On being honest in politics

 Eliciting preferences for truth-telling in a survey of politicians

From PNAS: 

Voters who would like to accurately evaluate the performance of politicians in office often rely on incomplete information and are uncertain whether politicians’ words can be trusted. Honesty is highly valued in politics because politicians who are averse to lying should in principle provide more trustworthy information. Despite the importance of honesty in politics, there is no scientific evidence on politicians’ lying aversion. We measured preferences for truth-telling in a sample of 816 elected politicians and study observable characteristics associated with honesty. We find that in our sample, politicians who are averse to lying have lower reelection rates, suggesting that honesty may not pay off in politics.

Therefore, in this case, truth-telling is a dominant non-rational strategy in politics. We still do need more evidence. Maybe the next presidential election will provide us a confirmation of this article.

Fact-Checker from WP

Bill Brandt at KBR-Mapfre Barcelona

Pricing panic, massageing data

The Price of Panic. How the Tyranny of Experts Turned a Pandemic into a Catastrophe

So, what caused the viral panic? The panic and lurching government overreach were inspired not so much by deaths people knew about firsthand, and not so much by the virus’s murky origins in China. They were sparked by a few forecasts that had the smell of science. The World Health Organization (WHO) favored a single, untested, apocalyptic model from Imperial College London. The United States government took its cues from the Institute for Health Metrics & Evaluation (IHME) at the University of Washington. We now know these models were so wrong they were like shots in the dark. After a few months, even the press admitted as much. But by then vast damage had been done.

But what of those experts? They treated predictive models—which are at best complex conjectures about future events—as if they were data. And then, when the models flopped, they began to massage the data. To get past this catastrophe we will need to forgive, but we should never forget. We should do whatever we can to dismantle such experts’ unchecked power over public policy.

These experts, however, could never have done so much damage without a gullible, self-righteous, and weaponized media that spread their projections far and wide. The press carpet-bombed the world with stories about impending shortages of hospital beds, ventilators, and emergency room capacity. They served up apocalyptic clickbait by the hour and the ton.

History shows that you will rarely lose your job making predictions if you’re wrong in the right direction. On the other hand, you may well lose it if you’re right in the wrong direction. Neither rulers nor subjects welcome the bearer of bad, but true, news. (Especially if it’s bad news for power-grabbing elites.)

Being wrong in the right direction, though, often reaps reward. Early pandemic models indicated that only prompt and massive state action could save us. The models were wrong—way off—but they were wrong in the right direction. They gave politicians justification for taking over almost every aspect of citizens’ lives. They gave the press clickbait galore. We’re not assuming malice here. We assume that many of these folks were moved by concern and even love for others. The issue is one of incentives and human nature, not bad intentions.

Our imagined pandemic model has made a huge mistake. How to explain that whopping error? In a perfect world, the experts who created the model, publicized, and used it to create public policy would reassess the assumptions they fed into the model—A, B, and C—find the mistake, and try new ones, which may better match the “observables.”

But we don’t live in a perfect world with perfect experts. What if experts are loath to admit that they were wrong? (We know that’s a real stretch, but stick with us here.) What if they have been feted by the press and promoted to positions of authority and power? They have other options besides the humiliating one of going back to the drawing board. For starters, such experts can stop using the word “predictions” to describe the forecasts that the model has been spitting out. Now they’re just “scenarios” or “guidelines” or “projections.” But these are just word games. There’s little daylight between a forecast, a prediction, a guess, a scenario, and a projection. All those words describe what the model is doing when it says, If A, B, and C are true, then something like X will happen, give or take a margin of error. If nothing like X happens, then something’s wrong with at least one of the model’s conceptual inputs, that is, with one of the propositions that that model assumed to be true. At least one of A, B, or C must be wrong.

It's only one view. Uncertainty is everywhere, and you may find snake-oil sellers in every corner.

PS. Tomorrow online presentation.

PS. Great post on pandemic models.

 

The burden of disease around the world

Global Burden of Disease (GBD) 2019

The Lancet latest issue: 

The potential to improve health by risk reduction is well reported in GBD 2019. All risks quantified in GBD collectively account for 48% of global DALYs. Exposure to many risks highly correlated with SDI has been steadily decreasing as global SDI has increased, including household air pollution; child growth failure; and unsafe water, sanitation, and handwashing. Additionally, there have been notable decreases in exposure to smoking. Figure 4 shows the annualised rate of change in exposure from 2010 to 2019 for select risk factors ordered by global attributable DALYs. Among the 15 leading causes of attributable DALYs shown, high systolic blood pressure, high fasting plasma glucose, high body-mass index (BMI), ambient particulate matter pollution, alcohol use, and drug use stand out because rates of exposure are increasing by more than 0·5% per year.

