Twitter, BBC, Bitly, and beyond

The new website went public on April 30. On its front page, a wavering graph rises and falls like a ticker at the New York Stock Exchange. Except, instead of averaging the value of thousands of companies, the hedonometer compiles and averages the emotional state of tens of millions of people.

“What it’s doing right now is measuring Twitter, checking the happiness of tweets in English,” says Chris Danforth, a UVM mathematician who co-led the creation of the site with fellow mathematician Peter Dodds.

But soon the hedonometer will be drawing in other data streams, like Google Trends, the New York Times, blogs, CNN transcripts, and text captured by the link-shortening service Bitly. And it will be data-mining in twelve languages.

Hedonometer.org is based on the research of Dodds and Danforth and their team in the Computational Story Lab at the University of Vermont’s Complex Systems Center, and the technology of Brian Tivnan, Matt McMahon and their team from MITRE, a not-for-profit organization that operates federal research and development centers and has expertise in big data analytics.

In February, the research team made headlines with the hedonometer. Studying geo-tagged tweets from cell phones, they reported on the happiest and saddest cities in America: Napa, CA, at the top and Beaumont, TX, at the bottom. In future versions of the new website, the researchers plan to make this kind of geographically linked data available, allowing as-it-happens observation of how a happiness signal varies, say, between Seattle and San Diego.

“Reporters, policymakers, academics — anyone — can come to the site,” says Danforth, “and see population-level responses to major events.”

Like the Boston Marathon bombings.

Boston’s impact

On Monday, April 15, reporters and TV crews from all over the world flocked to Boston to report on what they thought would be stories of athletic triumph. Instead, as the world now knows, two crude bombs near the finish line were detonated, killing three and injuring more than 260. Reporters turned to telling this new, tragic story. Many went out and started interviewing people. The stories were compelling; many people they spoke to around Boston seemed scared, angry and sad.

But suppose reporters wanted to find out how the bombings were affecting the mood of the world — in real-time. Was this horror registering in the global psyche, and how deeply?

“Many of the articles written in response to the bombing have quoted individual tweets reflecting qualitative micro-stories,” says Danforth. But capturing a few online comments or reactions on video does not necessarily reflect the overall mood of the English-speaking world anymore than talking to ten people in the park equals the US Census.

What if a reporter had also turned to the hedonometer? First, she’d have seen a dramatic downward spike in happiness for that day. Clearly, the Boston Marathon bombings were registering around the world. “Our instrument reflects a kind of quantitative macro-story,” Danforth says, “one that journalists can use to bring big data into an article attempting to characterize the public response to the incident.”

Then — in the same way that a stockbroker might drill down into a market average to get a sense of which companies are moving the markets the most — a reporter could dig deeper into the hedonometer’s data. There, she could see that “explosion,” “victims,” and “kill” are at the top of a list of trending words pushing the hedonometer down to its lowest ever point on April 15.

“They rise to the top because they are words that are negative,” Danforth says, “but primarily because they appear so much more than they usually do in the background in the ambient chatter of English.”

Emotional temperature

The hedonometer draws on what scientists call the “psychological valence” of about 10,000 words. Paid volunteers, using Amazon’s Mechanical Turk service, rated these words for their “emotional temperature,” says Dodds, director of UVM’s Complex Systems Center.

The volunteers ranked words they perceived as the happiest near the top of a 1-9 scale; sad words near the bottom. Averaging the volunteers’ responses, each word received a score: “happy” itself ranked 8.30, “hahaha” 7.94, “cherry” 7.04, and the more-neutral “pancake” 6.96. Truly neutral words, “and” and “the” scored 5.22 and 4.98. At the bottom, “crash” 2.60, the emoticon “:(“ 2.36, “war” 1.80, and “jail” 1.76.

Using these scores, the team collects some fifty million tweets from around the world each day—“then we basically toss all the words into a huge bucket,” says Dodds—and calculate the bucket’s average happiness score. As the site develops, the scientists anticipate that it will be gathering billions of words and sentences daily.

