Equal Strength or Dominant Teams: Policy Analysis of NFL

In North America, professional sports leagues operate mostly as cartels. They employ certain policies such as revenue sharing, salary caps to ensure that teams get high revenues and players get high wages. There are two major hypotheses regarding the talent distribution among the teams that would maximize the total revenues, dominant teams rule and equal strength team rule. This paper examines the revenue structure of National Football League and proposes policy recommendations regarding talent distribution among the teams. By using a unique, rich data set on game day stadium attendance and TV ratings I am able to measure the total demand as a function of involved teams’ talent levels. Reduced form regression results indicates that TV viewers are more interested in close games, on the other hand stadium attendees are more interested in home teams’ dominance. In order to identify the true effects of possible policy experiments, I estimate the parameters of the demand for TV as functions of team talent , fixed team and market variables by using partial linear model described as in Yatchew (1998) which uses non-parametric and difference-based estimators. I then estimate the demand for stadium attendance using random coefficients model by using normative priors for the 32 cities that hosts the teams. Estimated demand for TV ratings and stadium attendance corroborates the findings of reduced form regressions, stadium demand and TV demand working against each other. We therefore propose a “somewhat” equal strength team policy where big market teams has a slight advantage over the others. Total revenues of the league is maximized under such a policy.Perfect Competition, Dominant Team, Cartels

Optimized Adaptive Streaming Representations based on System Dynamics

Adaptive streaming addresses the increasing and heterogenous demand of multimedia content over the Internet by offering several encoded versions for each video sequence. Each version (or representation) has a different resolution and bit rate, aimed at a specific set of users, like TV or mobile phone clients. While most existing works on adaptive streaming deal with effective playout-control strategies at the client side, we take in this paper a providers' perspective and propose solutions to improve user satisfaction by optimizing the encoding rates of the video sequences. We formulate an integer linear program that maximizes users' average satisfaction, taking into account the network dynamics, the video content information, and the user population characteristics. The solution of the optimization is a set of encoding parameters that permit to create different streams to robustly satisfy users' requests over time. We simulate multiple adaptive streaming sessions characterized by realistic network connections models, where the proposed solution outperforms commonly used vendor recommendations, in terms of user satisfaction but also in terms of fairness and outage probability. The simulation results further show that video content information as well as network constraints and users' statistics play a crucial role in selecting proper encoding parameters to provide fairness a mong users and to reduce network resource usage. We finally propose a few practical guidelines that can be used to choose the encoding parameters based on the user base characteristics, the network capacity and the type of video content

A review and assessment of novice learning tools for problem solving and program development

There is a great demand for the development of novice learning tools to supplement classroom instruction in the areas of problem solving and program development. Research in the area of pedagogy, the psychology of programming, human-computer interaction, and cognition have provided valuable input to the development of new methodologies, paradigms, programming languages, and novice learning tools to answer this demand. Based on the cognitive needs of novices, it is possible to postulate a set of characteristics that should comprise the components an effective novice-learning tool. This thesis will discover these characteristics and provide recommendations for the development of new learning tools. This will be accomplished with a review of the challenges that novices face, an in-depth discussion on modem learning tools and the challenges that they address, and the identification and discussion of the vital characteristics that constitute an effective learning tool based on these tools and personal ideas

Fuel Panics - insights from spatial agent-based simulation

The United Kingdom has twice suffered major disruption as a result of fuel panics first in September 2000 coincident with a wave of fuel protests and more recently in March 2012 following politcal warnings of possible future supply chain disruption. In each case the disruption and economic consequences were serious. Fuel distribution is an example of a supply chain. Approaches to supply-chain planning based on linear programming are poorly suited to modelling non-equilibrium effects, while coarse-grained system dynamics models often fail to capture local phenomena which contribute to the evolution of global demand. In this Paper, we demonstrate that agent-based techniques offer a powerful framework for cosimulation of supply chains and consumers under conditions of transient demand. In the case of fuel panic crisis, we show that even a highly abstract model can reproduce a range of transient phenomena seen in the real world, and present a set of practical recommendations for policymakers faced with panic-buying

A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly

Developing a web-based assessment instrument

Public demand for affordable and quality higher education continues to pressure institutions to evaluate the effectiveness of the learning experiences. Traditional paper surveys have been utilized to gather data in the past. The World Wide Web can provide many of the same benefits in delivering a survey instrument while offering more convenience, lower costs, and a more flexible data set. This report of the graduate project documents the process employed when developing a web-based assessment instrument. It outlines a method and procedure for connecting a web site with a FileMaker Pro database. In addition, it provides documentation for the implementation of the instrument, and it proposes recommendations for future versions based on this model

