Decision-making: Risk school

Developing Computational Thinking Teaching Strategies to Model Pandemics and Containment Measures

COVID-19 has been extremely difficult to control. The lack of understanding of key aspects of pandemics has affected virus transmission. On the other hand, there is a demand to incorporate computational thinking (CT) in the curricula with applications in STEM. However, there are still no exemplars in the curriculum that apply CT to real-world problems such as controlling a pandemic or other similar global crises. In this paper, we fill this gap by proposing exemplars of CT for modeling the pandemic. We designed exemplars following the three pillars of the framework for CT from the Inclusive Mathematics for Sustainability in a Digital Economy (InMside) project by Asia-Pacific Economic Cooperation (APEC): algorithmic thinking, computational modeling, and machine learning. For each pillar, we designed a progressive sequence of activities that covers from elementary to high school. In an experimental study with elementary and middle school students from 2 schools of high vulnerability, we found that the computational modeling exemplar can be implemented by teachers and correctly understood by students. We conclude that it is feasible to introduce the exemplars at all grade levels and that this is a powerful example of Science Technology, Engineering, and Mathematics (STEM) integration that helps reflect and tackle real-world and challenging public health problems of great impact for students and their families.

Navigating cognition biases in the search of sustainability

We provide a conceptual review of the available knowledge on the role of human cognition biases for sustainability and sustainable behavior. Human cognition biases are defined as any deviation in decision making from the standard framework of rational choice. We distinguish between biases in individual decision making and biases in group decision making, and highlight the relevance of each for sustainable behavior. We find that while both categories may contribute to unsustainable behavior, human cognition biases in group settings might be central to understanding many of the current sustainability issues. Moreover, we argue that the effects of group-related biases may outweigh those on the individual level in driving unsustainable behavior, and that biases that have been discussed under various labels in the literature can be interpreted as manifestations of human cognition biases in group settings.

Medical education should teach heuristics rather than train them away

CONTEXT

Ample research suggests that most decisions are based on heuristics-simple rules of thumb-that violate prescriptions of logic and probability theory and should therefore be avoided. Yet findings on decision making in everyday work contexts support the idea that heuristics are in fact the very basis of good decision making if adapted to the challenges and performance criteria of the specific work domain. Because traditional pedagogies aim at circumventing heuristics in (clinical) decision making, ways in which to improve the use of heuristics via (medical) education have rarely been explored.

OBJECTIVE

To describe the rationale for teaching and learning proper use of heuristics, rather than stigmatising them, and to identify principles and potential implications for the design and improvement of pedagogies for training in clinical decision making.

RESULTS

Based on theory and evidence concerning heuristic decision making in everyday work domains, we suggest that heuristics should not be avoided as irrational or a mere source of errors, in particular in domains where errors are unavoidable. Instead, we should teach and learn how to use heuristics to make fewer and 'smarter' mistakes rather than 'dumb' ones. Based on concepts borrowed from signal detection and control theory, we demonstrate that, to improve heuristic decision making, teaching should focus on differential diagnoses and learning from feedback and mistakes, in teams and in contextually rich settings where the frequencies, costs and trade-offs between different types of errors (misses versus false alarms) can be experienced. We discuss three possible teaching formats and how to best implement them based on our findings.

CONCLUSIONS

The most promising way to train (future) physicians and other health professionals in clinical decision making is not to circumvent heuristics or correct deviations from logic and probability theory but to enhance the use of heuristics by improving perspicacity, that is, by tuning the (recognition) processes that underlie the domain-specific adaptive selection of heuristics and management of ensuing errors.

What is mechanistic evidence, and why do we need it for evidence-based policy?

It has recently been argued that successful evidence-based policy should rely on two kinds of evidence: statistical and mechanistic. The former is held to be evidence that a policy brings about the desired outcome, and the latter concerns how it does so. Although agreeing with the spirit of this proposal, we argue that the underlying conception of mechanistic evidence as evidence that is different in kind from correlational, difference-making or statistical evidence, does not correctly capture the role that information about mechanisms should play in evidence-based policy. We offer an alternative account of mechanistic evidence as information concerning the causal pathway connecting the policy intervention to its outcome. Not only can this be analyzed as evidence of difference-making, it is also to be found at any level and is obtainable by a broad range of methods, both experimental and observational. Using behavioral policy as an illustration, we draw the implications of this revised understanding of mechanistic evidence for debates concerning policy extrapolation, evidence hierarchies, and evidence integration.

On the Supposed Evidence for Libertarian Paternalism

Can the general public learn to deal with risk and uncertainty, or do authorities need to steer people’s choices in the right direction? Libertarian paternalists argue that results from psychological research show that our reasoning is systematically flawed and that we are hardly educable because our cognitive biases resemble stable visual illusions. For that reason, they maintain, authorities who know what is best for us need to step in and steer our behavior with the help of “nudges.” Nudges are nothing new, but justifying them on the basis of a latent irrationality is. In this article, I analyze the scientific evidence presented for such a justification. It suffers from narrow logical norms, that is, a misunderstanding of the nature of rational thinking, and from a confirmation bias, that is, selective reporting of research. These two flaws focus the blame on individuals’ minds rather than on external causes, such as industries that spend billions to nudge people into unhealthy behavior. I conclude that the claim that we are hardly educable lacks evidence and forecloses the true alternative to nudging: teaching people to become risk savvy.

[Competence in dealing with uncertainty: lessons to learn from the influenza pandemic (H1N1) 2009]

The influenza pandemic (H1N1) 2009 showed that many decision and policy makers do not trust citizens to effectively cope with uncertainty. We discuss three fundamental problems with the information management at the time: (1) knowledge of available and missing evidence was not communicated transparently and completely, (2) rather than informing citizens, officials often treated them paternalistically, and (3) public trust in vaccinations and institutions was damaged as a result of (1) and (2). We suggest the following measures to policy and decision makers in order to avoid similar problems in the future: transparent description of the situation instead of buzzwords such as "pandemic;" transparent communication of existing and missing evidence instead of dramatic estimates of death rates; disclosure of political decision processes and conflicts of interest.

Adherence with preventive medication in childhood asthma

Suboptimal adherence with preventive medication is common and often unrecognised as a cause of poor asthma control. A number of risk factors for nonadherence have emerged from well-conducted studies. Unfortunately, patient report a physician's estimation of adherence and knowledge of these risk factors may not assist in determining whether non-adherence is a significant factor. Electronic monitoring devices are likely to be more frequently used to remind patients to take medication, as a strategy to motivate patients to maintain adherence, and a tool to evaluate adherence in subjects with poor disease control. The aim of this paper is to review non-adherence with preventive medication in childhood asthma, its impact on asthma control, methods of evaluating non-adherence, risk factors for suboptimal adherence, and strategies to enhance adherence.