The “particle” rules governing human interactions in crowds

A group of physicists suggests humans follow particular rules while interacting in crowds:

Skinner and his coauthors, a pair of computer scientists at the University of Minnesota, looked at six data sets that capture individual movements in crowded places: four from natural settings, like video footage of pedestrians on a college campus, and two from laboratory experiments, in which participants were tracked with cameras as they navigated a corridor that narrowed into a bottleneck. Such data sets have become available only in the last five years, as improvements in camera technology and the field of computer vision have made large-scale pedestrian tracking possible.

Initially the researchers assumed they would find a “repulsive force” between people, like the one that pushes charged particles apart. As they looked closer, they realized it wasn’t that simple: There was a repulsive force between individuals, but it only operated sometimes. “Two people walking head first into each other have a strong interaction,” Skinner says, “but people walking side-by-side have almost no interaction.”

So the researchers went looking for a new rule. They found it in a variable they called “time to collision,” which explained many of the course adjustments they observed. The closer two people get to colliding, the more energy they expend getting out of each other’s way. To be technical, they found that the interaction between individuals in a crowd could be described as 1 over the square of the time to collision: As a collision becomes more imminent, the energy you apply to avoiding it goes up drastically.

Unlike with particles, the mechanism that produces these adjustments is an instinctive mental calculation rather than any kind of physical force. There’s also a limit to how far out we can—or need—to account for other people’s movement. When the time to collision was more than three seconds, the researchers found that the interaction energy between two pedestrians fell to zero, meaning people weren’t taking each other into account at all.

This seems to apply mainly to areas where people are going opposite directions, whether in an open concourse at a stadium or a crowded city crosswalk with two masses of people trying to get past each other. In these situations, there is usually an area of stronger flow on each side but then in the middle – between the two larger flows – is a zone where such collisions are imminent. I know because I often walk in such zones when in a hurry. It can be difficult in those situations to avoid people and not everyone likes to walk in such high-stakes areas where there is a higher probability of bumping into people.

The article goes on to talk about applications of these findings. I would guess that means having more clearly marked traffic flows and trying to avoid the weaker flows or neutral areas that I mentioned.

Viewing cities as crosses between stars and social networks

A new paper from a physicist suggests cities are “social reactors,” somewhere between social networks and stars:

Others have suggested that cities look and operate like biological organisms, but that is not the case, says Bettencourt. “A city is a bunch of people, but more importantly, it’s a bunch of people interacting, so hence the social network,” he explains. “What’s important are the properties of this social network: the scaling was giving us clues. But then when you think of this superlinearity, which means the socioeconomic outputs are the result of those interactions, are expressed as growing superlinear functions of populations, the only system that I could think of in nature is a star. A star does have this property – it’s essentially a nuclear reactor sustained by gravity and shines brighter (has greater luminosity) the larger its mass. So there’s a sense that this behavior that is sustained by and created by attractive interactions and whose output is proportional to rate of interactions, is what a city is and a star is, and so in that sense they are analogous.”…

The result is this “special social reactor” that adheres to four main assumptions about city dynamics and scaling:

1) There are “mixing populations”: basically, cities have attractive interactions and social outputs are the results of those, which leads to more social interactions.

2) There is “incremental network growth”: notably, the networks themselves and the supporting infrastructure develop gradually as the city grows. The infrastructure is decentralized as are the networks themselves. This is very different from an organism, says Bettencourt, whose internal “infrastructure” (analogous to a vascular system for example) develops basically all at once and has a centralized node.

3) “Human effort is bounded”: as he writes in his paper, “The increasing mental and physical demand from their inhabitants has been a pervasive concern to social scientists. Thus this assumption is necessary to lift an important objection to any conceptualization of cities as scale-invariant systems.” In other words, “The costs imposed on people by living in the city do not scale up,” he says, because as the number of social interactions increase, one doesn’t have to necessarily travel more to get to these interactions. “The city comes to you as it becomes denser,” he notes.

4) “Socioeconomic outputs are proportional to local social interactions”: this gives us an interesting snapshot of exactly what a city is – not just a conglomeration of individuals, but rather a concentration of social interactions.

Sounds interesting. Cities are both agglomerations of social interactions as well as have unique infrastructures (physical and social) that gives shape to and is shaped by these interactions.

People in mosh pits act predictably like gas molecules

A graduate student in physics argues the behavior of people in mosh pits is similar to that of gas molecules:

Being a physicist first and a mosher second (“fieldwork was independently funded”), the student, Jesse Silverberg, can’t help but notice curious patterns in what had always felt like the epitome of chaos. “Being on the outside for the first time, I was absolutely amazed at what I saw — there were all sorts of collective behaviors emerging that I never would have noticed from the inside.” So for an even better perspective, he turns to YouTube, to figure out what happens to people under the “extreme conditions” borne of a combination of “loud, fast music (130 dB, 350 beats per minute) … bright, flashing lights, and frequent intoxication.”