"Our method is only reasonable for large-scale texts, like what's available on the Web," Dodds says. "Any word or expression can be used in different ways. There's too much variability in individual expression," to use this approach to understand small groups or small samples. For example, “sick” may mean something radically different to a 14-year-old skateboarder than it does to his pediatrician.

But that's the beauty of big data. Each word is like an atom in the air when you’re trying to figure out the temperature. It’s the aggregate effect that registers, and no individual tweet or word makes much difference. In the Boston Marathon bombings example, positively scored words like “prayers” and “families” also spiked, but, obviously, not for positive reasons.

“If we remove ‘prayers,’ ‘love,’ and ‘families,’” says Chris Danforth, “it’s not going to change the day’s overall deviation from the background, because of all the other words.”

Changing which words are used to assess the overall emotional picture, “is like changing the filter on a lens you’re using,” explains Peter Dodds. “You can take out all the color, or you can turn up the contrast, but you can still see the picture.”

The verdict of consciousness

In 1881, a little-known book, Mathematical Psychics, published by Francis Edgeworth, asked the reader to “imagine an ideally perfect instrument, a psychophysical machine, continually registering the height of pleasure experienced by an individual, exactly according to the verdict of consciousness.”

In other words, a hedonometer. While Edgeworth’s was a thought experiment, Dodds and Danforth’s hedonometer is a real device. Of course, it doesn’t directly measure “the height of pleasure.” While the team is opening conversations with experts in brain scanning about how fMRI images might corroborate their remote-sensing approach, "we can’t — and really don’t want to — look inside people's heads," says Dodds.

Nor is their hedonometer “ideally perfect.” They’re working now to expand beyond the “atoms” of single words to explore the “molecules” of two-word expressions. But the hedonometer does work.

“The key piece is not whether we’re correctly measuring atoms and molecules,” says Brian Tivnan, a researcher from MITRE. “It’s the relative context that is so important: which is why the sudden drop from the Boston
Marathon bombings jumps out at you. The hedonometer shows the pulse of a society.”

Of course, happiness isn’t simple. Plato, Buddha, Freud and Tina Turner all pondered its meaning. Many Americans rank happiness as what they want most in life, but what is it, really?

“We’re not trying to tell you that contentment is better than happiness — we’re not trying to define the word,” says Danforth. The Nasdaq Index doesn’t capture the whole stock market. Gross Domestic Product doesn’t define the meaning of the economy. An EKG doesn’t tell a doctor everything about your heart. But all these aggregate measures, of something remote, are widely studied. The hedonometer may prove to be the same.

“We’re just saying we’re measuring something important and interesting,” says Chris Danforth. “And, now, sharing it with the world.

The finding that happiness might be contagious has a caveat, however: the phenomenon is not about simply following someone on Twitter, but two people connecting by directly replying to one another.

Researchers used a "hedomenter" for scoring the happiness level of words used in a tweet, measured on a scale of one to nine, with "love" rating 8.42 and "die" coming in at 1.74. "The study," according to the UK Huffington Post, "computed the happiness of each user by applying this 'hedometer' to all tweets authored by the user." Using this measure, contentment levels are higher the closer a user is to those who are very happy and declines with the degree of separation.

Read the story…

Lots of people gave it a try. One of them, a self-educated carpenter named John Harrison, invented the marine chronometer — a rugged and highly precise clock — that did the trick. For the first time, sailors could accurately determine their location at sea.

A centuries-old problem was solved. And, arguably, crowdsourcing was born.

Crowdsourcing is basically what it sounds like: posing a question or asking for help from a large group of people. Coined as a term in 2006, crowdsourcing has taken off in the internet era. Think of Wikipedia, and its thousands of unpaid contributors, now vastly larger than the Encyclopedia Britannica.

Crowdsourcing has allowed many problems to be solved that would be impossible for experts alone. Astronomers rely on an army of volunteers to scan for new galaxies. At climateprediction.net, citizens have linked their home computers to yield more than a hundred million hours of climate modeling; it’s the world’s largest forecasting experiment.

But what if experts didn’t simply ask the crowd to donate time or answer questions? What if the crowd was asked to decide what questions to ask in the first place?

Could the crowd itself be the expert?

That’s what a team at the University of Vermont decided to explore — and the answer seems to be yes.

