The risk of decision making with incomplete criteria weight information

2006 ◽  
Vol 36 (1) ◽  
pp. 195-205 ◽  
Author(s):  
Annika Kangas
Keyword(s):  
Decision Problem ◽  
Value Function ◽  
Rank Order ◽  
Decision Support Tools ◽  
Preference Information ◽  
True Value ◽  
Support Tools ◽  
Criteria Weights ◽  
The Value Function

In many cases, it may be difficult to obtain explicit information on criteria weights for multicriteria decision analysis. Usually, however, at least the relevant criteria can be assumed to be known, even if their weights are not. In addition, complete or incomplete rank order of these criteria can be known, and it may be possible to obtain estimates for at least some of the value-function parameters. With some decision support tools, such as stochastic multicriteria acceptability analysis (SMAA), it is possible to use incomplete information. The main results of SMAA are the probabilities of certain alternative obtaining a given rank, given all the information available. These probabilities can be used for choosing the most recommendable alternative. However, recommendations are risky when the preference information is incomplete. In this study, the risks are studied through a simulation study based on a previous forestry decision problem with multiple criteria. (1) The probability that the best alternative is recommended and (2) the expected losses in the value of value function due to choosing the wrong alternative are modelled as a function of the characteristics of the true value function and the best alternative. The results show that the quality of decisions improves very quickly with improving information on weights. Determining at least the complete rank order of criteria is advisable, especially if the importances vary markedly among the criteria.

scholarly journals OBLIGATION BLACKWELL GAMES AND P-AUTOMATA

2017 ◽  
Vol 82 (2) ◽  
pp. 420-452
Author(s):  
KRISHNENDU CHATTERJEE ◽  
NIR PITERMAN
Keyword(s):  
Markov Chains ◽  
Decision Problem ◽  
Value Function ◽  
Acceptance Condition ◽  
Parity Games ◽  
The Value Function

AbstractWe generalize winning conditions in two-player games by adding a structural acceptance condition called obligations. Obligations are orthogonal to the linear winning conditions that define whether a play is winning. Obligations are a declaration that player 0 can achieve a certain value from a configuration. If the obligation is met, the value of that configuration for player 0 is 1.We define the value in such games and show that obligation games are determined. For Markov chains with Borel objectives and obligations, and finite turn-based stochastic parity games with obligations we give an alternative and simpler characterization of the value function. Based on this simpler definition we show that the decision problem of winning finite turn-based stochastic parity games with obligations is in NP∩co-NP. We also show that obligation games provide a game framework for reasoning about p-automata.

Interactive versus static decision support tools for COVID-19: An experimental comparison (Preprint)

2021 ◽  
Author(s):  
Alice Röbbelen ◽  
Malte L Schmieding ◽  
Marvin Kopka ◽  
Felix Balzer ◽  
Markus A Feufel
Keyword(s):  
Decision Support ◽  
Mental Effort ◽  
Experimental Comparison ◽  
Control Group ◽  
Decision Support Tools ◽  
Self Assessment ◽  
Decision Algorithm ◽  
Support Tools ◽  
Case Vignettes

BACKGROUND During the COVID-19 pandemic, medical laypersons with symptoms indicative of a COVID-19 infection commonly seek guidance on whether and where to seek medical care. Numerous web-based decision support tools (DSTs) have been developed, both by public and commercial stakeholders, to assist their decision-making. Though most of the DST’s underlying algorithms are similar and simple decision trees, their mode of presentation differs: some DSTs present a static flowchart, while others are designed as a conversational agent, guiding the user through the decision tree’s node step-by-step in an interactive manner. OBJECTIVE To investigate whether interactive DSTs provide greater decision support than non-interactive (ie, static) flowcharts. METHODS We developed mock interfaces for two DST (one static, one interactive), mimicking patient-facing, freely available DSTs for COVID-19 related self-assessment. Their underlying algorithm was identical and based on the Center for Disease Control’s guidelines. We recruited adult US residents online. which participants. Participants appraised the appropriate social and care-seeking behavior for seven fictitious descriptions of patients (case vignettes). Participants in the experimental groups received either the static or interactive mock DST as support, while the control group appraised the case vignettes unsupported. We determined participants’ accuracy, decision certainty (after deciding) and mental effort to measure quality of decision support. Participants’ ratings of the DSTs’ usefulness, ease of use, trust and future intention to use the tools served as measure to analyze differences in participants’ perception of the tools. We used ANOVAs and t-tests to assess statistical significance. RESULTS Our survey yielded 196 responses. The mean number of correct assessments was higher in the experimental groups (interactive DST group: M=11.71, SD=2.37; static DST group: M=11.45, SD=2.48) than in the control group (M=10.17, SD=2.00; F(2,193)=8.6, p<.001). Decisional certainty was significantly higher in the experimental groups (interactive DST group: M=80.7%, SD=14.1%; static DST group: M=80.5%, SD=15.8%) compared to the control group (M=65.8%, SD=20.8%; F(2, 193)=15.7, p<.001). Differences for mental effort between the three study were non-significant. Effect sizes of differences between the two experimental groups were small and non-significant for all three measures of quality of decision support and most measures of users’ perception of the DSTs. CONCLUSIONS When the decision space is limited as is the case in common COVID-19 self-assessment DSTs, static flowcharts might prove as beneficial in enhancing decision quality as interactive tools. Given that static flowcharts reveal the underlying decision algorithm more transparently and require less effort to develop, they might prove more efficient in providing guidance to the public. Further research should validate our findings on different use cases, elaborate on the trade-off between transparency and convenience in DSTs, and investigate whether subgroups of users benefit more one type of user interface than the other.

Decision Support Tools: Realizing the Potential to Improve Quality of Care

2018 ◽  
Vol 34 (7) ◽  
pp. 821-826 ◽  
Author(s):  
Michelle M. Graham ◽  
Matthew T. James ◽  
John A. Spertus
Keyword(s):  
Quality Of Care ◽  
Decision Support ◽  
Decision Support Tools ◽  
Improve Quality ◽  
Support Tools

scholarly journals Decision-support tools via mobile devices to improve quality of care in primary healthcare settings

2021 ◽  
Vol 2021 (7) ◽  
Author(s):  
Smisha Agarwal ◽  
Claire Glenton ◽  
Tigest Tamrat ◽  
Nicholas Henschke ◽  
Nicola Maayan ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Decision Support ◽  
Mobile Devices ◽  
Primary Healthcare ◽  
Decision Support Tools ◽  
Improve Quality ◽  
Healthcare Settings ◽  
Support Tools

The value function for time-related decisions

2011 ◽  
Author(s):  
Anouk Festjens ◽  
Siegfried Dewitte ◽  
Enrico Diecidue ◽  
Sabrina Bruyneel
Keyword(s):  
Value Function ◽  
The Value Function
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