Category Archives: Systems

Complex adaptive systems, forecasting systems, systems design, systems architecture, modules, modular systems, modular design, system design patterns, forecasting systems that mine big data

Climate Change Language

We Need A Better Language for Climate Change – that Acts as a Call to Action

============================

Below is as essay that makes the case for a new six-box classification system for global climate change – two columns and three rows. The core idea here is to move climate change out of a subject for the editorial page and into a subject for daily new – much like how storms, earthquakes and epidemics are covered. We want a language that serves as a “call-to-action”.

The news would inform the world about climate-change related occurrences that have impacts that are “major”, “disaster”, or “global disaster”, and that are either “incidents” (one-time) or “recurring”.

I worked this out with Karen . I am the scribe. Obviously, this is DRAFT 1.

=============================
Climate Change Language

CREDIT: Karen Flanders-Reid
CREDIT: https://www.nytimes.com/2018/08/08/opinion/environment/california-wildfires-trump-zinke-climate-change.html

Karen and I read today’s NYT article about California wildfires, and found ourselves musing – is the language of climate change right? Why is a “wildfire” just an isolated incident? Why isn’t it part of a larger wildfire classification system (“BREAKING NEWS: THE CALIFORNIA WILDFIRE HAS JUST BEEN RECLASSIFIED AS CATEGORY V.”?

We went on to ask: if climate change is the critical issue of our day, why Why isn’t the wildfire in California an climate change incident – part of a larger climate change classification system?

Why do the NYT editorial writers have to scream – everything is related to climate change!!!! After all, news breaks when a Hurricane is re-classified: “BREAKING NEWS: THE TROPICAL STORM OVER CUBA HAS JUST BEEN RE-CLASSIFIED BY THE WEATHER SERVICE AS A HURRICANE.”

Why doesn’t climate change have its own global classification system? How do we move from the editorial opinion desk to the news desk? How do we move from “The science is being ignored.” To “BREAKING NEWS: THE WILDFIRES IN CALIFORNIA HAVE JUST BEEN RECLASSIFIED BY THE WEATHER SERVICE FROM A CLIMATE-RELATED INCIDENT (CRI) TO A CLIMATE-RELATED DISASTER (CRD).”

EXAMPLES OF POWERFUL GLOBAL CLASSIFICATION SYSTEMS

To identify a powerful classification system, and the new language it implies, it first would be useful to identify the other global classification systems that exist – especially those with imply a call to action.

There are at least four:

Storms; Classified by the World Meteorological Organization (WMO), using the Saffir–Simpson scale:

Tropical Depression
Tropical Storm
Hurricane/Cyclone Categories 1-5

Source: https://en.wikipedia.org/wiki/Maximum_sustained_wind

Earthquakes: Classified by the US Geological Service, using the Richter Scale:
Moderate (above 8)
Strong (7-7.9)
Major (6-6.9)
Great (5-5.9)

Infectious Disease; Classified by the global centers for disease control, the classes are:

Outbreak (more incident than expected)
Epidemic (spreads rapidly to many people)
Pandemic (spreads rapidly to many people globally)

Source: https://www.webmd.com/cold-and-flu/what-are-epidemics-pandemics-outbreaks#1

A NEW GLOBAL CLASSIFICATION SYSTEM FOR CLIMATE CHANGE

To Begin

We recommend s simple structure, with easily understood terms, that evolves over time:

Starts with a few terms, and adds terms over time.
Begins classifying major occurrences only, and evolves to classify most occurrences.
Begins classifying evidence-based occurrences only (where science is conclusive that the occurrence is climate-change-related) and evolves as science becomes increasingly conclusive.

Initial Terms

“Occurrence” – a natural phenomena that occurs somewhere

“Climate-Change-Related” (CR) – a shorthand for saying that the preponderance of science indicates that a given occurrence is a contributor to or the result of climate change.

“Incident” (I) – an episodic occurrence (with a beginning, middle, and end)
“Recurring” (R) – an on-going occurrence (no end in sight)

“Major” (M) – an occurrence with sufficient size to merit being classified.
“Disaster” (D) – an occurrence, with major impacts
“Global Disaster” (G) – an occurrence with major global impacts

Initial Classification System:

Climate-related Occurrences shall be identified.

Once identified, they shall be classified in one of six classes:

Either “incidents” or “recurring”.
Either “major”, “disaster”, or “global disaster”

“Climate-Change-Related Event” (CRE) – any occurrence that is deemed to be a contributor to climate-change.

“Climate-Change-Related Outcome” (CRO) – any occurrence that is deemed to be the result of to climate-change.

All major climate-change-related occurrences would be classified as follows:

CR Incident (CRE-I): An episodic event, with a beginning, a middle, and an end.
CR Disaster (CRE-D): An episodic event, with global impacts

The Weather Service would be tasked with implementation, and aligning with the World Meteorological Organization (WMO) and other agencies around the world.

History of US Immigration

Borders
A History of Border Security, Illegal and legal immigration

Overview

Regulating the flow of immigrants into the United States has a long, and often tawdry past.

Once regulated, entry then becomes “legal” or “illegal”. And “legal” entry is now generally highly restricted, on a temporary or permanent basis to three different routes: employment, family reunification, or humanitarian protection. All other entry: “illegal”.

Once regulated, borders then become “secure” or “insecure”. Because of trade, borders needed to be highly efficient for goods, and highly “secure” for people. This distinction, between the flow of goods and the flow of people, was an almost unenforceable dilemma, where billions have been expended to do …. the best we can.

Who should regulate? The Supreme Court settled that issue in 1875, opining that this was the role of the Federal Government. Up until then, it was a state responsibility.

How should it regulate? Congress decided that racial quotas were the answer in 1917. Before that time, they actually banned Asian immigration in 1875. The essential idea was to restrict immigration by race to a % of the race’s population in the US (2% of that population was frequently used, noting that 2% of nothing is nothing). The notion of racial quotas was maintained until 1965!

Would there be any exceptions to racial quotas?

Yes, for refugees and asylum-seekers. Congress responded to American sympathies for those fleeing communism and those feeing persecution. Recognizing “refugees” added significant new complexity.

Yes, for spouses and children of American citizens.

Yes, for those born in the Western Hemisphere.

Once regulated, politicians could rail against immigrants, but they rarely provided the funds to enforce the border laws. We severely curtailed legal immigration, and illegal immigration was the easily anticipated result. In 1952, Congress specified that legal immigration be limited to 175,455 per year!

Also easily anticipated, “illegals” brought massive issues for schools, health care, housing, etc. As the number of “illegals” grew, so grew the pressure to do something, anything, to reduce the pressure. Congress has been forced to act, as they did in 1986 when they granted amnesty to approximately 3 million illegals!

So the history of immigration in the United States includes major shifts in policy in 1875 (Supreme Court rules), 1891 (Federal bureaucracy formed), 1924 (racial quotas put in place), 1986 (racial quotas replaced and amnesty granted).

“Illegals” are out of control. Estimates of illegals are 3 million illegals in 1986, 7 million in 2001, and 12 million in 2017. As a % of U.S. population, “foreign-born” dropped from 14.7% in 1910 to 4.7% in 1970, and has been rising ever since. In 2013, there were 13.1% of the population who were foreign born (CREDIT:PEW).

Discussion
Immigration became a full-fledged subject for the nation in 1875, when the Supreme Court ruled that it was a Federal responsibility. Shortly thereafter, Congress stepped up and began excluding people – literally making it “illegal” for them to enter the United States. They banned Asians in 1875 and Chinese in 1882 (the “Asian Exclusion Act” and the “Chinese Exclusion Act” set the stage for all restrictions on immigration that would follow.

In 1891, the Federal Government took a big step: they created a bureaucracy to execute the laws. The Immigration Act of 1891 established a Commissioner of Immigration in the Treasury Department. With the two exceptions noted above, states regulated immigration before 1890.

Before then, this “nation of immigrants” actually had an immigration hiatus from 1790 to 1815, when “foreign-born” reached a low. Immigration as we now know it began with some force in 1830, when “foreign-born reached 9.7% of the population. By 1850, census estimates place immigrants at 1.7 million people, and “foreign-born” at 2.2 million. Between 1870 and 1910, foreign born hovered between 13% and 15% of population. It then started to dip, moving to 4.7% in 1970. It has been climbing since, reaching 13.1% in 2013.

Since then, waves of immigration brought the country waves of immigrants:

Between 1850 and 1930, 25 million Europeans immigrated. Italians, Greeks, Hungarians, Poles, and others speaking Slavic languages made up the bulk of this migration. But among them were 5 million Germans, 3.5 million British, and 4.5 million Irish. 2.5 to 4 million Jews were among them.

The twentieth century began with debates about immigration, and we have been debating the subject ever since.

In 1907, Congress created The Dillingham Commission to investigate the effects of immigration on the country. They wrote forty volumes on the subject.

In 1917, Congress changed the nation’s basic policy about immigration. We began setting “quotas” and limiting access based on literacy. The first such law was a literacy requirement in 1917.

In 1921, Congress adopted the Emergency Quota Act, set quotas. The National Origins Formula assigned quotas based on national origins. This complex legislation gave preference to immigrants from Central, Northern and Western Europe, severely limiting the numbers from Russia and Southern Europe, and declared all potential immigrants from Asia unworthy of entry into the United States (to our shame, this law made it virtually impossible for Jews fleeing Germany after 1934 to immigrate to the United States).

In 1924 , Congress adopted The Immigration Act of 1924. It set quotas for European immigrants so that no more than 2% of the 1890 immigrant stocks were allowed into America.

Interestingly, no quotas were set for people born in the Western Hemisphere.

This era, and its legislative framework, lasted until 1965. During this period, Congress recognized the notion of a “refugee” seeking “amnesty”. Jewish Holocaust survivors after the war, those fleeing Communist rule in Central Europe and Russia, Hungarians seeking refuge after their failed uprising in 1956, and Cubans after the 1960 revolution, and others moved the conscience of the nation.

In 1965, Congress adopted the Hart-Celler Act. It was a by-product of the civil rights revolution and a jewel in the crown of President Lyndon Johnson’s Great Society programs. It abolished the racially based quota system.The law replaced these quotas with new preferential categories. It gave particular preference to immigrants with U.S. relatives and job skills deemed critical.

In 1986, the Immigration Reform and Control Act (IRCA) was adopted. It created, for the first time, penalties for employers who hired illegal immigrants. IRCA, also granted amnesty to workers in the country illegally. In practice, amnesty was granted for about 3,000,000 illegal immigrants. Most were from Mexico. Legal Mexican immigrant family numbers were 2,198,000 in 1980, 4,289,000 in 1990 (includes IRCA), and 7,841,000 in 2000.

References

https://en.wikipedia.org/wiki/History_of_immigration_to_the_United_States

https://www.politico.com/magazine/story/2017/08/06/trump-history-of-american-immigration-215464

https://americanimmigrationcouncil.org/research/why-don’t-they-just-get-line

How U.S. immigration laws and rules have changed through history

http://assets.pewresearch.org/wp-content/uploads/sites/7/reports/39.pdf

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3407978/

SmartWatch Technology Reliably Detects Afib

The quantified self movement strikes again!

CREDIT: Cleveland Clinic Article on Detection of Afib via SmartWatch

Smartwatch Technology Reliably Detects Afib Prior to Cardioversion
Study suggests a role for KardiaBand when paired with physician review

