A survey of decision making and optimization under uncertainty

Keith, Andrew J.; Ahner, Darryl K.

doi:10.1007/s10479-019-03431-8

Cited by 58 publications

(22 citation statements)

References 204 publications

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“…The notion of uncertainty has been studied in a wide variety of fields, usually in connection to decision-making; a recent example is [15]. Most of these studies assume that decisionmaking processes are "executed" by humans.…”

Section: I R E L At E D S T U D I E Smentioning

confidence: 99%

Understanding Uncertainty in Self-adaptive Systems

Călinescu

Mirandola

Pérez-Palacín

et al. 2020

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

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Ensuring that systems achieve their goals under uncertainty is a key driver for self-adaptation. Nevertheless, the concept of uncertainty in self-adaptive systems (SAS) is still insufficiently understood. Although several taxonomies of uncertainty have been proposed, taxonomies alone cannot convey the SAS research community's perception of uncertainty. To explore and to learn from this perception, we conducted a survey focused on the SAS ability to deal with unanticipated change and to model uncertainty, and on the major challenges that limit this ability. In this paper, we analyse the responses provided by the 51 participants in our survey. The insights gained from this analysis include the view-held by 71% of our participants-that SAS can be engineered to cope with unanticipated change, e.g., through evolving their actions, synthesising new actions, or using default actions to deal with such changes. To handle uncertainties that affect SAS models, the participants recommended the use of confidence intervals and probabilities for parametric uncertainty, and the use of multiple models with model averaging or selection for structural uncertainty. Notwithstanding this positive outlook, the provision of assurances for safety-critical SAS continues to pose major challenges according to our respondents. We detail these findings in the paper, in the hope that they will inspire valuable future research on self-adaptive systems.

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Section: I R E L At E D S T U D I E Smentioning

confidence: 99%

Understanding Uncertainty in Self-adaptive Systems

Călinescu

Mirandola

Pérez-Palacín

et al. 2020

2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

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“…The notion of uncertainty has been studied in a wide variety of fields, often in connection with decision-making; a recent example is Reference [15]. A common assumption in such studies is that decision-making processes partly rely on humans.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty in Self-adaptive Systems: A Research Community Perspective

Mahdavi-Hezavehi

Weyns

Avgeriou

et al. 2020

ACM Trans. Auton. Adapt. Syst.

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One of the primary drivers for self-adaptation is ensuring that systems achieve their goals regardless of the uncertainties they face during operation. Nevertheless, the concept of uncertainty in self-adaptive systems is still insufficiently understood. Several taxonomies of uncertainty have been proposed, and a substantial body of work exists on methods to tame uncertainty. Yet, these taxonomies and methods do not fully convey the research community’s perception on what constitutes uncertainty in self-adaptive systems and on the key characteristics of the approaches needed to tackle uncertainty. To understand this perception and learn from it, we conducted a survey comprising two complementary stages in which we collected the views of 54 and 51 participants, respectively. In the first stage, we focused on current research and development, exploring how the concept of uncertainty is understood in the community and how uncertainty is currently handled in the engineering of self-adaptive systems. In the second stage, we focused on directions for future research to identify potential approaches to dealing with unanticipated changes and other open challenges in handling uncertainty in self-adaptive systems. The key findings of the first stage are: (a) an overview of uncertainty sources considered in self-adaptive systems, (b) an overview of existing methods used to tackle uncertainty in concrete applications, (c) insights into the impact of uncertainty on non-functional requirements, (d) insights into different opinions in the perception of uncertainty within the community and the need for standardised uncertainty-handling processes to facilitate uncertainty management in self-adaptive systems. The key findings of the second stage are: (a) the insight that over 70% of the participants believe that self-adaptive systems can be engineered to cope with unanticipated change, (b) a set of potential approaches for dealing with unanticipated change, (c) a set of open challenges in mitigating uncertainty in self-adaptive systems, in particular in those with safety-critical requirements. From these findings, we outline an initial reference process to manage uncertainty in self-adaptive systems. We anticipate that the insights on uncertainty obtained from the community and our proposed reference process will inspire valuable future research on self-adaptive systems.

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“…Since the work of Scarf (1958), many DRO models have been proposed and studied in the technical literature, especially over the last decade, in which DRO has attracted a lot of attention and become very popular in the field of optimization under uncertainty as an alternative to other paradigms. We refer the reader to Keith and Ahner (2021), Rahimian and Mehrotra (2019) for recent surveys on DRO and optimization under uncertainty. Naturally, the construction of the ambiguity set is key to the practical performance of DRO.…”

Section: Introductionmentioning

confidence: 99%

Partition-based distributionally robust optimization via optimal transport with order cone constraints

Esteban-Pérez

Morales

2021

4OR-Q J Oper Res

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In this paper we wish to tackle stochastic programs affected by ambiguity about the probability law that governs their uncertain parameters. Using optimal transport theory, we construct an ambiguity set that exploits the knowledge about the distribution of the uncertain parameters, which is provided by: (1) sample data and (2) a-priori information on the order among the probabilities that the true data-generating distribution assigns to some regions of its support set. This type of order is enforced by means of order cone constraints and can encode a wide range of information on the shape of the probability distribution of the uncertain parameters such as information related to monotonicity or multi-modality. We seek decisions that are distributionally robust. In a number of practical cases, the resulting distributionally robust optimization (DRO) problem can be reformulated as a finite convex problem where the a-priori information translates into linear constraints. In addition, our method inherits the finite-sample performance guarantees of the Wasserstein-metric-based DRO approach proposed by Mohajerin Esfahani and Kuhn (Math Program 171(1–2):115–166. 10.1007/s10107-017-1172-1, 2018), while generalizing this and other popular DRO approaches. Finally, we have designed numerical experiments to analyze the performance of our approach with the newsvendor problem and the problem of a strategic firm competing à la Cournot in a market.

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A survey of decision making and optimization under uncertainty

Cited by 58 publications

References 204 publications

Understanding Uncertainty in Self-adaptive Systems

Understanding Uncertainty in Self-adaptive Systems

Uncertainty in Self-adaptive Systems: A Research Community Perspective

Partition-based distributionally robust optimization via optimal transport with order cone constraints

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