The Inverse Shapley Value Problem

For $f$ a weighted voting scheme used by $n$ voters to choose between two candidates, the $n$ \emph{Shapley-Shubik Indices} (or {\em Shapley values}) of $f$ provide a measure of how much control each voter can exert over the overall outcome of the vote. Shapley-Shubik indices were introduced by Lloyd Shapley and Martin Shubik in 1954 \cite{SS54} and are widely studied in social choice theory as a measure of the "influence" of voters. The \emph{Inverse Shapley Value Problem} is the problem of designing a weighted voting scheme which (approximately) achieves a desired input vector of values for the Shapley-Shubik indices. Despite much interest in this problem no provably correct and efficient algorithm was known prior to our work. We give the first efficient algorithm with provable performance guarantees for the Inverse Shapley Value Problem. For any constant \eps > 0 our algorithm runs in fixed poly$(n)$ time (the degree of the polynomial is independent of \eps) and has the following performance guarantee: given as input a vector of desired Shapley values, if any "reasonable" weighted voting scheme (roughly, one in which the threshold is not too skewed) approximately matches the desired vector of values to within some small error, then our algorithm explicitly outputs a weighted voting scheme that achieves this vector of Shapley values to within error \eps. If there is a "reasonable" voting scheme in which all voting weights are integers at most \poly(n) that approximately achieves the desired Shapley values, then our algorithm runs in time \poly(n) and outputs a weighted voting scheme that achieves the target vector of Shapley values to within error $\eps=n^{-1/8}.

State estimation for aggressive flight in GPS-denied environments using onboard sensing

In this paper we present a state estimation method based on an inertial measurement unit (IMU) and a planar laser range finder suitable for use in real-time on a fixed-wing micro air vehicle (MAV). The algorithm is capable of maintaing accurate state estimates during aggressive flight in unstructured 3D environments without the use of an external positioning system. Our localization algorithm is based on an extension of the Gaussian Particle Filter. We partition the state according to measurement independence relationships and then calculate a pseudo-linear update which allows us to use 20x fewer particles than a naive implementation to achieve similar accuracy in the state estimate. We also propose a multi-step forward fitting method to identify the noise parameters of the IMU and compare results with and without accurate position measurements. Our process and measurement models integrate naturally with an exponential coordinates representation of the attitude uncertainty. We demonstrate our algorithms experimentally on a fixed-wing vehicle flying in a challenging indoor environment

False-Name Manipulation in Weighted Voting Games is Hard for Probabilistic Polynomial Time

False-name manipulation refers to the question of whether a player in a weighted voting game can increase her power by splitting into several players and distributing her weight among these false identities. Analogously to this splitting problem, the beneficial merging problem asks whether a coalition of players can increase their power in a weighted voting game by merging their weights. Aziz et al. [ABEP11] analyze the problem of whether merging or splitting players in weighted voting games is beneficial in terms of the Shapley-Shubik and the normalized Banzhaf index, and so do Rey and Rothe [RR10] for the probabilistic Banzhaf index. All these results provide merely NP-hardness lower bounds for these problems, leaving the question about their exact complexity open. For the Shapley--Shubik and the probabilistic Banzhaf index, we raise these lower bounds to hardness for PP, "probabilistic polynomial time", and provide matching upper bounds for beneficial merging and, whenever the number of false identities is fixed, also for beneficial splitting, thus resolving previous conjectures in the affirmative. It follows from our results that beneficial merging and splitting for these two power indices cannot be solved in NP, unless the polynomial hierarchy collapses, which is considered highly unlikely

Using Group Model Building to Understand Factors That Influence Childhood Obesity in an Urban Environment

Background: Despite increased attention, conventional views of obesity are based upon individual behaviors, and children and parents living with obesity are assumed to be the primary problem solvers. Instead of focusing exclusively on individual reduction behaviors for childhood obesity, greater focus should be placed on better understanding existing community systems and their effects on obesity. The Milwaukee Childhood Obesity Prevention Project is a community-based coalition established to develop policy and environmental change strategies to impact childhood obesity in Milwaukee, Wisconsin. The coalition conducted a Group Model Building exercise to better understand root causes of childhood obesity in its community. Methods: Group Model Building is a process by which a group systematically engages in model construction to better understand the systems that are in place. It helps participants make their mental models explicit through a careful and consistent process to test assumptions. This process has 3 main components: (1) assembling a team of participants; (2) conducting a behavior-over-time graphs exercise; and (3) drawing the causal loop diagram exercise. Results: The behavior-over-time graph portion produced 61 graphs in 10 categories. The causal loop diagram yielded 5 major themes and 7 subthemes. Conclusions: Factors that influence childhood obesity are varied, and it is important to recognize that no single solution exists. The perspectives from this exercise provided a means to create a process for dialogue and commitment by stakeholders and partnerships to build capacity for change within the community