A newly FDA-approved smartwatch accessory can record heart rhythm and successfully differentiate atrial fibrillation (AF) from normal sinus rhythm (SR) through an automated algorithm, according to a Cleveland Clinic investigation. The study, which will be presented March 11 at the American College of Cardiology’s 67th Scientific Session, also showed that the accuracy of interpretation gets even better when the accessory is supported by physician review.
The findings suggest that the wearable technology, known as KardiaBand™, can help screen patients before presentation for elective cardioversion to avoid unnecessary procedures, among other potential uses.
KardiaBand, which consists of a software app for an Apple Watch® and a sensor band that replaces one of the watch’s straps, provides a 30-second recording of an ECG rhythm strip when the wearer places a thumb on the sensor band. The app contains an algorithm for automated detection of AF.
“Our objective was to determine how accurately KardiaBand and its algorithm can differentiate AF from sinus rhythm compared with physician-interpreted 12-lead ECGs,” says senior author Khaldoun Tarakji, MD, MPH, a Cleveland Clinic electrophysiologist. In November 2017, the device became the first smartwatch healthcare accessory to be approved by the FDA, “but we wanted to test it ourselves to determine how well it would perform in clinical practice,” Dr. Tarakji explains.
Study essentials
To that end, he and Cleveland Clinic colleagues prospectively enrolled 100 consecutive patients (mean age, 68 ± 11 years) with chronic AF who were scheduled to undergo cardioversion. Upon presenting for the cardioversion procedure, all patients were given a KardiaBand-equipped smartwatch and trained in its use, after which they underwent traditional ECG assessment and a 30-second KardiaBand recording. If cardioversion was still indicated, they underwent ECG and KardiaBand testing after the procedure. KardiaBand recordings were then compared with the physician-reviewed ECGs and also reviewed by two blinded electrophysiologists, with these readings compared to ECG interpretations.
Eight patients did not undergo cardioversion because they presented in SR; these patients were excluded. Among the remaining patients, a total of 169 pairs of ECG and KardiaBand recordings were available for comparison (each patient had two before and two after cardioversion).
Key findings
• Of the 169 pre-cardioversion KardiaBand recordings, 57 fell out as “unclassified,” meaning that the KardiaBand algorithm did not draw a conclusion of either AF or SR.
• Among the remaining 112 pairs of recordings, the reviewing electrophysiologists determined that the KardiaBand algorithm correctly detected AF with 93 percent sensitivity and 84 percent specificity compared with ECG.
• When the blinded reviewers bypassed the automated algorithm and interpreted each patient’s KardiaBand strips against his or her ECG, sensitivity rose to 99 percent and specificity was 83 percent. Further, in the 57 unclassified cases, the reviewers were able to use the strips to correctly diagnose AF versus SR with 100 percent sensitivity and 80 percent specificity.
“This study shows that KardiaBand provides excellent sensitivity and good specificity in identifying AF,” says Dr. Tarakji. “The numbers improve further with physician overview of these recordings, indicating that even unclassified KardiaBand strip recordings could be of value to reading physicians.”
Smart devices demand smart use
KardiaBand carries the benefit of enabling patients to record their rhythm at any time, as opposed to only when they are wearing a Holter monitor or at a physician’s office. “We can catch intermittent episodes when they happen, and we’re not limited to a specific duration of monitoring time,” Dr. Tarakji says. He adds that wearable devices like this can also reduce time spent responding to false alarms if a recording taken at the same time shows normal rhythm.
Yet many questions remain about how KardiaBand and similar products may ultimately be used in practice. Dr. Tarakji cites a few examples:
• Which patients are best suited to this technology? For many patients dealing with AF, KardiaBand can provide reassurance when they need it. But for others, having constant access to their ECG data may lead them to check their rhythm obsessively, raising anxiety. “In general, however, patients value the instant feedback they get,” Dr. Tarakji observes.
• Do physicians have the IT infrastructure in place to make these devices part of their practice? Wearable devices can mean a flood of event reports to clinicians’ email boxes. At Cleveland Clinic, information from patients’ KardiaBands bypasses the email system and feeds into a cloud-computing platform that physicians can access anytime.
• How should clinicians respond to short episodes, particularly in asymptomatic patients? “We currently have a gap in our clinical knowledge about whether brief, random episodes that are asymptomatic warrant anticoagulation or not,” Dr. Tarakji explains, adding that ongoing studies are trying to address this important question.
“Future studies will focus on how we can use these smart devices intelligently to make sure they’re improving quality of care rather than just producing noise for physicians,” he observes.
A parallel goal, he says, is to ensure that the devices provide value by making care delivery more efficient. Noting that patients currently need to pay for KardiaBand out of pocket, Dr. Tarakji says that “developing a richer body of research evidence is the best way we can demonstrate cost-effectiveness to healthcare payers.”
Tech like this can’t be ignored
Indeed, KardiaBand could prove cost-effective by allowing patients who are in SR to avoid needless trips for elective procedures, such as in the case of the eight patients in the study who were found to be in SR when they presented for cardioversion and did not require the procedure. Other potential uses of KardiaBand for the longitudinal management of AF patients could well prove cost-effective too.
Regardless of how quickly such cost-effectiveness evidence may come, Dr. Tarakji says clinicians cannot be passive in the face of technologies like KardiaBand. “Patients will come to us with new products, and we can’t turn away,” he observes. “We need to test these products and find ways of responding to the information they deliver in a way that improves patient outcomes, all while remaining mindful of both patient and physician satisfaction.”
The researchers report that KardiaBand’s manufacturer, AliveCor, provided smartwatches for the study but was not involved in the study’s design, implementation, data analysis or interpretation.

Well-Being – Real Time Revisited

NOTE: This post revisits a post titled “Well-Being Real Time”. The original post was May, 2014, and can be found at: http://johncreid.com/2014/05/well-being-real-time/.

Well-Being – Real Time Revisited

Well-being is arguably the central mega-trend of the 21st century. As we look to the future, we have an obligation to “unpack” this dense concept, and find its essential component parts.

We describe these components here as “ACE” – ACT, CARE, and EAT. The wish we have for ourselves and for others is to be well. “Be Well” is our salutation and our call to actions.

How far out are we looking?

The future is now. ACE is here – together with real time measuring and monitoring. ACE is our pathway to greater and greater levels of personal well-being.

ACE measuring and monitoring will be supported by all elements of the quantified self movement. FitBit, Apple Watch, and so many other new monitoring devices will allow us to to bring personal well-being into a real-time modality.

ACE represents three pillars, each deceptively simple:

A – ACT: ACT is short for activity. The call to action is “stay active”. Well-being activity has physical activity at its center, but the pillar also embraces social activity, and activities of the mind. Staying active is a critical element of being well.
C – CARE: CARE is short for well-being care. The call to action is “care for yourself” and “care for others.”Well-being care of course has health care at its center, but there is so much more. e.g. genomics, massage, essential oils, acupuncture, etc. “Caring for myself” and “Caring for others” are elements of this pillar. “Preventive care” regular check-ups, colonoscopies after age 50, mammograms, pre-natal care for expecting mothers, etc.
E – EAT: EAT is short for eating and drinking. The call to action is “Eat well.” Well-being eating is the exploration of how what we eat and drink contributes to our well-being.

As simple as these pillars appear, each is complex: deep enough for a life-time of focus. Each represents bodies of research, skills, capabilities, and areas of professional endeavor. All together, these pillars represent pathway that each of us will follow as we attain greater and greater levels of personal well-being.

Discussion:

ACT

A – ACT (walking, running, calories burned etc)

Staying active is a critical element of being well. Well-being activity has physical activity at its center: sports, walking, lifting, climbing, yoga, and all of the other activities that light up a FitBit. The pillar also embraces activity of other kinds, e.g. social activity, and activities of the mind.

CARE

Well-being care is all about promoting health. Of course, it has health care at its center, but there is so much more. e.g. mental health, addictive behaviors, massage, genomics, essential oils, acupuncture, etc.

“Caring for myself” and “Caring for others” are elements of this pillar. “Preventive care”, eldercare and aging, palliative care are included, but so are regular check-ups, colonoscopies after age 50, mammograms, pre-natal care for expecting mothers, etc.

The ability to routinely monitor vital signs at home or at the office will be a part of this pillar. Lab work – including saliva, blood, and stool samples, will be more real time, more regular and less expensive. These trends will be one of the keys to progress in the care pillar. On the innovation side of this pillar will be many technologies, but breakthroughs in genomics will certainly be high on the list. Telemedicine is another innovation that will alter access to well-being care.

Predictive modeling will be more relevant than never. Am I headed for pre-diabetes? If so, what evidence shows me a path to avoid that condition?

CARE-MMEDS (what MEDS I take, what compliance I have, etc)

CARE-RResting Metabolic Rate (calories burned at rest)

CARE-VVITALS (pulse, BP, etc)

CARE-LLABS (blood testing, etc)

CARE-SSleep (duration, deep sleep, etc)

EAT

EAT is short for eating and drinking. The call to action is “Eat well.”

Well-being eating is the exploration of how what we eat and drink contributes to our well-being. Naturally, there is a social element, where eating and drinking together makes the experience more fulfilling. There is a physiological element, having to do with ingestion, osmosis, calories, glucose and glycogen, enzymes, etc. There is a psychological element, related to the feelings of satiety, or hunger, or thirst, and their related cravings. There is a sensory element, where sweet and sour contrasts, aromas, and their related metaphorical associations, play a part.

Eating delicious food and drink with friends is certainly a component. But achieving a balanced diet, with moderation as a central tenant,

On the one hand, this pillar is ancient. For thousands of years, elders have taught daughters and sons how to cook well. and cooking techniques have evolved

On the other hand, this pillar is ripe for innovation. The new breakthrough science related to the micro-biome is a part.

EATS (what I eat and drink, especially calories)

Implications

Monitoring all components of ACE (MEDS, Activity, Resting Metabolism,VITALS, EATS, LABS, Sleep) is now going to accelerate at an exponential rate.

There will be three settings where ACE monitoring will accelerate:

Employees in Workplaces: Employers will offer employees routine monitoring as part of employee benefits and/or health insurance.
Residents in Communities: Communities will offer residents routine monitoring as one of their amenities. Wellbeing facilities and programs will become as important as golf courses and swimming pools. Look for HOA’s,Condo and Coop associations, and subdivision developers to increasingly view MARVELS as critical to “place-making”.
Clients of service-providers: Hotels, spas, assisted-living centers, nursing homes, and many others will increasingly offer MARVELS monitoring as one of their base services.

The Privacy Imperative will be the critical success factor for all of these pushes into the future. It is foundational.

Without it, there will be no progress.

With it, personalized, real-time care will flourish. Each individual will be able to opt-in to his care-coaching community (and to opt-out whenever they choose), and get the extraordinary benefits that such a community can provide.

Want to talk to your well-being coach? FaceTime them, and they – with your permission – will help you sort out what’s going on with you.

Feel like you might need a check-in with a doctor? Send them an email – with your ACE history embedded in it, or get them on the phone or FaceTime, and see if they need you to come in.

The future is now.

BEWELL Centers will be everywhere. Look for:

DWELL CENTERS (part of BEWELL Centers) – for community ACE measuring and monitoring support. Target population is neighbors in the community.

Employee BEWELL CENTERS (part of BEWELL Centers) – for employees in workplaces ACE measuring and monitoring support. Target population is employees in the workplace.

CLIENT BEWELL CENTERS (Part of BEWELL Centers – for service-providers ACE measuring and monitoring support.Target population is clients of the service provider.
(Walgreens and CVS are already moving aggressively in this direction>

References:
The Privacy Imperative
LABS revolution
LABS By Disease
Quantified Self Movement

Amazon, BH, JPMorgan

With 1.2 million employees, Amazon, Berkshire Hathaway, and JP Morgan have decided to venture together into health care for their employees.

Following in the grand tradition of Henry Ford, who set up Henry Ford Hospital in Detroit, these three giants are stepping in too.

They have no illusions about how difficult it will be. But with premiums rising 19% per year, its clear that Congress is doing nothing, and someone has to do something.

“Planning for the new company is being led by Marvelle Sullivan Berchtold, a JPMorgan managing director who was previously head of the Swiss drugmaker Novartis’s mergers and acquisitions strategy; Mr. Combs; and Beth Galetti, a senior vice president at Amazon.”

The article points out that there are others working on this.

“Robert Andrews, chief executive of the Healthcare Transformation Alliance, a group of 46 companies, including Coca-Cola and American Express, that have banded together to lower health care costs.”

“Walmart contracted with groups like the Cleveland Clinic, Mayo and Geisinger, among others, to take care of employees who need organ transplants and heart and spine care.”

“Caterpillar, the construction equipment manufacturer, sets its own rules for drug coverage, which it has said saves it millions of dollars per year, even though it still uses a pharmacy benefit manager to process its claims.”

Suzanne Delbanco, the executive director for the Catalyst for Payment Reform, a nonprofit group that mainly represents employers”

=================
CREDIT: https://www.nytimes.com/2018/01/30/technology/amazon-berkshire-hathaway-jpmorgan-health-care.html?smid=nytcore-ipad-share&smprod=nytcore-ipad

TECHNOLOGY
Amazon, Berkshire Hathaway and JPMorgan Team Up to Try to Disrupt Health Care

By NICK WINGFIELD, KATIE THOMAS and REED ABELSON
JAN. 30, 2018
SEATTLE — Three corporate behemoths — Amazon, Berkshire Hathaway and JPMorgan Chase — announced on Tuesday that they would form an independent health care company for their employees in the United States.

The alliance was a sign of just how frustrated American businesses are with the state of the nation’s health care system and the rapidly spiraling cost of medical treatment. It also caused further turmoil in an industry reeling from attempts by new players to attack a notoriously inefficient, intractable web of doctors, hospitals, insurers and pharmaceutical companies.
It was unclear how extensively the three partners would overhaul their employees’ existing health coverage — whether they would simply help workers find a local doctor, steer employees to online medical advice or use their muscle to negotiate lower prices for drugs and procedures. While the alliance will apply only to their employees, these corporations are so closely watched that whatever successes they have could become models for other businesses.

Major employers, from Walmart to Caterpillar, have tried for years to tackle the high costs and complexity of health care, and have grown increasingly frustrated as Congress has deadlocked over the issue, leaving many of the thorniest issues to private industry. About 151 million Americans get their health insurance from an employer.
(Why will health care be so difficult for these companies to untangle? Analysis from The Upshot.)
But Tuesday’s announcement landed like a thunderclap — sending stocks for insurers and other major health companies tumbling. Shares of health care companies like UnitedHealth Group and Anthem plunged on Tuesday, dragging down the broader stock market.

That weakness reflects the strength of the new entrants. The partnership brings together Amazon, the online retail giant known for disrupting major industries; Berkshire Hathaway, the holding company led by the billionaire investor Warren E. Buffett; and JPMorgan Chase, the largest bank in the United States by assets.

