A Proxemic Account of Bayonet Fighting

The bayonet has long been regarded as an obsolete piece of military technology. This article advances a proxemic account of close combat, which points to its continuing utility under modern battlefield conditions. It is argued that bayonet training remains an effective way of preparing soldiers to psychologically dominate contested spaces on the battlefield. Thus, although other weapons and techniques can fulfil the same role, it can nevertheless be argued that the bayonet’s enduring presence is not simply the result of some atavistic military mind-set.     

Confidence coefficient of approximate two-sided confidence intervals for the binomial probability

The confidence coefficient of a two-sided confidence interval for the binomial parameter p is the infimum of the coverage probability of the interval as p ranges between 0 and 1. The confidence coefficients for five different approximate confidence intervals are computed and compared to the confidence coefficient for the two-sided Clopper-Pearson confidence interval. Pratt's approximation method [10] yields virtually the same confidence coefficients as the Clopper-Pearson interval, and is easily computed without resorting to interative methods.     

Netwar Revisited: The Fight for the Future Continues

This work continues to develop the 'netwar' concept that the authors introduced in 1993 and have expanded upon in their various RAND and other writings ever since. Deeper understanding of the nature, strengths and vulnerabilities of networks will prove useful in combating terrorism and transnational crime, but also in understanding militant social activism, both of the violently disruptive sort and that which aims at fostering the rise of a global civil society. This essay also assesses recent US performance in the terror war, and concludes by raising concerns over the possible rise of a new form of network-based fascism.     

The multicommodity network flow model revised to include vehicle per time period and node constraints

The minimum-cost formulation of the problem of determining multicommodity flows over a capacitated network subject to resource constraints has been treated in previobs papers. In those treatments only capacitated arcs were assumed and a uniform unit of measure like short tons was used for all commodities. This paper treats the effect of constraints on the nodes of the network, allows the commodities to be measured in their “natural” units and allows the network capacities to be expressed in vehicles per time period-in some cases giving a more accurate representation of the capacities of the network. This paper describes the solution procedure which uses the column generation technique; it also discusses computational experience.     

Optimum sequential search with discrete locations and random acceptance errors

Much work has been done in search theory; however, very little effort has occurred where an object's presence at a location can be accepted when no object is present there. The case analyzed is of this type. The number of locations is finite, a single object is stationary at one location, and only one location is observed each step of the search. The object's location has a known prior probability distribution. Also known are the conditional probability of acceptance given the object's absence (small) and the conditional probability of rejection given the object's presence (not too large); these Probabilities remain fixed for all searching and locations. The class of sequential search policies which terminate the search at the first acceptance is assumed. A single two-part optimization criterion is considered. The search sequence is found which (i) minimizes the probability of obtaining n rejections in the first n steps for all n, and (ii) maximizes the probability that the first acceptance occurs within the first n steps and occurs at the object's location for all n. The optimum sequential search policy specifies that the next location observed is one with the largest posterior probability of the object's presence (evaluated after each step from Bayes Rule) and that the object is at the first location where acceptance occurs. Placement at the first acceptance seems appropriate when the conditional probability of acceptance given the object's absence is sufficiently small. Search always terminates (with probability one). Optimum truncated sequential policies are also considered. Methods are given for evaluating some pertinent properties and for investigating the possibility that no object occurs at any location.