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We use close gubernatorial elections as a quasi-natural experiment to document a positive effect of political uncertainty on firm-level R&D. This finding is in contrast to the existing literature documenting a negative impact of political uncertainty on capital investment. We examine potential mechanisms and find that our results are consistent with the growth option view of R&D investment. The effect is stronger for politically sensitive and high-tech industries.The results are robust to different proxies for political uncertainty shocks. As predicted by models of investment under uncertainty, the real effects of political uncertainty critically depend on the type of the investment. JEL G18, G38, O31, O32 [ABSTRACT FROM AUTHOR]

Transporting ore from mines to ports is of significant interest in mining supply chains. These operations are commonly associated with growing costs and a lack of resources. Large mining companies are interested in optimally allocating resources to reduce operational costs. This problem has been previously investigated as resource constrained job scheduling (RCJS). While a number of optimisation methods have been proposed to tackle the deterministic problem, the uncertainty associated with resource availability, an inevitable challenge in mining operations, has received less attention. RCJS with uncertainty is a hard combinatorial optimisation problem that is challenging for existing optimisation methods. We propose an adaptive population-based simulated annealing algorithm that can overcome existing limitations of methods for RCJS with uncertainty, including pre-mature convergence, excessive number of hyper-parameters, and the inefficiency in coping with different uncertainty levels. This new algorithm effectively balances exploration and exploitation, by using a population, modifying the cooling schedule in the Metropolis-Hastings algorithm, and using an adaptive mechanism to select perturbation operators. The results show that the proposed algorithm outperforms existing methods on a benchmark RCJS dataset considering different uncertainty levels. Moreover, new best known solutions are discovered for all but one problem instance across all uncertainty levels. [ABSTRACT FROM AUTHOR]

Purpose: Training characteristics such as duration, frequency, and intensity can be manipulated to optimize endurance performance, with an enduring interest in the role of training-intensity distribution to enhance training adaptations. Training intensity is typically separated into 3 zones, which align with the moderate-, heavy-, and severe-intensity domains. While estimates of the heavy- and severe-intensity boundary, that is, the critical speed (CS), can be derived from habitual training, determining the moderate–heavy boundary or first threshold (T1) requires testing, which can be costly and time-consuming. Therefore, the aim of this review was to examine the percentage at which T1 occurs relative to CS. Results: A systematic literature search yielded 26 studies with 527 participants, grouped by mean CS into low (11.5 km·h−1; 95% CI, 11.2–11.8), medium (13.4 km·h−1; 95% CI, 11.2–11.8), and high (16.0 km·h−1; 95% CI, 15.7–16.3) groups. Across all studies, T1 occurred at 82.3% of CS (95% CI, 81.1–83.6). In the medium- and high-CS groups, T1 occurred at a higher fraction of CS (83.2% CS, 95% CI, 81.3–85.1, and 84.2% CS, 95% CI, 82.3–86.1, respectively) relative to the low-CS group (80.6% CS, 95% CI, 78.0–83.2). Conclusions: The study highlights some uncertainty in the fraction of T1 relative to CS, influenced by inconsistent approaches in determining both boundaries. However, our findings serve as a foundation for remote analysis and prescription of exercise intensity, although testing is recommended for more precise applications. [ABSTRACT FROM AUTHOR]

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Lean manufacturing (LM) applies different tools that help to eliminate waste as well as the operations that do not add value to the product or processes to increase the value of each performed activity. Here the main motivation is to study how quantitative modelling approaches can support LM tools even under system and environment uncertainties. The main contributions of the article are: (i) providing a systematic literature review of 99 works related to the modelling of uncertainty in LM environments; (ii) proposing a methodology to classify the reviewed works; (iii) classifying LM works under uncertainty; and (iv) identify quantitative models and their solution to deal with uncertainty in LM environments by identifying the main variables involved. Hence this article provides a conceptual framework for future LM quantitative modelling under uncertainty as a guide for academics, researchers and industrial practitioners. The main findings identify that LM under uncertainty has been empirically investigated mainly in the US, India and the UK in the automotive and aerospace manufacturing sectors using analytical and simulation models to minimise time and cost. Value stream mapping (VSM) and just in time (JIT) are the most used LM techniques to reduce waste in a context of system uncertainty. [ABSTRACT FROM AUTHOR]

Inspired by experimental evidence, we amend the recursive utility model to let risk aversion decrease with the temporal horizon. Our pseudo-recursive preferences remain tractable and retain appealing features of the long-run risk framework, notably its success at explaining asset pricing moments. In addition, our model addresses two challenges to the standard model. Calibrating the agents' preferences to explain the equity premium no longer implies an extreme preference for early resolutions of uncertainty. Horizon-dependent risk aversion helps resolve key puzzles in finance on the valuation of assets across maturities and captures the term structure of equity risk premiums and its dynamics. [ABSTRACT FROM AUTHOR]

Extensive research has examined the diffusion of innovations for products that can be trialed, and where the most adverse outcome, if a product fails, is a financial loss. However, less research has explored consumer responses to innovations in highly uncertain contexts characterized by health losses, lack of trialability, and the opportunity to free ride on other's adoption. This research focuses on vaccine decision making as a unique case within such contexts and extends the findings to other domains. Four studies (Ntotal = 1,796; five supplementary studies, Ntotal = 643) test the propositions of a formal model that incorporates uncertainty and others' choices into the adoption decision. The results show that consumers are surprisingly averse to products that are described as employing a new technology (e.g., mRNA technology) and require an "efficacy premium" to compensate for higher perceived uncertainty. However, considerable heterogeneity exists due to individual differences in technology readiness, trust in government, and risk attitudes. Notably, despite the prominent threat of free riding, a social proof nudge (communicating increasing population adoption) effectively reduces aversion to new technology. In this context, social proof information does not merely drive conformity or social learning, but instead increases adoption of new technology by alleviating perceived uncertainty. [ABSTRACT FROM AUTHOR]

Hybrid manufacturing systems utilising raw materials and returned end-of-life products for production are studied. The systems are failure-prone and subject to inventory, market demand and return uncertainties. Thanks to growing environmental and sustainability concerns, the manufacturing sector is currently experiencing a significant growth in the popularity of reverse logistics. However, the practical implementation of production control in such systems is challenging due to return flow uncertainty and variability. To address this challenge, an estimation-based control using the Kalman filter is proposed in this research. The demand and return models employed contain random and deterministic components, with the latter being time-invariant for demand and uncertain with seasonal variations for return. The processing steps used include the estimation of inventory levels and demand and return components, return forecasting allowing cost computation over a long horizon, and the determination of the production and disposal policies adapting to market variations and uncertainties. We classify the systems according to relationships between their production capacity, demand and return ranges. We then present an extensive numerical study of optimal policies for various system classes and show that adaptive policies outperform the conventional ones, thus proving the effectiveness of the proposed production control approach for complex industrially oriented systems. [ABSTRACT FROM AUTHOR]

Context: Rehabilitation after an anterior cruciate ligament injury is recommended to be started soon after the injury. When surgery is required, research supports the delivery of physiotherapy before anterior cruciate ligament reconstruction (prehabilitation) to optimize recovery and positive outcomes. Individuals attending prehabilitation have never been questioned regarding their adherence to prehabilitation, perception of utility in meeting needs, upcoming events, or anticipated recovery goals. Design: Mixed methods cross-sectional study: Methods: 25 individuals before anterior cruciate ligament reconstruction (43% of eligible individuals from 12 clinics during the delivery period) were surveyed on their mindset and recovery expectancies. Semistructured interviews conducted in 9 of 25 participants assessed their lived experience of prehabilitation. Results: Participants reported that preventing a reinjury (96% of responses) and feeling confident during daily activities about their knee (92%) were the higher rating expectations at this stage of their treatment course. Three themes were developed from the interviews and analyses. (1) Participants reported that prehabilitation was a period full of challenges with memories of the injury and uncertainties. (2) They viewed prehabilitation as a step to move forward by finding support and self-motivating. (3) They believed that prehabilitation would have positive impacts on the treatment outcomes. Participants were confident that prehabilitation would accelerate the recovery of muscle volume (88%) and strength (84%). Conclusion: Participants had positive experiences of prehabilitation, aligning with the findings on functional outcomes in the existing literature on prehabilitation. [ABSTRACT FROM AUTHOR]

This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agents intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

Humans are immensely curious and motivated to reduce uncertainty, but little is known about the neural mechanisms that generate curiosity. Curiosity is inversely associated with confidence, suggesting that it is triggered by states of low confidence (subjective uncertainty), but the neural mechanisms of this link, have been little investigated. Inspired by studies of sensory uncertainty, we hypothesized that visual areas provide multivariate representations of uncertainty, which are read out by higher-order structures to generate signals of confidence and, ultimately, curiosity. We scanned participants (17 female, 15 male) using fMRI while they performed a new task in which they rated their confidence in identifying distorted images of animals and objects and their curiosity to see the clear image. We measured the activity evoked by each image in the occipitotemporal cortex (OTC) and devised a new metric of "OTC Certainty" indicating the strength of evidence this activity conveys about the animal versus object categories. We show that, perceptual curiosity peaked at low confidence and OTC Certainty negatively correlated with curiosity, establishing a link between curiosity and a multivariate representation of sensory uncertainty. Moreover, univariate (average) activity in two frontal areas-vmPFC and ACC-correlated positively with confidence and negatively with curiosity, and the vmPFC mediated the relationship between OTC Certainty and curiosity. The results reveal novel mechanisms through which uncertainty about an event generates curiosity about that event.

AbstractPain anticipation during conditions of uncertainty can unveil intrinsic biases, and understanding these biases can guide pain treatment interventions. This study used machine learning and functional magnetic resonance imaging to predict anticipatory responses in a pain anticipation experiment. One hundred forty-seven participants that included healthy controls (n = 57) and individuals with current and/or past mental health diagnosis (n = 90) received cues indicating upcoming pain stimuli: 2 cues predicted high and low temperatures, while a third cue introduced uncertainty. Accurate differentiation of neural patterns associated with specific anticipatory conditions was observed, involving activation in the anterior short gyrus of the insula and the nucleus accumbens. Three distinct response profiles emerged: subjects with a negative bias towards high pain anticipation, those with a positive bias towards low pain anticipation, and individuals whose predictions during uncertainty were unbiased. These profiles remained stable over one year, were consistent across diagnosed psychopathologies, and correlated with cognitive coping styles and underlying insula anatomy. The findings suggest that individualized and stable pain anticipation occurs in uncertain conditions.

Periods of social mobility, such as attending college, can challenge ones status-based identity, leading to uncertainty around ones status in society. Status uncertainty is associated with poorer well-being and academic outcomes. Little is known, however, about what experiences lead to status uncertainty. The current longitudinal study investigated discrimination experiences and cultural mismatch as predictors of status uncertainty. We propose that discrimination indirectly predicts increased status uncertainty by increasing perceived cultural mismatch with the university. Participants were Latinx college students, all of whom were low-income and/or first generation to college. Discrimination experiences were measured at the end of participants first year. Cultural mismatch and status uncertainty were measured at the end of Year 2. Status uncertainty was measured again at the end of Year 3. Results indicated that students who experienced more frequent discrimination felt more cultural mismatch 1 year later, and, in turn, reported increased status uncertainty over the following year.

In real-world marketplaces, one may encounter an alternative that is inferior to another one in the assortment. While the presence of such seemingly irrelevant inferior alternatives should ostensibly have no influence on consumers' decisions, an extensive literature using stylized lab experiments has found that, surprisingly, their presence matters. Specifically, the dominance effect suggests that the presence of an inferior alternative shifts consumer's preferences toward the alternative made to be superior. However, null results in some recent lab studies and a lack of real-world evidence call into question whether, when, and how the effect exists. In this work, we find clear evidence that dominance matters in the wild. We also identify an important moderator for the dominance effect—preference uncertainty—and test it in both a real-world marketplace for digital freelance services and a lab experiment. Further, we find evidence for additional moderators that help explain how the effect works, including the count of dominated alternatives and the magnitude of dominance, consistent with a perceptual mechanism. This work is the first to use consequential field data to shed light on when and why the dominance effects occur, with implications for marketers, choice architects, user interface designers, and policymakers. [ABSTRACT FROM AUTHOR]

Physical activity is an effective, proactive intervention to reduce or reverse frailty and functional decline. However, uncertainty exists about the feasibility and impact of resistance training on multidimensional health in prefrail older adults in residential care. This mixed methods feasibility study assessed practicability with limited efficacy testing on health and functional outcomes. Eleven prefrail older adults participated in a 6-week progressive resistance training protocol three times per week. The intervention and measures were found to be appropriate and acceptable by those who completed the trial, with participants self-reporting improved well-being, mood, and function. Analysis identified several barriers to recruitment, including prior commitments, seasonal impact, and session timing, and offered potential solutions with further recommendations for program refinement prior to a definitive randomized controlled trial. These findings add to our understanding of prefrail older adults' preferences regarding participation in physical activity research and the perceived benefits of resistance training. This trial was registered with ClinicalTrials.gov: NCT03141879. [ABSTRACT FROM AUTHOR]

Expressions about opportunities are used unproblematically in everyday contexts. Yet, the question "What is an opportunity?" has posed a difficult riddle in the academic study of entrepreneurship. Drawing on the philosophy of Ludwig Wittgenstein, we explain that such perplexities are common when words are removed from ordinary language and intellectuals try to grasp what they name. Approaching the opportunity riddle differently, we ask, "How do entrepreneurs use the word 'opportunity?'" and elucidate an actualization theory of entrepreneurship attuned to the everyday understandings that underlie the meaningful use of the word. Bringing implicit understandings to the foreground contributes to (a) dissolving the mystification over the nature of "opportunity"; (b) clarifying the conceptual foundations of entrepreneurship theory; and (c) reorienting the field toward more conceptually precise ways of thinking about Knightian uncertainty, entrepreneurial success, and the entrepreneurial process. This paper also contributes to the methodology of management studies by demonstrating how attention to the logic of ordinary language can alert us to theoretical dead-ends and enable the development of theory that bridges academic and everyday understandings. [ABSTRACT FROM AUTHOR]

We advance a theoretical framework of how entrepreneurial ideas of employees are commercialized as a function of their uncertainty, firm-specificity, and appropriability. We argue that as uncertainty increases, the choice of commercialization mode will increasingly be driven by differences in subjective judgments of the idea's value, with firms having an advantage in assessing the true value of their employee's ideas relative to market because of their firm-specific nature. Building on this insight, we develop a formal model of commercialization choice that maps idea characteristics to commercialization outcomes—spinouts, internal commercialization, market mobility, or abandonment—while also predicting how these relationships will be moderated by the cost of startups, the value of the idea, and the institutional context, as well as how value will be created and appropriated within each mode. In particular, the model distinguishes between four different types of employee spinouts, including the previously neglected case where the employer sees value in an idea and wants to commercialize it, but the employee still prefers to start their own firm. We thus offer a more nuanced view of employee entrepreneurship, based on differences in subjective judgment under uncertainty, the firm-specific nature of employee knowledge, and appropriability regimes. [ABSTRACT FROM AUTHOR]

Purpose: Rugby union is a contact team sport demanding high levels of physical capacity, and understanding the match workloads can be useful to inform training. In this study, the factors influencing locomotion and contact workloads for offensive and defensive ball-in-play periods are quantified. Methods: Locomotion and contact metrics were collected from global positioning system units and videos for 31 professional players of a Super Rugby team across 14 games in the 2021 season. Data were analyzed with a generalized mixed-model procedure that included effects for type of play, playing position, match outcome, and ball-in-play time. Magnitudes were assessed with standardization, and evidence for substantial magnitudes was derived from sampling uncertainty. Results: When offense was compared to defense, most metrics showed decisively substantial increases (small to moderate) for forwards and backs. There was decisive evidence that locomotion metrics were substantially lower (large differences) and contact metrics were higher (very large differences) when comparing forwards to backs on offense and defense. When winning was compared to losing, there was good evidence that forwards experienced small increases in overall workload on defense, and backs experienced a small increase in high-speed running and a moderate decrease in contacts on offense. Match-to-match changes associated with ball-in-play time, attributed to fatigue, were decisive (moderate to very large) across most metrics for forwards and backs in offense and defense. Conclusions: The increased locomotion and contact workloads in offensive periods and the differing physical requirements between positions and match outcomes for both types of play are novel findings that should aid practitioners in designing effective training. [ABSTRACT FROM AUTHOR]

