Should we really wait until age 70 to receive Social Security benefits?

Everyone “knows” that you should defer receiving Social Security benefits until age 70.

But to what extent does this advice depend on the federal government making good on its Social Security commitments? The Social Security Administration trust fund is currently projected to run out of money in 15 years, and if no changes are made before then, in 2034 will be able to pay just 77% of scheduled benefits. Might it therefore be better to start receiving benefits at age 66—or even take early retirement at age 62?

There are at least two reasons to revisit this perennial question today. One is that Laurence Kotlikoff, a Boston University economics professor, recently took issuewith my column on this subject. Another is that, given that the midterms have produced an even more gridlocked Congress, the already-slim chances of a solution to the eventual Social Security funding shortfall have become even more remote.

My column on this subject, you may recall, focused on an analysis by Richard Band, the then-editor of Richard Band’s Profitable Investing newsletter. Band had just decided to begin receiving Social Security benefits at 66 rather than following the conventional wisdom of waiting until 70. Taking Band’s lead, I showed that, under certain assumptions about how the Federal government will handle its Social Security obligations, the present value of a retiree’s future Social Security benefits is higher if he begins receiving benefits at age 66 than at age 70.

Band wrote: “If you believe that Uncle Sam, saddled with a $20 trillion—and rapidly rising—national debt, will keep his Social Security promises flawlessly, you might as well wait until age 70 to take your benefits (assuming you’re in normal health).” Because he was skeptical, however, Band decided to “take the money and run.”

Kotlikoff’s analysis used different assumptions than I used in my earlier column and, not surprisingly, reached different conclusions. One big difference is that I assumed that Social Security would begin reducing benefits gradually before 2034, rather than waiting until then and facing a 23% cut in benefit payments. To the extent cuts occur earlier rather than later, of course, those cuts will have a bigger impact on the present value of all future payments.

The gradual cuts I put into my analysis were that benefits would be cut 0.5% a year starting in 2023. The start date of those cuts is arbitrary, of course, but the notion of such cuts is very much being considered. Some in Congress, for example, are proposing that there be a change to how the Social Security Administration calculates annual cost of living increases, the impact of which would be to lower the annual COLA by an estimated half a percentage point a year.

Another assumption that has a big impact on whether or not to begin receiving Social Security at 66 or 70 is the rate at which future payments are reduced in order to represent their “present value.” To the extent this rate, known as the discount rate, is larger, then future years’ payments will be deemed to be worth less—and taking Social Security earlier becomes the preferred course of action.

In my January column I assumed the discount rate would be equal to the yield on the 10-year Treasury, which at the time was about a half percentage point higher than the inflation rate. I’ve been unable to determine the discount rate used by the software on Kotlikoff’s website, but many software packages assume that the discount rate is the same as inflation. If so, then my assumption of a higher discount rate would be another reason my analysis found that it might be better to begin receiving benefits earlier rather than later.

To be sure, Kotlikoff is entirely reasonable in basing his analysis on the assumption that there will be no cuts until 2034. Given the gridlock in Washington, it’s certainly possible that no changes to Social Security’s funding will get enacted before then, which is the point at which current law would mandate a 23% cut in benefit payments.

Likewise, reasonable people can and do disagree on what is the proper discount rate.

But the existence of such disagreements between reasonable people just reinforces my broader point: The question of when to begin receiving Social Security is not as settled as many assume it to be. Don’t let the apparent precision of the numerical conclusions reached by various analyses (including mine!) cause you to overlook the crucial and dominant role played by those analyses’ assumptions.

https://www.marketwatch.com/story/should-we-really-wait-until-age-70-to-receive-social-security-benefits-2018-12-18?siteid=rss&rss=1

Eleven Economic Reports Delayed Due to Gov’t Shutdown: Do We Even Need Them?

Housing starts, retail sales, and business inventory reports are MIA this week. 8 prior reports are also missing.

Government workers failed to produce at least three economic reports this week alone due to the shutdown. Other reports went missing in action last week.

