Arrow’s theorem, multi-criteria decision problems and multi-attribute preferences in engineering design

Franssen, Maarten

doi:10.1007/s00163-004-0057-5

Cited by 95 publications

(72 citation statements)

References 13 publications

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“…But people do not take any countermeasures against common optical illusions, because in realistic circumstances, they do not appear with any significant frequency nor do they cause serious negative consequences. We propose that the concerns Hazelrigg (1998), Franssen (2005), and Saari (2006) describe are similar to optical illusions. They are interesting to know about, and they might even be useful tools for exploring the mechanisms of human decision-making, and it would be interesting to see if these ''cognitive illusions'' can explain some of the specific decisions from the jet engine industry as described by Koff (2004).…”

Section: On the Standing Of Counterexamples As Evidencementioning

confidence: 93%

Research in engineering design: the role of mathematical theory and empirical evidence

et al. 2010

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Section: On the Standing Of Counterexamples As Evidencementioning

confidence: 93%

Research in engineering design: the role of mathematical theory and empirical evidence

et al. 2010

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“…If a single objective function is used, one optimal solution to the problem will be sought. However, some theoretical work has shown that it is difficult or impossible to create an internally consistent aggregation function to achieve this (Franssen, 2005). At the other end of the spectrum, there is an emerging paradigm for "many" objective analysis of more than four objectives (Fleming et al, 2005;Woodruff et al, 2013).…”

Section: Current Statusmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

531

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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“…• ''Multi-criteria decision problems are still left largely unaddressed in engineering design'' (Franssen 2005). …”

Section: Motivationmentioning

confidence: 99%

“…4.4 suggest that investigating the reasons for a difference of opinion and exploring new options in light of what is revealed is more productive than using a carefully crafted election procedure to decide the matter. Franssen (2005) proposes that Arrow's theorem applies fully to multi-criteria decision-making because preferences are ''mental concepts neither logically or causally determined by'' physical parameters. The implication is that Arrow's stipulated conditions such as Unrestricted Domain and Minimal Liberty imply that preferences must be unrestricted by any demand for objectivity.…”

Section: Conclusion About Design Theorymentioning

confidence: 99%

The Pugh Controlled Convergence method: model-based evaluation and implications for design theory

et al. 2008

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This paper evaluates the Pugh Controlled Convergence method and its relationship to recent developments in design theory. Computer executable models are proposed simulating a team of people involved in iterated cycles of evaluation, ideation, and investigation. The models suggest that: (1) convergence of the set of design concepts is facilitated by the selection of a strong datum concept; (2) iterated use of an evaluation matrix can facilitate convergence of expert opinion, especially if used to plan investigations conducted between matrix runs; and (3) ideation stimulated by the Pugh matrices can provide large benefits both by improving the set of alternatives and by facilitating convergence. As a basis of comparison, alternatives to Pugh's methods were assessed such as using a single summary criterion or using a Borda count. These models suggest that Pugh's method, under a substantial range of assumptions, results in better design outcomes than those from these alternative procedures.

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Arrow’s theorem, multi-criteria decision problems and multi-attribute preferences in engineering design

Cited by 95 publications

References 13 publications

Research in engineering design: the role of mathematical theory and empirical evidence

Research in engineering design: the role of mathematical theory and empirical evidence

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

The Pugh Controlled Convergence method: model-based evaluation and implications for design theory

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