Dunbar's number is a popular estimate for the maximum size of social groups. But new research suggests that it's a fictitious number based on flimsy data and bad theory.
- A team of researchers recalculated Dunbar's number using his original methods and better data.
- Their estimates were as high as 520 and were stretched over a wide enough range as to be nearly useless.
- The authors suggest that the method used to calculate the number of friends a person can have is also theoretically unsound.
Since 1992, people have been talking about "Dunbar's number," the supposed upper limit of the number of people with whom a person can maintain stable social relationships. Named for British anthropologist Robin Dunbar, its value, rounded from 148 to 150, has permeated both professional and popular culture.
The Swedish taxation authority keeps offices under 150 people as a result of it, and the standard facilities of the W. L. Gore and Associates company are based around the concept. Dunbar's number was cited in Malcolm Gladwell's bestselling book Tipping Point, and it also has a fair amount of academic influence, the original paper having been cited 2,500 times.
It's also probably wrong.
Despite its fame, Dunbar's number has always been controversial. A new study out of Sweden and published in the journal Biology Letters suggests it might be both theoretically and empirically unsound.
Getting to 150
Less well known than the value of Dunbar's number is how he came up with it. The value of 150 is determined by looking at the ratio between the size of the neocortex in primates and the average size of groups they form. These ratios were then applied to data on the human brain, and the average value of roughly 150 relationships was determined.
The point of this study isn't to replace Dunbar's number but to dismiss the notion that such a number can be determined in the first place.
However, this number has always been the subject of debate. An alternative value based on empirical studies of American social groups is a much higher 291, nearly double that of Dunbar, and suggests that the median social network has 231 people in it. That value wasn't calculated by crunching other numbers; it kept coming up again and again when the authors of that study looked at the professional and social networks cultivated by different groups of people.
Yet, even in the face of critics and new studies, Dunbar's number always managed to hang on in popular and academic discourse. That is where this latest study comes in.
A new study with old methods but better data
In the new study, the researchers did similar calculations as Dunbar but with updated information on the size of monkey brains and social networks. While their average human group size was below Dunbar's estimate, the upper boundary of the 95 percent confidence interval ranged between 2 and 520 people depending on what methods were used. Nearly every method gave a range of possibilities with a maximum value higher than 150.
When the authors applied Dunbar's exact same methods from 1992 to their new data, they got an average group size of 69 people — but a 95% confidence interval between roughly 5 and 292. This is far too wide a range to be of any use.
Additionally, the authors discuss the flimsy nature of the theory behind Dunbar's number. Human brains often work differently than those of our nearest evolutionary cousins, as evidenced by our ability to create things like, "Stockholm, symphonies, and science." The idea that we would process social information exactly like other apes do is a bold and largely unsubstantiated claim.
They quote a study by Jan De Ruiter and their rejection of the idea that the ratio between monkey neocortex size and group composition can be carried over to humans:
"Dunbar's assumption that the evolution of human brain physiology corresponds with a limit in our capacity to maintain relationships ignores the cultural mechanisms, practices, and social structures that humans develop to counter potential deficiencies"
So, is there a new Dunbar number?
The point of this study isn't to replace Dunbar's number but to dismiss the notion that such a number can be determined in the first place. The authors go so far as to end their paper with:
"It is our hope, though perhaps futile, that this study will put an end to the use of 'Dunbar's number' within science and in popular media. 'Dunbar's number' is a concept with limited theoretical foundation lacking empirical support."
While this study may not be the death of Dunbar's number — after all, less empirically sound ideas have endured much longer — it may be the foundation for new attempts to determine how large our meaningful and stable social groups can be.
People often divide the world into "us" and "them" then forget about everybody else.
- A new study shows that our polarized "us" vs. "them" view of the world can be modeled mathematically.
- Those who don't fit easily into either group tend to be disliked.
- The model is not limited to politics and could be used to explain many aspects of society.