Economic Reports Not Produced

1. New Home Sales: Dec 27
2. Advance Retail Inventories: Dec 28
3. Advance Wholesale Inventories: Dec 28
4. Advance Trade: Dec 28
5. Construction Spending: Jan 3
6. Factory Orders: Jan 7
7. International Trade: Jan 8
8. Wholesale Trade: Jan 10
9. Retail Sales: Jan 16
10. Business Inventories: Jan 16
11. Housing Starts: Jan 17

Do we really need government employees compiling these reports or is this something private industry should do?

Mike Mish Shedlock@MishGEA

Eleven Economic Reports Delayed Due to Gov’t Shutdown: Do We Even Need Them?https://moneymaven.io/mishtalk/economics/eleven-economic-reports-delayed-due-to-gov-t-shutdown-do-we-even-need-them-yp-E1_4ROki1knbyiq7x7g/ …

3:02 PM – Jan 17, 2019

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10%Yes, by the Gov’t

28%Yes, by Private Industry

62%No, they are useless

61 votes•Final results

https://moneymaven.io/mishtalk/economics/eleven-economic-reports-delayed-due-to-gov-t-shutdown-do-we-even-need-them-yp-E1_4ROki1knbyiq7x7g/

Gene therapy promotes nerve regeneration

Researchers from the Netherlands Institute for Neuroscience (NIN) and the Leiden University Medical Center (LUMC) have shown that treatment using gene therapy leads to a faster recovery after nerve damage. By combining a surgical repair procedure with gene therapy, the survival of nerve cells and regeneration of nerve fibers over a long distance was stimulated for the first time. The discovery, published in the journal Brain, is an important step towards the development of a new treatment for people with nerve damage.

During birth or following a traffic accident, nerves in the neck can be torn out of the spinal cord. As a result, these patients lose their arm function, and are unable to perform daily activities such as drinking a cup of coffee. Currently, surgical repair is the only available treatment for patients suffering this kind of nerve damage. “After surgery, nerve fibers have to bridge many centimeters before reaching the muscles and nerve cells from which new fibers need to regenerate are lost in large numbers. Most regenerating nerve fiber do not reach the muscles. The recovery of arm function is therefore disappointing and incomplete,” explains researcher Ruben Eggers of the NIN.

Combination of treatments

By combining neurosurgical repair with gene therapy in rats, many of the dying nerve cells can be rescued and nerve fiber growth in the direction of the muscle can be stimulated.

In this study, the researchers used regulatable gene therapy with a growth factor that could be switched on and off by using a widely used antibiotic. “Because we were able to switch off the gene therapy when the growth factor was no longer needed, the regeneration of new nerve fibers towards the muscles was improved considerably,” says Ruben Eggers.

A stealth gene switch

To overcome the problem of the immune system recognizing and removing the gene switch, the researchers developed a hidden version, a so-called ‘stealth switch’. Professor Joost Verhaagen (NIN) explains: “The stealth gene switch is an important step forward towards the development of gene therapy for nerve damage. The use of a stealth switch improves the gene therapy rendering it even safer.”

The gene therapy is not yet ready for use in patients. While the ability to switch off a therapeutic gene is a large step forward, the researchers still found small amounts of the active gene when the switch was turned off. Therefore, further research is needed to optimize this therapy.

The research was funded by Wings for Life, the International Spinal Research Trust and a donation from the Dwarslaesiefonds.

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Story Source:

Materials provided by Netherlands Institute for Neuroscience – KNAW. Note: Content may be edited for style and length.

https://www.sciencedaily.com/releases/2019/01/190118083157.htm

Biotech and health applications with new knowledge on bacteria and viruses

University of Otago research to better understand how bacteria and their viruses interact and evolve will enable future studies to exploit the use of bacteria and their viruses for potential biotechnology and health applications.

Research led by Dr Simon Jackson and Associate Professor Peter Fineran, from the Department of Microbiology and Immunology, investigating the function of bacteria immune systems and what impact they have on the coevolution of bacteria and viruses was published today in a top tier scientific journal, Cell Host and Microbe.