They are moving into an industry where the lines between traditionally distinct areas, such as pharmacies, insurers and providers, are increasingly blurry. CVS Health’s deal last month to buy the health insurer Aetna for about $69 billion is just one example of the changes underway. Separately, Amazon’s potential entry into the pharmacy business continues to rattle major drug companies and distributors.
(Here’s a look at how the even the threat of Amazon’s entry into an industry can rattle stocks.)

The companies said the initiative, which is in its early stages, would be “free from profit-making incentives and constraints,” but did not specify whether that meant they would create a nonprofit organization. The tax implications were also unclear because so few details were released.
Jamie Dimon, the chief executive of JPMorgan Chase, said in a statement that the effort could eventually be expanded to benefit all Americans.

“The health care system is complex, and we enter into this challenge open-eyed about the degree of difficulty,” Jeff Bezos, Amazon’s founder and chief executive, said in a statement. “Hard as it might be, reducing health care’s burden on the economy while improving outcomes for employees and their families would be worth the effort.”

The announcement touched off a wave of speculation about what the new company might do, especially given Amazon’s extensive reach into the daily lives of Americans — from where they buy their paper towels to what they watch on television. It follows speculation that the company, which recently purchased the grocery chain Whole Foods, might use its stores as locations for pharmacies or clinics.
(We asked health care experts to imagine what the three corporations might do.)

“It could be big,” Ed Kaplan, who negotiates health coverage on behalf of large employers as the national health practice leader for the Segal Group, said of the announcement. “Those are three big players, and I think if they get into health care insurance or the health care coverage space, they are going to make a big impact.”

TAKING ON ‘THE HUNGRY TAPEWORM’
A look at the three companies that announced a joint health care initiative on Tuesday.

Total employees: 1.2 million 
Amazon: 540,000 
Berkshire Hathaway: 367,000
JPMorgan Chase: 252,000.
Individual strengths 
Amazon: logistics and technology
Berkshire Hathaway: insurance
JPMorgan Chase: finance.

Jeff Bezos of Amazon:
“The healthcare system is complex, and we enter into this challenge open-eyed about the degree of difficulty.”
Warren E. Buffett of Berkshire Hathaway:
“The ballooning costs of healthcare act as a hungry tapeworm on the American economy. Our group does not come to this problem with answers. But we also do not accept it as inevitable.”
Jamie Dimon of JPMorgan Chase:
“The three of our companies have extraordinary resources, and our goal is to create solutions that benefit our U.S. employees, their families and, potentially, all Americans.”

But others were less sure, noting that the three companies — which, combined, employ more than one million people — might still hold little sway over the largest insurers and pharmacy benefit managers, who oversee the benefits of tens of millions of Americans.

“This is not news in terms of jumbo employers being frustrated with what they can get through the traditional system,” said Sam Glick of the management consulting firm Oliver Wyman in San Francisco. He played down the notion that the three partners would have more success getting lower prices from hospitals and doctors. “The idea that they could have any sort of negotiation leverage with unit cost is a pretty far stretch.”

Even the three companies don’t seem to be sure of how to shake up health care. People briefed on the plan, who asked for anonymity because the discussions were private, said the executives decided to announce the initiative while still a concept in part so they can begin hiring staff for the new company.

Three people familiar with the partnership said it took shape as Mr. Bezos, Mr. Buffett, and Mr. Dimon, who are friends, discussed the challenges of providing insurance to their employees. They decided their combined access to data about how consumers make choices, along with an understanding of the intricacies of health insurance, would inevitably lead to some kind of new efficiency — whatever it might turn out to be.

“The ballooning costs of health care act as a hungry tapeworm on the American economy,” Mr. Buffett said in the statement. “Our group does not come to this problem with answers. But we also do not accept it as inevitable.”

Over the past several months, the three had met formally — along with Todd Combs, an investment officer at Berkshire Hathaway who is also on JPMorgan’s board — to discuss the idea, according to a person familiar with Mr. Buffett’s thinking.

The three chief executives saw one another at the Alfalfa Club dinner in Washington on Saturday, but by then each had already had dozens of conversations with the small in-house teams they had assembled. The plan was set.

Mr. Buffett’s motivation stems in part from conversations he has had with two people close to him who have been diagnosed with multiple sclerosis, according to the person. Mr. Buffett, the person said, believes the condition of the country’s health care system is a root cause of economic inequality, with wealthier people enjoying better, longer lives because they can afford good coverage As Mr. Buffett himself has aged — he is 87 — the contrast between his moneyed friends and others has grown starker, the person said.

The companies said they would initially focus on using technology to simplify care, but did not elaborate on how they intended to do that or bring down costs. One of the people briefed on the alliance said the new company wouldn’t replace existing health insurers or hospitals.

Planning for the new company is being led by Marvelle Sullivan Berchtold, a JPMorgan managing director who was previously head of the Swiss drugmaker Novartis’s mergers and acquisitions strategy; Mr. Combs; and Beth Galetti, a senior vice president at Amazon.

One potential avenue for the partnership might be an online health care dashboard that connects employees with the closest and best doctor specializing in whatever ailment they select from a drop-down menu. Perhaps the companies would strike deals to offer employee discounts with service providers like medical testing facilities.

“Each of those companies has extensive experience using transformative technology in their own businesses,” said John Sculley, the former chief executive of Apple who is now chairman of a health care start-up, RxAdvance. “I think it’s a great counterweight to what government leadership hasn’t done, which is to focus on how do we make this health care system sustainable.”

How Amazon Rattles Other Companies
The e-commerce giant’s actions – some big, like buying Whole Foods Markets; some smaller, like Amazon meal kits – have led to stock sell-offs for a wide range of businesses.

Erik Gordon, a professor at the University of Michigan’s Ross School of Business, predicted that the companies would attempt to modernize the cumbersome process of doctor appointments by making it more like booking a restaurant reservation on OpenTable, while eliminating the need to regularly fill out paper forms on clipboards.

“I think they will bring the customer-facing, patient-facing thing into your smartphone,” he said.

Amazon has long been mentioned by health care analysts and industry executives as a potential new player in the sector. While the company has remained quiet about its plans, some analysts noted that companies often use their own employees as a testing ground for future initiatives.

The entry of Amazon and its partners adds to the upheaval in an industry where much is changing, from government programs after the overhaul of the tax law to the uncertain future of the Affordable Care Act. All the while, medical costs have persistently been on the rise.

Nationwide, average premiums for family coverage for employees rose to $18,764 last year, an increase of 19 percent since 2012, according to the Kaiser Family Foundation. Workers are increasingly paying a greater share of those costs — they now pay 30 percent of the premium, in addition to high deductibles and growing co-payments.
“Our members’ balance sheets speak for themselves — health care is a growing cost at a time when other costs are either not rising or falling,” said Robert Andrews, chief executive of the Healthcare Transformation Alliance, a group of 46 companies, including Coca-Cola and American Express, that have banded together to lower health care costs.

Other major employers have also sought more direct control over their employees’ health care. Walmart contracted with groups like the Cleveland Clinic, Mayo and Geisinger, among others, to take care of employees who need organ transplants and heart and spine care. Caterpillar, the construction equipment manufacturer, sets its own rules for drug coverage, which it has said saves it millions of dollars per year, even though it still uses a pharmacy benefit manager to process its claims.

Suzanne Delbanco, the executive director for the Catalyst for Payment Reform, a nonprofit group that mainly represents employers, said controlling rising prices is especially hard in markets where a local hospital or medical group dominates. While some have tried to tackle the issue in different ways, like sending employees with heart conditions to a specific group, “it’s piecemeal,” she said.

She added, “There are so many opportunities to do this better.”

The issue is not solely a 21st-century concern: In 1915, Henry Ford became increasingly worried about the quality of health care available to his growing work force in Detroit, so he opened the Henry Ford Hospital. It is still in existence today.

Nick Wingfield reported from Seattle, Katie Thomas from Chicago and Reed Abelson from San Francisco. Michael J. de la Merced contributed reporting from London, and Emily Flitter from New York.

A version of this article appears in print on January 31, 2018, on Page A1 of the New York edition with the headline: 3 Giants Form Health Alliance, Rocking Insurers. Order Reprints| Today’s Paper|Subscribe

Homeostasis

One of the smartest guys in the room, Antonio Damasio, give his views about neuroscience and its relationship to pain, pleasure, and feelings. He points out that they all play a giant role in one of life’s most important concepts: homeostasis.

CREDIT: http://nautil.us/issue/56/perspective/antonio-damasio-tells-us-why-pain-is-necessary

Antonio Damasio Tells Us Why Pain Is Necessary
The neuroscientist explains why feelings evolved.

BY KEVIN BERGER
JANUARY 18, 2018

Following Oliver Sacks, Antonio Damasio may be the neuroscientist whose popular books have done the most to inform readers about the biological machinery in our heads, how it generates thoughts and emotions, creates a self to cling to, and a sense of transcendence to escape by. But since he published Descartes’ Error in 1994, Damasio has been concerned that a central thesis in his books, that brains don’t define us, has been muted by research that states how much they do. To Damasio’s dismay, the view of the human brain as a computer, the command center of the body, has become lodged in popular culture.

In his new book, The Strange Order of Things, Damasio, a professor of neuroscience and the director of the Brain and Creativity Institute at the University of Southern California, mounts his boldest argument yet for the egalitarian role of the brain. In “Why Your Biology Runs on Feelings,” another article in this chapter of Nautilus, drawn from his new book, Damasio tells us “mind and brain influence the body proper just as much as the body proper can influence the brain and the mind. They are merely two aspects of the very same being.”

BEYOND SCIENCE: Antonio Damasio, director of the Brain and Creativity Institute at USC, sings the glories of the arts in his new book, The Strange Order of Things: “The sciences alone cannot illuminate the entirety of human experience without the light that comes from art and humanities.”

The Strange Order of Things offers a sharp and uncommon focus on feelings, on how their biological evolution fueled our prosperity as a species, spurred science and medicine, religion and art. “When I look back on Descartes’ Error, it was completely timid compared to what I’m saying now,” Damasio says. He knows his new book may rile believers in the brain as emperor of all. “I was entirely open with my ideas,” he says. “If people don’t like it, they don’t like it. They can criticize it, of course, which is fair, but I want to tell them, because it’s so interesting, this is why you have feelings.”
In this interview with Nautilus, Damasio, in high spirits, explains why feelings deserve a starring role in human culture, what the real problem with consciousness studies are, and why Shakespeare is the finest cognitive scientist of them all.

One thing I like about The Strange Order of Things is it counters the idea that we are just our brains.

Oh, that idea is absolutely wrong.

Not long ago I was watching a PBS series on the brain, in which host and neurologist David Eagleman, referring to our brain, declares, “What we feel, what matters to us, our beliefs and our hopes, everything we are happens in here.”

That’s not the whole story. Of course, we couldn’t have minds with all of their enormous complexity without nervous systems. That goes without saying. But minds are not the result of nervous systems alone. The statement you quote reminds me of Francis Crick, someone whom I admired immensely and was a great friend. Francis was quite opposed to my views on this issue. We would have huge discussions because he was the one who said that everything you are, your thoughts, your feelings, your mental this and that, are nothing but your neurons. This is a big mistake, in my view, because we are mentally and behaviorally far more than our neurons. We cannot have feelings arising from neurons alone. The nervous systems are in constant interaction and cooperation with the rest of the organism. The reason why nervous systems exist in the first place is to assist the rest of the organism. That fact is constantly missed.

The concept of “homeostasis” is critical in your new book. What is homeostasis?

It’s the fundamental property of life that governs everything that living cells do, whether they’re living cells alone, or living cells as part of a tissue or an organ, or a complex system such as ourselves. Most of the time, when people hear the word homeostasis, they think of balance, they think of equilibrium. That is incorrect because if we ever were in “equilibrium,” we would be dead. Thermodynamically, equilibrium means zero thermal differences and death. Equilibrium is the last thing that nature aims for.

The importance of feeling is that it makes you critically aware of what you are doing in moral terms.

What we must have is efficient functioning of a variety of components of an organism. We procure energy so that the organism can be perpetuated, but then we do something very important and almost always missed, which is hoard energy. We need to maintain positive energy balances, something that goes beyond what we need right now because that’s what ensures the future. What’s so beautiful about homeostasis is that it’s not just about sustaining life at the moment, but about having a sort of guarantee that it will continue into the future. Without those positive energy balances, we court death.

What’s a good example of homeostasis?

If you are at the edge of your energy reserves and you’re sick with the flu, you can easily tip over and die. That’s one of the reasons why there’s fat accumulation in our bodies. We need to maintain the possibility of meeting the extra needs that come from stress, in the broad sense of the term. I poetically describe this as a desire for permanence, but it’s not just poetic. I believe it’s reality.

You write homeostasis is maintained in complex creatures like us through a constant interplay of pleasure and pain. Are you giving a biological basis to Freud’s pleasure principle—life is governed by a drive for pleasure and avoidance of pain?

