The next section is another gratuitous dunk on Confucius, but it’s also a warning about the perils of seeing strict linear relationships where there are none. Not only will you continually be disappointed/frustrated, you won’t know why.
In this story, Laozi suggests that Confucius’ model of a world in which every additional unit of virtue accumulated will receive its corresponding unit of social recognition is clearly not applicable to the age in which they lived.
Moreover, this results in a temptation is to blame others for not living up to your model. Thus, in the years following the 2007 crash, Lehman brothers were apostrophised for their greed, but in reality all they had done was respond as best they could to the incentives that society gave them. If we wanted them to behave less irresponsibly, we should have pushed government to adjust their incentives. They did precisely what we paid them to. If we didn’t want this outcome, we should have anticipated it and paid for something else.
The received wisdom among economists is that the US’s historical low interests rates are driven by high savings by aging boomers who are getting ready for, or in, retirement.
The idea is boomers have salted away so much cash that banks don’t bid for their savings, so interest rates fall.
But at last week’s Jackson Hole conference, a trio of economists presented a very different explanation for low interest, one that better fits the facts.
So we can’t really say that low interest rates are being caused by an aging population with high retirement savings, because while the US population is aging, it does not have high savings. Quite the contrary.
And, as Robert Armstrong points out in his analysis of the paper for the Financial Times, even in places like Japan, with large cohorts of retirees and near-retirees who do have adequate savings, rates are scraping bottom.
So why are rates so low? Well, the paper says it is being caused by high levels of savings — just not aging boomers’ savings. Rather, it’s the savings of the ultra-wealthy, the 1%, who are sitting on mountains of unproductive capital, chasing returns.
On page 166 of her 1952 book, in a chapter titled “The Bar Chart”, Spear shows very clearly an early form of a chart type called the Box Plot that she calls the “Range Bar.” …..
What’s interesting about this to me is that if you look up the Wikipedia page for Box Plot, at the present moment, you will not find Spear’s name appearing anywhere in the article. You will, however, read the following:
“Since the mathematician John W. Tukey introduced this type of visual data display in 1969, several variations on the traditional box plot have been described.”
The way I see it, the range bar appearing in Spear’s book is close enough in form to the box plot to warrant a mention on this Wikipedia page. Hopefully, by the time you read this, you’ll be able to find an updated page for the box plot with her name included on it.
On trips through Europe, Nightingale displayed a natural inclination to record data: distance and times traveled were neatly cataloged in her journal. She hoarded information pamphlets, especially those concerning laws, social conditions, and benevolent institutions. In a Parisian salon, Mary Clarke showed Nightingale how bold, independent, intelligent, and equal to men a woman can be.
In Egypt, Nightingale cruised the Nile and discovered ancient mysticism. Near Thebes, God called Florence Nightingale to nursing. God called me in the morning and asked me would I do good for him alone without reputation. But rich kids do not become nurses. Nursing was below Nightingale’s class. Her family disapproved.
During the months preceding the surge of SARS-CoV-2 infections this fall and winter, many public health officials expressed concern about the potential for a “double-barreled” respiratory virus season. In this scenario, healthcare facilities would be totally overwhelmed by: 1) patients afflicted by infections caused by endemic respiratory viruses (such as influenza) that occur during any normal year, and 2) a massive influx of coronavirus patients. Fortunately, such a catastrophe did not come to pass. The reason for this is an unprecedented reduction in flu prevalence for the 2020–21 season.
So perhaps a biological process, whereby viruses engage in some form of competition, or interactions, can better explain disappearances such as those currently being observed.
Subsequent research has borne out real world examples related to the phenomenon described by Simpson. According to a group of researchers at Yale, it is likely that a 2009 autumn rhinovirus epidemic interrupted the spread of influenza. The authors of that study write: “one respiratory virus can block infection with another through stimulation of antiviral defenses in the airway mucosa”. Results from another study, conducted in mice, support those findings. Mice were infected with either a rhinovirus or a murine coronavirus, and it was found that both attenuated influenza disease. Moreover, it was observed that the murine coronavirus infection reduced early replication of the influenza virus. In another study, negative interactions between noninfluenza and influenza viruses were suggested. According to the authors: “when multiple pathogens cocirculate this can lead to competitive or cooperative forms of pathogen–pathogen interactions. It is believed that such interactions occur among cold and flu viruses”. A recently published study examining the effects of interactions between an adenovirus and influenza in mice suggested that certain respiratory infections could impede “other viruses’ activities within the respiratory tract without attacking unrelated viruses directly”. Finally, in a paper entitled “A systematic approach to virus–virus interactions”, the authors state: “increasing evidence suggests that virus–virus interactions are common and may be critical to understanding viral pathogenesis”.
The sheer versatility and accessibility of the spreadsheet has made it the Swiss Army Knife of modern day productivity, inserting itself into almost every workflow across every industry. Over the past three decades, spreadsheets have become the de facto way for information to be collected, distributed and analysed.
But, as our operational and computational needs become ever greater, the limits of Excel become clear, and opportunities emerge for companies and tools to replace the spreadsheet.
3. Display distribution (supply) and administration (demand) data together for a more complete picture of the vaccine rollout.
To make sense of what was happening with COVID cases, charts from groups like the COVID-19 Tracking Project clustered trends on testing, cases, hospitalizations, and deaths for a more complete picture. Similarly, we can’t look at data on doses administered in isolation to understand how a country or state is performing on vaccine rollout.
The New York Times displays a combination of the percent of people given at least one shot or two shots and information about the doses distributed and the share of doses used. Together, these metrics give a high level snapshot of information about supply distributed and administered. Note that understanding demand requires knowing more than how many people received shots though, which is likely influenced by supply.