Household Use of Financial Planners: Measurement Considerations for Researchers

Citation: Heckman, Stuart J. and Seay, Martin C and Kim, Kyoung Tae and Letkiewicz, Jodi, Household Use of Financial Planners: Measurement Considerations for Researchers (November 2, 2016). Financial Services Review, Vol. 25, p. 427-446, 2016.Using the Certified Financial Planner (CFP) Board’s definition of financial planning, this paper evaluates the validity of the measures of financial planner use in publicly available datasets. A review of Financial Services Review, Journal of Personal Finance, Journal of Financial Planning, Journal of Family and Economic Issues, Journal of Consumer Affairs, and Journal of Financial Counseling and Planning identified seven datasets that were commonly used to investigate financial planner use. Of these, the two most promising measures were found in the Survey of Consumer Finances and the National Longitudinal Study of Youth (1979). However, an evaluation of these measures raises significant concerns related to their validity. This article critically evaluates these measures and provides insights into the development of better measures of financial planner use for the future

Southern Adventist University Graduate Handbook & Planner 2015-2016

https://knowledge.e.southern.edu/grad_student_handbook/1003/thumbnail.jp

Southern Adventist University Undergraduate Handbook & Planner 2016-2017

https://knowledge.e.southern.edu/student_handbook/1034/thumbnail.jp

Southern Adventist University Undergraduate Handbook & Planner 2015-2016

https://knowledge.e.southern.edu/student_handbook/1035/thumbnail.jp

Adaptive tactical behaviour planner for autonomous ground vehicle

Success of autonomous vehicle to effectively replace a human driver depends on its ability to plan safe, efficient and usable paths in dynamically evolving traffic scenarios. This challenge gets more difficult when the autonomous vehicle has to drive through scenarios such as intersections that demand interactive behavior for successful navigation. The many autonomous vehicle demonstrations over the last few decades have highlighted the limitations in the current state of the art in path planning solutions. They have been found to result in inefficient and sometime unsafe behaviours when tackling interactively demanding scenarios. In this paper we review the current state of the art of path planning solutions, the individual planners and the associated methods for each planner. We then establish a gap in the path planning solutions by reviewing the methods against the objectives for successful path planning. A new adaptive tactical behaviour planner framework is then proposed to fill this gap. The behaviour planning framework is motivated by how expert human drivers plan their behaviours in interactive scenarios. Individual modules of the behaviour planner is then described with the description how it fits in the overall framework. Finally we discuss how this planner is expected to generate safe and efficient behaviors in complex dynamic traffic scenarios by considering a case of an un-signalised roundabout

Southern Adventist University Graduate Handbook & Planner 2016-2017

https://knowledge.e.southern.edu/grad_student_handbook/1002/thumbnail.jp

“LEASING FINANCIERO Y RENTABILIDAD EN LA EMPRESA PLANNER BTL S.A.C. SAN BORJA AÑO 2016”

En esta investigación titulada Leasing Financiero y Rentabilidad en la empresa Planner BTL SAC San Borja año 2016, se planteó como objetivo principal determinar la relación que existe entre Leasing Financiero y Rentabilidad en la empresa Planner BTL SAC San Borja año 2016. La investigación realizada fue de tipo básico, de nivel descriptivo, con un diseño no experimental, de corte longitudinal, y con un diseño estadístico de tipo correlacional. La muestra estuvo conformada por 50 colaboradores de la empresa Planner BTL SAC San Borja año 2016. Los datos recolectados fueron procesados y analizados empleando el software SPSS versión 22. Una vez concluido la presente investigación del leasing financiero y la rentabilidad en la empresa Planner BTL SAC san Borja año 2016, se determinó que existe relación entre leasing financiero y rentabilidad en la empresa Planner BTL SAC san Borja año 2016. Ya que mediante la prueba de correlación demostramos que existe relación por que el valor de coeficiente de RHO DE SPEARMAN fue menos a 0.05 y esto nos permite inferir que la hipótesis alterna es aceptada

Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at https://github.com/matenure/GNN_planner. Data set is released at https://github.com/IBM/IPC-graph-dat

Online, interactive user guidance for high-dimensional, constrained motion planning

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration $\hat{q}$, which is used to bias the planner to go through $\hat{q}$. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafting domain-dependent heuristics

Learning the hidden human knowledge of UAV pilots when navigating in a cluttered environment for improving path planning

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWe propose in this work a new model of how the hidden human knowledge (HHK) of UAV pilots can be incorporated in the UAVs path planning generation. We intuitively know that human’s pilots barely manage or even attempt to drive the UAV through a path that is optimal attending to some criteria as an optimal planner would suggest. Although human pilots might get close but not reach the optimal path proposed by some planner that optimizes over time or distance, the final effect of this differentiation could be not only surprisingly better, but also desirable. In the best scenario for optimality, the path that human pilots generate would deviate from the optimal path as much as the hidden knowledge that its perceives is injected into the path. The aim of our work is to use real human pilot paths to learn the hidden knowledge using repulsion fields and to incorporate this knowledge afterwards in the environment obstacles as cause of the deviation from optimality. We present a strategy of learning this knowledge based on attractor and repulsors, the learning method and a modified RRT* that can use this knowledge for path planning. Finally we do real-life tests and we compare the resulting paths with and without this knowledge.Accepted versio