Yes, to a great extent. What’s so interesting is that for most of the existence of life on earth, all organisms have had this effective, automated machinery that operates for the purpose of maintenance and continuation of life. I like to call the organisms that only have that form of regulation, “living automata.” They can fight. They can cooperate. They can segregate. But there’s no evidence that they know that they’re doing so. There’s no evidence of anything we might call a mind. Obviously we have more than automatic regulation. We can control regulation in part, if we wish to. How did that come about?
Very late in the game of life there’s the appearance of nervous systems. Now you have the possibility of mapping the inside and outside world. When you map the inside world, guess what you get? You get feelings. Of necessity, the machinery of life is either in a state of reasonable efficiency or in a state of inefficiency, which is most often the case. Organisms with nervous systems can image these states. And when you start having imagery, you start having minds. Now you begin to have the possibility of responding in a way that you could call “knowledgeable.” That happens when organisms make images. A bad internal state would have been imaged as the first pains, the first malaises, the first sufferings. Now the organism has the possibility of knowingly avoiding whatever caused the pain or prefer a place or a thing or another animal that causes the opposite of that, which is well-being and pleasure.

Why would feelings have evolved?

Feelings triumphed in evolution because they were so helpful to the organisms that first had them. It’s important to understand that nervous systems serve the organism and not the other way around. We do not have brains controlling the entire operation. Brains adjust controls. They are the servants of a living organism. Brains triumphed because they provided something useful: coordination. Once organisms got to the point of being so complex that they had an endocrine system, immune system, circulation, and central metabolism, they needed a device to coordinate all that activity. They needed to have something that would simultaneously act on point A and point Z, across the entire organism, so that the parts would not be working at cross purposes. That’s what nervous systems first achieve: making things run smoothly.

Now, in the process of doing that, over millions of years, we have developed nervous systems that do plenty of other things that do not necessarily result in coordination of the organism’s interior, but happen to be very good at coordinating the internal world in relation to the outside world. This is what the higher reaches of our nervous system, namely the cerebral cortex, does. It gives us the possibilities of perceiving, of memorizing, of reasoning over the knowledge that we memorize, of manipulating all of that and even translating it into language. That is all very beautiful, and it is also homeostatic, in the sense that all of it is convenient to maintain life. It if were not, it would just have been discarded by evolution.

How does your thesis square with the hard problem of consciousness, how the physical tissue in our heads produces immaterial sensations?

Some philosophers of mind will say, “Well, we face this gigantic problem. How does consciousness emerge out of these nerve cells?” Well, it doesn’t. You’re not dealing with the brain alone. You have to think in terms of the whole organism. And you have to think in evolutionary terms.

The critical problem of consciousness is subjectivity. You need to have a “subject.” You can call it an I or a self. Not only are you aware right now that you are listening to my words, which are in the panorama of your consciousness, but you are aware of being alive, you realize that you’re there, you’re ticking. We are so distracted by what is going on around us that we forget sometimes that we are, A-R-E in capitals. But actually you are watching what you are, and so you need to have a mechanism in the brain that allows you to fabricate that part of the mind that is the watcher.
You do that with a number of devices that have to do, for example, with mapping the movements of your eyes, the position of your head, and the musculature of your body. This allows you to literally construct images of yourself making images. And you also have a layer of consciousness that is made by your perception of the outside world; and another layer that is made of appreciating the feelings that are being generated inside of you. Once you have this stack of processes, you have a fighting chance of creating consciousness.

Why do you object to comparing the brain to a computer?

In the early days of neuroscience, one of our mentors was Warren McCulloch. He was a gigantic figure of neuroscience, one of the originators of what is today computational neuroscience. When you go back to the ’40s and ’50s, you find this amazing discovery that neurons can be either active or inactive, in a way that can be described mathematically as zeroes and ones. Combine that with Alan Turing and you get this idea that the brain is like a computer and that it produces minds using that same simple method.

Religions have been one of the great causes of violence throughout history. But you can’t blame Christ for it.

That has been a very useful idea. And true enough, it explains a good part of the complex operations, that our brains produce such as language. Those operations require a lot of precision and are being carried out by cerebral cortex, with enormous detail, and probably in a basic computational mode. All the great successes of artificial intelligence used this idea and have been concerned with high-level reasoning. That is why A.I. has been so successful with games such as chess or Go. They use large memories and powerful reasoning.

Are you saying neural codes or algorithms don’t blend with living systems?

Well, they match very well with things that are high on the scale of the mental operations and behaviors, such as those we require for our conversation. But they don’t match well with the basic systems that organize life, that regulate, for example, the degree of mental energy and excitation or with how you emote and feel. The reason is that the operations of the nervous system responsible for such regulation relies less on synaptic signaling, the one that can be described in terms of zeroes and ones, and far more on non-synaptic messaging, which lends itself less to a rigid all or none operation.
Perhaps more importantly, computers are machines invented by us, made of durable materials. None of those materials has the vulnerability of the cells in our body, all of which are at risk of defective homeostasis, disease, and death. In fact, computers lack most of the characteristics that are key to a living system. A living system is maintained in operation, against all odds, thanks to a complicated mechanism that can fall apart as a result of minimal amounts of malfunction. We are extremely vulnerable creatures. People often forget that. Which is one of the reasons why our culture, or Western cultures in general, are a bit too calm and complacent about the threats to our lives. I think we are becoming less sensitive to the idea that life is what dictates what we should do or not do with ourselves and with others.

What is love for?

To protect, to cause flourishing, to give and receive pleasure, to procreate, to soothe. Endless great uses, as you can see.

How do emotions such as anger or sadness serve homeostasis?

At individual levels, both anger and sadness are protective. Anger lets your adversary know that you mean business and that there may be costs to attacking you. These days anger is an expression of sociopolitical conflicts. It is overused and has largely become ineffectual. Sadness is a prelude to mental hibernation. It lets you retreat and lick your wounds. It lets you plan a strategy of response to the cause of the wounds.

You say feelings spurred the creation of cultures. How so?

Before I started The Strange Order of Things, I was asking friends and colleagues how they thought cultures had begun. Invariably what people said was, “Oh, we’re so smart. We’re so intellectually powerful. We have all this reasoning ability. On top of it all, we have language—and there you are.” To which I say, “Fine, that’s true. How would you invent anything if you were stupid?” You would not. But the issue is to recognize the motive behind what you do. Why is it that you did it in the first place? Why did Moses come down from the mountain with Ten Commandments? Well, the Ten Commandments are representative of homeostasis because they tell you not to kill, not to steal, not to lie, not to do a lot of bad things. It sounds trivial but it’s not. We fail to think about motivation and so we do not factor it into the process of invention. We do not factor in the motives behind science or technology or governance or religion.

How does consciousness emerge out of nerve cells? Well, it doesn’t. You’re not dealing with the brain alone.

And there’s one more thing: The importance of feeling is that it makes you critically aware of what you are doing in moral terms. It forces you to look back and realize that what people were doing historically, at the outset, at the moment of invention of a cultural instrument or a cultural practice, was an attempt to reduce the amount of suffering and to maximize the amount of wellbeing not only for the inventor, but for the community around them. One person alone can invent a painting or a musical composition, but it is not meant for that person alone. And you do not invent a moral system or a government system alone or for yourself alone. It requires a society, a community.

The assertion that intellect is governed by feelings can sound New Age-y. It seems to undermine the powers of reason. How should we understand reason if it’s always motivated by subjective feelings?

Subjective simply means that it has a personal point of view, that it pertains to the self. It is compatible with “objective” facts and with truth. It is not about relativism. The fact that feelings motivate the use of knowledge and reason do not make the knowledge and the reason any less truthful or valid. Feelings are simply a call to action.

If humans formed societies and cultures to avoid suffering and pain, why do we have violence and wars?

Your question is very important. Take developments of political systems. On the face of it, when you look at Marxist ideas, you say, “This is obviously homeostatic.” What Marx and others were trying to do in the 19th century is confront and modify a social arrangement that was not equitable, that had some people suffering too much and some profiting too much. So having a system that produced equality made a lot of sense. In a way that is something that biological systems have been trying to do, quite naturally, for a long time. And when the natural systems do not succeed at improved regulation, guess what? They are weeded out by evolution because they promote illness.
Biological evolution, through genetic selection, eliminates those mechanisms. At the cultural level something comparable occurs. Seen in retrospect, Marxism as applied in Russia resulted in one of the worst tragedies of humankind. But Russian communism was ultimately weeded out by cultural selection. It took around 70 years to do it, but cultural selection did operate in a homeostatic way. It led to the fall of the Berlin Wall and the Soviet empire. It was a homeostatic correction achieved by social means.
The same reasoning applies to religions. For example, we can claim that religions have been one of the great causes of violence throughout history. But you certainly can’t blame Christ for that violence. He preached compassion, and the pardoning of enemies, and love. It does not follow that good recommendations can be implemented correctly and always produce good results. These facts in no way deny the homeostatic intent of religions.

You write, “The increasing knowledge of biology from molecules to systems reinforces the humanist project.” How so?

This knowledge gives us a broader picture of who we are and where we are in the history of life on earth. We had modest beginnings, and we have incorporated an incredible amount of living wisdom that comes from as far down as bacteria. There are characteristics of our personal and cultural behavior that can be found in single-cell organisms or in social insects. They clearly do not have the kind of highly developed brains that we have. In some cases, they don’t have any brain at all. But by analyzing this strange order of developments we are confronted with the spectacle of life processes that are complex and rich in spite of their apparent modesty, so complex and rich that they can deliver the high level of behaviors that we normally, quite pretentiously, attribute only to our great human smarts. We should be far more humble. That’s one of my main messages. In general, connecting cultures to the life process makes apparent a link that we have ignored for far too long.


What would you be if you weren’t a scientist?

When I was an adolescent, I often thought that I might become a philosopher or perhaps a playwright or filmmaker. That’s because I so admired what philosophers and storytellers had found about the human mind. Today when people ask me, “Who’s your most admired cognitive scientist?” I say Shakespeare. He knew it all and knew it with enormous precision. He didn’t have the nice fMRI scanner and electrophysiology techniques we have in our Institute. But he knew human beings. Watch a good performance of Hamlet, King Lear, or Othello. All of our psychology is there, richly analyzed, ready for us to experience and appreciate.

“On Demand”

Note: This post is a continuation of prior posts on complex, adaptive systems. This post focusses on the virtual workplace, the virtual retailer, the virtual employer, and their myriad manifestations in today’s world. These particular complex, adaptive systems will have the ability to rapidly expand or contract based on demand. And this is the point of this post: to explore the notion of “on demand”.

“On Demand”
Its so obvious …. but, then again, its not so obvious: “on demand” is the drumbeat of daily life. But the 21st century is putting the notion of “on demand” on steroids!

What is “on demand”?
I want a glass of wine, right now. I either pour myself one, buy one, or ask someone else to pour me one. “On demand”.

I need a hotel room, right now. Hotels inventory rooms. I rent one. “On demand”.

I need to haircut, right now. Barbers are open for business. I visit one. They are not busy so they take me. I buy the haircut. “On demand”.

Note that “on demand” wine needs an open bottle of wine to be available; the hotel room requires a hotel; the haircut requires a barber open for business;

In the 21st century, it seems clear to me that “on demand” will morph into smaller, more flexible slices. Consumers and companies will be able to purchase these slices when they want them, for as long as they want them.

It’s happening at lightning speed! There are so many examples. You can find them everywhere, in:

On Demand Transportation

The point: in the 20th century, you had to rent a car or bike or ride for a day from a business location, and now you can rent it for exactly as long as you want it from a street location.

Uber revolutionized the taxi business when they broke the paradigm and said “Effectively immediately, and car with driver can pick up a passenger and get paid to take them somewhere.” From a passenger’s POV, the result is revolutionary: I can get anywhere I want, anytime I want, by simply alerting a central intelligence on-line that I need ride from x to y at z time.

Every major city now rents bikes. Grab a bike at one stand and leave it at another stand. Take the bike from x to y at z time.

ZipCars are on-demand cars.

On Demand Work

The point: in the 20th century, you either had a job or you didn’t. Now you can have a job for a half hour of your choosing. “Temporary Help” agencies filled any gaps – when the job-holder was unable to work.

LiveOps revolutionized call center management by organizing workers to be available when the client wants the worker, for as long as the client wants the worker. They keep workers trained and on–the-ready, so they can deploy them virtually as needed.

On Demand Work Space

The point: In the 20th century, you worked someplace and employers employed workers in workplaces. Today workplaces are built for flexibility, so many employers can do their work with employees when they need the workplace and how they need the workplace for as long as they need the workplace.

Metro Studios and others are replacing the Hollywood “studio” with a flexible studio. Studios in the past built spaces for their filming needs. Metro Studios works with any client that will rent their massive spaces – for as long as the client needs the space, and not longer. Note that the “Studio” is a big box, easily repurposed to a warehouse or distribution center or big box store if demand shifts.

“Co-working” is exploding, and has revolutionized office work. A co-working space can be sized up or down as demand requires, for as long as demand requires. Co-working can suit the individual virtual worker, who can come in as they wish and stay as long as they wish. But, importantly, more and more companies are using co-working facilities in order to have flex space that suits them.

