Readmission discount factors in program evaluation. An output value analysis of an adult psychiatry program

The application of output value analysis, a type of benefit/cost analysis, to a psychiatric patient population is reported. A method for discounting the value of the program if a patient was readmitted within a year after discharge was introduced. The application of this discount factor reduces the value produced by the program and thereby reduces both productivity and effectiveness indices. When applied to groups known to differ in readmission rates, such as first admissions and readmissions or voluntary and involuntary admissions, the discount factor can accentuate group differences markedly. When selected diagnostic groups were compared, the discount factor could even reverse the relative standing of the groups.     

Dealing with logical omniscience: Expressiveness and pragmatics

We examine four approaches for dealing with the logical omniscience problem and their potential applicability: the syntactic approach, awareness, algorithmic knowledge, and impossible possible worlds. Although in some settings these approaches are equi-expressive and can capture all epistemic states, in other settings of interest (especially with probability in the picture), we show that they are not equi-expressive. We then consider the pragmatics of dealing with logical omniscience—how to choose an approach and construct an appropriate model.     

Validity, fairness, and group differences: Tough questions for selection testing.

Selection tests, such as those used for college admissions, present multiple dilemmas for psychometricians, who grapple with intractable problems in measurement, and the lay public, whose lives are affected by test scores they often do not understand or trust. Criterion studies utilize convenient criteria that have little meaningful significance, such as grades, and the validity coefficients are necessarily low because of range restrictions and the low reliability of grades. Both supporters and detractors of college admissions tests are correct in their assessment: College admissions tests can account for only a small percentage of the variance in success in school and in life, but even a small reduction of variance substantially reduces uncertainty and improves admissions decisions. Admissions testing can and should be improved. Numerous suggestions for improving admissions testing are presented, including ways to reduce group differences without sacrificing construct validity. The effect of the suggested changes on predictive validity is an empirical question and a question about fairness and values. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Preview tracking for discrete-time SISO systems

The preview tracking problem, that is, the problem of tracking a reference input when a window of its future values is known, is considered. This is done by showing how the set of all admissible regulated outputs of a system is modified when preview is incorporated. This allows the mechanism by which preview allows performance improvement to be seen more clearly . It also allows preview control systems to be designed using the powerful optimization techniques available for this framework. To exemplify this, some analytical results for the minimum l₁, l₂ and l_∞ norms of the tracking error in response to a step reference are derived for a simple system with preview. A worked example of some optimal compensator designs is also presented     

l/sub 1/-suboptimal pole placement design

We incorporate an arbitrary number of "candidate" positive distinct poles into the scalar l/sub 1/ optimization framework in order to obtain low-order rational suboptimal solutions to constrained l/sub 1/ optimization problems. Our approach uses a single linear program to minimize an upper bound on the l/sub 1/ norm and gives a solution which uses only the best out of the prespecified poles. Rational suboptimal solutions to a scalar two-block problem are obtained similarly.     

On time versus space III

Paul and Reischuk devised space efficient simulations of logarithmic cost random access machines and multidimensional Turing machines. We simplify their general space reduction technique and extend it to other computational models, including pointer machines, which model computations on graphs and data structures. Every pointer machine of time complexityT(n) can be simulated by a pointer machine of space complexityO(T(n)/logT(n)).