Prediction from the people

Josh Bongard and Paul Hines, professors in UVM’s College of Engineering and Mathematical Sciences, and their students, set out to discover if volunteers who visited two different websites could pose, refine, and answer questions of each other — that could effectively predict the volunteers’ body weight and home electricity use.

The experiment, the first of its kind, was a success: the self-directed questions and answers by visitors to the websites led to computer models that effectively predict user’s monthly electricity consumption and body mass index.

 Their results, "Crowdsourcing Predictors of Behavioral Outcomes,” were published in a recent edition of IEEE Transactions: Systems, Man and Cybernetics, a journal of the Institute of Electrical and Electronics Engineers.

“It’s proof of concept that a crowd actually can come up with good questions that lead to good hypotheses,” says Bongard, an expert on machine science.

In other words, the wisdom of the crowd can be harnessed to determine which variables to study, the UVM project shows — and at the same time provide a pool of data by responding to the questions they ask of each other.

“The result is a crowdsourced predictive model,” the Vermont scientists write.

Unexpected angles

Some of the questions the volunteers posed were obvious. For example, on the website dedicated to exploring body weight, visitors came up with the question: “Do you think of yourself as overweight?” And, no surprise, that proved to be the question with the most power to predict people’s body weight.

But some questions posed by the volunteers were less obvious. “We had some eye-openers,” Bongard says. “How often do you masturbate a month?” might not be the first question asked by weight-loss experts, but it proved to be the second-most-predictive question of the volunteer’s self-reported weights — more predictive than “how often do you eat during a day?”

“Sometimes the general public has intuition about stuff that experts miss — there’s a long literature on this,” Hines says.

“It’s those people who are very underweight or very overweight who might have an explanation for why they’re at these extremes — and some of those explanations might not be a simple combination of diet and exercise,” says Bongard. “There might be other things that experts missed.”

Cause and correlation

The researchers are quick to note that the variables revealed by the evolving Q&A on the experimental websites are simply correlated to outcomes — body weight and electricity use — not necessarily the cause.

"We’re not arguing that this study is actually predictive of the causes,” says Hines, “but improvements to this method may lead in that direction.”

Nor do the scientists make claim to being experts on body weight or to be providing recommendations on health or diet (though Hines is an expert on electricity, and the EnergyMinder site he and his students developed for this project has a larger aim to help citizens understand and reduce their household energy use.)

“We’re simply investigating the question: could you involve participants in the hypothesis-generation part of the scientific process?” Bongard says. “Our paper is a demonstration of this methodology.”

“Going forward, this approach may allow us to involve the public in deciding what it is that is interesting to study,” says Hines. “It’s potentially a new way to do science.”

And there are many reasons why this new approach might be helpful. In addition to forces that experts might simply not know about — “can we elicit unexpected predictors that an expert would not have come up with sitting in his office?” Hines asks — experts often have deeply held biases.

Faster discoveries

But the UVM team primarily sees their new approach as potentially helping to accelerate the process of scientific discovery. The need for expert involvement — in shaping, say, what questions to ask on a survey or what variable to change to optimize an engineering design — “can become a bottleneck to new insights,” the scientists write.

“We’re looking for an experimental platform where, instead of waiting to read a journal article every year about what’s been learned about obesity,” Bongard says, “a research site could be changing and updating new findings constantly as people add their questions and insights.”

The goal: “exponential rises,” the UVM scientists write, in the discovery of what causes behaviors and patterns — probably driven by the people who care about them the most. For example, “it might be smokers or people suffering from various diseases,” says Bongard. The team thinks this new approach to science could “mirror the exponential growth found in other online collaborative communities,” they write.

“We’re all problem-solving animals,” says Bongard, “so can we exploit that? Instead of just exploiting the cycles of your computer or your ability to say ‘yes’ or ‘no’ on a survey — can we exploit your creative brain?”

The University of Vermont has been named one of only 18 colleges and universities in the country to receive a highly coveted Integrative Graduate Education and Research Training, or IGERT, grant from the National Science Foundation, the first awarded in the state of Vermont. The UVM proposal was chosen from among 154 IGERT proposals submitted to the NSF in 2012.