Self-storage is exploding, giving companies and consumers the ability to get storage space when they want it, for as long as they want it.

On Demand Housing

The point: Hotels, long term rentals and short term rentals will have their place in tomorrow’s economy but ordinary people with extra space in their houses will make places available when, where, and however long they are needed.

AirB&B and VRBO have revolutionized the way we access temporary housing. Go on line, check out who’s offering what, and when, and then make your selection.

On Demand Entertainment

The point: entertainment was made available at a certain time, at a certain place (a concert venue, a movie theater, a movie channel, or a TV channel). No longer. Increasingly, consumers will get what they want, when they want it, for as long as they want it.

Netflix revolutionized on demand movies by letting consumer get what they want when they want it. They started with on-line movie rentals that requires physical shipping of CD’s, but rapidly moved to on-line downloads and streaming. Amazon is chasing them, but with amazing speed.

Cable companies are perfecting “on demand” movies. Select the movie you want and when you want to view it (including immediately), and press “play”.

Amazon is perfecting “on demand” books. Select the book you want, and how you want to read it, and press “buy”. Download and start reading right now. No shipping. No library schlepping.

On Demand Tools

The point: In the 20th century, the norm was “if you want a tool, buy it and put it in a safe place until you need it. The norm is changing to “when you need a tool, order it up for as when you need it, for as long as you need it.”

Home Depot and Loews both have lucrative side businesses that allow businesses and consumers to rent the exact tool they need for as long as they need it.

On Demand Medicine

The point: in the 20th century, when you needed a doctor, you would call the office and make an appointment. If it was urgent, you would beg for the appointment to be sooner rather than later. We are not yet at an inflection point, but the trends seem clear enough: if you need a doctor, you can get a doctor – when you need it, and through the medium that makes the most sense to you.

CVS and Walgreen’s both are perfecting the mini-clinic. Modeled after the convenience store revolution of the 1960’s, mini-clinics are inside the store, and require a sign-up sheet, and that’s all. If the doctor is available, they will see you.

Telemedicine is taking full advantage of Skype and other two-way video conference platforms. In the best case, a patient’s blood work, vital signs, and medical history can be on-line while the patient is online, so the doctor can have as much context as possible. And when the doctor also has a genetic history, in the future a myriad of risks that cannot be currently understood will be known.

Other examples of “On Demand”

On Demand Meals Fast food showed the way to drive-throughs; Then Domino’s showed the way to pizza “on demand” – when you want it, how you want it. Yesterday, this was delivered to my house: a spaghetti made out of squash, in a coconut curry, with a fresh salad and lasagna for the kids. Costs a bit more, but so worth it!

On Demand Internet There are no good examples at the present time, but isn’t it plausible that the average consumer could summon ultra-high-speed internet “on-demand”? The consumer is just fine most of the day with low-speed internet, for emails and searches, etc. But if they want to watch a movie, an want to avoid slow downloads, or breaks in streaming quality, then they are happy to pay for “express lane service”.

On Demand Inventory This is old news, but further illustrates the mega trend: procurement can now demand that materials and components contracted and scheduled arrive when and as needed, minimizing inventory carrying costs.

On-demand Event Space
There has always been a demand for highly flexible event space. This is the world of clubs, hotels, etc, where it is usual to build a big box in your space that can be outfitted to a client’s needs. Today, though, that has been professionalized through companies like Convene, who specializes in this business.

========================== APPENDIX ==========================
References:

Co-Working

Virtual Workplace and Virtual Retailer

Co-Working – Update

On-Demand Work Articles and Commentary
The New York Times article below refers to a mega-trend: on-demand work. The author refers to it as a “tectonic shift in how jobs are structured“.

The focus of the article is Liveops, but this is only illustrative of this larger trend. https://www.liveops.com. Their competitor is https://workingsolutions.com .

On their front page, LiveOps says: “It’s a highly skilled workforce of virtual agents who flex to meet customer needs.”

On-demand work is exploding in customer service call centers and sales.

Some points I found interesting:

Roughly 3,000,000 Americans find work this way – as independent contractors working on a virtual basis.

Since 2001, apparently the move to outsource call centers to India has reversed. Today, the focus is on quality, and so the new trend is toward employing American workers, on a contract basis, and on a virtual basis.

They are only paid while on the phone. This is roughly 75% of the time they “commit” (“commits” are made in half hour blocks)

Top performers get the first call. “Performance-Based Routing, so the top-performing agents on your programs get more calls. By aligning our agents’ incentives with your goals, each agent who answers the call will be invested in your business objectives. What’s more, you won’t be paying call centers for idle time.”

Clients hire LiveOp. In this article, TruStage Insurance is the client.

Liveops CEO is Greg Hanover. Their competitor, Working Solutions, was founded in 1996. LiveOps says they have 20,000 agents, which they refer to as “Liveops Nation”.

“We hand-pick our agents for their great phone voices and warm and friendly personalities.”

“Scalable and flexible contract center outsourcing – leveraging an on-demand distributed network.”

=====================
CREDIT: https://www.nytimes.com/2017/11/11/business/economy/call-center-gig-workers.html?smid=nytcore-ipad-share&smprod=nytcore-ipad
=====================

Plugging Into the Gig Economy, From Home With a Headset

A company called Liveops has become the
Uber of call centers by doting on its agents.
But is the work liberating, or dehumanizing?
By NOAM SCHEIBERNOV. 11, 2017

DURHAM, N.C. — The gathering in a private dining room at a Mexican restaurant had the fervent energy of a megachurch service, or maybe an above-average “Oprah” episode — a mix of revival-style confession and extravagant empathy. There were souls to be won.

“By the end of the day, Kelly’s going to be an agent,” the group’s square-jawed leader said. “Kelly went through the process a while ago, then life happens, now she’s back. Her commitment to me that she made earlier, she looked me right in the eyes and told me she’s going to be an agent.”
Paradise, for these pilgrims, lies at one end of a phone line.

The company behind this spectacle, Liveops, had invited several dozen freelance call-center agents to a so-called road show. Some of them may have answered your customer-service calls to Home Depot or AAA. All were among the more than 100,000 agents who work as independent contractors through on-demand platforms like the one Liveops operates, which uses big data, algorithms and gamelike techniques to match its agents to clients. What Uber is to cars, Liveops is to call centers.

The agents are part of a tectonic shift in how jobs are structured. More companies are pushing work onto freelancers, temps, contractors and franchisees in the quest for an ever more nimble profit-making machine. It is one reason a job category seemingly headed offshore forever — customer service representatives — has been thriving in call centers and home offices across the United States, supporting roughly three million workers.

While critics of the arrangement cite rising insecurity, some of Liveops’ star agents — like Emmett Jones in Chicago, who knows of his rivals primarily as numbers on a leader board — say the opportunity has been transformative.

The earnest gratitude of the agents assembled here, not far from the Raleigh-Durham Airport, affirmed that. To them, Liveops is a sustaining force, a way to earn a living while being present at home. A few had driven hours to attend. Many brought friends and family members who were considering joining “Liveops Nation,” too.

There were icebreakers (“Liveops Nation Bingo”). Gift-card raffles (“$150?” the chief executive quipped. “Who approved these things?”). Free enchiladas. Everyone was invited to schmooze.
“John, I heard your story about how you got to us is pretty great,” said the master of ceremonies, an impossibly sunny woman named Tara. “Would you mind telling all these people?”
When the mic came to John, a former insurance claims adjuster with a gray beard and several earrings, there was a sense of imminent revelation.

“I was working in another glass box over near here for six years,” he began. “I reached the point where it was either jump off the roof or walk out the front door.” The other agents laughed knowingly.

He continued: “My commute now is I walk down the hall, close the bedroom door behind me.” More laughter.
Then John’s voice softened: “This is good, this is good. I get paid for when I’m working, instead of souring when you get paid for 40 hours and work some more. So, I’m here.”
“Awesome,” Tara said, applause drowning her out. “I feel like John’s story mimics a lot of what we hear from people.”
According to Greg Hanover, a longtime Liveops official who became chief executive this summer, the company’s goal is to make agents feel as if they’re part of a movement, not just earning a wage.

“Where we want to be with this is what Mary Kay has done, multilevel marketing companies,” Mr. Hanover said, referring to the cosmetics distributor and its independent sales force. “The direction we need to head in for the community within Liveops Nation is that the agents are so happy, so satisfied with the purpose and meaning there, that they’re telling their story.”

It’s an ambition that feels almost radical compared with Uber, whose best-known exercise in worker outreach is a video of its former chief executive berating a driver. It was heartening to discover that on-demand work could be both financially viable and emotionally fulfilling.

That is, until I began to speak with Mr. Jones and some of his Liveops competitors. The more you talk with them, the more you detect a kind of Darwinian struggle behind the facade of community and self-actualization. You start to wonder: Is there really such a thing as a righteous gig-economy job, even if the company is as apparently well intentioned as Liveops? Or is there something about the nature of gig work that’s inescapably dehumanizing?

Just the Right Tone
Mr. Jones, who lives in Chicago, was the top rated Liveops agent for an insurer called TruStage for much of this year.
An AT&T technician for decades, he decided that he needed to be at home not long after his wife was diagnosed with vertigo in 2008. “I can’t work and be worried about how she’s doing,” he said.

A few years later, when his daughter told him of a friend who worked with Liveops, he was eager to sign up — but refused to send in his required voice test until it was close to perfect. “I must have did the voice test four or five times,” he said. “I wanted to make sure I gave the right tone that they were looking for.”

As a Liveops agent, Mr. Jones sells life policies to callers, often those who have just seen a television commercial for TruStage insurance. He estimates that he works roughly 40 hours each week, beginning around 8 most mornings, and that he makes about $20 an hour. He is such a valued worker that TruStage invited him to its headquarters earlier this year for a two-day visit by an elite group of agents, in which executives pumped them for insights about how to increase sales.

Roughly two decades ago, Liveops and its competitors typically connected callers to psychic hotlines, and in some cases less reputable services. Such businesses had frequent spikes in call volume, making it helpful to have an on-demand work force that could be abruptly ramped up.

“The only thing people were interested in was the abandonment rate” — that is, the number of people who would hang up in frustration from being kept on hold — said Kim Houlne, the chief executive of a Liveops rival called Working Solutions, which she founded in 1996.

The call center industry took a hit during the 2001 recession, when cost consciousness unleashed a wave of outsourcing to India. But within 10 years, many companies decided that the practice, known as offshoring, had been oversold. The savings on wages were often wiped out by lost business from enraged customers, who preferred to communicate with native English speakers.
“People don’t feel comfortable,” Ms. Houlne said, alluding to the overseas agents.

By the early part of this decade, quality was in fashion. The enormous amounts of data that companies like Liveops and Working Solutions collect allowed them to connect callers to the best possible agent with remarkable precision, while allowing big clients to avoid the overhead of a physical call center and full-time workers.

Today, in addition to sales calls, Liveops agents handle calls from people trying to file insurance claims, those in need of roadside assistance, even those with medical or financial issues relating to prescription drugs. The agents must obtain a certification before they can handle such calls, which sometimes takes weeks of online coursework.

Liveops goes to great lengths to attend to their needs, addressing technical-support issues, even answering agents’ emails to the chief executive within 24 hours.

Mr. Jones, like many of his fellow agents, thinks of himself as helping others in need. He said that many families will gather around a table after a loved one has died to discuss the burial. If the deceased relative had no insurance, he said, “A lot of times that table is going to clear.” If, on the other hand, he had even $2,000 in life insurance — the minimum that TruStage sells — “the family members are more inclined to say, ‘He did what he could, let me see what I could do to help out.’ You end up with $5,000 to $6,000. You can do a decent burial rather than none at all.”

Still, there is undeniably a brass-tacks quality to the work. Shortly after we hung up, I turned my attention to an assignment due that afternoon, only to receive more calls from Mr. Jones’s number. When I finally answered, he apologized for interrupting me, then came to the point. “I have a question for you,” he said. “Do you have life insurance?”

‘Where the Price Point Is’
Like Uber, Liveops expends considerable effort calculating demand for its agents. For example, if an auto insurance company is running a commercial on ESPN, Liveops will ask the company’s media buyer — that is, the intermediary that placed the ad — to predict how many calls such an ad is likely to generate. Liveops will adjust that prediction, using its own data showing how many calls similar ads have produced from similar audiences during a comparable time of year.

And like Uber, the Liveops focuses on “utilization” — in the Liveops case, the percentage of working agents actually on a call. Depending on the client, Liveops strives for rates of 65 percent to 75 percent. Lower than that and the agents, who make money only when they’re on a call, will complain that they’re not busy enough. Significantly higher and the system is vulnerable to a sudden increase in demand that could tie up the phone lines and keep callers waiting.

Liveops asks agents to schedule themselves in half-hour blocks, known as “commits,” for the upcoming week. If the company expects demand to be higher than the number of commits, it sends agents a message urging them to sign up. (Uber does something similar, except without formal scheduling.) Sometimes it will even offer financial incentives, like a bump in the rate earned for each minute they’re on a call, or a raffle-type scheme in which people accumulate tickets for the giveaway of an iPad or a cruise.

