Bounding the inefficiency of compromise

Social networks on the Internet have seen an enormous growth recently and play a crucial role in different aspects of today's life. They have facilitated information dissemination in ways that have been beneficial for their users but they are often used strategically in order to spread information that only serves the objectives of particular users. These properties have inspired a revision of classical opinion formation models from sociology using game-theoretic notions and tools. We follow the same modeling approach, focusing on scenarios where the opinion expressed by each user is a compromise between her internal belief and the opinions of a small number of neighbors among her social acquaintances. We formulate simple games that capture this behavior and quantify the inefficiency of equilibria using the well-known notion of the price of anarchy. Our results indicate that compromise comes at a cost that strongly depends on the neighborhood size

Bounding the Inefficiency of Compromise in Opinion Formation

Social networks on the Internet have seen an enormous growth recently and play a crucial role in different aspects of today’s life. They have facilitated information dissemination in ways that have been beneficial for their users but they are often used strategically in order to spread information that only serves the objectives of particular users. These properties have inspired a revision of classical opinion formation models from sociology using game-theoretic notions and tools. We follow the same modeling approach, focusing on scenarios where the opinion expressed by each user is a compromise between her internal belief and the opinions of a small number of neighbors among her social acquaintances. We formulate simple games that capture this behavior and quantify the inefficiency of equilibria using the well-known notion of the price of anarchy. Our results indicate that compromise comes at a cost that strongly depends on the neighborhood size

Quantum non-Gaussianity witnesses in the phase space

We address detection of quantum non-Gaussian states, i.e. nonclassical states that cannot be expressed as a convex mixture of Gaussian states, and present a method to derive a new family of criteria based on generic linear functionals. We then specialise this method to derive witnesses based on $s$-parametrized quasiprobability functions, generalising previous criteria based on the Wigner function. In particular we discuss in detail and analyse the properties of Husimi Q-function based witnesses and prove that they are often more effective than previous criteria in detecting quantum non-Gaussianity of various kinds of non-Gaussian states evolving in a lossy channel.Comment: 9 pages, 6 figure

Efficient Relocation of Spectrum Incumbents

Changes in technologies and in consumer demands have made prior radio spectrum allocations far from efficient. To address this problem the FCC has recently reallocated spectrum for more flexible use in bands that are partially occupied by incumbent license holders. Often, it is necessary for the new license holder to relocate incumbents to make efficient use of the spectrum. Regulations structuring the negotiation between incumbent and new entrant can promote efficiency. In particular, giving the new entrant the right to move the incumbent with compensation can reduce negotiation costs and promote efficiency when there is private information about spectrum values but good public information about the cost of relocating the incumbent. We examine the experience of broadband PCS entrants in relocating microwave incumbents. We conclude with some remarks on how these ideas might be applied to digital television spectrum.Bargaining; Auctions; Spectrum Auctions; Telecommunications Policy

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but impeded by speed of computation. We have developed new ways to extract features based on notional use of physical paradigms, with parameterisation that is more familiar to a scientifically-trained user, aiming to make best use of computational resource. We describe how analogies based on gravitational force can be used for low-level analysis, whilst analogies of water flow and heat can be deployed to achieve high-level smooth shape detection. These new approaches to arbitrary shape extraction are compared with standard state-of-art approaches by curve evolution. There is no comparator operator to our use of gravitational force. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision