On some ordinal models for decision making under uncertainty

Bouyssou, Denis; Pirlot, Marc

doi:10.1007/s10479-008-0329-y

Cited by 5 publications

(3 citation statements)

References 67 publications

(60 reference statements)

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“…• It is not difficult to find a condition strengthening condition OC and ensuring that ≿ φ is a weak order having at most two equivalence classes. Building on Bouyssou and Marchant (2007a) and Bouyssou and Pirlot (2009), it is simple to show that the following condition is exactly what is needed.…”

Section: Remark 10mentioning

confidence: 99%

Simple but Powerful Models of Stereotype Formation

Bouyssou¹,

Sanver²

2023

Revue Économique

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We propose simple models aiming at explaining the formation of stereotypes. A stereotype is an overall judgement brought by an observer over a group of individuals or objects. For each member of the group, we suppose that we observe a characteristic that belongs to a denumerable set. The formation of a stereotype about the group is governed by a perception function. Our basic model consists in decomposing a perception function into three steps: (i ) characteristics are recoded numerically so that higher numbers mean a higher support for the stereotype, (ii ) this vector of numbers is consistently aggregated into a single number, and (iii ) this number is compared to a threshold and the stereotype is accepted if the threshold is exceeded. We characterize perception functions that can be explained using such a model. We then study various extensions of our basic model.

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Section: Remark 10mentioning

confidence: 99%

Simple but Powerful Models of Stereotype Formation

Bouyssou¹,

Sanver²

2023

Revue Économique

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“…Opinions on how AI can replace the human brain functions, especially in complex decision making, are scarce and differ in statement and objectives. (Bouyssou & Pirlot, 2008); Nils J. Nilsson(2010); Hung T. Nguyen, E. Walker, 1996 ; (Wright &Schultz,2018) Nevertheless, a programmed machine can achieve limitations risky for human life. For example, an A Robot can go to Mars, defuse a bomb, explore the deepest parts of oceans, assist in relief work during disasters, and change its course and actions as desired by the controlling desk (Darrell and Allen, 2018).…”

Section: Introductionmentioning

confidence: 99%

Implementation of AI in Business Models: A Conceptual Study

A. Drave,

Rahman,

Drave

et al. 2021

Proceedings of the International Conference on Industrial Engineering and Operations Management

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This paper attempts to study factors that hinder the implementation, profitability and performance of Artificial Intelligence in Cost Leaders, Differentiators and Innovators and examine methods to overcome the failure factors. Artificial intelligence (AI) replicates human logic by using connected and programmed devices. Its implementation aids operational efficiencies and business decisions based on outputs from cognitive technologies, resulting in consistent product quality and manufacturing costs without significant human intervention. Moreover, implementing AI in all business environments may not always be profitable, as updating complex devices, software, and associated methodology inculcate financial implications.

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“…Computers are defined as artificial intelligence (AI), which Simon (1995) sees as mathematical and physical applications that are able to handle complexity, in contrast to traditional mathematical theorems. However, opinions and studies on the extent to which AI can be used for the same tasks as the human brain, especially in connection with decision making have been scarce and differ in focus, technology, and objective (Bouyssou and Pirlot 2008;Munguìa et al 2010;Nilsson 2010;Glock and Hochrein 2011;Nguyen et al 2018;Wright and Schultz 2018).…”

Section: Introductionmentioning

confidence: 99%

On the current state of combining human and artificial intelligence for strategic organizational decision making

Trunk

Birkel

Hartmann

2020

Bus Res

107

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Strategic organizational decision making in today’s complex world is a dynamic process characterized by uncertainty. Therefore, diverse groups of responsible employees deal with the large amount and variety of information, which must be acquired and interpreted correctly to deduce adequate alternatives. The technological potential of artificial intelligence (AI) is expected to offer further support, although research in this regard is still developing. However, as the technology is designed to have capabilities beyond those of traditional machines, the effects on the division of tasks and the definition of roles established in the current human–machine relationship are discussed with increasing awareness. Based on a systematic literature review, combined with content analysis, this article provides an overview of the possibilities that current research identifies for integrating AI into organizational decision making under uncertainty. The findings are summarized in a conceptual model that first explains how humans can use AI for decision making under uncertainty and then identifies the challenges, pre-conditions, and consequences that must be considered. While research on organizational structures, the choice of AI application, and the possibilities of knowledge management is extensive, a clear recommendation for ethical frameworks, despite being defined as a crucial foundation, is missing. In addition, AI, other than traditional machines, can amplify problems inherent in the decision-making process rather than help to reduce them. As a result, the human responsibility increases, while the capabilities needed to use the technology differ from other machines, thus making education necessary. These findings make the study valuable for both researchers and practitioners.

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On some ordinal models for decision making under uncertainty

Cited by 5 publications

References 67 publications

Simple but Powerful Models of Stereotype Formation

Simple but Powerful Models of Stereotype Formation

Implementation of AI in Business Models: A Conceptual Study

On the current state of combining human and artificial intelligence for strategic organizational decision making

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