Again like Uber, Liveops relentlessly tests the effectiveness of these tools. Referring to financial incentives, Jon Brown, the Liveops senior director of client services, said, “We’ve zeroed in on exactly what we need for an agent to go from 10 to 15 commits, from 15 commits to 20 commits. We know where the price point is, what drives behavior.”

And then there are the performance metrics. Liveops agents are rated according to what are called key performance indicators, which, depending on the customer, can include the number of sales they make, their success at upselling customers, and whether a caller would recommend the service based on their interaction.

Liveops makes clear that its agents’ ability to earn more money is closely tied to performance. “You’ve heard the term meritocracy?” said a Liveops official named Aimee Matolka at the North Carolina event. “When a call comes in, it routes in to that best agent. Yes, our router is that smart. You guys want to be that agent, I know you do. Otherwise you wouldn’t be here.”

It allows the agents to track their rankings obsessively through internal leader boards. (Liveops officials say that while the pressures of the job can preoccupy agents, it is up to them how much time to invest.)

“I lost the No. 1 spot, now I’m No. 2,” Mr. Jones said in early August, acknowledging that he checks his ranking frequently. “I thought about researching to find out who it is — you always want to know who’s the competition — but I said leave it.”

He added: “I’m a competitive person. We just toggle back and forth. If they see me jump back in, they work harder. They want that spot back.”

‘This Is My Phone Call’
My flight to Bangor, Me., was due after 9 p.m., and apparently sensing my unease with the North Country, the firefighter seated next to me asked if I had to far to drive when we landed. “About three hours north,” I confessed. “Watch out for moose,” he said. I assured him I’d driven around deer before. He stopped me short: If you hit a deer, you’ll kill them, he said. If you hit a moose, they’ll kill you.

I found Troy Carter, the agent who had recently surpassed Emmett Jones, at his home in Fort Fairfield the next day, wearing jeans, a button-down short-sleeve shirt, and a New England Patriots hat. There were no shoes on his feet, only white socks.

Like Mr. Jones, Mr. Carter said Liveops had been a blessing, allowing him to earn a living in a part of Maine so remote that my cellphone carrier welcomed me to Canada shortly after I pulled into his driveway.

When I told Mr. Carter that I had been in touch with his top competitor, he quickly pulled up the latest monthly rankings of Liveops agents selling TruStage insurance. He pointed out that while Mr. Jones, whom he recognized only by his identification number, 141806, had more sales — 87 to his 82 — he had far fewer paid sales, charged at the time of purchase rather than by invoice.

“The real thing is the paid application rate — they want it around 95 percent,” Mr. Carter said. “He has 87 sales, but only 65 percent paid, compared to my 94 percent.” This, he explained, was why he enjoyed the right to call himself the top agent for the month.

Mr. Carter is what you might call a serial entrepreneur. He once started an art supply website that folded within a few months, and a penny auction site called Bid Tree that foundered for lack of a marketing budget.

He sees Liveops, on which he spends 40 to 50 hours per week, as of a piece with these entrepreneurial efforts. In fact, it is something of a family business. His wife, Lori, handles incoming calls while he’s busy with customers. “I’m a housewife/secretary/receptionist,” she said. Even Mr. Carter’s 9-year-old son, Logan, plays a role. “At nighttime, he says the last part of his prayer based on how many sales I did today,” Mr. Carter said. “If it was a lot of sales, he’ll pray, ‘Dear Lord, help my dad get the same amount of sales tomorrow.’”

Though Liveops agents work from a script, Mr. Carter, like Mr. Jones, adds his own flourishes. Before asking a caller’s gender, as he is required to do, he will say, “Now I already know the answer to this question, but please confirm if you’re male or female.” Upon receiving the answer, he will pause momentarily before saying, “I told you I already knew the answer,” and break into a laugh.

He might make this identical joke, with identical timing, dozens of times in a workday. “It’s like a comedian has a little pause before a joke,” he told me. “It relaxes them right off.”

Even with these touches, results can vary widely. Two days earlier, Mr. Carter had made seven sales, only a few shy of his record. The day I turned up, he managed only one. He said some callers had the impression they could receive $25,000 of insurance for $9.95 per month — the commercial mentions both figures — and begged off when Mr. Carter told them that
Mr. Carter has done research on how to comport himself, including watching an instructional YouTube video by the former stockbroker who was the subject of the movie “The Wolf of Wall Street.” He believes the key is to come off as the alpha presence. “The one that asks the most questions is the one in control,” he said. “If they ask me questions — ‘How are you doing?’ — I’ll come back, ‘The question is how are you doing?’ This is my phone call, as much as I can make it.”

But on this day he repeatedly ran up against the limits of his powers. Even those who remained interested after 10 or 15 minutes of painstaking back-and-forth often demurred when Mr. Carter asked them for payment information. “This one guy was outside in a wheelchair,” Mr. Carter said of a caller who couldn’t produce his credit card. “He didn’t want to go in and get it. I said, ‘I’m fine waiting,’ but I can’t push him.”

These setbacks only seem to make Mr. Carter focus more. At one point, he made a swiping motion between his face and his headset with his index finger and middle finger. “They recommend that you keep the microphone two fingers away,” he said. “I’m always doing that — checking that it’s two fingers. I’ll do that for the rest of my life.”

It seemed, all in all, like a grueling way to make the slightly more than $30,000 that Mr. Carter estimates he takes in before taxes. “The good thing is he can take hours off,” Lori told me. “But then he can lose his spot. It’s always a fight for the top.”

I was reminded of the Alec Baldwin monologue from the movie “Glengarry Glen Ross,” except that the prize for having the most sales wouldn’t be a Cadillac, it would be a set of steak knives, because the Liveops analytics team had calculated that agents would give nearly as much effort for a prize worth a small fraction of the cost.

Of course, unlike the salesmen in that movie, the Liveops agents can’t really be fired — the third prize — because they weren’t employees to begin with.

A while later, Mr. Carter described a recent initiative in which agents were promised a bonus if 95 percent of their collective sales were paid up front. “I knew it wasn’t going to work as soon as they said it,” he told me, because a handful of agents with low paid rates could ruin everyone else’s chances.

“They did do a pullover sweatshirt for the top two,” he added, brightening. “I was second, so that’s coming.”

A version of this article appears in print on November 12, 2017, on Page BU1 of the New York edition with the headline: Paradise at the End of a Phone Line. Order Reprints| Today’s Paper|Subscribe

Thought Recognition and BCIS

The Economist kicked off their 2018 year with a bold prediction: “Brain-computer interfaces may change what it means to be human.”

In their lead article, they suggest that BCIS (Brain Computer Interfaces) like the BrainGate System are leading the way into a new world: where mind control works.

I feel like I did in 1979 when I first heard about the Apple II. The whole world was mainframe computing and time-sharing of those monsters, and yet two guys in a garage blow a massive hole through this paradigm, turn it on its head, and invent personal computing.

Think about it: personal computing had been evolving and constantly improving now for almost forty year!

Back then, I could see the future vaguely, in very partial outlines, without much practical effect, but with intense curiosity.

Another example is voice recognition. I still remember being introduced to the subject, way back in …. 1970? I got all excited about it, until I realized …. it sucked! And it wasn’t going to get much better anytime soon. But I remember saying to myself: I can’t be fooled by the first versions of voice recognition. I can’t lull myself to sleep. I need to watch this space because it will evolve and improve over time.

If you think about it, technology version 1-10 always sucks. The history of speech recognition in the 1950’s and 1960’s is, well, pathetic.

IBM’s SHOEBOX was introduced at the 1962 World’s Fair.
DARPA got involved in the late 1970’s, and then partnered with Carnegie Mellon on HARPY – a major advance.
Threshold Technology was formed then, in order to advance commercialization of primitive speech recognition.
And now we have SIRI.

And sure, enough, after almost 40 years of trying, voice recognition is getting really, really good. Can we see a time within the next 10 years when voice recognition replaces most keyboard applications?

I think so.

And so it is with this subject. We are at the very, very beginning, when it all sounds vague, with partial outlines, without much practical effect, and yet ….. it fills me with intense curiosity.

What could the next fifty years bring?

Is it possible that we will be able to think something, and have that something (a thought? a prescribed action? an essay?) become physical?

Read on…..

===============================

CREDIT: Economist Article on The Next Frontier

TECHNOLOGIES are often billed as transformative. For William Kochevar, the term is justified. Mr Kochevar is paralysed below the shoulders after a cycling accident, yet has managed to feed himself by his own hand. This remarkable feat is partly thanks to electrodes, implanted in his right arm, which stimulate muscles. But the real magic lies higher up. Mr Kochevar can control his arm using the power of thought. His intention to move is reflected in neural activity in his motor cortex; these signals are detected by implants in his brain and processed into commands to activate the electrodes in his arms.

An ability to decode thought in this way may sound like science fiction. But brain-computer interfaces (BCIs) like the BrainGate system used by Mr Kochevar provide evidence that mind-control can work. Researchers are able to tell what words and images people have heard and seen from neural activity alone. Information can also be encoded and used to stimulate the brain. Over 300,000 people have cochlear implants, which help them to hear by converting sound into electrical signals and sending them into the brain. Scientists have “injected” data into monkeys’ heads, instructing them to perform actions via electrical pulses.

As our Technology Quarterly in this issue explains, the pace of research into BCIs and the scale of its ambition are increasing. Both America’s armed forces and Silicon Valley are starting to focus on the brain. Facebook dreams of thought-to-text typing. Kernel, a startup, has $100m to spend on neurotechnology. Elon Musk has formed a firm called Neuralink; he thinks that, if humanity is to survive the advent of artificial intelligence, it needs an upgrade. Entrepreneurs envisage a world in which people can communicate telepathically, with each other and with machines, or acquire superhuman abilities, such as hearing at very high frequencies.

These powers, if they ever materialise, are decades away. But well before then, BCIs could open the door to remarkable new applications. Imagine stimulating the visual cortex to help the blind, forging new neural connections in stroke victims or monitoring the brain for signs of depression. By turning the firing of neurons into a resource to be harnessed, BCIs may change the idea of what it means to be human.

That thinking feeling
Sceptics scoff. Taking medical BCIs out of the lab into clinical practice has proved very difficult. The BrainGate system used by Mr Kochevar was developed more than ten years ago, but only a handful of people have tried it out. Turning implants into consumer products is even harder to imagine. The path to the mainstream is blocked by three formidable barriers—technological, scientific and commercial.

Start with technology. Non-invasive techniques like an electroencephalogram (EEG) struggle to pick up high-resolution brain signals through intervening layers of skin, bone and membrane. Some advances are being made—on EEG caps that can be used to play virtual-reality games or control industrial robots using thought alone. But for the time being at least, the most ambitious applications require implants that can interact directly with neurons. And existing devices have lots of drawbacks. They involve wires that pass through the skull; they provoke immune responses; they communicate with only a few hundred of the 85bn neurons in the human brain. But that could soon change. Helped by advances in miniaturisation and increased computing power, efforts are under way to make safe, wireless implants that can communicate with hundreds of thousands of neurons. Some of these interpret the brain’s electrical signals; others experiment with light, magnetism and ultrasound.

Clear the technological barrier, and another one looms. The brain is still a foreign country. Scientists know little about how exactly it works, especially when it comes to complex functions like memory formation. Research is more advanced in animals, but experiments on humans are hard. Yet, even today, some parts of the brain, like the motor cortex, are better understood. Nor is complete knowledge always needed. Machine learning can recognise patterns of neural activity; the brain itself gets the hang of controlling BCIS with extraordinary ease. And neurotechnology will reveal more of the brain’s secrets.

Like a hole in the head
The third obstacle comprises the practical barriers to commercialisation. It takes time, money and expertise to get medical devices approved. And consumer applications will take off only if they perform a function people find useful. Some of the applications for brain-computer interfaces are unnecessary—a good voice-assistant is a simpler way to type without fingers than a brain implant, for example. The idea of consumers clamouring for craniotomies also seems far-fetched. Yet brain implants are already an established treatment for some conditions. Around 150,000 people receive deep-brain stimulation via electrodes to help them control Parkinson’s disease. Elective surgery can become routine, as laser-eye procedures show.

All of which suggests that a route to the future imagined by the neurotech pioneers is arduous but achievable. When human ingenuity is applied to a problem, however hard, it is unwise to bet against it. Within a few years, improved technologies may be opening up new channels of communications with the brain. Many of the first applications hold out unambiguous promise—of movement and senses restored. But as uses move to the augmentation of abilities, whether for military purposes or among consumers, a host of concerns will arise. Privacy is an obvious one: the refuge of an inner voice may disappear. Security is another: if a brain can be reached on the internet, it can also be hacked. Inequality is a third: access to superhuman cognitive abilities could be beyond all except a self-perpetuating elite. Ethicists are already starting to grapple with questions of identity and agency that arise when a machine is in the neural loop.

These questions are not urgent. But the bigger story is that neither are they the realm of pure fantasy. Technology changes the way people live. Beneath the skull lies the next frontier.

