On the Compliance of Rationality Postulates for Inconsistency Measures: A More or Less Complete Picture

Thimm, Matthias

doi:10.1007/s13218-016-0451-y

Cited by 25 publications

(30 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Let us first look at the disagreement measures induced by I η . I η satisfies Monotony [15]. Therefore, D Iη will violate our majority principles as explained in Corollary 2.…”

Section: The η-Disagreement Measurementioning

confidence: 88%

“…Therefore, D Iη will violate our majority principles as explained in Corollary 2. However, I η is not adjunction invariant [15]. Therefore, Proposition 5 implies that…”

Section: The η-Disagreement Measurementioning

confidence: 96%

“…Hence, it basically performs a satisfiability test. There exist various other measures, see [15] for a recent overview. While there is an ongoing debate about what properties an inconsistency measure should satisfy, there is general agreement that it should be consistent in the sense that I(κ) = 0 if and only if κ is consistent.…”

Section: Basicsmentioning

confidence: 99%

“…Hence, the inconsistency value is greater than zero if and only if κ is inconsistent. Various other properties of inconsistency measures have been discussed [14,16,15]. We will present some of these later, when talking about corresponding properties of disagreement measures.…”

Section: Basicsmentioning

confidence: 99%

See 3 more Smart Citations

Measuring Disagreement among Knowledge Bases

Potyka¹

2018

EasyChair Preprints

View full text Add to dashboard Cite

When combining beliefs from different sources, often not only new knowledge but also conflicts arise. In this paper, we investigate how we can measure the disagreement among sources. We start our investigation with disagreement measures that can be induced from inconsistency measures in an automated way. After discussing some problems with this approach, we propose a new measure that is inspired by the $\eta$-inconsistency measure. Roughly speaking, it measures how well we can satisfy all sources simultaneously. We show that the new measure satisfies desirable properties, scales well with respect to the number of sources and illustrate its applicability in inconsistency-tolerant reasoning.

show abstract

“…Let us first look at the disagreement measures induced by I η . I η satisfies Monotony [15]. Therefore, D Iη will violate our majority principles as explained in Corollary 2.…”

Section: The η-Disagreement Measurementioning

confidence: 88%

“…Therefore, D Iη will violate our majority principles as explained in Corollary 2. However, I η is not adjunction invariant [15]. Therefore, Proposition 5 implies that…”

Section: The η-Disagreement Measurementioning

confidence: 96%

Section: Basicsmentioning

confidence: 99%

Section: Basicsmentioning

confidence: 99%

See 2 more Smart Citations

Measuring Disagreement among Knowledge Bases

Potyka¹

2018

EasyChair Preprints

View full text Add to dashboard Cite

show abstract

“…To support companies in this aim, inconsistency metrics have been proposed, providing insights on inconsistencies in decision-making during run-time. Examples include [6] for an overview and [8,29] for some recent surveys. An important family of such metrics are culpability measures, which aim to quantify the severity of inconsistency for individual business rules with a numerical value.…”

Section: Figure 1 Exemplary Business Process and Business Rulesmentioning

confidence: 99%

Effects of Visualization Techniques on Understanding Inconsistencies in Automated Decision-Making

Corea¹,

Nagel²,

Delfmann³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

The automation of business processes and decision-making has received major interest from practice and academia. As automation allows us to execute more processes (cases), monitoring automated decision-making is currently evolving into a big data analytics problem for companies. Thus, not only monitoring insights themselves, but also an effective use of such insights become important. In this context, the speed and ability to interpret data is closely related to the visualization of metrics and data. While various approaches for quantitative insights on automated decision-making have been proposed, there is currently no evidence as to how the specific visualization of such metrics helps companies to create more value from their data. In this report, we therefore present the results of an empirical experiment analyzing the cognitive effects of different visualization techniques for quantitative insights on understanding inconsistencies in automated decisionmaking data.

show abstract

Measuring and Computing Database Inconsistency via Repairs

Bertossi

2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

On the Compliance of Rationality Postulates for Inconsistency Measures: A More or Less Complete Picture

Cited by 25 publications

References 30 publications

Measuring Disagreement among Knowledge Bases

Measuring Disagreement among Knowledge Bases

Effects of Visualization Techniques on Understanding Inconsistencies in Automated Decision-Making

Measuring and Computing Database Inconsistency via Repairs

Contact Info

Product

Resources

About