Simple “Market Value” Bargaining Model for Weighted Voting Games: Characterization and Limit Theorems

Feld, Grofman and Ray (2003) offer a bargaining model for weighted voting games that is a close relative of the nucleolus and the kernel. They look for a set of weights that preserves winning coalitions that has the property of minimizing the difference between the weight of the smallest and the weight of the largest Minimum Winning Coalition. They claim that such a set of weights provides an a priori measure of a weighted voter’s bribeworthiness or market value. Here, after reviewing the basic elements of their model, we provide a characterization result for this model and show its links to other bargaining model approaches in the literature. Then we offer some limit results showing that, with certain reasonable conditions on the distributions of weights, as the size of the voting body increases, the values of bribeworthiness we calculate will approach both the weights themselves and the Banzhaf scores for the weighted voting game. We also show that, even for relatively small groups using weighted voting, such as the membership of the European Council of Ministers (and its precedessors) 1958-2003, similarities among the usual a priori power scores, bribeworthiness/market value, and the weights themselves, will be quite strong

A two-dimensional Fermi liquid with attractive interactions

We realize and study an attractively interacting two-dimensional Fermi liquid. Using momentum resolved photoemission spectroscopy, we measure the self-energy, determine the contact parameter of the short-range interaction potential, and find their dependence on the interaction strength. We successfully compare the measurements to a theoretical analysis, properly taking into account the finite temperature, harmonic trap, and the averaging over several two-dimensional gases with different peak densities

Bayesian Surprise in Indoor Environments

This paper proposes a novel method to identify unexpected structures in 2D floor plans using the concept of Bayesian Surprise. Taking into account that a person's expectation is an important aspect of the perception of space, we exploit the theory of Bayesian Surprise to robustly model expectation and thus surprise in the context of building structures. We use Isovist Analysis, which is a popular space syntax technique, to turn qualitative object attributes into quantitative environmental information. Since isovists are location-specific patterns of visibility, a sequence of isovists describes the spatial perception during a movement along multiple points in space. We then use Bayesian Surprise in a feature space consisting of these isovist readings. To demonstrate the suitability of our approach, we take "snapshots" of an agent's local environment to provide a short list of images that characterize a traversed trajectory through a 2D indoor environment. Those fingerprints represent surprising regions of a tour, characterize the traversed map and enable indoor LBS to focus more on important regions. Given this idea, we propose to use "surprise" as a new dimension of context in indoor location-based services (LBS). Agents of LBS, such as mobile robots or non-player characters in computer games, may use the context surprise to focus more on important regions of a map for a better use or understanding of the floor plan.Comment: 10 pages, 16 figure

Scale invariance and viscosity of a two-dimensional Fermi gas

We investigate the collective excitations of a harmonically trapped two-dimensional Fermi gas from the collisionless (zero sound) to the hydrodynamic (first sound) regime. The breathing mode, which is sensitive to the equation of state, is observed at a frequency two times the dipole mode frequency for a large range of interaction strengths and temperatures, and the amplitude of the breathing mode is undamped. This provides evidence for a dynamical SO(2,1) scaling symmetry of the two-dimensional Fermi gas. Moreover, we investigate the quadrupole mode to measure the shear viscosity of the two-dimensional gas and study its temperature dependence

Efficient Immunization Strategies for Computer Networks and Populations

We present an effective immunization strategy for computer networks and populations with broad and, in particular, scale-free degree distributions. The proposed strategy, acquaintance immunization, calls for the immunization of random acquaintances of random nodes (individuals). The strategy requires no knowledge of the node degrees or any other global knowledge, as do targeted immunization strategies. We study analytically the critical threshold for complete immunization. We also study the strategy with respect to the susceptible-infected-removed epidemiological model. We show that the immunization threshold is dramatically reduced with the suggested strategy, for all studied cases.Comment: Revtex, 5 pages, 4 ps fig

Decentralization in Bitcoin and Ethereum Networks

Blockchain-based cryptocurrencies have demonstrated how to securely implement traditionally centralized systems, such as currencies, in a decentralized fashion. However, there have been few measurement studies on the level of decentralization they achieve in practice. We present a measurement study on various decentralization metrics of two of the leading cryptocurrencies with the largest market capitalization and user base, Bitcoin and Ethereum. We investigate the extent of decentralization by measuring the network resources of nodes and the interconnection among them, the protocol requirements affecting the operation of nodes, and the robustness of the two systems against attacks. In particular, we adapted existing internet measurement techniques and used the Falcon Relay Network as a novel measurement tool to obtain our data. We discovered that neither Bitcoin nor Ethereum has strictly better properties than the other. We also provide concrete suggestions for improving both systems.Comment: Financial Cryptography and Data Security 201

You are only as safe as your riskiest contact: Effective Covid-19 vaccine distribution using local network information

When vaccines are limited, prior research has suggested it is most protective to distribute vaccines to the most central individuals – those who are most likely to spread the disease. But surveying the population’s social network is a costly and time-consuming endeavour, often not completed before vaccination must begin. This paper validates a local targeting method for distributing vaccines. That is, ask randomly chosen individuals to nominate for vaccination the person they are in contact with who has the most disease-spreading contacts. Even better, ask that person to nominate the next person for vaccination, and so on. To validate this approach, we simulate the spread of COVID-19 along empirical contact networks collected in two high schools, in the United States and France, pre-COVID. These weighted networks are built by recording whenever students are in close spatial proximity and facing one another. We show here that nomination of most popular contacts performs significantly better than random vaccination, and on par with strategies which assume a full survey of the population. These results are robust over a range of realistic disease-spread parameters, as well as a larger synthetic contact network of 3000 individuals