This article appeared in the Leaders section of the print edition under the headline “The next frontier”

================== REFERENCE: History of Speech Recognition =====

CREDIT:

PC WORLD ARTICLE ON HISTORY OF SPEECH RECOGNITION

Speech Recognition Through the Decades: How We Ended Up With Siri

By Melanie Pinola
PCWorld | NOV 2, 2011 6:00 PM PT

Looking back on the development of speech recognition technology is like watching a child grow up, progressing from the baby-talk level of recognizing single syllables, to building a vocabulary of thousands of words, to answering questions with quick, witty replies, as Apple’s supersmart virtual assistant Siri does.

Listening to Siri, with its slightly snarky sense of humor, made us wonder how far speech recognition has come over the years. Here’s a look at the developments in past decades that have made it possible for people to control devices using only their voice.

1950s and 1960s: Baby Talk
The first speech recognition systems could understand only digits. (Given the complexity of human language, it makes sense that inventors and engineers first focused on numbers.) Bell Laboratories designed in 1952 the “Audrey” system, which recognized digits spoken by a single voice. Ten years later, IBM demonstrated at the 1962 World’s Fair its “Shoebox” machine, which could understand 16 words spoken in English.

Labs in the United States, Japan, England, and the Soviet Union developed other hardware dedicated to recognizing spoken sounds, expanding speech recognition technology to support four vowels and nine consonants.
They may not sound like much, but these first efforts were an impressive start, especially when you consider how primitive computers themselves were at the time.

1970s: Speech Recognition Takes Off

Speech recognition technology made major strides in the 1970s, thanks to interest and funding from the U.S. Department of Defense. The DoD’s DARPA Speech Understanding Research (SUR) program, from 1971 to 1976, was one of the largest of its kind in the history of speech recognition, and among other things it was responsible for Carnegie Mellon’s “Harpy” speech-understanding system. Harpy could understand 1011 words, approximately the vocabulary of an average three-year-old.

Harpy was significant because it introduced a more efficient search approach, called beam search, to “prove the finite-state network of possible sentences,” according to Readings in Speech Recognition by Alex Waibel and Kai-Fu Lee. (The story of speech recognition is very much tied to advances in search methodology and technology, as Google’s entrance into speech recognition on mobile devices proved just a few years ago.)

The ’70s also marked a few other important milestones in speech recognition technology, including the founding of the first commercial speech recognition company, Threshold Technology, as well as Bell Laboratories’ introduction of a system that could interpret multiple people’s voices.

1980s: Speech Recognition Turns Toward Prediction
Over the next decade, thanks to new approaches to understanding what people say, speech recognition vocabulary jumped from about a few hundred words to several thousand words, and had the potential to recognize an unlimited number of words. One major reason was a new statistical method known as the hidden Markov model.
Rather than simply using templates for words and looking for sound patterns, HMM considered the probability of unknown sounds’ being words. This foundation would be in place for the next two decades (see Automatic Speech Recognition—A Brief History of the Technology Development by B.H. Juang and Lawrence R. Rabiner).

Equipped with this expanded vocabulary, speech recognition started to work its way into commercial applications for business and specialized industry (for instance, medical use). It even entered the home, in the form of Worlds of Wonder’s Julie doll (1987), which children could train to respond to their voice. (“Finally, the doll that understands you.”)
See how well Julie could speak:

However, whether speech recognition software at the time could recognize 1000 words, as the 1985 Kurzweil text-to-speech program did, or whether it could support a 5000-word vocabulary, as IBM’s system did, a significant hurdle remained: These programs took discrete dictation, so you had … to … pause … after … each … and … every … word.

Next page: Speech recognition for the masses, and the future of speech recognition

The Dying Algorithm

CREDIT: NYT Article on the Dying Algorithm

This Cat Sensed Death. What if Computers Could, Too
By Siddhartha Mukherjee
Jan. 3, 2018

Of the many small humiliations heaped on a young oncologist in his final year of fellowship, perhaps this one carried the oddest bite: A 2-year-old black-and-white cat named Oscar was apparently better than most doctors at predicting when a terminally ill patient was about to die. The story appeared, astonishingly, in The New England Journal of Medicine in the summer of 2007. Adopted as a kitten by the medical staff, Oscar reigned over one floor of the Steere House nursing home in Rhode Island. When the cat would sniff the air, crane his neck and curl up next to a man or woman, it was a sure sign of impending demise. The doctors would call the families to come in for their last visit. Over the course of several years, the cat had curled up next to 50 patients. Every one of them died shortly thereafter.
No one knows how the cat acquired his formidable death-sniffing skills. Perhaps Oscar’s nose learned to detect some unique whiff of death — chemicals released by dying cells, say. Perhaps there were other inscrutable signs. I didn’t quite believe it at first, but Oscar’s acumen was corroborated by other physicians who witnessed the prophetic cat in action. As the author of the article wrote: “No one dies on the third floor unless Oscar pays a visit and stays awhile.”
The story carried a particular resonance for me that summer, for I had been treating S., a 32-year-old plumber with esophageal cancer. He had responded well to chemotherapy and radiation, and we had surgically resected his esophagus, leaving no detectable trace of malignancy in his body. One afternoon, a few weeks after his treatment had been completed, I cautiously broached the topic of end-of-life care. We were going for a cure, of course, I told S., but there was always the small possibility of a relapse. He had a young wife and two children, and a mother who had brought him weekly to the chemo suite. Perhaps, I suggested, he might have a frank conversation with his family about his goals?

But S. demurred. He was regaining strength week by week. The conversation was bound to be “a bummah,” as he put it in his distinct Boston accent. His spirits were up. The cancer was out. Why rain on his celebration? I agreed reluctantly; it was unlikely that the cancer would return.

When the relapse appeared, it was a full-on deluge. Two months after he left the hospital, S. returned to see me with sprays of metastasis in his liver, his lungs and, unusually, in his bones. The pain from these lesions was so terrifying that only the highest doses of painkilling drugs would treat it, and S. spent the last weeks of his life in a state bordering on coma, unable to register the presence of his family around his bed. His mother pleaded with me at first to give him more chemo, then accused me of misleading the family about S.’s prognosis. I held my tongue in shame: Doctors, I knew, have an abysmal track record of predicting which of our patients are going to die. Death is our ultimate black box.

In a survey led by researchers at University College London of over 12,000 prognoses of the life span of terminally ill patients, the hits and misses were wide-ranging. Some doctors predicted deaths accurately. Others underestimated death by nearly three months; yet others overestimated it by an equal magnitude. Even within oncology, there were subcultures of the worst offenders: In one story, likely apocryphal, a leukemia doctor was found instilling chemotherapy into the veins of a man whose I.C.U. monitor said that his heart had long since stopped.

But what if an algorithm could predict death? In late 2016 a graduate student named Anand Avati at Stanford’s computer-science department, along with a small team from the medical school, tried to “teach” an algorithm to identify patients who were very likely to die within a defined time window. “The palliative-care team at the hospital had a challenge,” Avati told me. “How could we find patients who are within three to 12 months of dying?” This window was “the sweet spot of palliative care.” A lead time longer than 12 months can strain limited resources unnecessarily, providing too much, too soon; in contrast, if death came less than three months after the prediction, there would be no real preparatory time for dying — too little, too late. Identifying patients in the narrow, optimal time period, Avati knew, would allow doctors to use medical interventions more appropriately and more humanely. And if the algorithm worked, palliative-care teams would be relieved from having to manually scour charts, hunting for those most likely to benefit.

Avati and his team identified about 200,000 patients who could be studied. The patients had all sorts of illnesses — cancer, neurological diseases, heart and kidney failure. The team’s key insight was to use the hospital’s medical records as a proxy time machine. Say a man died in January 2017. What if you scrolled time back to the “sweet spot of palliative care” — the window between January and October 2016 when care would have been most effective? But to find that spot for a given patient, Avati knew, you’d presumably need to collect and analyze medical information before that window. Could you gather information about this man during this prewindow period that would enable a doctor to predict a demise in that three-to-12-month section of time? And what kinds of inputs might teach such an algorithm to make predictions?
Avati drew on medical information that had already been coded by doctors in the hospital: a patient’s diagnosis, the number of scans ordered, the number of days spent in the hospital, the kinds of procedures done, the medical prescriptions written. The information was admittedly limited — no questionnaires, no conversations, no sniffing of chemicals — but it was objective, and standardized across patients.

These inputs were fed into a so-called deep neural network — a kind of software architecture thus named because it’s thought to loosely mimic the way the brain’s neurons are organized. The task of the algorithm was to adjust the weights and strengths of each piece of information in order to generate a probability score that a given patient would die within three to 12 months.

The “dying algorithm,” as we might call it, digested and absorbed information from nearly 160,000 patients to train itself. Once it had ingested all the data, Avati’s team tested it on the remaining 40,000 patients. The algorithm performed surprisingly well. The false-alarm rate was low: Nine out of 10 patients predicted to die within three to 12 months did die within that window. And 95 percent of patients assigned low probabilities by the program survived longer than 12 months. (The data used by this algorithm can be vastly refined in the future. Lab values, scan results, a doctor’s note or a patient’s own assessment can be added to the mix, enhancing the predictive power.)

So what, exactly, did the algorithm “learn” about the process of dying? And what, in turn, can it teach oncologists? Here is the strange rub of such a deep learning system: It learns, but it cannot tell us why it has learned; it assigns probabilities, but it cannot easily express the reasoning behind the assignment. Like a child who learns to ride a bicycle by trial and error and, asked to articulate the rules that enable bicycle riding, simply shrugs her shoulders and sails away, the algorithm looks vacantly at us when we ask, “Why?” It is, like death, another black box.

Still, when you pry the box open to look at individual cases, you see expected and unexpected patterns. One man assigned a score of 0.946 died within a few months, as predicted. He had had bladder and prostate cancer, had undergone 21 scans, had been hospitalized for 60 days — all of which had been picked up by the algorithm as signs of impending death. But a surprising amount of weight was seemingly put on the fact that scans were made of his spine and that a catheter had been used in his spinal cord — features that I and my colleagues might not have recognized as predictors of dying (an M.R.I. of the spinal cord, I later realized, was most likely signaling cancer in the nervous system — a deadly site for metastasis).
It’s hard for me to read about the “dying algorithm” without thinking about my patient S. If a more sophisticated version of such an algorithm had been available, would I have used it in his case? Absolutely. Might that have enabled the end-of-life conversation S. never had with his family? Yes. But I cannot shake some inherent discomfort with the thought that an algorithm might understand patterns of mortality better than most humans. And why, I kept asking myself, would such a program seem so much more acceptable if it had come wrapped in a black-and-white fur box that, rather than emitting probabilistic outputs, curled up next to us with retracted claws?

Siddhartha Mukherjee is the author of “The Emperor of All Maladies: A Biography of Cancer” and, more recently, “The Gene: An Intimate History.”

Regulatory State and Redistributive State

Will Wilkinson is a great writer, and spells out here two critical aspects of government:

The regulatory state is the aspect of government that protects the public against abuses of private players, protects property rights, and creates well-defined “corridors” that streamline the flows of capitalism and make it work best. It always gets a bad rap, and shouldn’t. The rap is due to the difficulty of enforcing regulations on so many aspects of life.

The redistributive state is the aspect of government that deigns to shift income and wealth from certain players in society to other players. The presumption is always one of fairness, whereby society deems it in the interests of all that certain actors, e.g. veterans or seniors, get preferential distributions of some kind.

He goes on to make a great point. These two states are more independent of one another than might at first be apparent. So it is possible to dislike one and like another.

Personally, I like both. I think both are critical to a well-oiled society with capitalism and property rights as central tenants. My beef will always go to issues of efficiency and effectiveness?

On redistribution, efficiency experts can answer this question: can we dispense with the monthly paperwork and simply direct deposit funds? Medicare now works this way, and the efficiency gains are remarkable.

And on regulation, efficiency experts can answer this question: can private actors certify their compliance with regulation, and then the public actors simple audit from time to time? Many government programs work this way, to the benefit of all.

ON redistribution, effectiveness experts can answer this question: Is the homeless population minimal? Are veterans getting what they need? Are seniors satisfied with how government treats them?

On regulation, effectiveness experts can answer this question: Is the air clean? Is the water clean? Is the mortgage market making food loans that help people buy houses? Are complaints about fraudulent consumer practices low?

CREDIT: VOX Article on Economic Freedom by Will Wilkinson

By Will Wilkinson
Sep 1, 2016

American exceptionalism has been propelled by exceptionally free markets, so it’s tempting to think the United States has a freer economy than Western European countries — particularly those soft-socialist Scandinavian social democracies with punishing tax burdens and lavish, even coddling, welfare states. As late as 2000, the American economy was indeed the freest in the West. But something strange has happened since: Economic freedom in the United States has dropped at an alarming rate.

Meanwhile, a number of big-government welfare states have become at least as robustly capitalist as the United States, and maybe more so. Why? Because big welfare states needed to become better capitalists to afford their socialism. This counterintuitive, even paradoxical dynamic suggests a tantalizing hypothesis: America’s shabby, unpopular safety net is at least partly responsible for capitalism’s flagging fortunes in the Land of the Free. Could it be that Americans aren’t socialist enough to want capitalism to work? It makes more sense than you might think.