Viruses infecting bacteria are called bacteriophages (“phages” for short) and are the most abundant biological entities on the planet influencing many aspects of our lives and the global ecosystem.

Dr Jackson says the war between phages and bacteria is ever-present and many bacteria protect themselves using immune defences known as CRISPR-Cas systems.

“Research to understand more about the interactions between phages and bacteria, particularly how bacterial CRISPR-Cas immunity functions, is being exploited internationally in many ground-breaking biotechnological applications including gene editing,” Dr Jackson explains.

“We think this area of research holds a lot of promise for biotechnology applications and might also be an important consideration for the use of phages to treat infectious diseases.

“For example, because phages kill specific bacteria, they can be used as alternatives to antibiotics to treat some infectious diseases and can even kill antibiotic resistant bacteria.”

Bacterial adaptive immunity is similar in concept to human adaptive immunity. Bacteria must first become “vaccinated” against specific phages, which involves the bacteria storing a short snippet of viral DNA, termed a “spacer,” used to recognise and defend against future infections.

In a previous study examining how CRISPR-Cas systems acquire spacers, the Otago research team found that often bacteria acquire “incorrect” spacers, known as “slipped spacers.” At the time, they did not know whether the incorrect or imprecise slipped spacers were functional.

Associate Professor Fineran, a molecular microbiologist, says their initial observations were surprising and showed these slipped spacers were very efficient at boosting bacterial immunity by stimulating bacteria to acquire extra spacers targeting the same phage. This unexpected role increases immune diversity, which is important for bacteria to protect against the rapidly evolving phages.

“Several groups had previously identified the occurrence of imprecisely acquired or slipped spacers. However, no-one had previously considered whether they were functional or what impact they might have on immunity,” Associate Professor Fineran explains.

“By showing they are functional and can provide benefit to bacteria, we have revealed an unexpected complexity to the evolutionary battle between bacteria and phages.”

If in the future, researchers can determine how immunity is first gained, they may be able to either prevent or promote it for different applications, Associate Professor Fineran says.

“For example, in the dairy industry for the production of cheese and yoghurt it is beneficial for bacteria to have resistance against phages, whereas if phages are used as antimicrobials, emergence of immunity would be undesirable — akin to antibiotic resistance.”

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Story Source:

Materials provided by University of Otago. Note: Content may be edited for style and length.

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Journal Reference:

1. Simon A. Jackson, Nils Birkholz, Lucía M. Malone, Peter C. Fineran. Imprecise Spacer Acquisition Generates CRISPR-Cas Immune Diversity through Primed Adaptation. Cell Host & Microbe, 2019; DOI: 10.1016/j.chom.2018.12.014

https://www.sciencedaily.com/releases/2019/01/190118095941.htm

Medicare Covers Adaptive Biotechnologies’ ClonoSeq Assay

Adaptive Biotechnologies said after the close of the market on Thursday that Medicare contractor Palmetto GBA has established coverage of its next-generation sequencing-based assay, ClonoSeq, for Medicare patients with multiple myeloma and B-cell acute lymphoblastic leukemia.

The US Food and Drug Administration granted de novo premarket authorization to ClonoSeq last year to detect and monitor minimal residual disease by sequencing DNA from bone marrow of patients with some blood cancers.

The coverage is effective immediately. Another Medicare contractor, Noridian, also is covering ClonoSeq, Adaptive said.

“Availability of sensitive, specific, and standardized MRD testing is increasingly crucial to the delivery of optimal patient care in both multiple myeloma and ALL,” Nikhil Munshi, director of basic and correlative science at the Jerome Lipper Multiple Myeloma Center at Dana-Farber Cancer Institute, said in a statement. “Medicare coverage for the ClonoSeq assay will help ensure that eligible patients across the US have access to a highly advanced option for MRD assessment to support more personalized treatment decisions across their course of care.”

