Utility Maximization, Choice and Preference

Aleskerov, Fuad; Monjardet, Bernard

doi:10.1007/978-3-662-04992-1

Cited by 83 publications

(102 citation statements)

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“…In this family of methods, the expected utilities (probabilistically weighted figures of merit) associated with the outcomes of each decision option are compared, and the option with the greatest expected utility is selected (see, for example, Aleskerov et al 2007). Since the identities of the preferred decisions are sensitive to the input probability distributions (which can be contentious-see Section 2.2), there is often some reluctance in real-world settings to adopt such methods wholesale.…”

Section: Classical Decision Methodsmentioning

confidence: 99%

Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

Unwin¹,

Moss²,

Rice³

et al. 2011

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SummaryThis white paper describes the results of new research to develop an uncertainty characterization (UC) process to help address the challenges of regional climate change mitigation and adaptation decisions. This research is being carried out as part of the integrated Regional Earth System Model (iRESM) initiative, a new scientific framework developed at Pacific Northwest National Laboratory to evaluate the interactions between human and environmental systems and mitigation and adaptation decisions at regional scales. The framework integrates a regional climate model; a regional energyeconomy model; and highly spatially-resolved models of crop productivity, building energy demands, electricity infrastructure operation and expansion, and water supply and management. The iRESM framework is intended to help regional stakeholders (scientists as well as decision makers) understand the consequences of climate change as well as the consequences of policies to mitigate or adapt to such change within regions.The initiative has developed the following four science questions to guide its research:• How do intrinsic regional characteristics shape, enhance, or constrain regional mitigation and adaptation opportunities?• How do projected changes in mean climate versus climate extremes affect the development of adaptation and mitigation strategies?• How might interactions between management decisions and natural processes contribute to rapid or nonlinear changes, and do they contribute to climate feedbacks?• How will adaptation and mitigation strategies interact in the next few decades in terms of achieving their respective goals?An important consideration for the iRESM initiative is that Earth system mechanisms and future changes are imperfectly understood and in some cases deeply uncertain-especially at the level of resolution required for regional analyses and decision making. The UC process developed for the initiative addresses uncertainty by first identifying through sensitivity analysis the key uncertainties in data inputs, individual model structures, and coupled models that are important for particular stakeholder questions and evaluation criteria. These key uncertainties are then characterized and propagated to determine the robustness of the framework's results for the particular questions, thereby providing insights for researchers and decision makers alike. The process differs from many traditional applications of uncertainty quantification (UQ) because of its focus on stakeholder needs and its allowance for qualitative and semi-quantitative methods for describing uncertainty. The process not only permits the dimensionality of the UQ problem to be reduced, it also allows research efforts to be targeted at the uncertainties that really matter for the question in hand.This decision-specific orientation for UC has multiple implications for the iRESM initiative that will continue to be explored, including: the importance of stakeholder interactions and the development of methods for communicating results; the dev...

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Section: Classical Decision Methodsmentioning

confidence: 99%

Characterizing Uncertainty for Regional Climate Change Mitigation and Adaptation Decisions

Unwin¹,

Moss²,

Rice³

et al. 2011

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“…. , last equivalence class of K. (Aleskerov, Bouyssou, and Monjardet 2007) that the relation K • defined letting, for all a, b, c ∈ A,…”

Section: Binary Relationsmentioning

confidence: 99%

“…In all these examples, we define by its boolean matrix, so that it is easy to check that the relations considered below are semiorders. Indeed, if it is possible to arrange the rows and columns of the boolean matrix in the same order so that the boolean matrix is stepped, we know that the relation is a semiorder (Aleskerov et al 2007). It is clear that this relation is a semiorder on X.…”

Section: A Proof Of Lemma 12mentioning

confidence: 99%

Multiattribute preference models with reference points

Bouyssou¹,

Marchant²

2013

European Journal of Operational Research

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In the context of multiple attribute decision making, preference models making use of reference points in an ordinal way have recently been introduced in the literature. This text proposes an axiomatic analysis of such models, with a particular emphasis on the case in which there is only one reference point. Our analysis uses a general conjoint measurement model resting on the study of traces induced on attributes by the preference relation and using conditions guaranteeing that these traces are complete. Models using reference points are shown to be a particular case of this general model. The number of reference points is linked to the number of equivalence classes distinguished by the traces. When there is only one reference point, the induced traces are quite rough, distinguishing at most two distinct equivalence classes. We study the relation between the model using a single reference point and other preference models proposed in the literature.

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“…Basic references on this issue can be considered: [102], [161], [222], [103], [230], [152], [247], [251], [209], [105], [4], [199]. In this article we adopt the notation introduced in [230].…”

Section: Introduction and Notationmentioning

confidence: 99%