Purpose: To compare the effects of unilateral flywheel training (FT), using a rotational conical pulley, including multidirectional movements with either preplanned or random unanticipated/unexpected executions on functional performance in football players. Methods: A total of 32 young male football players were randomly assigned to an FT program including preplanned unilateral multidirectional movements (PTG, n = 11), an FT executing the same unilateral movements through random (ie, right or left leg) unanticipated (ie, verbal or visual cue) or unexpected (ie, moment where the cue was provided) situations (UTG, n = 11), or a control group (n = 10) that followed their football training routine. FT consisted of 1 set × 5–12 repetitions of 4 exercises performed once a week for 10 weeks. Intermittent endurance performance, repeated unilateral and bilateral jumping ability, change-of-direction (COD) ability, linear sprint velocity, preplanned repeated-sprint ability (RSA), and uncertainty RSA (RSA-RANDOM) were assessed preintervention and postintervention. Results: Significant improvements were found in RSA-RANDOM performance (P <.05, effect size [ES] range: UTG [1.83–2.16], PTG [0.69–0.73]) and COD (P <.05, ES: UTG = 1.34, PTG = 0.98]) in both training groups. Furthermore, significant improvements were also found in intermittent endurance performance (P =.016, ES = 0.37) and sprinting (P =.006, ES = 0.45) in UTG. No changes in any variable were found in the control group. No significant between-groups differences (P >.05) were reported between UTG and PTG, while differences were observed to the control group in unilateral jumping ability, COD, and RSA-RANDOM for UTG, and in RSA-RANDOM for PTG. Conclusions: A 10-week unilateral FT improved RSA-RANDOM and COD ability in youth football players, so both preplanned and unexpected situations should be included on strength training. [ABSTRACT FROM AUTHOR]

Carbon tax represents governments' approach to steering the economy towards a greener future. Our research question focuses on the impact of carbon tax policy on a firm's production decision and revenue and on total social welfare. In particular, we assume that a firm's decision is subject to its behavioural considerations or, in other words, its risk attitude. We model a government plan to charge a carbon tax, and a risk-averse (or risk-neutral) firm needs to plan its production and estimate its profit if the carbon tax policy is implemented. We show that it is possible for a risk-averse firm's optimal profit, in some cases, to be higher than that of a risk-neutral firm when facing a carbon tax. This implies that the risk-averse attitude is not necessarily harmful to the firm's profit. For operations management scholars, our model highlights the importance of integrating firms' behavioural responses into the carbon tax price. Our model suggests that for governments to implement carbon tax policy effectively, they should make carbon prices vary under certain conditions and not worry about price variation antagonising firms at large. [ABSTRACT FROM AUTHOR]

This article proposes a new method for examining the impact on a firm's investment of uncertainty reflected in its stock-return volatility. We simultaneously address the endogeneity of uncertainty and mismeasurement in Tobin's Q , but earlier empirical work often neglects one of the two issues. Our nonparametric estimates further suggest that the relation between investment and uncertainty is significantly decreasing and strongly concave. This result contrasts with the existing literature that widely adopts linear regressions. Ignoring nonlinearity or measurement error in Q can lead to a substantial estimation bias. However, the bias due to the endogeneity of uncertainty is small. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) are important for many biological processes, but predicting them from sequence data remains challenging. Existing deep learning models often cannot generalize to proteins not present in the training set and do not provide uncertainty estimates for their predictions. To address these limitations, we present TUnA, a Transformer-based uncertainty-aware model for PPI prediction. TUnA uses ESM-2 embeddings with Transformer encoders and incorporates a Spectral-normalized Neural Gaussian Process. TUnA achieves state-of-the-art performance and, importantly, evaluates uncertainty for unseen sequences. We demonstrate that TUnAs uncertainty estimates can effectively identify the most reliable predictions, significantly reducing false positives. This capability is crucial in bridging the gap between computational predictions and experimental validation.

