Elicitation, assessment, and pooling of expert judgments using possibility theory

Sandri, Sandra; Dubois, Didier; Kalfsbeek, H. W.

doi:10.1109/91.413236

Cited by 80 publications

(54 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Possibility theory is another useful choice for representing uncertain information ( [18,53,3], etc), especially when an agentÕs knowledge is not sufficient to provide either a probabilistic or a mass assignment.…”

Section: Possibility Theorymentioning

confidence: 99%

Fusion rules for merging uncertain information

Hunter

Liu

2006

Information Fusion

View full text Add to dashboard Cite

In previous papers, we have presented a logic-based framework based on fusion rules for merging structured news reports. Structured news reports are XML documents, where the textentries are restricted to individual words or simple phrases, such as names and domain-specific terminology, and numbers and units. We assume structured news reports do not require natural language processing. Fusion rules are a form of scripting language that define how structured news reports should be merged. The antecedent of a fusion rule is a call to investigate the information in the structured news reports and the background knowledge, and the consequent of a fusion rule is a formula specifying an action to be undertaken to form a merged report. It is expected that a set of fusion rules is defined for any given application. In this paper we extend the approach to handling probability values, degrees of beliefs, or necessity measures associated with textentries in the news reports. We present the formal definition for each of these types of uncertainty and explain how they can be handled using fusion rules. We also discuss the methods of detecting inconsistencies among sources.

show abstract

Section: Possibility Theorymentioning

confidence: 99%

Fusion rules for merging uncertain information

Hunter

Liu

2006

Information Fusion

View full text Add to dashboard Cite

show abstract

“…Assessors have to cope up with different conceptualizations of uncertainty described in Table 1. Ambiguity during RP may arise for several reasons [40,41]:…”

Section: Ambiguity: Software Requirements Contextmentioning

confidence: 99%

4A Frameworks for Requirements Prioritization

Voola¹,

Babu²

2013

IJCA

View full text Add to dashboard Cite

Prioritization decisions in general aim at conducting assessment of several alternatives that are characterized by multiple conflicting attributes, which are intertwined by the competing preferences of multiple assessors. These assessments personifying various forms of ambiguity such as uncertainty, ignorance, vagueness and fuzziness have to be aggregated to generate reliable collective priorities. The objective of this paper is to introduce 4A prioritization frameworks with alternatives at the centre surrounded by the four facets: Attributes, Assessors, Ambiguity and Aggregation. Elements constituting the framework are discussed in a general context and then related to software requirements. The frameworks introduced have confronted a wide scope of further research.

show abstract

“…In the latter case the issue has been subject of research for more than 200 years and resulted in well known paradoxes, such as rank reversals in the Borda count, the Condorcet paradox of non-transitivity, and Arrow's impossibility theorem [4]. Risk management methods for the aggregation of expert assessments have been addressed by Beinat et al [3], Cooke [9], DeWispelare et al [10], Sandri et al [20], and Myung et al [18]. The procedures proposed in the literature to aggregate expert assessments can be classified as behavioral and mechanical [10].…”

Section: Orm For Multi-expert Situationsmentioning

confidence: 99%

Multi-expert operational risk management

Beroggi¹,

Waliace²

2000

IEEE Trans. Syst., Man, Cybern. C

View full text Add to dashboard Cite

Abstract-Operational risk management is the process of monitoring, evaluating, and changing courses of actions with potential detrimental consequences in real time. In this paper, we extend the decision models proposed in the literature for individual risk managers to account for situations where multiple risk managers are involved. For this purpose, two dynamic and adaptive preference aggregation models for cardinal and ordinal assessments are proposed and discussed. The mechanical aspects of the models are then validated using field data collected from experienced operational risk managers in an individual-expert setting. Sensitivity analysis indicates that the models have enough flexibility to be adapted to account for behavioral considerations. The paper closes with a research agenda.

show abstract

Elicitation, assessment, and pooling of expert judgments using possibility theory

Cited by 80 publications

References 24 publications

Fusion rules for merging uncertain information

Fusion rules for merging uncertain information

4A Frameworks for Requirements Prioritization

Multi-expert operational risk management

Contact Info

Product

Resources

About