What he found, of course, was the “collective phenomenon consisting of 10^1 to 10^2 participants commonly referred to as a mosh pit.” And he was able to prove his initial observation: While the individual movements of moshers may be random, their collective behavior follows a few simple rules…

Look familiar? Moshers, as they “move randomly, colliding with one another in an undirected fashion,” seem a lot like gas particles, the researchers note. Or, as Silverberg explained to me: “It turns out that the statistical description we use for gasses matches the behavior of people in mosh pits. In other words, people bounce around like the molecules in a gas.” And they can be understood using the same basic principles we use to study those molecules.

Using videos of heavy metal concerts, write the authors, allows them to study crowd behavior in a way that staged experiments haven’t been able to. According to Silverberg, the unique circumstances (re: loud music and intoxication) of mosh pits are applicable to other instances of collective motion, like riots or emergency situations, where panicked crowds tend to break into random, slightly hysterical motion. Better understanding their dynamics might serve to improve safety measures in buildings or stadiums. If nothing else, they may serve as a useful reference to EMTs in the very pits where the research originated (per one study, 37 percent of injuries that took place over the course of a four-day music festival were “related to moshing activity”).

Interesting research. While many may not typically think of physics providing insights into social interaction, a lot of good work has emerged from physics in recent decades on social networks.

This is a funny statement: “the fieldwork was independently funded.” It could be even better if an independent granting agency was willing to fund such research with mosh pits to develop insights into collective behavior. If the project was pitched at looking for insights into safety with such crowds, I imagine some funding could be found.

The world of McDonalds, McQuarks, and McMansions

Wired has a few recent pieces that are related to McMansions. First, an “Alt Text” piece parodies other “theoretical particles” that might follow the recent Higgs-Boson news:


This subatomic particle is found in all McDonald’s food, and is the reason that all the menu offerings — including the burgers, shakes and dipping sauces — taste “McDonaldy,” as if they were all just carved out of a big lump of McSubstance. Currently, the McQuark is the universe’s only trademarked subatomic particle, although Motorola, maker of the Photon smartphone, is attempting to gain traction against Apple’s battery of lawsuits by patenting actual photons.

Wired‘s Matt Simon follows up and defines McMansions:

The most widely used of these pejoratives is McMansions. These are the quickly produced cookie-cutter homes that some say lack taste.

It would be interesting to hear more from McDonald’s about how they feel about the expanding usage of such terms, particularly McMansion. According to Wikipedia, McDonalds was not too happy about the term “McJobs”:

The term “McJob” was added to Merriam-Webster’s Collegiate Dictionary in 2003, over the objections of McDonald’s. In an open letter to Merriam-Webster, Cantalupo denounced the definition as a “slap in the face” to all restaurant employees, and stated that “a more appropriate definition of a ‘McJob’ might be ‘teaches responsibility.'” Merriam-Webster responded that “[they stood] by the accuracy and appropriateness of [their] definition.”

On 20 March 2007, the BBC reported that the UK arm of McDonald’s planned a public petition to have the OED’s definition of “McJob” changed. Lorraine Homer from McDonald’s stated that the company feels the definition is “out of date and inaccurate”. McDonald’s UK CEO, Peter Beresford, described the term as “demeaning to the hard work and dedication displayed by the 67,000 McDonald’s employees throughout the UK”. The company would prefer the definition to be rewritten to “reflect a job that is stimulating, rewarding … and offers skills that last a lifetime.”…

According to Jim Cantalupo, former CEO of McDonald’s, the perception of fast-food work being boring and mindless is inaccurate, and over 1,000 of the men and women who now own McDonald’s franchises began behind the counter.Because McDonald’s has over 400,000 employees and high turnover, Cantalupo’s contention has been criticized as being invalid, working to highlight the exception rather than the rule.

In 2006, McDonald’s undertook an advertising campaign in the United Kingdom to challenge the perceptions of the McJob. The campaign, developed by Barkers Advertising and supported by research conducted by Adrian Furnham, professor of psychology at University College London, highlighted the benefits of working for the organization, stating that they were “Not bad for a McJob”. So confident were McDonald’s of their claims that they ran the campaign on the giant screens of London’s Piccadilly Circus.

Instead of trying to change or block the definition, why doesn’t McDonald’s try to introduce its version of a “Mc-” term that it can then work to define? Of course, such things can be quickly turned around on the Internet but McDonald’s has plenty of resources and reach. I’m sure they could develop a positive version and there are still plenty of people going to their restaurants…

Sociologist discusses the Living Earth Simulator

A sociologist explains a little bit more about the Living Earth Simulator that aims to model society:

Time travel: probably not going to happen any time soon. At least, not in the physical, “Back to the Future” sense. But that doesn’t stop us from trying to peek into the future. In the December issue of Scientific American, writer David Weinberger chats with Dirk Helbing, a Swiss physicist and sociologist who is pitching a project called the Living Earth Simulator, a billion-euro computer system that would absorb vast amounts of data, use it to model global-scale systems — economies, governments, etc. — and predict the future.

