Search results for "aspiration level"

showing 7 items of 7 documents

Interactive Decision Aids

2011

Decision support systems assist people in making a decision or choosing a course of action in a nonroutine situation that requires judgment (Haubl and Trifts 2000; Kasper 1996). In online webstores, vendors can easily offer highly interactive types of decision support. These co-called interactive decision aids (IDA) “help consumers in making informed purchase decisions amidst the vast availability of online product offerings” (Wang and Benbasat 2009, p. 3). However, the application of IDA is not restricted to purchase decisions. They are general enough to be of use in any kind of choice task where alternatives are known.

Course of actionDecision support systemKnowledge managementComputer sciencebusiness.industryDecision aidsPreference elicitationProduct (category theory)Aspiration levelRecommender systembusinessTask (project management)
researchProduct

INTACMATO: An IIMT-Prototype

2011

In the previous chapter, we pointed out that although users evaluate IIMT very positively, only a limited number of IIMT are offered. We assume that the main reason for this is that online sellers do not know which IIMT they should offer and what they should exactly look like. We address these two aspects in this chapter. Firstly, we review literature on IDA in the field of human interaction. We discuss several drawbacks of current approaches as well as the resulting requirements for the design of IIMT. Secondly, we break down observed decision-making behavior into typical steps decision makers apply in their decision processes. These steps indicate which IIMT would offer appropriate decisi…

Decision support systembusiness.industryHuman interactionComputer scienceCollaborative filteringUsabilityAspiration levelDecision processbusinessData scienceField (computer science)Attribute level
researchProduct

A Priori Methods

1998

In the case of a priori methods, the decision maker must specify her or his preferences, hopes and opinions before the solution process. The difficulty is that the decision maker does not necessarily know beforehand what it is possible to attain in the problem and how realistic her or his expectations are. The working order in these methods is: 1) decision maker, 2) analyst.

Mathematical optimizationMultiobjective optimization problemWeighting coefficientComputer scienceOrder (business)Goal programmingA priori and a posterioriAspiration levelDecision maker
researchProduct

Interactive Method NIMBUS for Nondifferentiable Multiobjective Optimization Problems

1997

An interactive method, NIMBUS, for nondifferentiable multiobjective optimization problems is introduced. The method is capable of handling several nonconvex locally Lipschitzian objective functions subject to nonlinear (possibly nondifferentiable) constraints. The idea of NIMBUS is that the decision maker can easily indicate what kind of improvements are desired and what kind of impairments are tolerable at the point considered. The decision maker is asked to classify the objective functions into five different classes: those to be improved, those to be improved down to some aspiration level, those to be accepted as they are, those to be impaired till some upper bound, and those allowed to …

Mathematical optimizationNonlinear systemMultiobjective optimization problemComputer sciencePoint (geometry)Aspiration levelDecision makerUpper and lower boundsMulti-objective optimization
researchProduct

Adolescents’ Self-Concordance, School Engagement, and Burnout Predict Their Educational Trajectories 1This paper is part of a series on “Youth Develo…

2009

This study investigated whether self-concordance of adolescents’ achievement-related goal predicts their school engagement and lack of burnout during upper secondary school as well as their subsequent educational trajectories. We also examined whether goal effort and progress mediate these associations. The sample consisted of 614 17-year-old upper secondary school students, who were surveyed three times: (1) in the second grade of upper secondary, (2) in the third grade of upper secondary school, and (3) one year later. The results showed that when adolescents pursued their achievement-related goal for internal reasons, they also invested effort in their goal, which was reflected in a hig…

Secondary levelArts and Humanities (miscellaneous)ConcordanceeducationStudent engagementAspiration levelAcademic achievementSchool engagementBurnoutPsychologyGeneral PsychologyPredictive factorDevelopmental psychologyEuropean Psychologist
researchProduct

Towards Automatic Testing of Reference Point Based Interactive Methods

2016

In order to understand strengths and weaknesses of optimization algorithms, it is important to have access to different types of test problems, well defined performance indicators and analysis tools. Such tools are widely available for testing evolutionary multiobjective optimization algorithms. To our knowledge, there do not exist tools for analyzing the performance of interactive multiobjective optimization methods based on the reference point approach to communicating preference information. The main barrier to such tools is the involvement of human decision makers into interactive solution processes, which makes the performance of interactive methods dependent on the performance of huma…

aspiration level021103 operations researchComputer sciencebusiness.industryComputer Science::Neural and Evolutionary Computation0211 other engineering and technologiespreference information02 engineering and technologyMachine learningcomputer.software_genreMulti-objective optimizationTest (assessment)testing framework0202 electrical engineering electronic engineering information engineeringdecision maker’s preferencesmultiobjective optimization020201 artificial intelligence & image processingEMOPerformance indicatorArtificial intelligencebusinesscomputerAutomatic testing
researchProduct

An Artificial Decision Maker for Comparing Reference Point Based Interactive Evolutionary Multiobjective Optimization Methods

2021

Comparing interactive evolutionary multiobjective optimization methods is controversial. The main difficulties come from features inherent to interactive solution processes involving real decision makers. The human can be replaced by an artificial decision maker (ADM) to evaluate methods quantitatively. We propose a new ADM to compare reference point based interactive evolutionary methods, where reference points are generated in different ways for the different phases of the solution process. In the learning phase, the ADM explores different parts of the objective space to gain insight about the problem and to identify a region of interest, which is studied more closely in the decision phas…

aspiration levelsMathematical optimizationComputer sciencepäätöksenteko02 engineering and technologySpace (commercial competition)interactive methodsDecision makerMulti-objective optimizationmonitavoiteoptimointidecision makingmany-objective optimizationoptimointiRegion of interestmonimuuttujamenetelmät020204 information systemsPerformance comparison0202 electrical engineering electronic engineering information engineeringBenchmark (computing)020201 artificial intelligence & image processingperformance comparison
researchProduct