UVM will receive approximately $3 million over five years to create an innovative, multi-disciplinary graduate program supporting 22 doctoral students who will be trained to analyze and develop smart grid systems. UVM will also hire two faculty members as part of the grant.  

A smart grid is an intelligent, digitally enabled electric grid that gathers, distributes and acts on information about the behavior of consumers and suppliers in order to improve the efficiency, reliability, cost and sustainability of electricity services. A smart grid can also better serve new technologies, such as plug-in hybrid electric vehicles, and more effectively assimilate renewable energy, such as solar and wind power, that is not produced at a uniform, predictable rate.    

UVM's new graduate program will create a generation of multidisciplinary scientists who are capable of analyzing the entire smart grid system – integrating technology, human behavior and public policy – to understand the complex dynamics of the next generation of electric power systems. The ultimate goal of the program is to develop the scientific/engineering research workforce necessary to allow intelligent deployment of smart grids that provide efficient power delivery in keeping with society's needs.

The growth of smart grids has been hampered in part by a workforce unable to fully exploit the integrated nature of the intelligent digital technology, a deficiency the new program will directly address, according to Domenico Grasso, vice president for Research and dean of the Graduate College at UVM. 

“In the past we might have viewed technical issues like energy loading and consumer concerns like variable pricing as separate,” Grasso said. “Now we know all these factors touch one another and have to be viewed holistically to devise solutions that can move us forward.” 

TRI critical to success

Though UVM has tried for IGERT funding in the past, Grasso believes the renewed institutional commitment to investments in innovative and strategic research played a significant role in the success of this grant.  Specifically, UVM's Transdisciplinary Research Initiative, or TRI, specifically the Complex Systems and Neuroscience, Behavior and Health spires, were critical components in the winning bid, according to Grasso. “Both Complex Systems and NBH were instrumental in our obtaining the grant and will be at the heart of the new curriculum,” he said.  The first students will enroll beginning in the fall of 2012.

In addition to the spires, two other factors were important in UVM's successful application for the IGERT, said Jeffrey Marshall, a professor in the School of Engineering in the College of Engineering and Mathematical Sciences, who spearheaded the grant:  the Vermont Advanced Computer Center at UVM, which will provide the computational power the initiative will need, and UVM's ongoing partnership with Sandia National Laboratories, one of whose areas of focus is the development and deployment of smart grid technology.

“Multi-disciplinary research and education of the sort sponsored by the IGERT program are an ideal fit with the smaller, more connected structure of UVM, where faculty from all different parts of the university generally know each other and work closely together,” said Marshall.

Marshall also credited Vermont senator Bernie Sanders for helping position the university to win the grant. Sanders has played a critical role in a variety of smart grid initiatives, including facilitating and helping fund the Sandia partnership with the university and the state, and securing funds to establish the Center for Energy Transformation and Innovation housed at UVM.

Advancing Vermont's leadership in smart grid

The grant will significantly advance the state of Vermont's leadership position in smart grid technology and UVM's role in helping propel that advance, Marshall said. In 2009 the state received a $69 million federal grant from the U.S. Department of Energy, matched by the state's electric utilities, for a total of $168 million, to install the country's first statewide smart-metering system.

The IGERT grant is complementary to the DOE grant, producing a professionally trained workforce able to make strategic use of the data gathered from the smart meters. Marshall expects that a significant number of graduates will stay in Vermont. The program is also open to working professionals in the state, who will be able to take courses. Professionals will also teach in the program. 

Other UVM faculty who are co-investigators on the IGERT grant with Marshall include Margaret Eppstein in Computer Science, Stephen Higgins in Psychiatry, Paul Hines in Engineering, and Chris Koliba  in Community Development and Applied Economics. Diann Gaalema in Psychiatry, Cynthia Forehand in the Graduate College and Grasso were also important contributors in the development of the project. Faculty participating on the project come from many university departments in addition to those above, including Computer Science, Mathematics, Economics, and Psychology as well as researchers and staff from Sandia, the Vermont Law School, Champlain College, and the ECHO Center.

The grant will also help support UVM's strategic initiative to increase diversity in its graduate programs.

