Asteroid Deflection Campaign Design Integrating Epistemic Uncertainties

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Whereas the ability of deep networks to produce useful predictions on many kinds of data has been amply demonstrated, estimating the reliability of these predictions remains challenging. Sampling approaches such as MC-Dropout and Deep Ensembles have emerge ...

We introduce contextual stochastic bilevel optimization (CSBO) -- a stochastic bilevel optimization framework with the lower-level problem minimizing an expectation conditioned on some contextual information and the upper-level decision variable. This fram ...

At the conceptual stage, building performance simulation (BPS) based evaluations are being increasingly used for tasks such as ranking of competing massing design proposals. However, such conceptual stage evaluations suffer from information deficiency in b ...

Random Fourier features (RFFs) provide a promising way for kernel learning in a spectral case. Current RFFs-based kernel learning methods usually work in a two-stage way. In the first-stage process, learn-ing an optimal feature map is often formulated as a ...

The design of efficient energy systems, through the development of new technologies and the improvement of current ones, requires the use of rigorous process synthesis methods for generating and analysing design alternatives. We introduce a digital twin of ...

Service-level requirements play a crucial role in eliminating stock-outs in a production pipeline. However, delivering a specific service level can become an unattainable goal given the various uncertainties influencing both the production pipeline and cus ...

There is a need for a tool that facilitates safety decision-making in the academic environment. As this environment is very different from that of industry or other public sectors, there is no information available on the factors that influence the decisio ...

It is natural for humans to judge the outcome of a decision under uncertainty as a percentage of an ex-post optimal performance. We propose a robust decision-making framework based on a relative performance index. It is shown that if the decision maker’s p ...

We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be exp ...

The reuse of structural components in new buildings has great potential to reduce the environmental impacts of the construction sector but remains uncommon practice. An obstacle to its wider implementation is the lack of robust assessment methods and decis ...

This paper studies the operation of multi-agent networks engaged in multi-task decision problems under the paradigm of simultaneous learning and adaptation. Two scenarios are considered:one in which a decision must be taken among multiple states of nature ...

Design and verification of structures in modern codes of practice account for a safety format, ensuring that the probability of failure does not exceed a given threshold. Although specific safety formats are proposed in some cases for special types of stru ...

Making decisions is part and parcel of being human. Among a set of actions, we want to choose the one that has the highest reward. But the uncertainty of the outcome prevents us from always making the right decision. Making decisions under uncertainty can ...

Driving is a very challenging task to automatize despite how naturally and efficiently it may come to experienced human drivers. The complexity stems from the need to (i) understand the surrounding context and forecast how it is likely to evolve, (ii) plan ...

We investigate the willingness of individuals to persist at exploration in the face of failure. Prior research suggests that the organization's "tolerance for failure" may motivate greater exploration by the individual. Little is known, however, about how ...

Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...

The PETALE experimental programme in the CROCUS reactor intends to provide integral measurements to constrain stainless steel nuclear data. This article presents the tools and the methodology developed to design and optimize the experiments, and its operat ...

Uncertainty presents a problem for both human and machine decision-making. While utility maximization has traditionally been viewed as the motive force behind choice behavior, it has been theorized that uncertainty minimization may supersede reward motivat ...

Many decisions rely on how we evaluate potential outcomes and estimate their corresponding probabilities of occurrence. Outcome evaluation is subjective because it requires consulting internal preferences and is sensitive to context. In contrast, probabili ...

The human capacity to compute the likelihood that a decision is correct—known as metacognition—has proven difficult to study in isolation as it usually cooccurs with decision making. Here, we isolated postdecisional from decisional contributions to metacog ...

Asteroid Deflection Campaign Design Integrating Epistemic Uncertainties

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ZigZag: Universal Sampling-free Uncertainty Estimation Through Two-Step Inference

Contextual Stochastic Bilevel Optimization

Risk of incorrect choices due to uncertainty in BPS evaluations of conceptual-stage neighbourhood-scale building designs

End-to-end kernel learning via generative random Fourier features

Overcoming decision paralysis—A digital twin for decision making in energy system design

Limiting the impact of supply chain disruptions in the face of distributional uncertainty in demand

Safety Decision-Making for Laboratory Processes in Academia

Relatively Robust Decisions

A Pareto Dominance Principle for Data-Driven Optimization

Decision Framework to Balance Environmental, Technical, Logistical, and Economic Criteria When Designing Structures With Reused Components

Decision Learning and Adaptation Over Multi-Task Networks

A consistent safety format and design approach for brittle systems and application to textile reinforced concrete structures

Multi-armed Bandits in Action

Motion planning for CAVs in mixed traffic, a study on roundabouts

Motivating Innovation: The Effect of Loss Aversion on the Willingness to Persist

Wasserstein Distributionally Robust Learning

Uncertainty propagation for the design study of the PETALE experimental programme in the CROCUS reactor

Information Theoretic Characterization of Uncertainty Distinguishes Surprise From Accuracy Signals in the Brain

Context effects on probability estimation

Disentangling the origins of confidence in speeded perceptual judgments through multimodal imaging