Moving towards digital governance of university scholars: instigating a post-truth university culture

Journal of Management and Governance - Governance models are increasingly driven by information technology (IT) and are being applied to measure the performance of all kinds of organisational...     

Tripartite guanxi: resolving kin and non-kin discontinuities in Chinese connections

Theory and Society - A consensus holds that guanxi, understood as dyadic connections consolidated affectively and mobilized to achieve the purposes of members, exists in three forms (family guanxi,...     

Applications of Machine Learning Methods to Predict Readmission and Length-of-Stay for Homeless Families: The Case of Win Shelters in New York City

ABSTRACT

New York City faces the challenge of an ever-increasing homeless population with almost 60,000 people currently living in city shelters. In 2015, approximately 25% of families stayed longer than nine months in a shelter, and 17% of families with children that exited a homeless shelter returned to the shelter system within 30 days of leaving. This suggests that “long-term” shelter residents and those that re-enter shelters contribute significantly to the rise of the homeless population living in city shelters and indicate systemic challenges to finding adequate permanent housing. This article focuses on our preliminary work with Win (Women-in-Need) shelters to understand the factors that predict readmission and length-of-stay of homeless families. We create a unified, comprehensive database of the homeless population being served by Win shelters, accounting for more than 6,000 homeless families. We apply logistic regression models and an unsupervised clustering algorithm to identify predictors of re-entry and long-term length-of-stay. Citizenship, age, medical conditions, employment, and history of foster care or shelter stays as a child are found to be significant predictors. The results of the K-means clustering identify three primary groups, consistent with previous typologies characterized by transitionally homeless, episodically homeless, and chronically homeless.     

Sequential sampling in the search for new shared species

In microbial sciences, as well as other disciplines, it is often valuable to sample communities in a sequential or group sequential manner, in order to determine their structure or their similarity. We develop sequential sampling procedures to accomplish this by first assuming that one observation is drawn with replacement from each population at a time. Suppose that the sampling is terminated after n pairs of observations and k shared species were discovered, and assume that we receive payoff h(k)−cn, where h(k) is non-decreasing and the sampling cost c is non-negative. Similar to Rasmussen and Starr (1979), we show that an optimal stopping rule exists if h(k+1)−h(k) is non-increasing. An analogous result holds for group sequential sampling. This leads to using an estimate of the probability of discovering new shared species as a stopping indicator for comparing two populations with respect to the similarity index. We show by simulation and real examples that this is a feasible approach which can help to reduce the sample size.     

Dynamic Pricing and Demand Estimation for a Single Unique Asset*

We address the situation of a firm that needs to dispose of a large, expensive asset (e.g., car, machine tool, earth mover, turbine, house, airplane), with or without a given deadline (and either known or unknown to the buyer). If a deadline exists, the asset is salvaged at a known value which may be zero, or even negative if there is a disposal cost. The asset has a known holding cost and may also have an initial nominal (undiscounted) price. The question is how, if at all, the price should be discounted as time progresses to maximize the expected proceeds. We use a dynamic recursion where each decision stage can be optimized based on classic economic monopoly pricing theory with a demand intensity function estimated from sales data, and show that the model is well‐behaved in the sense that the optimal price and optimal expected revenue monotonically decline as the deadline approaches. We test the model by comparing its optimal price pattern to the official pricing policy practiced at a used‐car dealer. We then extend the model to situations where the buyer knows the seller's deadline and thus may alter his behavior as the deadline approaches.