Launched in 1997, the IGERT is the National Science Foundation's flagship interdisciplinary training program, educating U.S. Ph.D. scientists and engineers by building on the foundations of their disciplinary knowledge with interdisciplinary training. The IGERT program spans science, technology, engineering, mathematics and social sciences.

“Our work to develop and test technologies to rapidly diagnose infectious disease is enhanced by this joint facility,” says Dr. Hill, assistant professor in the School of Engineering. “It is also a great environment to do some research cross-fertilization.”

"This renovation greatly improves our bio- and chemical-related engineering research facilities and capabilities for growth. More importantly, by linking faculty with different expertise, the space will foster student and faculty interaction across subdisciplines within engineering," says Dr. Holmén, associate professor in the School of Engineering whose environmental engineering research focuses on characterizing processes affecting the transport and fate of organic chemicals and airborne particles from agriculture and transportation sources.  Dr. Holmén’s expertise ranges from the study of nanoparticles in vehicle exhaust to herbicide gas/particle partitioning at the farm scale.

“My research group is pleased to have this space to support our interdisciplinary work on biological feedback control systems,” says Dr. Dunlop, assistant professor in the School of Engineering.  “The opportunity for greater interaction with colleagues and students in the other research areas is an important step towards supporting transdisciplinary research within CEMS.”

“The interdisciplinary research of my students will be enhanced by the close proximity of distinguished professors and their respective students,” says Dr. Oldinski, an assistant professor in the School of Engineering and an assistant professor in the Department of Orthopaedics and Rehabilitation in the College of Medicine.

The Dunlop Lab studies how microorganisms use feedback to respond to changes in their environment.  The focus is on engineering novel control systems in cells and studying how robust, predictable behavior is achieved with naturally occurring feedback loops.  Dr. Dunlop’s interest is in processes that are dynamic or stochastic and the researchers use fluorescent proteins and time-lapse microscopy to image single cells over the course of many hours. Solutions using engineered control systems are being applied to problems in bioenergy and medical research.

The Hill Lab is focused on two primary research areas. The first area centers on the development of technologies to rapidly determine the identity of pathogenic bacteria. Mass spectrometry is the primary tool used to rapidly “fingerprint” bacteria in contexts ranging from food to the human lung. The second area focuses on studying how organic phosphorus compounds are cycled in the environment, with an emphasis on directing the release of phosphate near plant roots rather than in places where it can leach into nearby waterbodies and cause algal blooms.

Dr. Holmén leads the Transportation-Air Quality Laboratory (TAQ Lab) which aims to understand and model factors affecting vehicle exhaust emissions as they relate to effects on human and environmental health.  With a focus on unregulated pollutants such as air toxics and nanoparticles, both primary exhaust composition and secondary transformation processes are quantified at high temporal resolution under real-world vehicle operating conditions.  Studies on hybrid and conventional gasoline and diesel vehicles operating on diverse fuels, including biodiesel blends from multiple feedstocks, position the TAQ Lab with a unique dataset for modeling tailpipe emissions of the future on-road vehicle fleet.

Dr. Oldinski is the director of the Engineered Biomaterials Research Laboratory in the School of Engineering. Dr. Oldinski’s research encompasses the fundamental understanding and development of polymeric materials for biological applications with a specific emphasis on tissue regeneration and drug delivery. The research in her laboratory involves: (i) developing novel polymeric materials and precursors; (ii) utilizing processing techniques to fabricate scaffolds with the desired micro- and macroscopic structures both spatially and temporally; (iii) investigating the interaction of cells with these materials while developing materials-based techniques to control cell differentiation; and (iv) using polymers to control the delivery of therapeutic molecules.

The renovation and creation of this new laboratory space is the result of a commitment from the UVM College of Engineering and Mathematical Sciences to empower the research faculty and to enhance the undergraduate teaching experience for students.

How brains do that is an area of rapidly expanding research — and Sejnowski is one of the field’s most celebrated investigators.

Sejnowski will speak on one of these brain tricks — a remarkable way that neurons efficiently represent visual information — on Friday, Feb. 24 at 2 p.m. at the Davis Auditorium at Fletcher Allen Health Care.

