Recently, scholars have advanced an ideal of the entrepreneurial state in which industrial policy is pursued in a mission-directed manner. Crucially, this perspective does not merely call for the heavier use of industrial policy, but envisions the state as a central focal point, mobilising society around the pursuit of a common mission. Using the historical example of East Asia's developmental state, which closely resembles its contemporary variant, I demonstrate that mission-directionality – should it be consistently applied – tends towards the pursuit of a singular overarching mission, and could require the use of authoritarian and disciplinary mechanisms to sustain mission focus in an environment of uncertainty. In turn, this potential risk arises because mission-directionality seeks to transcend the otherwise directionless nature of market-based and democratic decision-making through the use of bureaucratic discretion, to align the behaviour of social actors in a cohesive and directional manner.

The performance and confidence in fault detection and diagnostic systems can be undermined by data pipelines that feature multiple compounding sources of uncertainty. These issues further inhibit the deployment of data-based analytics in industry, where variable data quality and lack of confidence in model outputs are already barriers to their adoption. The methodology proposed in this paper supports trustworthy data pipeline design and leverages knowledge gained from one fully-observed data pipeline to a similar, under-observed case. The transfer of uncertainties provides insight into uncertainty drivers without repeating the computational or cost overhead of fully redesigning the pipeline. A SHAP-based human-readable explainable AI (XAI) framework was used to rank and explain the impact of each choice in a data pipeline, allowing the decoupling of positive and negative performance drivers to facilitate the successful selection of highly-performing pipelines. This empirical approach is demonstrated in bearing fault classification case studies using well-understood open-source data.

Threat perception in international relations has received much academic attention and continues to do so. Other contributions to this special section on how leaders feel security dangers or perceive threats with radical uncertainty are closely intertwined with this article’s focus on threats that are vague and not immediately perceptible. Humans possess a capacity for thinking about and imagining the future known as prospection. Faced with threatening futures, can governments prepare their civil servants to systematically manage uncertainties and anticipate dangers? Drawing on empirical data from interviews with foresight practitioners in the United Kingdom and Singapore, this article examines how governments are nudging civil servants to deploy futures techniques as part of threat perception.

During the Cold War, logical rationality – consistency axioms, subjective expected utility maximization, Bayesian probability updating – became the bedrock of economics and other social sciences. In the 1970s, logical rationality underwent attack by the heuristics-and-biases program, which interpreted the theory as a universal norm of how individuals should make decisions, although such an interpretation is absent in von Neumann and Morgenstern’s foundational work and dismissed by Savage. Deviations in people’s judgments from the theory were thought to reveal stable cognitive biases, which were in turn thought to underlie social problems, justifying governmental paternalism. In the 1990s, the ecological rationality program entered the field, based on the work of Simon. It moves beyond the narrow bounds of logical rationality and analyzes how individuals and institutions make decisions under uncertainty and intractability. This broader view has shown that many supposed cognitive biases are marks of intelligence rather than irrationality, and that heuristics are indispensable guides in a world of uncertainty. The passionate debate between the three research programs became known as the rationality wars. I provide a brief account from the ‘frontline’ and show how the parties understood in strikingly different ways what the war entailed.

The robots of tomorrow should be endowed with the ability to adapt to drastic and unpredicted changes in their environment and interactions with humans. Such adaptations, however, cannot be boundless: the robot must stay trustworthy. So, the adaptations should not be just a recovery into a degraded functionality. Instead, they must be true adaptations: the robot must change its behaviour while maintaining or even increasing its expected performance and staying at least as safe and robust as before. The RoboSAPIENS project will focus on autonomous robotic software adaptations and will lay the foundations for ensuring that they are carried out in an intrinsically trustworthy, safe and efficient manner, thereby reconciling open-ended self-adaptation with safety by design. RoboSAPIENS will transform these foundations into ‘first time right’-design tools and platforms and will validate and demonstrate them.

This paper distinguishes news about short-lived events from news about changes in longer term prospects using surveys of expectations. Employing a multivariate GARCH-in-Mean model for the US, the paper illustrates how the different types of news influence business cycle dynamics. The influence of transitory output shocks can be relatively large on impact but gradually diminishes over two to three years. Permanent shocks drive the business cycle, generating immediate stock price reactions and gradually building output effects, although they have more immediate output effects during recessions through the uncertainties they create. Markedly different macroeconomic dynamics are found if these explicitly identified types of news or uncertainty feedbacks are omitted from the analysis.

Drylands provide multiple essential services to human society, and dryland vegetation is one of the foundations of these services. There is a paradox, however, in the vegetation productivity–precipitation relationship in drylands. Although water is the most limiting resource in these systems, a strong relationship between precipitation and productivity does not always occur. Such a paradox affects our understanding of dryland vegetation dynamics and hinders our capacity to predict dryland vegetation responses under future climates. In this perspective, we examine the possible causes of the dryland precipitation–productivity paradox. We argue that the underlying reasons depend on the location and scale of the study. Sometimes multiple factors may interact, resulting in a less significant relationship between vegetation growth and water availability. This means that when we observe a poor correlation between vegetation growth and water availability, there are potentially missing sources of water input or a lack of consideration of other important processes. The paradox could also be related to the inaccurate measurement of vegetation productivity and water availability indicators. Incorporating these complexities into predictive models will help us better understand the complex relationship between water availability and dryland ecosystem processes and improve our ability to predict how these ecosystems will respond to the multiple facets of climate change.

This editorial describes the Cass Review findings and the extraordinary challenge we all face in managing uncertainty amid a toxic and highly polarised debate. Children and young people will only get the best care if patients and professionals join forces to seek answers collaboratively and respectfully.