Professional Regulations
Harrison Jamie, Rob Innes, Tim van Zwanenberg, Sir Denis Pereira Gray in The New GP Changing roles and the modern NHS, 2018
More recently, decision theory has been explored. This involves constructing mathematical models to determine the relative merits of different decisions.10 These models deal with probabilities. An example might be a doctor considering whether to prescribe a drug (known to cause severe side-effects in a proportion of patients) to a patient suffering from a condition which has multiple risk factors. Out of this work has emerged the approach that we now call evidence-based medicine (EBM). The double-blind randomised control trial (RCT) has become the gold standard for research design, because meta-analyses of such studies are the best way of generating the data which support decision theory modelling and thus EBM. However, meta-analyses only calculate the odds more accurately.
Clinical Trial Designs
Gary L. Rosner, Purushottam W. Laud, Wesley O. Johnson in Bayesian Thinking in Biostatistics, 2021
The components needed to apply Bayesian decision theory are the following. First, one needs to specify the space of possible actions one will consider. This action space may consist of nominal discrete points, such as whether or not to stop the study, or the set of actions may be essentially continuous, as when one decides on the number of future patients to enroll. Secondly, there will be a utility function. This utility function can relate each action to a loss function, a gain, or a combination of the two, such as a function that relates cost to benefit. Next, one needs to specify a sampling distribution that characterizes the stochastic nature of the data the study will collect. This distribution often includes model parameters, some of which may relate to the primary study outcome, such as the probability of response or the treatment-specific risk of an event. Finally, one needs a characterization of the uncertainty associated with the model parameters (i.e., prior distributions). One needs to account for all of this uncertainty when deciding which action to take based on the expected utility that would result from each action. Once one has the expected utility for each action, considering a discrete action space to simplify the discussion, one can choose the action that is associated with the maximal expected utility.
Philosophy in the Ipcc
Evelyn Brister, Robert Frodeman in A Guide to Field Philosophy, 2020
Here is an example. Traditionally, the IPCC expresses its scientific conclusions in terms of likelihoods: it is extremely likely that human beings have caused observable climate change, it is likely that warming will be less than two degrees if cumulative emissions remain below one trillion tonnes of carbon, and so on. But elementary decision theory, which is not part of science, is that decision-making should not depend on likelihoods alone. The right decision to make is not necessarily the one that is likely to have the best result. For example, a ship should carry lifeboats, even though it is unlikely that they will ever be used to save lives. The reason is that, in the very unlikely event of the ship’s sinking, the result will be dreadfully bad if it has no lifeboats. In determining whether to carry lifeboats, this badness should be discounted by its small probability, but even so it outweighs the cost of the lifeboats. This example is no more than common sense, but decision theory goes further. It tells us that decisions should be based on mathematical expectations of value. The likelihoods stated in IPCC reports are insufficient for good decision-making.
I would rather be vaguely right than precisely wrong
Published in Scandinavian Journal of Primary Health Care, 2022
Reidar Brumer Bratvold, Svein R. Kjosavik
The only way doctors can help patients is by making decisions, whether it is to conduct tests, give advice, prescribe drugs or treatments, or refer to another doctor. Even when a GP helps the patient by listening, understanding and being compassionate, she has made a decision to do so. Such decision-making is not easy and requires an evaluation of many complex and uncertain factors. Still, if the clinician regularly makes mediocre decisions, she may never accomplish the things that are important to the patients in her care, to herself or to the healthcare system she represents. Empirical evidence demonstrates that clinicians, as well as people in general, often make suboptimal decisions [1,2]. Even when clinicians make decisions based on good quality information, they may be inconsistent and biased. Decision theory, which has been developed over more than 300 years, provides both an overall paradigm and a set of tools to help decision-makers construct and analyze models of decision situations.
Games surgeons play
Published in British Journal of Neurosurgery, 2019
Patrick Mitchell
There are other possible approaches though. One of these is the study of decision theory and in particular decision quality. A decision such as whether to have a surgical operation or not can be formally modeled by estimating the likely benefits and costs of surgery and comparing them with the likely benefits and costs of conservative treatment. When this is done as objectivity and accurately as possible, given the available data, the result will fall into three broad categories: clearly surgery is a bad idea, clearly surgery is a good idea or the situation is ambiguous. Modern research methods are not great at capturing and measuring the anxiety, discomfort and inconvenience that surgery causes and so frequently in an ambiguous situation, a patient will reasonably choose not to have the operation.
Goals- and Burdens-based DMC as Expressions of Value Rather than Manifestations of DMC
Published in The American Journal of Bioethics, 2022
Peter Maloy Koch
It is also worth pointing out the nature of decisions. A decision, in the ordinary use of the term, requires options as the object of the decision (Cambridge Dictionary n.d.). In more philosophical accounts of decision-theory, options are also identified as a necessary condition for a decision (Steele and Stefansson 2020). The underlying capacity to make decisions, then, should at a minimum reflect the capacity to balance or weigh options—a view which is captured by the Comparative Account of Decision Making and helps explain its role as the standard account of DCM. It seems that neither Burdens-based nor the Goals-based models reflect what is ordinarily meant by a decision—that is, balancing and then selecting among options, however trivial the stakes. Rather, these models seem to reflect the capacity to express one’s commitments or values, which is different in kind (and not merely degree) from making a decision. Referring to such alternative capacities as a Decision Making Capacity seems to stretch the notion of decision making too broadly; it does not accurately reflect what is ordinarily meant by the terms that constitute decision making capacity.
Related Knowledge Centers
- Statistical Significance
- Hyperbolic Discounting
- Expected Value
- Frequentist Inference
- Statistical Hypothesis Testing
- Anchoring Effect
- Life Expectancy
- Bounded Rationality
- Distinction Bias
- Gambler'S Fallacy