How confirmation bias contributes to polarization

Confirmation bias in the utilization of others’ opinion strength

Humans tend to discount information that undermines past choices and judgments. This confirmation bias has significant impact on domains ranging from politics to science and education. Little is known about the mechanisms underlying this fundamental characteristic of belief formation. Here we report a mechanism underlying the confirmation bias. Specifically, we provide evidence for a failure to use the strength of others’ disconfirming opinions to alter confidence in judgments, but adequate use when opinions are confirmatory. This bias is related to reduced neural sensitivity to the strength of others’ opinions in the posterior medial prefrontal cortex when opinions are disconfirming. Our results demonstrate that existing judgments alter the neural representation of information strength, leaving the individual less likely to alter opinions in the face of disagreement.

The notion that the strength of disconfirming opinion is not necessarily proportionate to its impact on belief change is in accord with anecdotal and ‘real-world’ observations in domains ranging from science to politics. The underlying process is remarkably flexible, with the neural circuitry involved switching on a trial-bytrial basis from high sensitivity to relative neglect, contingent on whether the opinion is confirmatory or disconfirming. This process may leave the individual less likely to alter opinions in the face of disagreement.

Interesting article from Nature. So what? Is there any exercise to train the neural sensitivity of our posterior medial prefrontal cortex? If so, I would suggest these exercise to some guys.

Bloomberg The year in pictures. Police brutality. Barcelona Airport October 2019.

Ethical algorithms like hammers?

Main messages:

All decision-making—including that carried out by human beings—is ultimately algorithmic. The difference is that human decision-making is based on logic or behaviors that we struggle to precisely enunciate. If we humans had the ability to describe our own decision-making processes precisely enough, then we could in fact represent them as computer algorithms. So the choice is not whether to avoid using algorithms or not, but whether or not we should use precisely specified algorithms.

Machine learning is a powerful tool that has many extant and potential benefits.  Technology companies such as Google and Facebook of course rely on products powered by machine learning for much of their revenue—but as these techniques grow in applicability, their scope and societal benefits grow as well.

The result is that, at least for a while, the critics of the algorithmic approach may often be right. There are many consequential domains where algorithmic tools are still too naive and primitive to be fully trusted with decision-making. This is because to model the forest, we need to start with the trees. This book offers a snapshot of exciting strands of research aimed at developing ethical algorithms, many of which are still in their very earliest days.

Global pharmaceutical market vs. local regulators

Regulating Medicines in a Globalized World: The Need for Increased Reliance Among Regulators

As defined by the World Health Organization (WHO), recognition occurs when a regulatory authority accepts the regulatory decision of another authority “as its own decision;”— reliance takes place when a regulatory authority takes into account the work products of another authority (e.g., inspection reports, scientific assessment reports, joint assessment reports produced together with another authority) to help inform the receiving authority's own regulatory decision, which, in the end may differ from the made by the initial authority using the same products. 

Recognition and reliance are the first steps towards an improvement of real coordination between regulatory bodies. If the pharmaceutical market is global, the regulators should cooperate for a coherent global regulation.

Anchoring and adjusting heuristic

Effect of Teaching Bayesian Methods Using Learning by Concept vs Learning by Example on Medical Students’ Ability to Estimate Probability of a Diagnosis

Why do we need more bayesian reasoning?. Can we improve clinical decision making? The answer in this JAMA article.

Although bayesian reasoning has been widely promoted in the literature as a diagnostic strategy, there has been scant evidence that teaching bayesian reasoning actually improves diagnostic accuracy. Providing learners with relatively brief instruction on these abstract concepts appeared to significantly improve their diagnostic performance in comparison with simply providing a number of relevant examples or no relevant instruction.

 The previously reported discrepancy between human probability estimation and optimal bayesian probability estimation has become traditionally accepted in the psychology and medical literature as cognitive biases, including base-rate neglect, anchoring bias, confirmation bias, and representativeness, all of which suggest suboptimal revision and have been purported to be a primary cause of diagnostic error.13,14 However, on closer scrutiny, much of this evidence was derived from situations that were not representative of the typical diagnostic setting. One highly cited study9 used a screening situation in which the base rate was very low and any positive test result, even one with excellent operating characteristics, would have most likely been a false-positive result. Participants in that study also appeared to exhibit semantic confusion by confusing the posttest probability with the conditional probability that was presented in the problem-solving exercise.

Medicine as a data science (7)

Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril

The National Academy of Medicine’s Special Publication: Artificial Intelligence in Health Care: The Hope, The Hype, The Promise, The Peril synthesizes current knowledge to offer a reference document for relevant health care stakeholders such as: AI model developers, clinical implementers, clinicians and patients, regulators, and policy makers, to name a few. It outlines the current and near-term AI solutions; highlights the challenges, limitations, and best practices for AI development, adoption, and maintenance; offers an overview of the legal and regulatory landscape for AI tools designed for health care application; prioritizes the need for equity, inclusion, and a human rights lens for this work; and outlines key considerations for moving forward.

A must read

Pharmaceutical policies

Medicamentos, innovación tecnológica y economía; (2019), nº 160

This issue of Papeles de Economia Española explains the current situation of pharmaceutical policies in Spain. These are the topics:

La industria farmacéutica en la actualidad: un vistazo a sus características

II. LA I+D+I EN LA INDUSTRIA FARMACÉUTICA
La evolución reciente y perspectivas de la innovación
de medicamentos
Ética, medicamentos e innovación
La economía de la I+D en la industria farmacéutica: un resumen
Innovación y competencia en el sector farmacéutico en la época de la medicina de precisión
La innovación y la industria farmacéutica en España
La I+D en el sector farmacéutico español en el período 2003-2015

III. ASPECTOS EMPRESARIALES Y ECONÓMICOS
La evolución de la organización empresarial en la industria farmacéutica
La política de la competencia en la industria farmacéutica
La contribución del sector farmacéutico al crecimiento, a las exportaciones y a la inversión en España

IV. LA PRESTACIÓN FARMACÉUTICA EN LAS COMUNIDADES AUTÓNOMAS
Gestión de la prestación farmacéutica y compras públicas en la Comunidad de Madrid
Procedimiento de selección de medicamentos en atención primaria en Andalucía
La experiencia de gestión de los medicamentos en Cataluña. Objetivos de salud y económicos

V. LA EVALUACIÓN DE LA EFICIENCIA DE LAS INTERVENCIONES Y TECNOLOGÍAS SANITARIAS
La trayectoria metodológica de la evaluación de la eficiencia y su futuro
El valor de las innovaciones médicas
La evaluación de la eficiencia de intervenciones y tecnologías sanitarias en España

VI. LA VISIÓN DEL SECTOR BIOFARMACÉUTICO DESDE LAS EMPRESAS
Industria farmacéutica en España: innovación y compromiso social
El sector de los medicamentos genéricos en España
Medicamentos biosimilares: una oportunidad para el Sistema Nacional de Salud
40 años de autocuidado en España.
Un sector consolidado y emergente

Books of the year

The Economist
Financial Times
Prospect (economics)

Top ten books of 2019 by Eric Topol