Well, maybe. Weinberger speaks with researchers who point out the roadblocks. While it’s possible to model small systems, such as highway and pedestrian traffic, getting a read on the economy, the environment and public health all at once is a much more complicated process. For instance, how would you account for feedback loops in the system — that is, what happens when the computer model’s conclusions alter the situation that it’s modeling? And if you can’t understand the process through which the model generates an answer, the whole thing is just a giant Magic 8 Ball, anyway. The computer may call upon world leaders to “set fire to all the world’s oil wells,” writes Weinberger. “That will not be actionable advice if the policymaker cannot explain why it’s right.”

So data mining will not be encouraged or will the model’s supervisors insist that every discovered pattern come with an explanation?

Interestingly, Helbing is also featured in a recent article in The Economist about pedestrian traffic:

In 1995 Mr Helbing and Peter Molnar, both physicists, came up with a “social force” computer model that used insights from the way that particles in fluids and gases behave to describe pedestrian movement. The model assumed that people are attracted by some things, such as the destination they are heading for, and repelled by others, such as another pedestrian in their path. It proved its worth by predicting several self-organising effects among crowds that are visible in real life.

One is the propensity of dense crowds spontaneously to break into lanes that allow people to move more efficiently in opposing directions. Individuals do not have to negotiate their way through a series of encounters with oncoming people; they can just follow the person in front. That works better than trying to overtake. Research by Mr Moussaid suggests that the effect of one person trying to walk faster than the people around them in a dense crowd is to force an opposing lane of pedestrians to split in two, which has the effect of breaking up the lane next door, and so on. Everyone moves slower as a result.

Two quick thoughts:

1. Combining physics and sociology to explain social behavior seems to be growing in popularity. Here is what I assume: the physics side brings experience in dealing with complex models and a more naturalistic way of explaining human behavior while sociologists bring more theories and knowledge about human contingencies. (But I could be wrong.) It does seem like the combination of these two disciplines could uniquely bridge the gap between the natural and social sciences.

2. Overall, I assume there will be many more projects like this. Getting the data is not so much a problem and we have the computing power to calculate complex models. If this does increase, this will mean some changes within the discipline of sociology: a shift toward mathematical sociology (making regression look relatively simple), thinking about “natural laws” in a way that sociology has generally avoided, and viewing the world in a different way (individuals operating within complex systems).

A sociological case for scientific innovation

A physicist makes a sociological case for innovation in the sciences:

So, here is a general, sociological case for why we researchers should always be ahead of our time, even at the cost of frustrating ourselves trying to solve insoluble problems. Suppose that the tank of a given field has another 10 or even 15 years of gas left in it. Why should we abandon the field and try to train our students in a different area? Good training in science doesn’t depend on the subject. But more important, why not enter a new field in which, like almost any subject in the life sciences, the time left to have fun does not have a foreseeable upper bound?…

Scientists are conservative even when their job description could be succinctly summarized as “innovator” because the culture in which we operate is chock full of traditions that represent the opposite of innovation and intellectual freedom. We are still afraid of making mistakes—even simple terminology mistakes!—even though in the age of Google those can be corrected in an instant! We are still organizing our institutions of higher learning around power centers (the departments) that are built like fortresses meant to divide people instead of bringing them together!…

In conclusion, let me offer a note of hope, but of caution, too.

Many of the scientific questions that await answers will, I hope, be solved in the second part of this century. Then, having solved the last of the big puzzles—that is, having explained the origin of life—scientists will turn their attention and the power of their quantitative tools toward explaining the sociological complications that arise when these very complex machines called Homo sapiens interact with each other. Let us hope the fruits of that research will respect the freedom of our minds—and of our bodies too!

Sounds good: a physicist arguing that once some of the big natural science issues are solved, attention should be turned to studying human interaction. However, does this suggest that the view from physics that disciplines that study human interaction, like sociology, aren’t doing an adequate job?

It would be interesting to see a companion piece here that summarizes research regarding scientific innovation: how many scientists do switch fields or even subfields of study, how many feel like they could actually do this, and what do they feel are barriers to this.

Possible Fermilab “breakthough” illustrates statistical significance

Scientists at Fermilab may be on the verge of a scientific breakthrough regarding “a new elementary particle or a new fundamental force of nature.” There is just one problem:

But scientists on the Fermilab team say there is about a 1 in 1,000 chance that the results are a statistical fluke — odds far too high for them to claim a discovery.