His lecture, “Suspicious Coincidences in the Brain,” is free and open to the public.

Brain spikes

Sejnowski is the Francis Crick Professor at the Salk Institute for Biological Studies where he directs their Computational Neurobiology Laboratory. He is also an investigator with the Howard Hughes Medical Institute and holds academic appointments at the University of California, San Diego.

In his lecture, Sejnowski will focus on a strange brain phenomena called a “spike coincidence” in which a group of brain cells — neurons — fire at the same time.

“I will show how rare spike coincidences can be used efficiently to represent important visual events,” Sejnowski says.

And these coincidences are part of a larger suite of signals, both biochemical and electrical -- some “analog," “some “digital,” he says — that the brain uses to efficiently handle visual inputs.

Going further, Sejnowski will describe how this brain architecture can be reproduced with computer technologies to “simplify the early stages of visual processing.”

This work is part of the long-range goal of Sejnowski's laboratory to understand the computational powers of brains and to find the principles that link a brain to behavior.

More information

Terry Sejnowski has published more than 300 scientific papers and 12 books, including The Computational Brain, with Patricia Churchland. He was elected an IEEE Fellow in 2000, an AAAS Fellow in 2006, to the Institute of Medicine in 2008, the National Academy of Sciences in 2010 and the National Academy of Engineering in 2011.

His lecture is jointly sponsored by the University of Vermont’s Complex Systems and Neuroscience, Behavior and Health Spires as part of UVM’s Complex Systems Speaker Series.

The Davis Auditorium is located on the concourse between the Given Building and the west pavilion wing of Fletcher Allen Health Care.

"The afternoon of May 6, 2010 was among the strangest in economic history. Starting at 2:42 p.m. EDT, the Dow Jones stock index fell 600 points in just 6 minutes. Its nadir represented the deepest single-day decline in that market’s 114-year history. By 3:07 p.m., the index had rebounded. The “flash crash,” as it came to be known, was big, unexpected and scary — and a new study says flash events actually happen routinely, at speeds so fast they don’t register on regular market records, with potentially troubling consequences for market stability."

Read the whole story >>

So one might expect the New York Times to contain, on average, more negative and unhappy types of words — like “war,” “ funeral,” “cancer,” “murder” — than positive, happy ones — like “love,” “peace” and “hero.”

Or take Twitter. A popular image of what people tweet about may contain a lot of complaints about bad days, worse coffee, busted relationships and lousy sitcoms. Again, it might be reasonable to guess that a giant bag containing all the words from the world’s tweets — on average — would be more negative and unhappy than positive and happy.

But new research shows just the opposite.

“English, it turns out, is strongly biased toward being positive,” said Peter Dodds, an applied mathematician at the University of Vermont.

The UVM team’s study “Positivity of the English Language,” is presented in the Jan. 11 issue of the journal PLoS ONE.

Two happiness studies

This new study complements another study the same Vermont scientists presented in the Dec. 7 issue of PLoS ONE, “Temporal Patterns of Happiness and Information in a Global Social Network.”

That work attracted wide media attention showing that average global happiness, based on Twitter data, has been dropping for the past two years.

Combined, the two studies show that short-term average happiness has dropped — against the backdrop of the long-term fundamental positivity of the English language.

Universal positivity

In the new study, Dodds and his colleagues gathered billions of words from four sources: twenty years of the New York Times, the Google Books Project (with millions of titles going back to 1520), Twitter and a half-century of music lyrics.

“The big surprise is that in each of these four sources it’s the same,” says Dodds. “We looked at the top 5,000 words in each, in terms of frequency, and in all of those words you see a preponderance of happier words.”

Or, as they write in their study, “a positivity bias is universal,” both for very common words and less common ones and across sources as diverse as tweets, lyrics and British literature.

Homo narrativus

Why is this? “It’s not to say that everything is fine and happy,” Dodds says. “It’s just that language is social.”

In contrast to traditional economic theory, which suggests people are inherently and rationally selfish, a wave of new social science and neuroscience data shows something quite different: that we are a pro-social storytelling species. As language emerged and evolved over the last million years, positive words, it seems, have been more widely and deeply engrained into our communications than negative ones.

