an important aspect of the economic and social fabric of the United States. The number and total assets of nonprofits registered with the Internal Revenue Service (IRS) under the section 501(c)(3) of the tax code have grown significantly over the past decade. Given the significant share of donations in supporting the activities of nonprofits, it is important for donors to have a better understanding of their operations and governance. As the number of nonprofits with similar objectives increases, it becomes overly complicated for donors to make a choice that is consistent with their own purpose for giving. The goal of this paper is to develop an analytic framework for selecting a nonprofit from among competing alternatives. Specifically, we propose a process in which consultants or financial advisors help donors evaluate nonprofits using a set of financial and governance criteria to generate a ranked short list of alternatives for further evaluation. Donors differ in their criteria for evaluating the performance of nonprofits. The methodology we use allows donors to incorporate their preferences for specific criteria to the selection of a nonprofit in a consistent manner.

Testing Market Response to Auditor Change Filings: a comparison of machine learning classifiers

The use of textual information contained in company filings with the Securities Exchange Commission (SEC), including annual reports on Form 10-K, quarterly reports on Form 10-Q, and current reports on Form 8-K, has gained the increased attention of finance and accounting researchers. In this paper we use a set of machine learning methods to predict the market response to changes in a firm\u27s auditor as reported in public filings. We vectorize the text of 8-K filings to test whether the resulting feature matrix can explain the sign of the market response to the filing. Specifically, using classification algorithms and a sample consisting of the Item 4.01 text of 8-K documents, which provides information on changes in auditors of companies that are registered with the SEC, we predict the sign of the cumulative abnormal return (CAR) around 8-K filing dates. We report the correct classification performance and time efficiency of the classification algorithms. Our results show some improvement over the naïve classification method

A Recursive Algorithm For Fractionally Differencing Long Data Series

We propose a recursive algorithm to fractionally difference time series data. The algorithm eliminates the need to evaluate the gamma function directly, and hence avoids the overflow problem that arises when fractionally differencing a long data series. The proposed algorithm can be implemented using any general matrix programming language. An implementation using SAS is presented. The algorithm and the code provide a practical approach to including fractional differencing as part of a time series data analysis

On Safety Assessment of Automated Driving Systems Using Simulation-based Testing and Formal Methods