“That’s no more than what physicists tend to call an ‘observation’ or an ‘indication,’ ” said Caltech physicist Harvey Newman.

For the finding to be considered real, researchers have to reduce the chances of a statistical fluke to about 1 in a million.

One of the key concepts in a statistics or social research course is statistical significance, where researchers say that they are 95% certain (or more) that their result is not just the result due to their sample or chance but that it actually reflects the population or reality. These scientists at Fermilab then want to be really sure that the results reflect reality as they want to reduce their possible error to 1 in a million.

Beyond working with the calculations, the scientists are also hoping to replicate their findings and rule out other explanations for what they are seeing:

Researchers hope that more data compiled at Fermilab will shed light on the matter, or that the Large Hadron Collider in Geneva will be able to replicate the findings. “We will know this summer when we double the data sets and see if it is still there,” said physicist Rob Roser of Fermilab, who is a spokesman for the project…

What the team must to do now, Roser said, is “eliminate all the mundane explanations.” They have been working on that, he said, and decided it was time to go public and let others know what they had found so far.

And science rolls on.

Quick Review: How Music Works

I like reading about music so I recently thought I would take a chance with a recently published book by British physicist John Powell: How Music Works: The Science and Psychology of Beautiful Sounds, From Beethoven to the Beatles. A few thoughts about this text which is intended for a general audience:

1. One of the key things this text tries to do is explain why we have the music structure today that we do. So he includes explanations about musical modes that developed in history (of which we use two today, one the major scale and one the minor scale) and how instruments, like the harp, can be configured to produce notes.

2. One of the most interesting things to me in this book was the fact that agreement about modern notes didn’t happen until a conference in 1939. Before that, an A in Leipzig and an A in Paris might not be the same sound. It wasn’t until this conference that a particular frequency (A = 440) was set so that all instruments could be set to the same pitches. And even then, Powell suggests choosing this particular frequency occurred not because it is a better sound but rather because it is somewhere in the middle and seemed good. To think that the sounds we know today are really a social construction is intriguing.

3. There are number of little discussions that a reader might find interesting about perfect pitch, the physics of sound versus noise, how we can rate sound intensity (and he does not like the decibel system), and whether there are certain keys that are happier or sadder (the conclusion: no, they all share the same patterns of notes).

4. While I enjoyed a number of these shorter discussions, I wonder whether someone with limited or no musical knowledge could take much from this book. At various points, Powell suggests one doesn’t need to know how to play or read music to understand the discussions but I think it would be difficult. To his credit, Powell does suggest that anyone of any age can learn music – yes, it takes time (and he invokes Gladwell’s rule of 10,000 hours needed for expertise) but he suggests the idea that some people are musical and others are not does not hold water.

Overall, a book with some interesting points. The discussion bogs down in places and may be difficult for those with little music knowledge but it is an interesting start in considering how music is made.

Quick Review: The Canon

When recently at the Field Museum in Chicago, I encountered several books in the bookstore. I tracked down one of them, a former bestseller, down at the library: The Canon: A Whirligig Tour of the Beautiful Basics of Science by Natalie Angier. A few quick thoughts about the book:

1. This book is an overview of the basic building blocks of science (there are the chapters in order): thinking scientifically, probabilities, scale (different sizes), physics, chemistry, evolutionary biology, molecular biology, geology, and astronomy. Angier interviewed a number of scientists and she both quotes and draws upon their ideas. For someone looking for a quick understanding of these subjects, this is a decent find. From this book, one could delve into more specialized writings.

2. Angier is a science writer for the New York Times. While she tries to bring exuberance to the subject, her descriptions and adjectives are often over the top. This floweriness was almost enough to stop me from reading this book at a few points.

3. To me, the most rewarding chapters were the first three. As a social scientist, I could relate to all three of these and plan to bring some of these thoughts to my students. Thinking scientifically is quite different than the normal experience most of us have of building ideas and concepts on anecdotal data.

a. A couple of the ideas stuck out to me. The first is a reminder about scientific theories: while some think a theory means that it isn’t proven yet so it can be disregarded, scientists view theories differently. Theories are explanations that are constantly being built upon and tested but they often represent the best explanations scientists currently have. A theory is not a law.

b. The second was about random data. Angier tells the story of a professor who runs this activity: at the beginning of class, half the students are told to flip a coin 100 times and record the results. The other half of the students are told to make up the results for 100 imaginary coin flips. The professor leaves the room while the students do this. When she returns, she examines the different recordings and most of the time is able to identify which were the real and imaginary results. How? Students don’t quite understand random data; usually after two consecutive heads or tails, they think they have to have the opposite result. In real random data, there can be runs of 6 of 7 heads or tails in a row even as the results tend to average out in the end.

Overall, I liked the content of the book even as I was often irritated with its delivery. For a social scientist, this was a profitable read as it helped me understand subjects far afield.