Mind the Uncertainty: Risk-Aware and Actively Exploring Model-Based Reinforcement Learning

We introduce a simple but effective method for managing risk in model-based reinforcement learning with trajectory sampling that involves probabilistic safety constraints and balancing of optimism in the face of epistemic uncertainty and pessimism in the face of aleatoric uncertainty of an ensemble of stochastic neural networks.Various experiments indicate that the separation of uncertainties is essential to performing well with data-driven MPC approaches in uncertain and safety-critical control environments

The Value of Private Risk Versus the Value of Public Risk: An Experimental Analysis of the Johannesson et al. Conjecture

In 1996 Johannesson et al. published a paper in this journal entitled “The Value of Private Safety versus the Value of Public Safety.” Based on preliminary evidence from a hypothetical contingent valuation study, these authors argue that consumers behave as “pure altruists” and reject the notion of paternalistic preferences for safety in a coercive tax setting. These pure altruists consider the cost of a program that might be imposed on other voters when they decide whether to vote for or against public safety programs. The authors conclude that further empirical research in this area is warranted. This paper presents a set of laboratory economics experiments to test Johannesson et al.’s conjecture under controlled conditions in which participants face an actual risk of financial loss. The laboratory results extend those of Johannesson et al., providing strong evidence of pure altruism but limited support for paternalistic altruism for risk.Altruism, risk, voting, public goods, Research Methods/ Statistical Methods, Risk and Uncertainty, D81, D64, H41, C91, C92, D72,

HEALTH VALUE IN FOOD SAFETY SURVEILLANCE INITIATIVES

Recognizing the increasing concern about the potential health effects of genetically modified foods, this research provides a framework for economic value of monitoring genetically modified food for their potential long-term human health effects. This is with the view of helping policy makers improve resource allocation decisions in the face of competing public health initiatives. Using a hypothetical population exposed to a hypothetical product, we estimate the health value associated with a post-market surveillance initiative. The analysis indicate that the principal challenge confronting decision makers in the implementation of post-market surveillance initiatives is prioritising products for monitoring given the uncertainty associated with outcomes and effects.Food Consumption/Nutrition/Food Safety,

An audit of uncertainty in multi-scale cardiac electrophysiology models

Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’

Safety netting; best practice in the face of uncertainty

© 2020 Safety netting is a recognised General Practitioner (GP) diagnostic strategy often used in the face of uncertainty to help ensure that a patient with unresolved or worsening symptoms knows when and how to access further advice. It is an important way of reducing clinical risk. In the context of the COVID-19 pandemic and the rapid move to mainly remote consultations within the musculoskeletal field, safety netting is an important strategy to embed within all consultations. Only those presenting with potentially serious conditions are offered face to face consultations. Screening for Red Flags and any indication of a serious cause of symptoms is always first line in any consultation, however, clinical presentations are not always black and white with patients falling into a clear diagnostic category. With patients minds more focussed on COVID-19 symptoms this can be problematic. With the additional ramifications of public health social restrictions, onward management can be a conundrum. Many people with risk factors of serious pathology are also as a consequence, vulnerable to contracting COVID-19. In situations of uncertain clinical presentations, to avoid unnecessary social contact, safety netting can help to monitor symptoms over time until the clinical context becomes more certain. Embedding safety netting within physiotherapy best practice could be a silver lining in this pandemic black cloud

Disentangled Uncertainty and Out of Distribution Detection in Medical Generative Models

Trusting the predictions of deep learning models in safety critical settings such as the medical domain is still not a viable option. Distentangled uncertainty quantification in the field of medical imaging has received little attention. In this paper, we study disentangled uncertainties in image to image translation tasks in the medical domain. We compare multiple uncertainty quantification methods, namely Ensembles, Flipout, Dropout, and DropConnect, while using CycleGAN to convert T1-weighted brain MRI scans to T2-weighted brain MRI scans. We further evaluate uncertainty behavior in the presence of out of distribution data (Brain CT and RGB Face Images), showing that epistemic uncertainty can be used to detect out of distribution inputs, which should increase reliability of model outputs

Incorporating epistemic uncertainty into the safety assurance of socio-technical systems

In system development, epistemic uncertainty is an ever-present possibility when reasoning about the causal factors during hazard analysis. Such uncertainty is common when complicated systems interact with one another, and it is dangerous because it impairs hazard analysis and thus increases the chance of overlooking unsafe situations. Uncertainty around causation thus needs to be managed well. Unfortunately, existing hazard analysis techniques tend to ignore unknown uncertainties, and system stakeholders rarely track known uncertainties well through the system lifecycle. In this paper, we outline an approach to managing epistemic uncertainty in existing hazard analysis techniques by focusing on known and unknown uncertainty. We have created a reference populated with a wide range of safety-critical causal relationships to recognise unknown uncertainty, and we have developed a model to systematically capture and track known uncertainty around such factors. We have also defined a process for using the reference and model to assess possible causal factors that are suspected during hazard analysis. To assess the applicability of our approach, we have analysed the widely-used MoDAF architectural model and determined that there is potential for our approach to identify additional causal factors that are not apparent from individual MoDAF views. We have also reviewed an existing safety assessment example (the ARP4761 Aircraft System analysis) and determined that our approach could indeed be incorporated into that process. We have also integrated our approach into the STPA hazard analysis technique to demonstrate its feasibility to incorporate into existing techniques. It is therefore plausible that our approach can increase safety assurance provided by hazard analysis in the face of epistemic uncertainty

Incorporating epistemic uncertainty into the safety assurance of socio-technical systems

In system development, epistemic uncertainty is an ever-present possibility when reasoning about the causal factors during hazard analysis. Such uncertainty is common when complicated systems interact with one another, and it is dangerous because it impairs hazard analysis and thus increases the chance of overlooking unsafe situations. Uncertainty around causation thus needs to be managed well. Unfortunately, existing hazard analysis techniques tend to ignore unknown uncertainties, and system stakeholders rarely track known uncertainties well through the system lifecycle. In this paper, we outline an approach to managing epistemic uncertainty in existing hazard analysis techniques by focusing on known and unknown uncertainty. We have created a reference populated with a wide range of safety-critical causal relationships to recognise unknown uncertainty, and we have developed a model to systematically capture and track known uncertainty around such factors. We have also defined a process for using the reference and model to assess possible causal factors that are suspected during hazard analysis. To assess the applicability of our approach, we have analysed the widely-used MoDAF architectural model and determined that there is potential for our approach to identify additional causal factors that are not apparent from individual MoDAF views. We have also reviewed an existing safety assessment example (the ARP4761 Aircraft System analysis) and determined that our approach could indeed be incorporated into that process. We have also integrated our approach into the STPA hazard analysis technique to demonstrate its feasibility to incorporate into existing techniques. It is therefore plausible that our approach can increase safety assurance provided by hazard analysis in the face of epistemic uncertainty