America’s falling economic freedom

From 1970 to 2000, the American economy was the freest in the West, lagging behind only Asia’s laissez-faire city-states, Hong Kong and Singapore. The average economic freedom rating of the wealthy developed member countries of the Organization for Economic Cooperation and Development (OECD) has slipped a bit since the turn of the millennium, but not as fast as America’s.
“Nowhere has the reversal of the rising trend in the economic freedom been more evident than in the United States,” write the authors of Fraser Institute’s 2015

Economic Freedom of the World report, noting that “the decline in economic freedom in the United States has been more than three times greater than the average decline found in the OECD.”

The economic freedom of selected countries, 1999 to 2016. Heritage Foundation 2016 Index of Economic Freedom

The Heritage Foundation and the Canadian Fraser Institute each produce an annual index of economic freedom, scoring the world’s countries on four or five main areas, each of which breaks down into a number of subcomponents. The main rubrics include the size of government and tax burdens; protection of property rights and the soundness of the legal system; monetary stability; openness to global trade; and levels of regulation of business, labor, and capital markets. Scores on these areas and subareas are combined to generate an overall economic freedom score.

The rankings reflect right-leaning ideas about what it means for people and economies to be free. Strong labor unions and inequality-reducing redistribution are more likely to hurt than help a country’s score.

So why should you care about some right-wing think tank’s ideologically loaded measure of economic freedom? Because it matters. More economic freedom, so measured, predicts higher rates of economic growth, and higher levels of wealth predict happier, healthier, longer lives. Higher levels of economic freedom are also linked with greater political liberty and civil rights, as well as higher scores on the left-leaning Social Progress Index, which is based on indicators of social justice and human well-being, from nutrition and medical care to tolerance and inclusion.

The authors of the Fraser report estimate that the drop in American economic freedom “could cut the US historic growth rate of 3 percent by half.” The difference between a 1.5 percent and 3 percent growth rate is roughly the difference between the output of the economy tripling rather than octupling in a lifetime. That’s a huge deal.
Over the same period, the economic freedom scores of Canada and Denmark have improved a lot. According to conservative and libertarian definitions of economic freedom, Canadians, who enjoy a socialized health care system, now have more economic freedom than Americans, and Danes, who have one of the world’s most generous welfare states, have just as much.
What the hell’s going on?

The redistributive state and the regulatory state are separable

To make headway on this question, it is crucial to clearly distinguish two conceptually and empirically separable aspects of “big government” — the regulatory state and the redistributive state.

The redistributive state moves money around through taxes and transfer programs. The regulatory state places all sorts of restrictions and requirements on economic life — some necessary, some not. Most Democrats and Republicans assume that lots of regulation and lots of redistribution go hand in hand, so it’s easy to miss that you can have one without the other, and that the relationship between the two is uneasy at best. But you can’t really understand the politics behind America’s declining economic freedom if you fail to distinguish between the regulatory and fiscal aspects of the economic policy.

Standard “supply-side” Republican economic policy thinking says that cuts in tax rates and government spending will unleash latent productive potential in the economy, boosting rates of growth. And indeed, when taxes and government spending are very high, cuts produce gains by returning resources to the private sector. But it’s important to see that questions about government control versus private sector control of economic resources are categorically different from questions about the freedom of markets.

Free markets require the presence of good regulation, which define and protect property rights and facilitate market processes through the consistent application of clear law, and an absence of bad regulation, which interferes with productive economic activity. A government can tax and spend very little — yet still stomp all over markets. Conversely, a government can withdraw lots of money from the economy through taxes, but still totally nail the optimal balance of good and bad regulation.

Whether a country’s market economy is free — open, competitive, and relatively unmolested by government — is more a question of regulation than a question of taxation and redistribution. It’s not primarily about how “big” its government is. Republicans generally do support a less meddlesome regulatory approach, but when they’re in power they tend to be much more persistent about cutting taxes and social welfare spending than they are about reducing economically harmful regulatory frictions.

If you’re as worried about America’s declining economic freedom as I am, this is a serious problem. In recent years, the effect of cutting taxes and spending has been to distribute income upward and leave the least well-off more vulnerable to bad luck, globalization, “disruptive innovation,” and the vagaries of business cycles.
If spending cuts came out of the military’s titanic budget, that would help. But that’s rarely what happens. The least connected constituencies, not the most expensive ones, are the first to get dinged by budget hawks. And further tax cuts are unlikely to boost growth. Lower taxes make government seem cheaper than it really is, which leads voters to ask for more, not less, government spending, driving up the deficit. Increasing the portion of GDP devoted to paying interest on government debt isn’t a growth-enhancing way to return resources to the private sector.

Meanwhile, wages have been flat or declining for millions of Americans for decades. People increasingly believe the economy is “rigged” in favor of the rich. As a sense of economic insecurity mounts, people anxiously cast about for answers.

Easing the grip of the regulatory state is a good answer. But in the United States, its close association with “free market” supply-side efforts to produce growth by slashing the redistributive state has made it an unattractive answer, even with Republican voters. That’s at least part of the reason the GOP wound up nominating a candidate who, in addition to promising not to cut entitlement spending, openly favors protectionist trade policy, giant infrastructure projects, and huge subsidies to domestic manufacturing and energy production. Donald Trump’s economic policy is the worst of all possible worlds.

This is doubly ironic, and doubly depressing, once you recognize that the sort of big redistributive state supply-siders fight is not necessarily the enemy of economic freedom. On the contrary, high levels of social welfare spending can actually drive political demand for growth-promoting reform of the regulatory state. That’s the lesson of Canada and Denmark’s march up those free economy rankings.

The welfare state isn’t a free lunch, but it is a cheap date

Economic theory tells you that big government ought to hurt economic growth. High levels of taxation reduce the incentive to work, and redistribution is a “leaky bucket”: Moving money around always ends up wasting some of it. Moreover, a dollar spent in the private sector generally has a more beneficial effect on the economy than a dollar spent by the government. Add it all up, and big governments that tax heavily and spend freely on social transfers ought to hurt economic growth.

That matters from a moral perspective — a lot. Other things equal, people are better off on just about every measure of well-being when they’re wealthier. Relative economic equality is nice, but it’s not so nice when relatively equal shares mean smaller shares for everyone. Just as small differences in the rate at which you put money into a savings account can lead to vast differences in your account balance 40 years down the road, thanks to the compounding nature of interest, a small reduction in the rate of economic growth can leave a society’s least well-off people much poorer in absolute terms than they might have been.

Here’s the puzzle. As a general rule, when nations grow wealthier, the public demands more and better government services, increasing government spending as a percentage of GDP. (This is known as “Wagner’s law.”) According to standard growth theory, ongoing increase in the size of government ought to exert downward pressure on rates of growth. But we don’t see the expected effect in the data. Long-term national growth trends are amazingly stable.

And when we look at the family of advanced, liberal democratic countries, countries that spend a smaller portion of national income on social transfer programs gain very little in terms of growth relative to countries that spend much more lavishly on social programs. Peter Lindert, an economist at the University of California Davis, calls this the “free lunch paradox.”

Lindert’s label for the puzzle is somewhat misleading, because big expensive welfare states are, obviously, expensive. And they do come at the expense of some growth. Standard economic theory isn’t completely wrong. It’s just that democracies that have embraced generous social spending have found ways to afford it by minimizing and offsetting its anti-growth effects.

If you’re careful with the numbers, you do in fact find a small negative effect of social welfare spending on growth. Still, according to economic theory, lunch ought to be really expensive. And it’s not.

There are three main reasons big welfare states don’t hurt growth as much as you might think. First, as Lindert has emphasized, they tend to have efficient consumption-based tax systems that minimize market distortions.
When you tax something, people tend to avoid it. If you tax income, as the United States does, people work a little less, which means that certain economic gains never materialize, leaving everyone a little poorer. Taxing consumption, as many of our European peers do, is less likely to discourage productive moneymaking, though it does discourage spending. But that’s not so bad. Less consumption means more savings, and savings puts the capital in capitalism, financing the economic activity that creates growth.

There are other advantages, too. Consumption taxes are usually structured as national sales taxes (or VATs, value-added taxes), which are paid in small amounts on a continuous basis, are extremely cheap to collect (and hard to avoid), while being less in-your-face than income taxes, which further mitigates the counterproductively demoralizing aspect of taxation.

Big welfare states are also more likely to tax addictive stuff, which people tend to buy whatever the price, as well as unhealthy and polluting stuff. That harnesses otherwise fiscally self-defeating tax-avoiding behavior to minimize the costs of health care and environmental damage.
Second, some transfer programs have relatively direct pro-growth effects. Workers are most productive in jobs well-matched to their training and experience, for example, and unemployment benefits offer displaced workers time to find a good, productivity-promoting fit. There’s also some evidence that health care benefits that aren’t linked to employment can promote economic risk-taking and entrepreneurship.

Fans of open-handed redistributive programs tend to oversell this kind of upside for growth, but there really is some. Moreover, it makes sense that the countries most devoted to these programs would fine-tune them over time to amplify their positive-sum aspects.

This is why you can’t assume all government spending affects growth in the same way. The composition of spending — as well as cuts to spending — matters. Cuts to efficiency-enhancing spending can hurt growth as much as they help. And they can really hurt if they increase economic anxiety and generate demand for Trump-like economic policy.

Third, there are lots of regulatory state policies that hurt growth by, say, impeding healthy competition or closing off foreign trade, and if you like high levels of redistribution better than you like those policies, you’ll eventually consider getting rid of some of them. If you do get rid of them, your economic freedom score from the Heritage Foundation and the Fraser Institute goes up.
This sort of compensatory economic liberalization is how big welfare states can indirectly promote growth, and more or less explains why countries like Canada, Denmark, and Sweden have become more robustly capitalist over the past several decades. They needed to be better capitalists to afford their socialism. And it works pretty well.

If you bundle together fiscal efficiency, some offsetting pro-growth effects, and compensatory liberalization, you can wind up with a very big government, with very high levels of social welfare spending and very little negative consequences for growth. Call it “big-government laissez-faire.”

The missing political will for genuine pro-growth reform

Enthusiasts for small government have a ready reply. Fine, they’ll say. Big government can work through policies that offset its drag on growth. But why not a less intrusive regulatory state and a smaller redistributive state: small-government laissez-faire. After all, this is the formula in Hong Kong and Singapore, which rank No. 1 and No. 2 in economic freedom. Clearly that’s our best bet for prosperity-promoting economic freedom.

But this argument ignores two things. First, Hong Kong and Singapore are authoritarian technocracies, not liberal democracies, which suggests (though doesn’t prove) that their special recipe requires nondemocratic government to work. When you bring democracy into the picture, the most important political lesson of the Canadian and Danish rise in economic freedom becomes clear: When democratically popular welfare programs become politically nonnegotiable fixed points, they can come to exert intense pressure on fiscal and economic policy to make them sustainable.

Political demand for economic liberalization has to come from somewhere. But there’s generally very little organic, popular democratic appetite for capitalist creative destruction. Constant “disruption” is scary, the way markets generate wealth and well-being is hard to comprehend, and many of us find competitive profit-seeking intuitively objectionable.

It’s not that Danes and Swedes and Canadians ever loved their “neoliberal” market reforms. They fought bitterly about them and have rolled some of them back. But when their big-government welfare states were creaking under their own weight, enough of the public was willing, thanks to the sense of economic security provided by the welfare state, to listen to experts who warned that the redistributive state would become unsustainable without the downsizing of the regulatory state.

A sound and generous system of social insurance offers a certain peace of mind that makes the very real risks of increased economic dynamism seem tolerable to the democratic public, opening up the political possibility of stabilizing a big-government welfare state with growth-promoting economic liberalization.

This sense of baseline economic security is precisely what many millions of Americans lack.

Learning the lesson of Donald Trump
America’s declining economic freedom is a profoundly serious problem. It’s already putting the brakes on dynamism and growth, leaving millions of Americans with a bitter sense of panic about their prospects. They demand answers. But ordinary voters aren’t policy wonks. When gripped by economic anxiety, they turn to demagogues who promise measures that make intuitive economic sense, but which actually make economic problems worse.

We may dodge a Trump presidency this time, but if we fail to fix the feedback loop between declining economic freedom and an increasingly acute sense of economic anxiety, we risk plunging the world’s biggest economy and the linchpin of global stability into a political and economic death spiral. It’s a ridiculous understatement to say that it’s important that this doesn’t happen.

Market-loving Republicans and libertarians need to stare hard at a framed picture of Donald Trump and reflect on the idea that a stale economic agenda focused on cutting taxes and slashing government spending is unlikely to deliver further gains. It is instead likely to continue to backfire by exacerbating economic anxiety and the public’s sense that the system is rigged.

If you gaze at the Donald long enough, his fascist lips will whisper “thank you,” and explain that the close but confusing identification of supply-side fiscal orthodoxy with “free market” economic policy helps authoritarian populists like him — but it hurts the political prospects of regulatory state reforms that would actually make American markets freer.

Will Wilkinson is the vice president for policy at the Niskanen Center.