Finite-particle representations and states of the canonical commutation relations.

Massachusetts Institute of Technology. Dept. of Mathematics. Thesis. 1966. Ph.D.Bibliography: leaves 104-106.Ph.D

Nonequilibrium stationary states and equilibrium models with long range interactions

It was recently suggested by Blythe and Evans that a properly defined steady state normalisation factor can be seen as a partition function of a fictitious statistical ensemble in which the transition rates of the stochastic process play the role of fugacities. In analogy with the Lee-Yang description of phase transition of equilibrium systems, they studied the zeroes in the complex plane of the normalisation factor in order to find phase transitions in nonequilibrium steady states. We show that like for equilibrium systems, the ``densities'' associated to the rates are non-decreasing functions of the rates and therefore one can obtain the location and nature of phase transitions directly from the analytical properties of the ``densities''. We illustrate this phenomenon for the asymmetric exclusion process. We actually show that its normalisation factor coincides with an equilibrium partition function of a walk model in which the ``densities'' have a simple physical interpretation.Comment: LaTeX, 23 pages, 3 EPS figure

Transfer of Emergency Service Deployment Models to Operating Agencies

Six deployment models for emergency service agencies were developed, field-tested, and documented during a two-year period ending October 1975. During the following eighteen months, records were kept of the extent to which the models were acquired by operating agencies and actually used for making deployment changes. The number of acquisitions ranged from zero for one model to 39 for another. Over half of those who acquire these models actually use them, and, except for one model, nearly all users made operational changes based on the output. Differences among the models illuminate the implementation process.

A Patrol Car Allocation Model: Background

Before designing a computer program for allocating police patrol cars by time and geography, a review was undertaken of previously existing programs of this type. Nearly all of the programs calculated queuing statistics for the collection of patrol cars by assuming a steady-state system with calls for service arriving within priority levels according to Poisson processes and having independent, identical, exponentially distributed service times. Unavailabilities of patrol cars for reasons other than calls for service were handled in the models either by artificially increasing the arrival rate of calls or by assuming that the number of servers is smaller than the number of patrol cars. Some programs calculated additional performance measures such as travel times and preventive patrol frequencies. All the programs had the capabilities to describe performance statistics for an allocation proposed by the user, but they differed in their capabilities to prescribe desirable allocations. None of the programs had achieved general acceptance because each had virtues and inadequacies not present in the others.government: services, police, programming: multiple criteria, queues: applications

Methods for Allocating Urban Emergency Units

An urban emergency service system provides mobile units (vehicles) to respond to requests for service which can occur at any time and any place throughout a city. This paper surveys recent quantitative work aimed at improving the allocation policies of these systems, including determining the number of units on duty, designing response areas and patrol patterns, and locating service facilities. Recent models which provide insight into system operation are proposed to replace traditional rules-of-thumb as guides to allocation decision-making. The methods discussed are applicable to police and fire departments, emergency ambulance services, and certain other emergency service.U. S. Department of Housing and Urban Development under Grant H-1056 and in part by the National Science Foundation under Grants GK-16471 and GI-5

Methods for Allocating Urban Emergency Units: A Survey

An urban emergency service system provides mobile units (vehicles) to respond to requests for service which can occur at any time and any place throughout a city. This paper describes the common characteristics and operational problems of these systems and surveys the various methods, both traditional and recently developed, which may be used for allocating their units. Aspects of allocation policy discussed include (1) determining the number of units to have on duty, (2) locating the units, (3) designing their response areas or patrol areas, (4) relocating units, and (5) planning preventive-patrol patterns for police cars. Typical policy changes which may be suggested by the use of quantitative allocation models include selective queuing of low priority calls, varying the number of units on duty (and their locations) by time of day, dispatching units other than the closest ones to certain incidents, relocating units as unavailabilities begin to develop, and assigning police cars to overlapping patrol sectors. As a result of making such changes, it is often possible to reduce queuing and travel time delays, improve the balance of workload among units, and enhance the amount of preventive patrol where needed.

Motivated information processing and group decision refusal

Group decision making has attracted much scientific interest, but few studies have investigated group decisions that do not get made. Based on the Motivated Information Processing in Groups model, this study analysed the effect of epistemic motivation (low vs. high) and social motivation (proself vs. prosocial) on group decision refusal (the decision to delay choice and refuse all options). In a laboratory experiment, groups had to negotiate diverse preferences and choose one of three options or refuse all. When epistemic motivation was low decisions were made quickly, whereas high epistemic motivation more often led to refusal. This effect was partly mediated by perceived information insufficiency. Social motivation did not affect refusal, but proself motivation led to longer discussions, greater task conflict and more forcing behavior than prosocial motivation. Further, forcing was negatively related to decision refusal, but only when epistemic motivation was low