Waiting for important news is uniquely anxiety provoking, and expectations for one's outcome fluctuate throughout the wait. Emotional volatility is typically associated with negative outcomes, but little is known about volatility in expectations. In Study 1, law graduates (N = 248) estimated their chances of passing the bar exam every 2 weeks during the wait for results. Greater volatility in expectations, operationalized as the frequency with which outcome expectations changed during the wait, was associated with greater worry and more negative emotionality throughout the wait. Study 2 partially replicated these findings in a sample of Trump and Biden supporters (N = 444) awaiting the result of the 2020 presidential election. Study 2 also demonstrated a causal link between constrained (vs. volatile) expectations and worry. Our findings have implications for how best to manage one's expectations while awaiting important news, with the goal of minimizing worry and other negative emotions.

We investigate a stochastic distributed appointment assignment and scheduling problem, which consists of assigning appointments to distributed service units and determining service sequences at each service unit. In particular, the service time duration and release time uncertainties are well-considered. The solution to this generic problem finds interesting applications in distributed production systems, healthcare systems, and post-disaster operations. We formulate the problem as a two-stage stochastic program to minimise the total transportation cost and expected makespan, idle time or overtime, and apply the sample average approximation method to make the problem tractable. We then develop a stochastic logic-based Benders decomposition method, decomposing the problem into a master problem and a subproblem. The master problem determines the appointment assignment variables, and the subproblem handles the sequence and service start time variables. Benders optimality cuts are generated from the subproblem's solution and added to the master problem. The developed stochastic logic-based method is advantageous since it can manage many scenarios in parallel. We further consider each appointment's due date, minimise the weighted earliness and tardiness, and adjust the developed method to solve this variant. Experiments on random instances demonstrate the excellent performance of the proposed model and methods. [ABSTRACT FROM AUTHOR]

In this paper, we adopt a multistakeholder governance perspective to study how people collectively respond to a grand challenge. Specifically, we show how working through governance obstacles—that is, coordinating and collaborating challenges arising from a multistakeholder governance approach to responding to grand challenges—can erode actors' ability to mitigate these wicked problems. We illustrate this process through an in-depth case study of WaterHealthOrg, a multistakeholder initiative established to address degrading water health in Australia's critical Great Barrier Reef region. Our findings reveal how, in an effort to avoid group paralysis or dissolution, actors employ specific practices to address governance obstacles. By doing so, actors set off a cumulative self-reinforcing process, driving them to consolidate rather than critically reflect on and adapt their collective response. Drawing on these insights, we develop a conceptual process model of how efforts to manage multistakeholder governance obstacles can generate governance traps that shape participants' ability to collectively respond and, ultimately, mitigate grand challenges. [ABSTRACT FROM AUTHOR]

Emotions shape how people understand and interact with others. Here, we review evidence on the relationship between anxiety—a future-oriented emotion characterized by negative valence, high arousal, and uncertainty—and mentalizing—the ascription of mental content to other agents. We examine three aspects of this relationship: how people with anxiety disorders perform on mentalizing tasks relative to controls; how situational anxiety alters mentalizing performance; and how autistic people, who experience the impacts of mentalizing differences, are at high risk of anxiety. We propose a bidirectional model for understanding how short-term and longer term anxiety are related to mentalizing. Key to this relationship is the aversive experience of uncertainty and the motivations that result from it.

In times of crises, public health leaders may claim that trials of public health interventions are unethical. One reason for this claim can be that equipoise-i.e. a situation of uncertainty and/or disagreement among experts about the evidence regarding an intervention-has been disturbed by a change of collective expert views. Some might claim that equipoise is disturbed if the majority of experts believe that emergency public health interventions are likely to be more beneficial than harmful. However, such beliefs are not always justified: where high quality research has not been conducted, there is often considerable residual uncertainty about whether interventions offer net benefits. In this essay we argue that high-quality research, namely by means of well-designed randomized trials, is ethically obligatory before, during, and after implementing policies in public health emergencies (PHEs). We contend that this standard applies to both pharmaceutical and non-pharmaceutical interventions, and we elaborate an account of equipoise that captures key features of debates in the recent pandemic. We build our case by analyzing research strategies employed during the COVID-19 pandemic regarding drugs, vaccines, and non-pharmaceutical interventions; and by providing responses to possible objections. Finally, we propose a public health policy reform: whenever a policy implemented during a PHE is not grounded in high-quality evidence that expected benefits outweigh harms, there should be a planned approach to generate high-quality evidence, with review of emerging data at preset time points. These preset timepoints guarantee that policymakers pause to review emerging evidence and consider ceasing ineffective or even harmful policies, thereby improving transparency and accountability, as well as permitting the redirection of resources to more effective or beneficial interventions.

The possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern1-3. For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system1. Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.

BACKGROUND: Adverse radiation effect (ARE) following stereotactic radiosurgery (SRS) for brain metastases is challenging to distinguish from tumor progression. This study characterizes the clinical implications of radiologic uncertainty (RU). METHODS: Cases reviewed retrospectively at a single-institutional, multi-disciplinary SRS Tumor Board between 2015-2022 for RU following SRS were identified. Treatment history, diagnostic or therapeutic interventions performed upon RU resolution, and development of neurologic deficits surrounding intervention were obtained from the medical record. Differences in lesion volume and maximum diameter at RU onset versus resolution were compared with paired t-tests. Median time from RU onset to resolution was estimated using the Kaplan-Meier method. Univariate and multivariate associations between clinical characteristics and time to RU resolution were assessed with Cox proportional-hazards regression. RESULTS: Among 128 lesions with RU, 23.5% had undergone ≥ 2 courses of radiation. Median maximum diameter (20 vs. 16 mm, p  6 and > 12 months in 25% and 7% of cases, respectively. Higher total EQD2 prior to RU onset (HR = 0.45, p = 0.03) and use of MR perfusion (HR = 0.56, p = 0.001) correlated with shorter time to resolution; larger volume (HR = 1.05, p = 0.006) portended longer time to resolution. Most lesions (57%) were diagnosed as ARE. Most patients (58%) underwent an intervention upon RU resolution; of these, 38% developed a neurologic deficit surrounding intervention. CONCLUSIONS: RU resolution took > 6 months in > 25% of cases. RU may lead to suboptimal outcomes and symptom burden. Improved characterization of post-SRS RU is needed.

This study investigates the role of human intervention in artificial intelligence/machine learning (AIML)-driven predictions. By doing so, we distinguish between three different types of human-AIML collaboration: automation, adjustable automation, and augmentation. We theorize that prediction uncertainty and time horizon represent two critical determinants of forecast accuracy. Based on a field experiment involving AIML-driven demand forecasts approximately 1,888 stock-keeping units in the retail industry, we rely on a multivalued treatment effect methodology to measure the effects of human-AIML collaboration on forecast accuracy. Our findings show that human intervention complements AIML-driven forecasts most effectively (augmentation) in predictions with long time horizons and low uncertainty. However human intervention is least likely to contribute to the effectiveness of AIML predictions (automation) in environments with short time horizons and high uncertainty. We discuss implications for extant theory and propose a framework outlining the conditions in which human intervention is most likely to add predictive value to human-AIML collaborations. [ABSTRACT FROM AUTHOR]

The need for business agility, that is, the capacity to adapt to changing external circumstances (and even to shape them), is high on the agenda of executives around the world, as they face up to increasing levels of uncertainty. But uncertainty still abounds about what business agility is, and most executives still struggle to master this capability. The introduction to this special issue reviews what we know and still do not know about business agility and introduces the six articles and suggests a path for more research. [ABSTRACT FROM AUTHOR]

Although agility is often associated with rapid speed and flexibility, having processes for "deep reflection" is also crucial. This includes the need for collective dialogs across and outside organizations to build greater awareness of, and attention to, strategic issues. How do managers involve a wider range of stakeholder voices in strategy as they pursue agility? This article identifies three practices that synergistically contribute to agility and conceptualizes them in a framework for managers called the "Strategy Making as Polyphony Wheel." The work outlines several implications for managerial practice and research. [ABSTRACT FROM AUTHOR]