Charles Sang, Adaptive’s senior vice president of diagnostics, said that the establishment of Medicare coverage “demonstrates the clinical relevance of MRD assessment and underscores the benefit that this test delivers in the management of myeloma and ALL patients.”

https://www.genomeweb.com/sequencing/medicare-covers-adaptive-biotechnologies-clonoseq-assay#.XEO1a9JKjIU

Large Human Microbiome Analysis Discovers Thousands of New Species

By analyzing more than 9,000 metagenomes from human microbiome samples, an international team of researchers has uncovered more than 150,000 microbial genomes, many of which represent species that have never before been described.

Much of the diversity of the human microbiome remains unexplored, particularly among body sites other than the gut and among non-Westernized populations. Researchers led by Nicola Segata at the University of Trento in Italy reconstructed microbial genomes from more than 9,000 metagenomes from various human populations, geographies, lifestyles, and ages. As they reported in Cell today, they uncovered nearly 5,000 species-level genome bins, 77 percent of which were not present in public databases.

“We genetically characterized and catalogued a large number of bacteria and archaea that are part of the human microbiome, but remained so far unexplored, uncharacterized, and undescribed,” Segata said in a statement. “We also observed that many of these microbes tend to be only rarely identified in Westernized populations, most probably as a indirect consequence of the complex industrialization processes.”

The researchers used a large-scale metagenomic assembly approach to reconstruct bacterial and archaeal genomes from 9,316 metagenomes, generated by short-read sequencing. The metagenomes came from 46 datasets and included samples from different populations, body sites, and host ages. From these, the researchers reconstructed 154,723 genomes.

This, they noted, more than doubles the about 150,000 microbial genomes that were previously publicly available.

Using an all-versus-all genetic distance quantification and clustering approach, they organized these 154,723 genomes into about 5,000 species-level genome bins (SGBs). About 4,930 SGBs were from 22 known phyla and 345 could not be assigned a taxonomic family.

Still, 77 percent of the SGBs represented species without any publicly available genome, the researchers noted. These unknown SGBs include, on average, nine reconstructed genomes. Most of these unknown SGBs represent rare human-associated microbes — about half of the unknown SGBs contained one reconstructed genome — though the researchers noted that some are more prevalent.

Having these unknown SGBs increased mappability of metagenomic reads, the researchers noted. The read mappability for stool samples increased by 29 percent to 87.5 percent, and for the oral cavity by 26 percent to 82 percent. In particular, they increased the mappabilty of the gut microbiomes from non-Westernized populations, which now reached 83 percent.

The researchers dubbed one of the most prevalent unknown SGBs “Candidatus Cibiobacter qucibialis.” Through a phylogenetic analysis, they placed this candidate species between Faecalibacterium and Ruminococcus, both key members of the gut microbiome. Within this phylogenetic analysis, Ca. Cibiobacter qucibialis genomes from non-Westernized populations clustered together in a monophyletic subtree.

Functional profiling of Ca. Cibiobacter qucibialis found differences between the Westernized and non-Westernized strains. The non-Westernized strains, for instance, included the full tryptophan metabolism operon, while the Westernized strains did not. This, they noted, could be a sign of divergent evolution.

Even among well-studied microbiome bacteria, like Bacteroides, the researchers uncovered additional intra-species diversity.

But the addition of gut microbiomes from rural, non-Westernized populations in Madagascar uncovered a number of unknown SGBs, including in the Firmicutes and Actinobacteria phyla, as well as in other phyla that are not always associated with humans.

Functional annotation of these SGBs also uncovered some differences in what modules were enriched, though the researchers noted that the same functions were often encoded in both non-Westernized and Westernized microbiomes, but sometimes by different enzymes and pathways.

This, they said, suggests there are numerous ways the gut microbiome adapts to the diversity of its human hosts.

https://www.genomeweb.com/sequencing/large-human-microbiome-analysis-discovers-thousands-new-species#.XEO0U9JKjIU

AI is better at bluffing than professional gamblers

The act of gambling on games of chance has been around for as long as the games themselves. For as long as there’s been money to be made wagering on the uncertain outcomes of these events, bettors have been leveraging mathematics to give them an edge on the house. As gaming has moved from bookies and casinos into the digital realm, gamblers are beginning to use modern computing techniques, especially AI and machine learning (ML), to increase their odds of winning.