 Success in reducing the disease burden from causes of communicable, maternal, neonatal, and nutritional  CMNN diseases by global collective action to fund key programmes should be celebrated. Catch-up social and economic development is fuelling more rapid health progress in the lower socio-demographic index (SDI) quintiles. But there is reason to believe that, although the past 70 years have largely been a story of sustained improvements in health, rising exposure to crucial risks, such as high BMI, high fasting plasma glucose, and ambient particulate matter pollution, as well as stagnant exposure to many other behavioural risks, including diet quality and physical activity, might attenuate progress. Most alarmingly, the mortality decreases in cardiovascular diseases of the past half a century have slowed substantially, or even reversed, in some nations with high SDI. 

Good news!

 

Check the resource center.

The Bio Revolution has alredy started

The world is at a “special moment” as revolutionary advances in biology converge with progress in computing automation and Artificial Intelligence (Matthias Evers, Senior Partner at McKinsey & Company)

The Bio Revolution / Reshaping the EU’s industrial strategy, from competition to trade policy

Dying from COVID

 Magnitude, demographics and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries

The total mortality effect of the COVID-19 pandemic is the difference between the observed number of deaths from all causes and the number of deaths had the pandemic not occurred, which is not directly measurable. The most common approach to calculating the number of deaths had the pandemic not occurred has been to use the average number of deaths over previous years—for example, the most recent 5 years—for the corresponding week or month when the comparison is made. This approach, however, does not take into account changes in population size and age structure, nor long- and short-term trends in mortality, which are particularly pronounced for some age groups52,53. Nor does this approach account for time-varying factors, such as temperature, that are largely external to the pandemic but also affect death rates.

We developed an ensemble of 16 Bayesian mortality projection models that each make an estimate of weekly death rates that would have been expected if the COVID-19 pandemic had not occurred. We used multiple models because there is inherent uncertainty in the choice of model that best predicts death rates in the absence of pandemic. 

I suggest you to find where is Spain... 

Comparison of percent increase in deaths from any cause as a result of the COVID-19 pandemic between men and women, for all ages and by age group.

 

Markets in Healthcare, some weaknesses and what to do about them

 The Role of Market Forces in U.S. Health Care

No market functions perfectly, however, and health care markets are more imperfect than most.

The article explains weaknesses of the market and some potential fine tunning to avoid them. And says: 

Many of the “single-payer” health care systems around the world have some market components, and many are actually expanding the role of markets. The more important question is how government and markets can complement one another. Essentially, we do not need to abandon markets — we can make them better. Specifically, relatively incremental actions, such as continued support for ACA  marketplaces, continued efforts to increase the effectiveness of transparency initiatives, procompetitive reforms to reduce the deleterious consequences of provider consolidation,5 and regulations to prevent the most severe market failures, such as limits on surprise billing or more aggressive caps on excessive prices in the commercial market, seem like first-order ways to improve market functioning with a relatively light touch.  If we fail to improve market functioning, stronger government involvement will most likely be needed.

My impression is that unless there is stronger government involvement there will no be equity of access, specially after covid pandemic.

 

Milgrom and Wilson on auctions and Nobel Prize

The Prize in Economic Sciences 2020

The quest for perfect auction

Additional readings

When basic and applied sciences meet together:

Milgrom and Wilson’s ground-breaking initial work should be regarded as basic research. They wanted to use and develop game theory to analyse how diferent actors behave strategically when they each have access to diferent information. Auctions – with their clear rules that govern this strategic behaviour – comprised a natural arena for their research. However, auctions have gained in practical signifcance and, since the mid-1990s, they have been increasingly used in the distribution of complex public assets, such as frequency bands, electricity and natural resources. Fundamental insights from auction theory provided the foundation for constructing new auction formats that overcame these new challenges. 

The new auction formats are a beautiful example of how basic research can subsequently generate inventions that beneft society. The unusual feature of this example is that the same people developed the theory and the practical applications. The Laureates’ ground-breaking research about auctions has thus been of great beneft, for buyers, sellers and society as a whole.

Long time ago, I studied my PhD with Milgrom-Roberts book on organizational economics, later I published an article on auctions for primary care in Hacienda Publica, using Milgrom ideas. It is a well deserved Prize, I know what it really means.