In most of the great debates, many possible stances get reduced to two options in a hurry. In American politics, we often frame all debates as "Democrats versus Republicans" and proceed to label every policy or action as belonging to one of those factions. Anybody else, particularly those in the middle, are quickly swept aside and forgotten.
According to a new study, this tendency is not only common but is so ingrained in our thinking that you can make a formula to describe it.
"We know what happens to people who stay in the middle of the road. They get run down." — Aneurin Bevan
A team of researchers at the Santa Fe Institute studied the system using a commonly applied model to try to "solve" for where social boundaries appear on a political scale. The researchers added cognitive and social components -- which often drive people into an "us vs. them" style of thinking -- to the model's equations. They hypothesized that people would devise measurable camps of "us" and "them" which would appear in the models. Those who don't fit cleanly into these roles would tend to be overlooked.
The results of the model were compared to data from surveys in the 1980s, which included questions about how respondents felt about members of other political groups, to check for accuracy. Importantly, during that decade, many people were asked about political independents in the center, deemed "inbetweeners" by the researchers, as well as about their stance on members of other parties.
Though one might suspect that people viewed those in the middle as potential allies, the tendency to divide the world into two buckets simply excludes those in the middle. The model predicted, and the survey's from the 80s confirmed, that both those on the left and right viewed centrists unfavorably.
Lead author Vicky Chuqiao Yang explained the unfortunate situation of these centrists:
"By being 'inbetweeners,' independents are viewed as unfavorably as the other party by both sides, and left out. So Independents get the worst of both worlds, and there are downstream consequences."
Graph showing how members of both major American parties viewed fellow party members, those of the other party, and independents. The values came from the American National Election Studies surveys. Credit: Yang et al.
The authors speculate that this phenomenon could cause those in the middle to drift towards the extremes in hopes of avoiding the ills of being in the middle. Over time, this could lead to a highly polarized society, as nobody is left in the middle at all.
These findings are not necessarily limited to politics. The model could be applied to how society views people of indeterminate race or who do not fit cleanly into categories of sexuality, for example. Additionally, because group labels evolve over time, we should see the formation of entirely new groups and inbetweeners. In the study, the authors note that the model was limited for exactly this reason and hope that future studies will expand on the concept.
Could we have predicted COVID-19 through social media trends?
- The first human cases of COVID-19 (subsequently named SARS-Cov-2) were first reported by officials in Wuhan City, China, in December 2019. The first cases of the virus in Europe were discovered at the end of January 2020.
- Although there were really no preventative measures that could have completely stopped the pandemic, a new study takes a retrospective look at the months preceding the rapid spread of this virus.
- Researchers suggest that, in a successive phase of the pandemic (or any pandemic), monitoring social media could help public health authorities mitigate the risks of a contagion resurgence.
Although there were really no preventative measures that could have completely stopped the pandemic, a new study takes a retrospective look at the months preceding the rapid spread of this virus. Specifically, this study focused on Twitter to decide if there were 'warning signs' of the upcoming pandemic on social media.
Could social media have predicted the pandemic?
Geo-localization of pneumonia-related tweets posted across Europe since December 2019.
(A) Number of users discussing pneumonia between 15 December 2019 and 21 January 2020, after filtering out press releases and news accounts. (B) Relative variation in number of users discussing pneumonia between winter seasons 2019 and 2020.
Since January 2020, when the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) began to spread from China to Europe and the United States, criticism has intensified over the ways public health authorities across the globe could have better managed the threat.
Throughout this pandemic, different surveillance strategies have been used to monitor the spread of the virus, including sentinel surveillance systems, household surveys, lab-based surveys, community-based surveys, and the Integrated Disease Surveillance and Response (IDSR) framework. More recently, social media outlets have been used for monitoring epidemics and informing the judgments and decisions of public health officials and experts.
A new study conducted by researchers at IMT School for Advanced Studies Lucca analyzed data from Twitter to uncover early warning signs of COVID-19 outbreaks in Europe during the winter season of 2019-2020. On December 31, 2019, WHO (World Health Organization) was informed of the first "cases of pneumonia of unknown etiology." Tracking spikes in pneumonia trends was a big part of this study.
Why focus on pneumonia?
Pneumonia is the most severe condition induced by COVID-19. Additionally, the flu season in 2020 was milder than in previous years, which means there were fewer cases of flu-induced pneumonia.
The study used "pneumonia" as a keyword to track potential COVID-19 induced cases.
The study created a unique database including all public messages posted on Twitter between December 1, 2014 and March 1, 2020. This search included the seven most commonly spoken languages: English, Germany, French, Italian, Spanish, Polish, and Dutch.
There were several adjustments made to avoid overestimations on the number of tweets mentioning cases of pneumonia during this time. Most notably, the study removed the effects on posting activity of COVID-19 related news that appeared up to January 21, 2020 (the day this virus was recognized as a serious transmissible disease), due to the fact that most tweets after this date mentioning pneumonia would be related to the COVID-19 outbreak even if they did not use the word COVID in the tweet.
The analysis shows an increase in tweets mentioning the keyword "pneumonia" in most of the European countries included in the study as early as January 2020.
In Italy, for example, where the first lock-down measures to contain the spread of COVID-19 were introduced on February 22, 2020 - the increase rate in mentions of the keyword during the first weeks of 2020 differs substantially from the rate observed in the same weeks of the previous years.
This could indicate that potentially hidden infection hotspots were identifiable several weeks before the announcement of the first local source of the virus in Italy (which happened on February 20, 2020, in Codogno, Italy.) France exhibited a similar pattern, whereas Spain, Poland, and the U.K. witnessed a delay of two weeks.
The analysis of tweets was then correlated to the regions where the first cases of infections were later reported.
The authors discovered through geo-localization that over 13,000 tweets in this same period came from the regions where the first cases of COVID-19 were later reported.
"Our study adds on to the existing evidence that social media can be a useful tool of epidemiological surveillance. They can help intercept the first signs of a new disease, before it proliferates undetected, and also track its spread," explains Massimo Riccaboni, full professor of Economics at the IMT School, to Eurekalert. Massimo coordinated the large-scale research effort.
How could this help in the future?
Researchers suggest that, in a successive phase of the pandemic (or any pandemic), monitoring social media could help public health authorities mitigate the risks of a contagion resurgence. For example, by adopting stricter measures of social distancing where the infections appear to be increasing. The researchers of the study suggest that these tools could also be a way forward to an integrated epidemiological surveillance system that is globally managed by international health organizations.
Would you ever have sex with a robot?
- In 2016, "Harmony", the world's first AI sex robot was designed by a tech firm called Realbotix.
- According to 2020 survey data, more than one in five Americans (22 percent) say they would consider having sex with a robot. This is an increase from a survey conducted in 2017.
- Robots (and robotic tech) already play a vital role in speeding up manufacturing, packaging, and processing across various industries.
From homemade dildos to Harmony, the AI sex robot
"...amid an economic crisis, with restaurants and retailers closing their doors and larger companies laying off and furloughing employees, the sex tech industry is booming."
A Bustle article published in April 2020, weeks after COVID-19 was declared a pandemic, explored the drastic boost in the sex tech industry. According to the research, Dame Products (a popular sex toy retailer) experienced a 30 percent increase in sales between the months of February to April, and popular sexual wellness brand Unbound reported selling twice as many toys as normal in this period.
While the new coronavirus was crashing the economy in other ways, the sex tech industry was one of the few that actually saw improvements, likely due to people all over the world being advised, encouraged, and in some instances forced to stay at home.
Something similar happened in 2008, during the recession: the sex toy industry was one of the only industries at the time that didn't gravely suffer.
The evolution of sex tech from stone dildos to artificial intelligence.
The history of sex toys is quite interesting. A 28,000-year-old siltstone dildo was uncovered in Germany in 2005. Luxury bronze dildos have also been found in China that are at least 2,000 years old.
Aside from various materials being shaped into dildos, there has always been an interest in how to advance sex technology, even before it involved actual technology at all.
- The 1700s: Steam-powered vibrators (such as the Manipulator).
- The 1800s—1900s: The invention of the first electric vibrator (the Pulsoson) and "beauty tools" being used for sexual satisfaction (such as the Polar Cub massager)
- The 1920s—1940s: The introduction of hand-held massagers (the Andis Vibrator) and compact devices (such as the Oster Stim-U-Lax)
- The 1940s—1960s: Japan introduced the "Cadillac of Vibrators" (The Hitachi Magic Wand), which eventually made it's way to America.
- 1965: The invention of silicone, which most modern sex toys are made of.
- The 1980s—1990s: The invention of the rabbit-style vibrator, made more popular with one of the first showings of a sex toy on television ("Sex and the City").
- The 2000s: Visual porn website Pornhub launched and sex toys became increasingly popular. Erotic literature also became more common and popular, with "50 Shades of Grey" and others like it.
- The 2010s and beyond: Sex toys and technology start to blend, and the world's first internet-controlled sex toy was launched in 2010 by Lovense.
In 2016, "Harmony", the world's first AI sex robot was designed by a tech firm called Realbotix.
From television shows to real-life applications, artificial intelligence (AI) is becoming more and more popular in all areas of human life.
Credit: Willyam Bradberry on Shutterstock
In 2020, more than one in five Americans (22 percent) say they would consider having sex with a robot. YouGov conducted a study in February 2020 that compared results from a similar study from 2017.
According to the results, 6 percent more people in 2020 are comfortable with the idea of having sex with a robot than in 2017.
YouGov points out that the increase in consideration is particularly significant among American adults between the ages of 18-34 years old. Additionally, how people feel about having sex with a robot has also changed. In 2020, 27 percent of Americans said they would consider it cheating if they had a partner who had sex with a robot during the relationship, compared to the 32 percent reported in 2017.
"If you had a partner who had sex with a robot, would you consider it cheating?"
The results from this interesting study also reveal that many people (42 percent) believe having sex with a robot is safer than having sex with a human stranger.
Robots (and robotic tech) already play a vital role in speeding up manufacturing, packaging, and processing across various industries. From television shows to real-life applications, artificial intelligence is becoming more and more popular in all areas of human life.
According to YouGov, "a Bloomberg report outlining Amazon's plans for an Alexa-powered robot that follows and helps you around the home may redefine how these machines service humans in the near future."
Study finds quantum entanglement could, in principle, give a slight advantage in the game of blackjack.
With that knowledge, they can then estimate the cards still in the deck, and those most likely to be dealt out next, all to help each player decide how to place their bets, and as a team, gain an advantage over the dealer.
This calculating strategy, known as card-counting, was made famous by the MIT Blackjack Team, a group of students from MIT, Harvard University, and Caltech, who for several decades starting in 1979, optimized card-counting and other techniques to successfully beat casinos at blackjack around the world — a story that later inspired the book "Bringing Down the House."
Now researchers at MIT and Caltech have shown that the weird, quantum effects of entanglement could theoretically give blackjack players even more of an edge, albeit a small one, when playing against the house.
In a paper published this week in the journal Physical Review A, the researchers lay out a theoretical scenario in which two players, playing cooperatively against the dealer, can better coordinate their strategies using a quantumly entangled pair of systems. Such systems exist now in the laboratory, although not in forms convenient for any practical use in casinos. In their study, the authors nevertheless explore the theoretical possibilities for how a quantum system might influence outcomes in blackjack.
They found that such quantum communication would give the players a slight advantage compared to classical card-counting strategies, though in limited situations where the number of cards left in the dealer's deck is low.