“If you want to remain in a social contract with other people, you can’t be a…,” well, Dodds here used a word that is rather too negative to be fit to print — which makes the point.

Twitter downer

This new work adds depth to the Twitter study that the Vermont scientists published in December that attracted attention from NPR, Time magazine and other media outlets.

“After that mild downer story, we can say, ‘But wait — there's still happiness in the bank,” Dodds notes. “On average, there's always a net happiness to language.”

Both studies drew on a service from Amazon called Mechanical Turk. On this website, the UVM researchers paid a group of volunteers to rate, from one to nine, their sense of the “happiness” — the emotional temperature — of the 10,222 most common words gathered from the four sources. Averaging their scores, the volunteers rated, for example, “laughter” at 8.50, “food” 7.44, “truck” 5.48, “greed” 3.06 and “terrorist” 1.30.

The Vermont team — including Dodds, Isabel Kloumann, Chris Danforth, Kameron Harris, and Catherine Bliss — then took these scores and applied them to the huge pools of words they collected. Unlike some other studies — with smaller samples or that elicited strong emotional words from volunteers — the new UVM study, based solely on frequency of use, found that “positive words strongly outnumber negative words overall.”

Confirming Pollyanna

This seems to lend support to the so-called Pollyanna Principle, put forth in 1969, that argues for a universal human tendency to use positive words more often, easily and in more ways than negative words.

Of course, most people would rank some words, like “the,” with the same score: a neutral 5. Other words, like “pregnancy,” have a wide spread, with some people ranking it high and others low. At the top of this list of words that elicited strongly divergent feelings: “profanities, alcohol and tobacco, religion, both capitalism and socialism, sex, marriage, fast foods, climate, and cultural phenomena such as the Beatles, the iPhone, and zombies,” the researchers write.

“A lot of these words — the neutral words or ones that have big standard deviations — gets washed out when we use them as a measure,” Dodds notes. Instead, the trends he and his team have observed are driven by the bulk of English words tending to be happy.

If we think of words as atoms and sentences as molecules that combine to form a whole text, “we’re looking at atoms,” says Dodds. “A lot of news is bad,” he says, and short-term happiness may rise and and fall like the cycles of the economy, “but the atoms of the story — of language — are, overall, on the positive side.”

Dodds was interviewed on NPR's "Marketplace," the social media website Mashable created this video, and stories appeared in:

The gross domestic product of the United States — that oft-cited measure of economic health — has been ticking upward for the last two years.

But what would you see if you could see a graph of gross domestic happiness?

A team of scientists from the University of Vermont have made such a graph — and the trend is down.

Reporting in the Dec. 7 issue of the journal PLoS ONE, the team writes, “After a gradual upward trend that ran from January to April, 2009, the overall time series has shown a gradual downward trend, accelerating somewhat over the first half of 2011.”

“It appears that happiness is going down,” said Peter Dodds, an applied mathematician at UVM and the lead author on the new study.

Twitteronomics

How does he know this? From Twitter. For three years, he and his colleagues gathered more than 46 billion words written in Twitter tweets by 63 million Twitter users around the globe.

In these billions of words is not a view of any individual’s state of mind. Instead, like billions of moving atoms add up to the overall temperature of a room, billions of words used to express what people are feeling resolve into a view of the relative mood of large groups.

These billions of words contain everything from “the” to “pancakes” to “suicide.” To get a sense of the emotional gist of various words, the researchers used a service from Amazon called Mechanical Turk. On this website, they paid a group of volunteers to rate, from one to nine, their sense of the “happiness” — the emotional temperature — of the ten thousand most common words in English. Averaging their scores, the volunteers rated, for example, “laughter” at 8.50, “food” 7.44, “truck” 5.48, “greed” 3.06 and “terrorist” 1.30.

The Vermont team then took these scores and applied them to the huge pool of words they collected from Twitter. Because these tweets each have a date and time, and, sometimes, other demographic information — like location — they show changing patterns of word use that provide insights into the way groups of people are feeling.

The new approach lets the researchers measure happiness at different scales of time and geography — whether global patterns over a workweek — or on Christmas.

