Does Autoenrollment Affect Employer Contributions?

Summarizes research on how automatically enrolling employees in 401(k) plans in order to raise participation rates increases costs for employers and affects their matching contribution rates and, in turn, the retirement security of eligible employees

von Neumann-Morgenstern and Savage Theorems for Causal Decision Making

Causal thinking and decision making under uncertainty are fundamental aspects of intelligent reasoning. Decision making under uncertainty has been well studied when information is considered at the associative (probabilistic) level. The classical Theorems of von Neumann-Morgenstern and Savage provide a formal criterion for rational choice using purely associative information. Causal inference often yields uncertainty about the exact causal structure, so we consider what kinds of decisions are possible in those conditions. In this work, we consider decision problems in which available actions and consequences are causally connected. After recalling a previous causal decision making result, which relies on a known causal model, we consider the case in which the causal mechanism that controls some environment is unknown to a rational decision maker. In this setting we state and prove a causal version of Savage's Theorem, which we then use to develop a notion of causal games with its respective causal Nash equilibrium. These results highlight the importance of causal models in decision making and the variety of potential applications.Comment: Submitted to Journal of Causal Inferenc

Efectos de la implementación de terapia asistida por perros (TAP) en pacientes humanos con trastornos cognitivos

La terapia asistida por animales (TAA) es un tipo de tratamiento terapéutico donde se utilizan animales con el propósito de ayudar a combatir diferentes enfermedades, tanto físicas como psicológicas. Se realizó una revisión bibliográfica acerca de los efectos que tiene la implementación de la Terapia Asistida por Perros (TAP) en la salud de los pacientes, especialmente en aquellos con trastornos cognitivos. Se evidencia la importancia del perro como el animal más idóneo para la TAA, debido a la facilidad de este animal en crear lazos afectivos con el ser humano. Además, es importante resaltar que la raza del perro no es relevante para su utilización en la TAA, mientras que sea un animal dócil y obediente. También es importante implementar frecuentemente la TAP con el fin de incrementar las posibilidades de mejoría de los pacientes

An Analysis of Potential Tax Incentives to Increase Charitable Giving in Puerto Rico

Compares options for improving tax incentives for charitable giving, including lifting the ceiling on deductions as a percentage of adjusted gross income, and estimated effects on nonprofits in Puerto Rico, where average giving is high relative to AGI

Do Households Have a Good Sense of Their Retirement Preparedness?

The National Retirement Risk Index (NRRI) measures the percentage of working-age households who are ‘at risk’ of being financially unprepared for retirement today and in coming decades. The calculations show that even if households work to age 65 and annuitize all their financial assets, including the receipts from reverse mortgages on their homes, 44 percent will be ‘at risk’ of being unable to maintain their standard of living in retirement. An extension of the analysis to account explicitly for health care costs in retirement raises the share of ‘at risk’ households from 44 percent to 61 percent. This brief examines whether households have a good sense of their own retirement preparedness — do their retirement expectations match the reality that they face? Do people ‘at risk’ know that they are ‘at risk?’ The first section summarizes the NRRI and compares households’ self-assessed preparedness to the objective measure provided by the NRRI. The second section describes the characteristics of households associated with being too optimistic or too pessimistic. The last section of this brief introduces health care costs into the analysis.

Do State Economics or Individual Characteristics Determine Whether Older Men Work?

The difference in labor force participation rates of men aged 55-64 across the United States is astounding. For example, West Virginia has a participation rate below 60 percent, while South Dakota has a participation rate approaching 90 percent (see Figure 1). This fact in itself has significant implications for the pressures that states will face as the baby boom starts to retire in the face of a contracting retirement income system, declining housing prices, and a lackluster stock market. Despite these marked differences, little is known about the reasons for such variations in work patterns. An earlier brief, using the Current Population Survey for the period 1977-2007, demonstrated that the differences in the labor force participation of older men were related to labor market conditions, the nature of employment, and the employee characteristics in each state as well as to a “pseudo replacement rate.” These variables explained more than one-third of the total variation...

Artificial intelligence and machine learning in the era of digital transformer monitoring: Exciting developments at Hitachi Energy

The era of digitalization brings new challenges and new paradigms since transformer users and manufacturers alike are moving towards digital solutions. This transition requires new approaches, new architectures, and new ways of looking at data collection, storage, and assessment. Speed and reliability of actionable information become essential at a time when data is ubiquitous, loads are more complex, and energy production moves from traditional plants to distributed generation. This article intends to show some of the ongoing efforts at Hitachi Energy to address these and other demanding technical and economic issues. Our wind power forecast approach deals with the problem of uncertainty in upcoming power demand. We propose a machine learning model to predict power demand to improve the calculation of loadability and cooling / hotspot calculations. Similarly, our Bushing Tan δ and Capacitance Fault Detection solution uses the error of a model to detect problems with Tan δ and capacitance. Our Probabilistic Fault Tree describes an open-source approach that uses Bayesian networks to find the probability of failure of a specific transformer. Finally, we describe two publications made by our team regarding the use of synthetic data created using the Duval Pentagons to generate a model that diagnoses transformer faults; and a patent regarding the creation of an infrastructure that uses blockchain to anonymize users and provide them with information about their transformer fleet using artificial intelligence

Artificial intelligence and machine learning in the era of digital transformer monitoring: Exciting developments at Hitachi Energy

The era of digitalization brings new challenges and new paradigms since transformer users and manufacturers alike are moving towards digital solutions. This transition requires new approaches, new architectures, and new ways of looking at data collection, storage, and assessment. Speed and reliability of actionable information become essential at a time when data is ubiquitous, loads are more complex, and energy production moves from traditional plants to distributed generation. This article intends to show some of the ongoing efforts at Hitachi Energy to address these and other demanding technical and economic issues. Our wind power forecast approach deals with the problem of uncertainty in upcoming power demand. We propose a machine learning model to predict power demand to improve the calculation of loadability and cooling / hotspot calculations. Similarly, our Bushing Tan δ and Capacitance Fault Detection solution uses the error of a model to detect problems with Tan δ and capacitance. Our Probabilistic Fault Tree describes an open-source approach that uses Bayesian networks to find the probability of failure of a specific transformer. Finally, we describe two publications made by our team regarding the use of synthetic data created using the Duval Pentagons to generate a model that diagnoses transformer faults; and a patent regarding the creation of an infrastructure that uses blockchain to anonymize users and provide them with information about their transformer fleet using artificial intelligence