"It's pretty small in terms of the actual magnitude of the expected quantum advantage," says first author Joseph Lin, a former graduate student at MIT. "But if you imagine the players are extremely rich, and the deck is really low in number, so that every card counts, these small advantages can be big. The exciting result is that there's some advantage to quantum communication, regardless of how small it is."
Lin's MIT co-authors on the paper are professor of physics Joseph Formaggio, associate professor of physics Aram Harrow, and Anand Natarajan of Caltech, who will start at MIT in September as assistant professor of electrical engineering and computer science.
Entanglement is a phenomenon described by the rules of quantum mechanics, which states that two physically separate objects can be "entangled," or correlated with each other, in such a way that the correlations between them are stronger than what would be predicted by the classical laws of physics and probability.
In 1964, physicist John Bell proved mathematically that quantum entanglement could exist, and also devised a test — known a Bell test — that scientists have since applied to many scenarios to ascertain if certain spatially remote particles or systems behave according to classical, real-world physics, or whether they may exhibit some quantum, entangled states.
"One motivation for this work was as a concrete realization of the Bell test," says Harrow of the team's new paper. "People wrote the rules of blackjack not thinking of entanglement. But the players are dealt cards, and there are some correlations between the cards they get. So does entanglement work here? The answer to the question was not obvious going into it."
After casually entertaining the idea during a regular poker night with friends, Formaggio decided to explore the possibility of quantum blackjack more formally with his MIT colleagues.
"I was grateful to them for not laughing and closing the door on me when I brought up the idea," Formaggio recalls.
In blackjack, the dealer deals herself and each player a face-up card that is public to all, and a face-down card. With this information, each player decides whether to "hit," and be dealt another card, or "stand," and stay with the cards they have. The goal after one round is to have a hand with a total that is closer to 21, without going over, than the dealer and the other players at the table.
In their paper, the researchers simulated a simple blackjack setup involving two players, Alice and Bob, playing cooperatively against the dealer. They programmed Alice to consistently bet low, with the main objective of helping Bob, who could hit or stand based on any information he gained from Alice.
The researchers considered how three different scenarios might help the players win over the dealer: a classical card-counting scenario without communication; a best-case scenario in which Alice simply shows Bob her face-down card, demonstrating the best that a team can do in playing against the dealer; and lastly, a quantum entanglement scenario.
In the quantum scenario, the researchers formulated a mathematical model to represent a quantum system, which can be thought of abstractedly as a box with many "buttons," or measurement choices, that is shared between Alice and Bob.
For instance, if Alice's face-down card is a 5, she can push a particular button on the quantum box and use its output to inform her usual choice of whether to hit or stand. Bob, in turn, looks at his face-down card when deciding which button to push on his quantum box, as well as whether to use the box at all. In the cases where Bob uses his quantum box, he can combine its output with his observation of Alice's strategy to decide his own move. This extra information — not exactly the value of Alice's card, but more information than a random guess — can help Bob decide whether to hit or stand.
The researchers ran all three scenarios, with many combinations of cards between each player and the dealer, and with increasing number of cards left in the dealer's deck, to see how often Alice and Bob could win against the dealer.
After running thousands of rounds for each of the three scenarios, they found that the players had a slight advantage over the dealer in the quantum entanglement scenario, compared with the classical card-counting strategy, though only when a handful of cards were left in the dealer's deck.
"As you increase the deck and therefore increase all the possibilities of different cards coming to you, the fact that you know a little bit more through this quantum process actually gets diluted," Formaggio explains.
Nevertheless, Harrow notes that "it was surprising that these problems even matched, that it even made sense to consider entangled strategy in blackjack."
Do these results mean that future blackjack teams might use quantum strategies to their advantage?
"It would require a very large investor, and my guess is, carrying a quantum computer in your backpack will probably tip the house," Formaggio says. "We think casinos are safe right now from this particular threat."
This research was funded, in part, by the National Science Foundation, the Army Research Office, the U.S. Department of Energy, and the MIT Undergraduate Research Opportunities Program (UROP).