But that betting blade cuts both ways, as researchers work to design artificial intelligences capable of beating professional players at their own game — and even out-wagering sportsbooks.

The rate at which machine-learning AI systems have caught up and overtaken the skills of their human opponents has accelerated at a frightening pace in the past few years. IBM’s Watson famously wiped the floor against Jeopardy‘s master class of players in 2011. AlphaGo, from Google’s DeepMind division, beat European Go champion Fan Hui less than three years ago, in 2016, before mopping up South Korean professional Goplayer Lee Sedol two months later and posting a 60-0 record in online matches against some of the best players on the planet a year later.

The year 2017 saw AlphaZero, an AlphaGo offshoot, demolish the world champion chess program, Stockfish 8, in a 100-game matchup after spending just four hours learning how to play. And while it took a bit longer to master Dota 2 — “3500 simulated years” longer — OpenAI’s digital competitor managed to best the top amateur players in 2018.

AI has proved itself quite literally capable of beating humans at their own games, but does that hold true when the chips are down and real money is on the line? As the Libratus system from Carnegie Mellon University showed in 2017, the answer remains a resounding yes.

It wasn’t so much of a poker tournament as it was a three-week curb stomping. Professional poker players Jason Les, Dong Kyu Kim, Daniel McAulay and Jimmy Chou spent 20 days playing 120,000 hands of heads-up, no-limit Texas Hold’em against the AI but wound up losing by a margin of more than $1.76 million in the end.

“In the start here, we lost the first day,” Les told Engadget in 2018. “Whatever — not a big deal. And then we were losing, but then we fought back up to nearly equal. We were feeling really confident!” But confidence wasn’t enough to halt the gambling juggernaut’s advance. “It just kept improving every single day, and we started going backwards and backwards,” he continued.

The loss stung for Les and Kim who, two years prior, beat the pants off another poker AI, Claudico. That said, their drubbing wasn’t nearly as bad as what Lengpudashi, Libratus’ second iteration, put World Series veteran Alan Du and a team of engineers through later that year. Even though the humans tried to apply machine-learning lessons gleaned from the original tournament, the result was another bloodbath. The AI won by a landslide after more than 36,000 hands were played.

“People think that bluffing is very human — it turns out that’s not true,” Libratus co-developer Noam Brown said in a statement. “A computer can learn from experience that if it has a weak hand and it bluffs, it can make more money.”

Robert De Niro’s Casino character, Sam “Ace” Rothstein, was a living actuarial table for the Las Vegas mob. His encyclopedic knowledge of sports variables allowed him to make betting (and winning) look easy. Today, thanks to the rise of big data analytics and algorithmic AI, virtually any schmuck at the local sportsbook can perform at Rothstein’s level.

In fact, sports betting — whether it’s guessing who will win outright or what the margin of victory will be — is well suited for machine-learning applications. As Rory Bunker and Fadi Thabtah of Auckland University of Technology and the Nelson Marlborough Institute of Technology, respectively, illustrate in their 2017 study, “A machine learning framework for sport result prediction,” estimating the outcome of sports is a fairly straightforward affair for machine-learning systems.

“One of the common machine learning (ML) tasks, which involves predicting a target variable in previously unseen data, is classification,” the researchers write. “The aim of classification is to predict a target variable (class) by building a classification model based on a training dataset, and then utilizing that model to predict the value of the class of test data.”

With these models, clubs and managers can better size up their opponents and formulate better strategies to win more matches, while sportsbooks and individual bettors can more accurately estimate the game’s outcome ahead of time. “In sport prediction, large numbers of features can be collected including the historical performance of the teams, results of matches, and data on players,” the researchers continue, “to help different stakeholders understand the odds of winning or losing forthcoming matches.”