CELLO: A fast algorithm for Covariance Estimation

We present CELLO (Covariance Estimation and Learning through Likelihood Optimization), an algorithm for predicting the covariances of measurements based on any available informative features. This algorithm is intended to improve the accuracy and reliability of on-line state estimation by providing a principled way to extend the conventional fixed-covariance Gaussian measurement model. We show that in experiments, CELLO learns to predict measurement covariances that agree with empirical covariances obtained by manually annotating sensor regimes. We also show that using the learned covariances during filtering provides substantial quantitative improvement to the overall state estimate. © 2013 IEEE.United States. National Aeronautics and Space AdministrationSiemens Corporate ResearchUnited States. Office of Naval Research. Multidisciplinary University Research InitiativeMicro Autonomous Consortium Systems and Technolog

Developing a framework for the analysis of power through depotentia

Stakeholder participation in tourism policy-making is usually perceived as providing a means of empowerment. However participatory processes drawing upon stakeholders from traditionally empowered backgrounds may provide the means of removing empowerment from stakeholders. Such an outcome would be in contradiction to the claims that participatory processes improve both inclusivity and sustainability. In order to form an understanding of the sources through which empowerment may be removed, an analytical perspective has been developed deriving from Lukes�s views of power dating from 1974. This perspective considers the concept of depotentia as the removal of �power to� without speculating upon the underlying intent and also provides for the multidimensionality of power to be examined within a single study. The application of this analytical perspective has been tested upon findings of the government-commissioned report of the Countryside and Community Research Unit in 2005. The survey and report investigated the progress of Local Access Forums in England created in response to the Countryside and Rights of Way Act 2000. Consideration of the data from this perspective permits the classification of individual sources of depotentia which can each be addressed and potentially enable stakeholder groups to reverse loss of empowerment where it has occurred

The role of dissociation-related beliefs about memory in trauma-focused treatment

OBJECTIVE: Dysfunctional cognitions play a central role in the development of post-traumatic stress disorder (PTSD). However the role of specific dissociation-related beliefs about memory has not been previously investigated. This study aimed to investigate the role of dissociation-related beliefs about memory in trauma-focused treatment. It was hypothesized that patients with the dissociative subtype of PTSD would show higher levels of dissociation-related beliefs, dissociation-related beliefs about memory would decrease after trauma-focused treatment, and higher pre-treatment dissociation-related beliefs would be associated with fewer changes in PTSD symptoms.METHOD: Post-traumatic symptoms, dissociative symptoms, and dissociation-related beliefs about memory were assessed in a sample of patients diagnosed with PTSD ( n = 111) or the dissociative subtype of PTSD ( n  = 61). They underwent intensive trauma-focused treatment consisting of four or eight consecutive treatment days. On each treatment day, patients received 90 min of individual prolonged exposure (PE) in the morning and 90 min of individual eye movement desensitization and reprocessing (EMDR) therapy in the afternoon. The relationship between dissociation-related beliefs about memory and the effects of trauma-focused treatment was investigated. RESULTS: Dissociation-related beliefs about memory were significantly associated with PTSD and its dissociative symptoms. In addition, consistent with our hypothesis, patients with the dissociative subtype of PTSD scored significantly higher on dissociation-related beliefs about memory pre-treatment than those without the dissociative subtype. Additionally, the severity of these beliefs decreased significantly after trauma-related treatment. Contrary to our hypothesis, elevated dissociation-related beliefs did not negatively influence treatment outcome.CONCLUSION: The results of the current study suggest that dissociation-related beliefs do not influence the outcome of trauma-focused treatment, and that trauma-focused treatment does not need to be altered specifically for patients experiencing more dissociation-related beliefs about memory because these beliefs decrease in association with treatment.</p