Technical note: Characterization of binomial semivalues through delegation games

Semivalues are allocation rules for cooperative games that assign to each player in a given game a weighted sum of his marginal contributions to all coalitions he belongs to, where the weighting coefficients depend only on the coalition size. Binomial semivalues are a special class of semivalues whose weighting coefficients are obtained by means of a unique parameter. In particular, the Banzhaf value is a binomial semivalue. In this article, we provide an axiomatic characterization for each binomial semivalue. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007     

Rectilinear m-Center problem

In this article we consider the unweighted m-center problem with rectilinear distance. We preent an O(n^m–2 log n) algorithm for the m-center problem where m ≥ 4.     

Book Reviews

Strategic Geography. By Hugh Faringdon. Routledge, London (1989), ISBN 0-415-00980-4, £40.00

Field Artillery and Firepower. By J. B. A. Bailey. The Military Press, Oxford (1989), ISBN 0-85066-810-7, £25.00 (hardback), ISBN 0-85066-811-5, £14.50 (paperback)

U.S.-Soviet Security Cooperation. Edited by Alexander L. George, Philip J. Farley and Alexander Dallin. Oxford University Press, New York (1988), ISBN 0-19-505397-4, £30.00 (hardback), $19.95 (paperback)

Non-production by Industry of Chemical-warfare Agents: Technical Verification under a Chemical Weapons Convention. Edited by S. J. Lundin. SIPRI/Oxford University Press, Oxford (1988), ISBN 0-19-829129-9, £19.50

Ireland's Terrorist Trauma: Interdisciplinary Perspectives. Edited by Alan O'Day and Yonah Alexander. Harvester Wheatsheaf, Hemel Hempstead (1989), ISBN 7450-049003, £27.50 ($48.65)

Pilots and Rebels: the Use of Aircraft in Unconventional Warfare 1918-1988. By P. A. Towle. Brassey's (UK), London (1989), ISBN 0-08-036712-7, £29.95 ($53.95)     

On estimating population characteristics from record-breaking observations. i. parametric results

Consider an experiment in which only record-breaking values (e.g., values smaller than all previous ones) are observed. The data available may be represented as X₁,K₁,X₂,K₂, …, where X₁,X₂, … are successive minima and K₁,K₂, … are the numbers of trials needed to obtain new records. We treat the problem of estimating the mean of an underlying exponential distribution, and we consider both fixed sample size problems and inverse sampling schemes. Under inverse sampling, we demonstrate certain global optimality properties of an estimator based on the “total time on test” statistic. Under random sampling, it is shown than an analogous estimator is consistent, but can be improved for any fixed sample size.     

Tabular aids for fitting weibull moment estimates

Moment estimators for the parameters of the Weibull distribution are considered in the context of analysis of field data. The data available are aggregated, with individual failure times not recorded. In this case, the complexity of the likelihood function argues against the use of maximum-likelihood estimation, particularly for relatively large sets of data, and moment estimators are a reasonable alternative. In this article, we derive the asymptotic covariance matrix of the moment estimators, and provide listings for BASIC computer programs which generate tables useful for calculation of the estimates as well as for estimating the asymptotic covariance matrix using aggregated data.     

Hybrid heuristics for minimum cardinality set covering problems

Minimum cardinality set covering problems (MCSCP) tend to be more difficult to solve than weighted set covering problems because the cost or weight associated with each variable is the same. Since MCSCP is NP-complete, large problem instances are commonly solved using some form of a greedy heuristic. In this paper hybrid algorithms are developed and tested against two common forms of the greedy heuristic. Although all the algorithms tested have the same worst case bounds provided by Ho [7], empirical results for 60 large randomly generated problems indicate that one algorithm performed better than the others.