Efficient investment in children

If children are society’s most precious resource, as many would argue, how should we invest in them? To gain insight into this question, the authors develop a dynamic, general-equilibrium model in which children differ by ability. Parents invest time and money in their offspring, depending on their altruism, to help them grow into more productive adults. The authors characterize the efficient allocation, then compare it with the outcome that arises when financial markets are incomplete. They also examine the situation where childcare markets are lacking and analyze the consequences of impure altruism.

Large Group Work: Identity Development and Its Significance for Achieving Race Equality

This article examines and analyses the effectiveness of experiential large group work (between 24–35 students) in delivering community and youth work training at Goldsmiths1. It specifically focuses on students’ development and experience in their understanding of racism and identity. The training has refined a model of learning and teaching that combines large group work and experiential learning. It is in this arena that students explore and critically reflect on their life and work experiences. They learn to process and articulate their feelings and understandings across a wide range of issues that come from learning how to inwardly reflect and to develop an awareness of themselves and change. The article explores the experience of group work training and the significance of students’ development of their racial identity in effectively addressing racism. It draws on both the work of Paulo Freire (1972; 1995) on education and Pat de Maré (1975; 1991) on large groups. The article begins by examining some of the literature on both race and large groups followed by an exploration of racial identity and its development in the group work process in terms of distinct phases. It then discusses the importance of the large group and its relevance to development of racial identities. It concludes by highlighting the significance of the issue of racial identity in addressing racism

Genetically elevated high-density lipoprotein cholesterol through the cholesteryl ester transfer protein gene does not associate with risk of Alzheimer's disease

Introduction: There is conflicting evidence whether high-density lipoprotein cholesterol (HDL-C) is a risk factor for Alzheimer's disease (AD) and dementia. Genetic variation in the cholesteryl ester transfer protein (CETP) locus is associated with altered HDL-C. We aimed to assess AD risk by genetically predicted HDL-C. Methods: Ten single nucleotide polymorphisms within the CETP locus predicting HDL-C were applied to the International Genomics of Alzheimer's Project (IGAP) exome chip stage 1 results in up 16,097 late onset AD cases and 18,077 cognitively normal elderly controls. We performed instrumental variables analysis using inverse variance weighting, weighted median, and MR-Egger. Results: Based on 10 single nucleotide polymorphisms distinctly predicting HDL-C in the CETP locus, we found that HDL-C was not associated with risk of AD (P > .7). Discussion: Our study does not support the role of HDL-C on risk of AD through HDL-C altered by CETP. This study does not rule out other mechanisms by which HDL-C affects risk of AD

Admission control with incomplete information to a finite buffer queue

We consider the problem of admission control to a multiserver finite buffer queue under partial information. The controller cannot see the queue but is informed immediately if an admitted customer is lost due to buffer overflow. Turning away (i.e., blocking) customers is costly and so is losing an admitted customer. The latter cost is greater than that of blocking. The controller's objective is to minimize the average cost of blocking and rejection per incoming customer. Lin and Ross [11] studied this problem for multiserver loss systems. We extend their work by allowing a finite buffer and the arrival process to be of the renewal type. We propose a control policy based on a novel state aggregation approach that exploits the regenerative structure of the system, performs well, and gives a lower bound on the optimal cost. The control policy is inspired by a simulation technique that reduces the variance of the estimators by not simulating the customer service process. Numerical experiments show that our bound varies with the load offered to the system and is typically within 1% and 10% of the optimal cost. Also, our bound is tight in the important case when the cost of blocking is low compared to the cost of rejection and the load offered to the system is high. The quality of the bound degrades with the degree of state aggregation, but the computational effort is comparatively small. Moreover, the control policies that we obtain perform better compared to a heuristic suggested by Lin and Ross. The state aggregation technique developed in this article can be used more generally to solve problems in which the objective is to control the time to the end of a cycle and the quality of the information available to the controller degrades with the length of the cycle

Fixed vs. random proportions demand models for the assortment planning problem under stockout-based substitution

We consider the problem of determining the optimal assortment of products to offer in a given product category when each customer is characterized by a type, which is a list of products he is willing to buy in decreasing order of preference. We assume consumer-driven, dynamic, stockout-based substitution and random proportions of each type. No efficient method to obtain the optimal solution for this problem is known to our knowledge. However, if the number of customers of each type is a fixed proportion of demand, there exists an efficient algorithm for solving for the optimal assortment. We show that the fixed proportions model gives an upper bound to the optimal expected profit for the random proportions model. This bound allows us to obtain a measure of the absolute performance of heuristic solutions. We also provide a bound for the component-wise absolute difference in expected sales between the two models, which is asymptotically tight as the inventory vector is made large, while keeping the number of products fixed. This result provides us with a lower bound to the optimal expected profit and a performance guarantee for the fixed proportions solution in the random proportions model

Fixed vs. random proportions demand models for the assortment planning problem under stockout-based substitution

We consider the problem of determining the optimal assortment of products to offer in a given product category when each customer is characterized by a type, which is a list of products he is willing to buy in decreasing order of preference. We assume consumer-driven, dynamic, stockout-based substitution and random proportions of each type. No efficient method to obtain the optimal solution for this problem is known to our knowledge. However, if the number of customers of each type is a fixed proportion of demand, there exists an efficient algorithm for solving for the optimal assortment. We show that the fixed proportions model gives an upper bound to the optimal expected profit for the random proportions model. This bound allows us to obtain a measure of the absolute performance of heuristic solutions. We also provide a bound for the component-wise absolute difference in expected sales between the two models, which is asymptotically tight as the inventory vector is made large, while keeping the number of products fixed. This result provides us with a lower bound to the optimal expected profit and a performance guarantee for the fixed proportions solution in the random proportions model