Single Parameter Combinatorial Auctions with Partially Public Valuations

We consider the problem of designing truthful auctions, when the bidders' valuations have a public and a private component. In particular, we consider combinatorial auctions where the valuation of an agent $i$ for a set $S$ of items can be expressed as $v_if(S)$, where $v_i$ is a private single parameter of the agent, and the function $f$ is publicly known. Our motivation behind studying this problem is two-fold: (a) Such valuation functions arise naturally in the case of ad-slots in broadcast media such as Television and Radio. For an ad shown in a set $S$ of ad-slots, $f(S)$ is, say, the number of {\em unique} viewers reached by the ad, and $v_i$ is the valuation per-unique-viewer. (b) From a theoretical point of view, this factorization of the valuation function simplifies the bidding language, and renders the combinatorial auction more amenable to better approximation factors. We present a general technique, based on maximal-in-range mechanisms, that converts any $\alpha$-approximation non-truthful algorithm ($\alpha \leq 1$) for this problem into $\Omega(\frac{\alpha}{\log{n}})$ and $\Omega(\alpha)$-approximate truthful mechanisms which run in polynomial time and quasi-polynomial time, respectively

Addressing biased occurrence data in predicting potential Sierra Nevada red fox habitat for survey prioritization

The Sierra Nevada red fox Vulpes vulpes necator is listed as a threatened species under the California Endangered Species Act. It originally occurred throughout California’s Cascade and Sierra Nevada mountain regions. Its current distribution is unknown but should be determined in order to guide management actions. We used occurrence data from the only known population, in the Lassen Peak region of northern California, combined with climatic and remotely sensed variables, to predict the species’ potential distribution throughout its historic range. These model predictions can guide future surveys to locate additional fox populations. Moreover, they allow us to compare the relative performances of presence-absence (logistic regression) and presence-only (maximum entropy, or Maxent) modeling approaches using occurrence data with potential false absences and geographical biases. We also evaluated the recently revised Maxent algorithm that reduces the effect of geographically biased occurrence data by subsetting background pixels to match biases in the occurrence data. Within the Lassen Peak region, all models had good fit to the test data, with high values for the true skill statistic (76–83%), percent correctly classified (86–92%), and area under the curve (0.94–0.96), with Maxent models yielding slightly higher values. Outside the Lassen Peak region, the logistic regression model yielded the highest predictive performance, providing the closest match to the fox’s historic range and also predicting a site where red foxes were subsequently detected in autumn 2010. Subsetting background pixels in Maxent reduced but did not eliminate the effect that geographically biased occurrence data had on prediction results relative to the Maxent model using full background pixels

The Grand Gleaners Project Analysis

The Heartside Gleaning Initiative (HGI) is a Grand Rapids based non-profit organization that collects unsold produce from the city’s two farmers markets and distributes it to the low-income residents of the Heartside Neighborhood. Through their efforts they increase access to healthy and local foods as well as combat food waste in the Heartside Community. Seeing the value of this work, we, an interdisciplinary team of Grand Valley State University students, sought to further the mission of the HGI by focusing predominantly on two vital aspects of any non-profit business: promoting and funding. In the winter semester of 2015 we established three courses of action. Our first course of action was to create a crowdfunding “how-to” guide for the HGI. By illustrating how to proficiently utilize crowdfunding platforms this “how-to” guide will also aid other nonprofits. Our second course of action was to develop a storyboard intended to be used in the creation of a short promotional video for the HGI. Finally, we contacted local film professionals to aid HGI in completing a promotional video during its 2015 gleaning season. It is our hope that these efforts will increase the exposure for the Heartside Gleaning Initiative and provide them with the tools to efficiently utilize this exposure