Project description:Two experiments investigated how category information is used in decision making under uncertainty and whether the framing of category information influences how it is used. Subjects were presented with vignettes in which the categorization of a critical item was ambiguous and were asked to choose among a set of actions with the goal of attaining the desired outcome for the main character in the story. The normative decision making strategy was to base the decision on all possible categories; however, research on a related topic, category-based induction, has found that people often only consider a single category when making predictions when categorization is uncertain. These experiments found that subjects tend to consider multiple categories when making decisions, but do so both when it is and is not appropriate, suggesting that use of multiple categories is not driven by an understanding of whether categories are relevant to the decision. Similarly, although a framing manipulation increased the rate of multiple-category use, it did so in situations in which multiple-category use both was and was not appropriate.
Project description:Decision-makers often are faced with uncertain situations in which they have incomplete information. While risky decisions include the probabilities of the possible outcomes, ambiguous decisions involve both unknown probabilities and unknown outcomes. Prior research has suggested that there are differences in how men and women evaluate risk, but evidence related to gender and ambiguity is mixed. The present work approaches this problem from a novel angle, focusing on the use of information that is present rather than the impact of information that is absent. It examines how individuals assign value in uncertain decisions based on the partial information they do have. While a main effect of gender on value is not observed, there is an enhanced "optimism bias" in how both favorable and unfavorable information influences the subjective value of ambiguous financial prospects for male compared to female participants. Unpacking these effects suggests multiple mechanisms, including a significant contribution of risk processing. Specifically, favorable and unfavorable information are over- and underweighted respectively in male participants' estimated likelihood of a winning outcome, and unfavorable information is underweighted in estimating certainty. There also is an interaction of gender and risk preferences, such that value increases more for male participants as the subjectively estimated likelihood of winning increases. A second experiment demonstrates this risk interaction effect is also observed for objective probabilities of winning, suggesting that the relationship between value and risk uses similar mechanisms across layers of uncertainty.
Project description:Challenges arise when treatment to improve maternal health brings the possibility of risk to fetal health. The coronavirus disease 2019 (COVID-19) vaccine is the most recent, but hardly the only, example. Because pregnant patients are often specifically excluded from trials of new therapies, this is often the dilemma that patients and providers face when considering new treatments. In this study, we used the COVID-19 vaccine as an exemplar to question the broader issue of how society, in general, and obstetricians, in particular, should balance obligations to pregnant women's right of access to new therapeutic agents with the physician's desire to protect the fetus from potential risks. We will argue that in almost all circumstances (with few exceptions, as will also be discussed), maternal benefit and respect for autonomy create the uncertainty that absent safety data bring. Consequently, if pregnant women choose to try new interventions and treatments, such as the COVID-19 vaccination, they should be offered those new regimens and their decision supported. In addition, we will argue that the right solution to avoid the dilemma of absent data is to include pregnant individuals in clinical trials studying new treatments, drugs, and other therapies. We will also discuss the basis for our opinion, which are mainstream obstetrical ethics, precedents in law (supreme court ruling that forbids companies to exclude women from jobs that might pose a risk to the fetus), and historic events (thalidomide). The ethical framework includes the supposition that sacrifice to improve fetal outcome is a virtue and not a mandate. Denying a pregnant patient treatment because of threats to their life can create absurd and paradoxical consequences. Either requiring abortion or premature delivery before proceeding with treatments to optimize maternal health, or risking a patient's own life and ability to parent a child by delaying treatment brings clear and significant risks to fetal and/or neonatal outcomes. With rare exceptions, properly and ethically balancing such consequential actions cannot be undertaken without considering the values and goals of the pregnant patient. Therefore, active participation of both the pregnant patient and their physician in shared decision making is needed.
Project description:Every day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. uncertainty about waiting time in a queue, affects decisions and confidence when outcome is stochastic and continuous. How does one evaluate and choose between an option with unreliable but high expected reward, and an option with more certain but lower expected reward? Here we used an experimental design where two choices' payoffs took continuous values, to examine the effect of outcome variance on decision and confidence. We found that our participants' probability of choosing the good (high expected reward) option decreased when the good or the bad options' payoffs were more variable. Their confidence ratings were affected by outcome variability, but only when choosing the good option. Unlike perceptual detection tasks, confidence ratings correlated only weakly with decisions' time, but correlated with the consistency of trial-by-trial choices. Inspired by the satisficing heuristic, we propose a "stochastic satisficing" (SSAT) model for evaluating options with continuous uncertain outcomes. In this model, options are evaluated by their probability of exceeding an acceptability threshold, and confidence reports scale with the chosen option's thus-defined satisficing probability. Participants' decisions were best explained by an expected reward model, while the SSAT model provided the best prediction of decision confidence. We further tested and verified the predictions of this model in a second experiment. Our model and experimental results generalize the models of metacognition from perceptual detection tasks to continuous-value based decisions. Finally, we discuss how the stochastic satisficing account of decision confidence serves psychological and social purposes associated with the evaluation, communication and justification of decision-making.
Project description:Many institutions worldwide are considering how to include uncertainty about future changes in sea-levels and storm surges into their investment decisions regarding large capital infrastructures. Here we examine how to characterize deeply uncertain climate change projections to support such decisions using Robust Decision Making analysis. We address questions regarding how to confront the potential for future changes in low probability but large impact flooding events due to changes in sea-levels and storm surges. Such extreme events can affect investments in infrastructure but have proved difficult to consider in such decisions because of the deep uncertainty surrounding them. This study utilizes Robust Decision Making methods to address two questions applied to investment decisions at the Port of Los Angeles: (1) Under what future conditions would a Port of Los Angeles decision to harden its facilities against extreme flood scenarios at the next upgrade pass a cost-benefit test, and (2) Do sea-level rise projections and other information suggest such conditions are sufficiently likely to justify such an investment? We also compare and contrast the Robust Decision Making methods with a full probabilistic analysis. These two analysis frameworks result in similar investment recommendations for different idealized future sea-level projections, but provide different information to decision makers and envision different types of engagement with stakeholders. In particular, the full probabilistic analysis begins by aggregating the best scientific information into a single set of joint probability distributions, while the Robust Decision Making analysis identifies scenarios where a decision to invest in near-term response to extreme sea-level rise passes a cost-benefit test, and then assembles scientific information of differing levels of confidence to help decision makers judge whether or not these scenarios are sufficiently likely to justify making such investments. Results highlight the highly-localized and context dependent nature of applying Robust Decision Making methods to inform investment decisions.
Project description:Making accurate decisions based on unreliable sensory evidence requires cognitive inference. Dysfunction of n-methyl-d-aspartate (NMDA) receptors impairs the integration of noisy input in theoretical models of neural circuits, but whether and how this synaptic alteration impairs human inference and confidence during uncertain decisions remains unknown. Here we use placebo-controlled infusions of ketamine to characterize the causal effect of human NMDA receptor hypofunction on cognitive inference and its neural correlates. At the behavioral level, ketamine triggers inference errors and elevated decision uncertainty. At the neural level, ketamine is associated with imbalanced coding of evidence and premature response preparation in electroencephalographic (EEG) activity. Through computational modeling of inference and confidence, we propose that this specific pattern of behavioral and neural impairments reflects an early commitment to inaccurate decisions, which aims at resolving the abnormal uncertainty generated by NMDA receptor hypofunction.
Project description:Decisions are often based on imprecise, uncertain or vague information. Likewise, the consequences of an action are often equally unpredictable, thus putting the decision maker into a twofold jeopardy. Assuming that the effects of an action can be modeled by a random variable, then the decision problem boils down to comparing different effects (random variables) by comparing their distribution functions. Although the full space of probability distributions cannot be ordered, a properly restricted subset of distributions can be totally ordered in a practically meaningful way. We call these loss-distributions, since they provide a substitute for the concept of loss-functions in decision theory. This article introduces the theory behind the necessary restrictions and the hereby constructible total ordering on random loss variables, which enables decisions under uncertainty of consequences. Using data obtained from simulations, we demonstrate the practical applicability of our approach.
Project description:It is often difficult to synthesize information about the risks and benefits of recommended management strategies in older patients with end-stage renal disease since they may have more comorbidity and lower life expectancy than patients described in clinical trials or practice guidelines. In this review, we outline a framework for individualizing end-stage renal disease management decisions in older patients. The framework considers three factors: life expectancy, the risks and benefits of competing treatment strategies, and patient preferences. We illustrate the use of this framework by applying it to three key end-stage renal disease decisions in older patients with varying life expectancy: choice of dialysis modality, choice of vascular access for hemodialysis, and referral for kidney transplantation. In several instances, this approach might provide support for treatment decisions that directly contradict available practice guidelines, illustrating circumstances when strict application of guidelines may be inappropriate for certain patients. By combining quantitative estimates of benefits and harms with qualitative assessments of patient preferences, clinicians may be better able to tailor treatment recommendations to individual older patients, thereby improving the overall quality of end-stage renal disease care.
Project description:AimsTo characterize the histomorphological features of endometrial carcinomas (ECs) harbouring polymerase ε (POLE) mutations.Methods and resultsForty-three ECs with POLE mutations were compared with a cohort of 202 ECs. Most POLE-mutated ECs were endometrioid [34/43 (79%)]; the remaining tumours were mixed [6/43 (14%)], serous [2/43 (5%)], and clear cell [1/43 (2%)]. The endometrioid carcinomas were predominantly International Federation of Gynecology and Obstetrics grade 3 (27/43, 63%). The histotype distribution did not differ from that of control ECs (P = 0.69), but the grade of the EC was higher (P < 0.0005). Both nuclear grade and mitotic index were significantly higher in POLE-mutated ECs than in the comparison cohort. POLE-mutated ECs were associated with peritumoral lymphocytes and numerous tumour-infiltrating lymphocytes. Lymphovascular invasion was present in 20 of 43 tumours. Adjuvant radiotherapy and adjuvant chemotherapy would be offered in up to 80% and 40% of patients, respectively, on the basis of stage, grade, lymphovascular invasion, and histotype.ConclusionsPOLE-mutated ECs are typically of high grade, with prominent lymphocytic infiltration, but they are not sufficiently distinctive to allow accurate diagnosis based on routine haematoxylin and eosin staining. Even though POLE-mutated tumours are associated with an excellent prognosis, current guidelines for giving adjuvant treatment for EC result in most patients receiving adjuvant therapy.