Automated vehicles are assumed to play an important role in the future of mobility, but their operation must be provably safe. They consist of automated driving systems (ADSs) that perform various automated driving tasks without the active participation of a human driver. These automated driving tasks can be mainly categorized as perception, decision-making, and motion control. These tasks must be accomplished by the components of an ADS, which must be seamlessly integrated to ensure safety. The complexity of the ADS architecture makes the safety assessment rather challenging. This complexity is further exacerbated when automated vehicles need to interact in different traffic situations. Design, verification, and testing of ADSs as simulation models provide a safer and cost-efficient early development opportunity compared to real-world testing. To this end, a capable simulation framework that incorporates the simulation models of ADSs must be developed for designing, implementing, and testing these models in a traffic simulation. The main contributions of this thesis are denoted as (i), (ii), and (iii). Safety assessment of ADS can be done either experimentally by (i) simulation-based testing in (ii) a simulation framework or theoretically (iii) using formal methods. Simulation-based testing requires two components: (i) efficient testing strategies for different ADS components and (ii) a simulation framework containing the models of ADS components for applying these testing strategies. Simulation-based testing alone cannot prove or guarantee safety. In order to complement the safety assessment process, whenever applicable, (iii) formal methods must be utilized to derive theoretical safety proofs for certain types of systems for a set of assumptions. Formal methods for synthesis include methods such as correct-by-construction of control protocols and reachability analysis for dynamic systems, which can be used to design provably safe decision-making and control algorithms. The correct-by-construction synthesis of discrete control protocols can be used as safety filters for decision-making algorithms, such as autonomous intersection management algorithms, to verify the safety of taken actions. The reachability analysis is useful for predicting trajectories for possible maneuvers in a finite time horizon for an automated vehicle on a highway. By over-approximating these ego vehicle trajectories, safety verification of possible maneuvers can be done by comparing them to the possible trajectories of other vehicles. A game-theoretical decision-making approach, such as minimax, can augment safety in maneuver planning by considering the worst-case situations up to a finite time horizon. Such an online maneuver planning algorithm reconsiders the maneuvers at each planning cycle in a receding horizon fashion. However, to apply formal methods, certain assumptions must be made about complex parts of ADSs, and therefore, simulation-based testing is still needed to check the validity of these assumptions in simulation models. Safety assessment with a holistic approach is presented that combines the previously mentioned contributions of this thesis (i), (ii), and (iii) into a workflow of modeling, design/synthesis, and testing. Such an approach is essential for developing safe algorithms for ADSs in a simulation framework.:Kurzfassung v Abstract vii Contents ix 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Scope of the Thesis . . . . . . . . . . . . . . . . . . . . . . . 1 1.3 Research Questions . . . . . . . . . . . . . . . . . . . . . . . 2 1.4 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . 3 2 Safety Assessment of Automated Driving Systems - State of the Art 5 2.1 State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Definition of ADS . . . . . . . . . . . . . . . . . . . . 5 2.1.2 Meaning of Safety for ADS . . . . . . . . . . . . . . . 8 2.1.3 Testing for Safety . . . . . . . . . . . . . . . . . . . . 12 2.1.4 Simulation Frameworks for ADSs and AVs . . . . . . 14 2.1.5 Roles of Formal Methods . . . . . . . . . . . . . . . . 16 2.2 Challenges and Contributions . . . . . . . . . . . . . . . . . 18 2.2.1 Challenges in the State-of-the-Art . . . . . . . . . . . 18 2.2.2 The Contributions . . . . . . . . . . . . . . . . . . . 21 3 Simulation-based Testing using Fault Injection 23 3.1 Related Work and Preliminaries . . . . . . . . . . . . . . . . 24 3.1.1 Fault Injection . . . . . . . . . . . . . . . . . . . . . 24 3.1.2 Fault Types and Parameters . . . . . . . . . . . . . . 27 3.1.3 Testing for ADS safety using FI . . . . . . . . . . . . 30 3.1.4 Metrics and Specifications for Safety Evaluation . . . 33 3.1.5 Simulative Error Propagation Analysis . . . . . . . . 35 3.2 Developing a Testing Strategy using Fault Injection . . . . . 36 3.2.1 Automated Testing . . . . . . . . . . . . . . . . . . . 37 3.2.2 Using Domain-specific Knowledge . . . . . . . . . . . 40 3.2.3 Smart Testing Strategy . . . . . . . . . . . . . . . . . 41 3.3 Application of Testing Strategies . . . . . . . . . . . . . . . 42 3.3.1 Testing of ACC Systems for Fault Tolerance using Fault Injection . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.3.2 Discovering Fault Parameter Space using Smart Testing Strategy . . . . . . . . . . . . . . . . . . . . . . . 48 3.4 General Functionalities for Efficient Tools . . . . . . . . . . . 52 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4 A Framework for Simulating Automated Driving Systems in Traffic 55 4.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 56 4.1.1 Levels of Detail in Traffic Simulation . . . . . . . . . 56 4.1.2 Traffic Simulations and Scenario-based Testing . . . . 59 4.1.3 Generic ADS Architecture . . . . . . . . . . . . . . . 64 4.2 Preliminaries and Definitions . . . . . . . . . . . . . . . . . . 65 4.2.1 Map and Path Planning . . . . . . . . . . . . . . . . 66 4.2.2 Decision Making and Trajectories . . . . . . . . . . . 67 4.2.3 Vehicle Motion Control . . . . . . . . . . . . . . . . . 68 4.3 Mapping the ADS structure into a Simulation Model . . . . 72 4.3.1 Sensor-based Perception . . . . . . . . . . . . . . . . 72 4.3.2 V2X Communication . . . . . . . . . . . . . . . . . . 73 4.3.3 Global Path Planner . . . . . . . . . . . . . . . . . . 75 4.3.4 Behavioral Planner/Maneuver Planner . . . . . . . . 78 4.3.5 Longitudinal and Lateral Motion Control . . . . . . . 80 4.4 Interfaces and Layering between Modules . . . . . . . . . . . 81 4.4.1 Relations between Discrete Decision-Making and Continuous Control . . . . . . . . . . . . . . . . . . . . . 82 4.4.2 Vehicles and the Infrastructure - Autonomous Intersection Management . . . . . . . . . . . . . . . . . . . . 83 4.5 Instantiating a Model-based Traffic Simulation . . . . . . . . 86 4.5.1 Traffic Simulation Environment Architecture . . . . . 88 4.5.2 Road Network and the Map Format . . . . . . . . . . 91 4.5.3 Scenario-based Traffic Simulation as Test Cases . . . 95 4.5.4 Overview of the Simulation Framework with Fault Injection . . . . . . . . . . . . . . . . . . . . . . . . . . 97 4.6 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 4.6.1 Urban Traffic Simulations . . . . . . . . . . . . . . . 101 4.6.2 Fault-Error-Failure Chain Analysis for Safety Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 5 Using Formal Methods for Safe Algorithms Design 111 5.1 Control Protocol Synthesis . . . . . . . . . . . . . . . . . . . 111 5.1.1 Related Work and Preliminaries . . . . . . . . . . . . 111 5.1.1.1 Finite State Transition Systems . . . . . . . 112 5.1.1.2 Linear Temporal Logic and Büchi Automaton 113 5.1.1.3 Correct-by-Construction Control Protocol Synthesis . . . . . . . . . . . . . . . . . . . 114 5.1.2 Application in an Autonomous Intersection Management Algorithm . . . . . . . . . . . . . . . . . . . . . 116 5.1.2.1 Modeling the Intersection and the Behaviors of the Vehicles . . . . . . . . . . . . . . . . 116 5.1.2.2 Specifications for Synthesis . . . . . . . . . 120 5.1.2.3 Algorithm for Safe Decision-Making for AIM 122 5.2 Game-Theoretical Decision-Making and Trajectory Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 5.2.1 Related Work and Preliminaries . . . . . . . . . . . . 125 5.2.1.1 Game-Theoretical Minimax Decision-Making 126 5.2.1.2 Reachability Analysis for Trajectory Generation . . . . . . . . . . . . . . . . . . . . . . 127 5.2.1.3 Motion in Frenet Coordinates . . . . . . . . 130 5.2.1.4 Modeling of AVs and Maneuvers . . . . . . 132 5.2.2 Application in a Safe Maneuver Planning Algorithm . 137 5.2.2.1 Fixed Abstraction and the Over- Approximation of Trajectories . . . . . . . . 138 5.2.2.2 Safety Quantification of Maneuvers . . . . . 140 5.2.2.3 Minimax Decision-Making for Safe Maneuver Planning . . . . . . . . . . . . . . . . . . . 143 5.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 6 Safety Assessment with a Holistic Approach 151 6.1 Overview and the Application of the Approach . . . . . . . . 152 6.2 Case Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 6.2.1 Case Study 1: Safety of an Autonomous Intersection Management Algorithm . . . . . . . . . . . . . . . . 155 6.2.1.1 Modeling . . . . . . . . . . . . . . . . . . . 155 6.2.1.2 Design/Synthesis . . . . . . . . . . . . . . . 157 6.2.1.3 Testing and Results . . . . . . . . . . . . . 159 6.2.1.4 Conclusion . . . . . . . . . . . . . . . . . . 161 6.2.2 Case Study 2: Safety of a Maneuver Planning Algorithm for Highway Driving . . . . . . . . . . . . . . . 162 6.2.2.1 Modeling . . . . . . . . . . . . . . . . . . . 163 6.2.2.2 Design/Synthesis . . . . . . . . . . . . . . . 163 6.2.2.3 Testing and Results . . . . . . . . . . . . . 167 6.2.2.4 Conclusion . . . . . . . . . . . . . . . . . . 175 7 Conclusions 177 7.1 Main Findings . . . . . . . . . . . . . . . . . . . . . . . . . . 178 7.2 Answers to the Research Questions . . . . . . . . . . . . . . 179 7.3 Possible Future Directions . . . . . . . . . . . . . . . . . . . 181 Appendix A Additional Details 185 A.1 Rigid Bodies of the Vehicles . . . . . . . . . . . . . . . . . . 185 A.2 Collision Detection . . . . . . . . . . . . . . . . . . . . . . . 186 A.3 Trajectory Tracking in Frenet Coordinates . . . . . . . . . . 187 References 18

