1,595 research outputs found

    The Dynamics of a Genetic Algorithm for a Simple Learning Problem

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    A formalism for describing the dynamics of Genetic Algorithms (GAs) using methods from statistical mechanics is applied to the problem of generalization in a perceptron with binary weights. The dynamics are solved for the case where a new batch of training patterns is presented to each population member each generation, which considerably simplifies the calculation. The theory is shown to agree closely to simulations of a real GA averaged over many runs, accurately predicting the mean best solution found. For weak selection and large problem size the difference equations describing the dynamics can be expressed analytically and we find that the effects of noise due to the finite size of each training batch can be removed by increasing the population size appropriately. If this population resizing is used, one can deduce the most computationally efficient size of training batch each generation. For independent patterns this choice also gives the minimum total number of training patterns used. Although using independent patterns is a very inefficient use of training patterns in general, this work may also prove useful for determining the optimum batch size in the case where patterns are recycled.Comment: 28 pages, 4 Postscript figures. Latex using IOP macros ioplppt and iopl12 which are included. To appear in Journal of Physics A. Also available at ftp://ftp.cs.man.ac.uk/pub/ai/jls/GAlearn.ps.gz and http://www.cs.man.ac.uk/~jl

    Efficiency and Equity in the Use of Eminent Domain, with Local Externalies

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    In Shapiro and Pincus (2008), we proposed a method for arriving at just compensation of private owners of urban land, in cases like Kelo v New London, in which government has plans to use eminent domain to `take' private properties, to be assembled into a single parcel for some public purpose. The required quantum of just compensation can be discovered when the public purpose is to be pursued via private use of the assembled land parcel, and when the private user can be selected through an auction of the assembled land. This paper extends the auction mechanism to include properties which lie outside the area `taken' or resumed by government, but which will be affected by the new use made of the assembled area. The auction provides an efficiency test: does the proposed change in use increase the aggregate value of the land to be resumed plus the affected properties? Local externalities are internalised through the auction. We briefly discuss the political economy of the mechanism.

    Reinvigorating Horizontal Merger Enforcement

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    The past forty years have witnessed a remarkable transformation in horizontal merger enforcement in the United States. With no change in the underlying statute, the Clayton Act, the weight given to market concentration by the federal courts and by the federal antitrust agencies has declined dramatically. Instead, increasing weight has been given to three arguments often made by merging firms in their defense: entry, expansion and efficiencies. We document this shift and provide examples where courts have approved highly concentrating mergers based on limited evidence of entry and expansion. We show using merger enforcement data and a survey we conducted of merger practitioners that the decline in antitrust enforcement is ongoing, especially at the current Justice Department. We then argue in favor of reinvigorating horizontal merger enforcement by partially restoring the structural presumption and by requiring strong evidence to overcome the government's prima facie case. We propose several routes by which the government can establish its prima facie case, distinguishing between cases involving coordinated vs. unilateral anti-competitive effects.

    The L2H2 Auction: Efficiency and Equity in the Assemblage of Land for Public Use

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    The burden of redevelopment projects, whether or not they ultimately benefit the communities in which they are undertaken, is borne disproportionately by those displaced. Neighborhoods are destroyed and residents are made to leave a home they love, compensated only by its market value. The benefits and costs of redevelopment can only be estimated since there are no direct market tests. Here a mechanism, developed as an extension of two recent papers, by Lehavi and Lichts (L2) and by Heller and Hill (H2), provides a market-based efficiency test for a proposed project and a compensation rule that alleviates the disproportionate burden on displaced residents. Assembled property is sold at an auction. The reserve price (the lowest price at which the assembled property will be sold) is set so that all displaced residents receive at least their personal value of their property. A successful bid, one that claims the assembled property, is sufficient proof of efficiency.

    A Real-Time Novelty Detector for a Mobile Robot

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    Recognising new or unusual features of an environment is an ability which is potentially very useful to a robot. This paper demonstrates an algorithm which achieves this task by learning an internal representation of `normality' from sonar scans taken as a robot explores the environment. This model of the environment is used to evaluate the novelty of each sonar scan presented to it with relation to the model. Stimuli which have not been seen before, and therefore have more novelty, are highlighted by the filter. The filter has the ability to forget about features which have been learned, so that stimuli which are seen only rarely recover their response over time. A number of robot experiments are presented which demonstrate the operation of the filter.Comment: 8 pages, 6 figures. In Proceedings of EUREL European Advanced Robotics Systems Masterclass and Conference, 200

    Novelty Detection for Robot Neotaxis

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    The ability of a robot to detect and respond to changes in its environment is potentially very useful, as it draws attention to new and potentially important features. We describe an algorithm for learning to filter out previously experienced stimuli to allow further concentration on novel features. The algorithm uses a model of habituation, a biological process which causes a decrement in response with repeated presentation. Experiments with a mobile robot are presented in which the robot detects the most novel stimulus and turns towards it (`neotaxis').Comment: 7 pages, 5 figures. In Proceedings of the Second International Conference on Neural Computation, 200