A (slightly) faster algorithm for klee's measure problem

2008 ◽  
Author(s):  
Timothy M. Chan
Keyword(s):  
Measure Problem

scholarly journals The measure problem in no-collapse (many worlds) quantum mechanics

2017 ◽  
Vol 26 (03) ◽  
pp. 1730008 ◽  
Author(s):  
Stephen D. H. Hsu
Keyword(s):  
Wave Function ◽  
Quantum Mechanics ◽  
Probability Measure ◽  
Ab Initio ◽  
Subjective Probability ◽  
State Vector ◽  
A Priori ◽  
Born Rule ◽  
Many Worlds ◽  
Measure Problem

We explain the measure problem (cf. origin of the Born probability rule) in no-collapse quantum mechanics. Everett defined maverick branches of the state vector as those on which the usual Born probability rule fails to hold — these branches exhibit highly improbable behaviors, including possibly the breakdown of decoherence or even the absence of an emergent semi-classical reality. Derivations of the Born rule which originate in decision theory or subjective probability (i.e. the reasoning of individual observers) do not resolve this problem, because they are circular: they assume, a priori, that the observer occupies a non-maverick branch. An ab initio probability measure is sometimes assumed to explain why we do not occupy a maverick branch. This measure is constrained by, e.g. Gleason’s theorem or envariance to be the usual Hilbert measure. However, this ab initio measure ultimately governs the allocation of a self or a consciousness to a particular branch of the wave function, and hence invokes primitives which lie beyond the Everett wave function and beyond what we usually think of as physics. The significance of this leap has been largely overlooked, but requires serious scrutiny.

Are Good Problem-Solvers Also Creative?

1969 ◽  
Vol 24 (1) ◽  
pp. 139-146 ◽  
Author(s):  
Norman R. F. Maier ◽  
Junie C. Janzen
Keyword(s):  
Sex Differences ◽  
Problem Solving ◽  
Junior College ◽  
Subjective Measure ◽  
College Sample ◽  
Increasing Trend ◽  
Measure Problem ◽  
Problem Solvers ◽  
The University ◽  
Objective Type

The purpose of this experiment was to determine whether Ss, superior in solving difficult problems having objectively correct solutions, also achieve solutions rated “creative” or superior for a problem with several possible answers. To avoid the issue of sex differences, only female Ss were used (96 from a university and 55 from a junior college). Four difficult objective-type problems were used to measure problem-solving ability and the “Changing Work Procedure” (CWP) problem was used for the subjective measure. The case produces 3 types of solutions. One type, called Integrative, was regarded as “creative” and “superior” in previous studies. The results showed a significantly better performance on all problems for the university than the junior college sample. Ss who reached the Integrative solution solved significantly more of the objective problems, and an increasing trend for Integrative solutions with increasing success on objective problems was evident for both populations. It is concluded that superior problem solvers also generate solutions that are rated as creative when several solutions to a given problem are possible.

Optimization Methods in Continuous Improvement Models

2019 ◽  
Vol 6 (1) ◽  
pp. 46-59
Author(s):  
Brian J. Galli
Keyword(s):  
Continuous Improvement ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Problem Analysis ◽  
Measure Problem ◽  
Continuous Improvement Model ◽  
Current Utilization ◽  
And Control ◽  
Theory Of Constraint

There are numerous processes used to implement quality, such as TQM, 6 Sigma, and Lean. For these quality processes to remain effective, a continuous improvement model is required and implemented from time to time. Some of these models include Define, Measure, Analyse, Improve and Control (DMAIC); Plan, Do, Check, and Act (PDCA); Identify, Measure, Problem Analysis, Remedy, Operationalize, Validate, and Evaluate (IMPROVE); and Theory of Constraint (TOC). Furthermore, continuous improvement tools need to remain effective through the use of optimization techniques to produce the best possible outcomes. This article discusses some of the current utilization of these tools and proposes different optimizing techniques and variations to make robust quality implementation tools.

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