What’s more, these systems are in no way left wanting for training data. Any number of Major League Baseball stats can easily be gleaned from MLB.com, Baseball Reference, Sean Lahman’s database and Retrosheet, for example, just as advanced NHL stats can be found at Hockey Referenceand NFL data is available from NFL.com, ESPN or Pro Football Reference. Even the ATP tour for tennis has begun collecting analytic data to improve the game for players and fans alike, having teamed with Infosys in 2015.

“In US sports, we’re data junkies,” ATP chair umpire Ali Nili explained to Forbes in 2018. “If you look at an NBA game, afterward you look at stats. If you’re not a fan of the game they look like gibberish but a 6-year-old fan of the sport can translate it for you. The scope of data collection these days has no end. Tennis is no different, we’re getting more data and using more and more information.”

With so much data so freely available, it’s no surprise that a number of enterprising outfits are already leveraging AI and ML to perform seemingly impossible feats of predictive sports analysis. UK-based Stratagem, for example, is training AI to extract actionable patterns from football, baseball and tennis matches and use that data to make better bets.

The company currently employs human analyzers to track matches, then combines the data with odds from a variety of bookies to improve its wagering, but it is also in the process of developing a deep neural network to perform the same task in real time simply by watching a broadcast feed of the match, according to The Verge.

“Football [soccer] is such a low-scoring game that you need to focus on these sorts of metrics to make predictions,” Stratagem founder Andreas Koukorinis told The Verge in 2018. “If there’s a shot on target from 30 yards with 11 people in front of the striker and that ends in a goal, yes, it looks spectacular on TV, but it’s not exciting for us. Because if you repeat it 100 times the outcomes won’t be the same. But if you have Lionel Messi running down the pitch and he’s one-on-one with the goalie, the conversion rate on that is 80 percent. We look at what created that situation. We try to take the randomness out, and look at how good the teams are at what they’re trying to do, which is generate goal-scoring opportunities.”

Even more impressive are the predictions made by Unanimous AI. In 2016, the company released its “swarm intelligence” platform UNU, which “enables groups to get together as online swarms… combining their thoughts, opinions, and intuitions in real-time to answer questions, make predictions, reach decisions… as a unified collective intelligence,” according to a press statement. Using UNU, Unanimous managed a superfecta at that year’s Kentucky Derby. That means the company correctly predicted which four horses would cross the finish line first, in order, beating 540-to-1 odds.

“We were reluctant to take this challenge,” David Baltaxe, Unanimous’ chief information officer, said in a statement. “Nobody here knows anything about horse racing, and it’s notorious for being unpredictable. Still, UNU surprises us again and again, so we recruited a swarm of volunteers through an online ad. The whole thing took 20 minutes.”

The following February, Unanimous’ system correctly predicted the outcome of Super Bowl LI — down to the precise 34-28 final score — then went on to correctly pick 11 of that year’s 18 Oscar winners.

Should the capabilities of AI and ML systems continue to improve — and there’s no evidence to suggest that they won’t — this technology could fundamentally upend the world of professional sports betting. However, there are still limitations to what these systems can accomplish. For example, current predictive systems don’t have a means of accounting for a team’s mojo, how well the players “click” with one another or shifts in a game’s momentum.

“In some team sports, very good players don’t do much that’s measurable,” Adam Kucharski, author of The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling, told Digital Trends last February. “A very good player might just get into a good position. Tackle rates won’t show that. It’s their positioning and intuitive behavior that is having an influence.”

But with future advancements in supplementary fields like machine vision, these predictive engines will become even more potent. Say, for example, Jonathan Toews from the Blackhawks crumples on the ice after a vicious slash to the knee. Existing algorithms can’t currently take his injury into account and update their odds, at least not until the results from the team doctor are reported. But with machine vision, future systems may well be able to suss out the seriousness of his injury simply by gauging how badly he’s grimacing.

Ultimately, this technology will reach a singularity wherein we’ll be using AI-driven analytics to place real-time bets on robo-death matches. At least, with any luck we will.

https://www.engadget.com/2019/01/17/ai-is-better-at-bluffing-than-professional-gamblers/