On Teaching Multi-Criteria Decision Making with a Robot Assistant

We propose a system and method for a robot assistant for teaching multi-attribute decision making (MCDM). Through questions and answers in natural language, the robot assistant learns the user’s preferences on multiple criteria involving a selection decision and makes recommendations using data on each criterion and the learned user preferences. It will include a use-case demonstration where NAO the robot will assist a human in forming a simple portfolio of mutual funds. Presenters will illustrate the architecture of the robot assisted MCDM and describe a method that is extensively used to structure complex decision problems and has been applied to a variety of problems in a diverse set of disciplines, such as selecting a project, selecting a life insurance contract, selecting public relations firms, deciding on library acquisitions, hostage negotiations, selecting sites for wildlife management, and selecting a nonprofit for donation

ESG Investing: A Decision-Making Paradox?

Debates about the attributes of environmental, social, and governance (ESG) investing are ongoing. The definitions, integration methods, performance outcomes, and data consistency are all developing. Against this backdrop, we explore whether ESG investing constitutes a decision-making paradox in which selecting a desirable set of stocks for inclusion in an ESG portfolio simultaneously requires adopting a desirable decision-making method to select those stocks. Comparing the results from two multi-criteria decision-making methods for selecting ESG portfolio stocks, we illustrate the sensitivity of the resulting selections to various approaches. We recommend a series of practical steps for resolving the paradox

Modeling Market Reactions to Auditor Changes Using Variable Selection Algorithms: A Meta-Analysis

Market reactions to auditor change filings have been studied over a long period in the literature. We provide a review of the literature on market response to auditor changes and identify a superset of variables used in published research. Applying methods from machine learning to optimize variable selection, we build models that explain market reaction to auditor changes. We compare the performance of our models with the performance of the models that use subsets of variables examined in a select list of studies in the literature. Our meta-analysis results in an improvement in model fit compared to the analysis used in prior studies

Sensor characterization for multisensor odor-discrimination system

In recent years, with the advent of new and cheaper sensors, the use of olfactory systems in homes, industries, and hospitals has a new start. Multisensor systems can improve the ability to distinguish between complex mixtures of volatile substances. To develop multisensor systems that are accurate and reliable, it is important to take into account the anomalies that may arise because of electronic instabilities, types of sensors, and air flow. In this approach, 32 metal oxide semiconductor sensors of 7 different types and operating at different temperatures have been used to develop a multisensor olfactory system. Each type of sensor has been characterized to select the most suitable temperature combinations. In addition, a prechamber has been designed to ensure a good air flow from the sample to the sensing area. The multisensor system has been tested with good results to perform multidimensional information detection of two fruits, based on obtaining sensor matrix data, extracting three features parameters from each sensor curve and using these parameters as the input to a pattern recognition system. (C) 2012 Elsevier B.V. All rights reserved.Cueto Belchí, AD.; Rothpfeffer, N.; Pelegrí Sebastiá, J.; Chilo, J.; García Rodríguez, D.; Sogorb Devesa, TC. (2013). Sensor characterization for multisensor odor-discrimination system. Sensors and Actuators A: Physical. 191:68-72. doi:10.1016/j.sna.2012.11.039S687219