Assessing leanness level with demand dynamics in a multi-stage production system

Purpose – The purpose of this paper is to present a dynamic model to measure the degree of system’s leanness under dynamic demand conditions using a novel integrated metric. Design/methodology/approach – The multi-stage production system model is based on a system dynamics approach. The leanness level is measured using a new developed integrated metric that combines efficiency, WIP performance as well as service level. The analysis includes design of experiment technique at the initial analysis to examine the most significant parameters impacting the leanness score and then followed by examining different dynamic demand scenarios. Two scenarios were examined: one focussed low demand variation with various means (testing the impact of demand volumes) while the second focussed on high demand variation with constant means (testing the impact of demand variability). Findings – Results using the data from a real case study indicated that given the model parameters, demand rate has the highest impact on leanness score dynamics. The next phase of the analysis thus focussed on investigating the effect of demand dynamics on the leanness score. The analysis highlighted the different effects of demand variability and volumes on the leanness score and its different components leading to various demand and production management recommendations in this dynamic environment. Research limitations/implications – The presented lean management policies and recommendations are verified within the scope of similar systems to the considered company in terms of manufacturing settings and demand environment. Further research will be carried to extend the dynamic model to other dynamic manufacturing and service settings. Practical implications – The developed metric can be used not only to assess the leanness level of the systems which is very critical to lean practitioners but also can be used to track lean implementation progress. In addition, the presented analysis outlined various demand management as well as lean implementation policies that can improve the system leanness level and overall performance. Originality/value – The presented research develops a novel integrated metric and adds to the few literature on dynamic analysis of lean systems. Furthermore, the conducted analysis revealed some new aspects in understanding the relation between demand (variability and volume) and the leanness level of the systems. This will aid lean practitioners to set better demand and production management policies in today’s dynamic environment as well as take better decisions concerning lean technology investments

Conflict-Affected Youth Livelihoods Programming: Bridging The Gap Between Research & Practice

The current thesis project consists of a programmatic mapping of existing policy and programming related to conflict-affected youth livelihoods and a Rapid Evidence Assessment (REA) of the literature to identify the evidence base for effective interventions. The programmatic mapping identified key actors in the field and existing policy and programming, revealing a need for: data regarding evidence-based interventions; demand-driven intervention strategies; cross-sectoral partnerships providing holistic programming approaches; increased outreach to vulnerable sub-populations; and increased youth participation in program design, implementation, management, and evaluation. The REA revealed a severe shortage of evidence-based practice in this area, but sheds light on the value of cash grants for startup businesses, on-the-job training, demand-side market-driven programs, and combination strategies for increased employment. The joint findings diagnose a nonfunctioning system in which agencies continue to invest in youth livelihoods in conflict settings despite lacking data about effective interventions. The thesis concludes with a set of recommendations for researchers, policymakers, and practitioners to increase accountability in the provision of humanitarian and development assistance for youth livelihoods in conflict to improve youth development outcomes (including health)

Prospective of Roof Rain Water Harvesting (RRWH) in Kesses Constituency, Uasin Gishu County, Kenya

As a water scarce country, Kenya has witness an increased investment in Rain Water Harvesting (RWH) projects. Most of Roof RWH research is centred on the potential and implementation of RWH systems, however not much focus has been placed on examining the demand satisfaction of these systems. The main goal of this study was to demonstrate in spatial domains, the large potential for RRWH in Kesses Constituency and thereby provide a tool for advocacy and decision support, for RRWH in Kenya. This research was based on literature revision, statistical analysis of precipitation data set (January, 1994 to December, 2013), rainwater laboratory tests (Hardness, pH, turbidity, Chlorine, e-coli, suspended matter and colour) and concise field study using key informant interviews and structured questionnaires. The results were analyzed by Descriptive statistical methods. In addition analysis of variance ANOVA, Statgraphics Centurion XVI and Microsoft Excel were used. The study concluded that each of the study wards received a high RRWH reliability based on the amount of water available in storage and secondly, that RRWH can satisfy the minimum demand requirement throughout a year, given sufficient guttered roof area. Numerous recommendations were also made on correlated issues. Keywords: Harvesting, Kesses, Precipitation, Rainwater, Roof.