And stretched out over the last three years, these patterns of word use show a drop in average happiness.

Or at least a drop in happiness for those who use Twitter. “It does skew toward younger people and people with smartphones and so on — but Twitter is nearly universal now,” Dodds said, “Every demographic is represented."

“Twitter is a signal,” Dodds said, “just like looking at the words in the New York Times or Google Books.” (Word sources that the team is also exploring in related studies). “They’re all a sample,” he says. “And indeed everything we say or write is a distortion of what goes on inside our head.”

But — like GDP is a distortion of the hugely complex interactions that make up the economy and yet is still useful — the new approach by the UVM team provides a powerful sense of the rising and falling pulse of human feelings.

Getting serious about happiness

“Individual happiness is a fundamental societal metric,” the researchers write in their study. Indeed the ultimate goal of much public policy is to improve and protect happiness. But measuring happiness has been exceedingly difficult by traditional means, like self-reporting in social science surveys. Some of the problems with this approach are that people often don’t tell the truth in surveys and the sample sizes are small.

And so efforts to measure happiness have been “overshadowed by more readily quantifiable economic indicators such as gross domestic product,” the study notes.

The new approach lets the UVM researchers almost instantaneously look over the “collective shoulder of society,” Dodds says. “We get a sense of the aggregate expressions of millions of people,” says Dodds’ colleague Chris Danforth, a mathematician and a co-author of the study, while they are communicating in a “more natural way,” he says. And this opens the possibility of taking regular measures of happiness in near real-time — measurements that could have applications in public policy, marketing and other fields.

The study describes hundreds of insights from the Twitter data, like a clear weekly happiness signal “with the peak generally occurring over the weekend, and the nadir on Monday and Tuesday,” they write. And over each day happiness seems to drop from morning to night. “It’s part of the general unraveling of the mind that happens over the course of the day,” said Dodds.

In the long-term graph that shows an overall drop in happiness, various ups and downs are clearly visible. While the strongest up-trending days are annual holidays like Christmas and Valentine’s Day, “all the most negative days are shocks from outside people’s routines,” Dodds say. Clear drops can be seen with the spread of swine flu, announcement of the U.S. economic bailout, the tsunami in Japan and even the death of actor Patrick Swayze.

On the dashhboard

“In measuring happiness, we construct a tunable, real-time, remote sensing, and non-invasive, text-based hedonometer,” the Vermont scientists write. In other words, a happiness sensor.

Right now the sensor is only available to the researchers, but Dodds, Danforth and their colleagues have in mind a tool that could go “on the dashboard” of policy makers, Dodds says. Or, perhaps, on a real estate website for people exploring communities into which they might move, or, simply, “if someone is flying in a plane they could look at this dashboard and see how the city below them is feeling,” he says.

Of course feelings change quickly and the nature of happiness itself is one of the most complex, profound issues of human experience.

“There is an important psychological distinction between an individual's current, experiential happiness and their longer term, reflective evaluation of their life,” the scientists write, “and in using Twitter, our approach is tuned to the former kind.”

And looking ahead, the Vermont scientists hope that by following the written expressions of individual Twitter users over long time periods, they’ll be able to infer details of happiness dynamics “such as individual stability, social correlation and contagion and connections to well-being and health.”

Dodds and his colleagues are no strangers to the debates over the role of happiness that can be traced back through Brave New World to Jeremy Bentham, Thomas Aquinas, and Aristotle. “By measuring happiness, we're not saying that maximizing happiness is the goal of society,” Dodds says. “It might well be that we need to have some persistent degree of grumpiness for cultures to flourish.”

Nevertheless, this study provides a new view on a compelling question: why does happiness seem to be declining?

How long do you suppose it would take them to do the same calculations? A thousand years? A million years? Not even close. If they had started calculating at the start of the Big Bang, with nary a snack or bathroom break, they wouldn’t be halfway done, if recent estimates in Science are correct.

And the corollary to this vast computing power is the vast amount of information now being produced and stored by our computers, satellites, cell phones and social media. A recent IBM estimate notes that at least ninety percent of all the information created by humanity has been produced in the last two years.

This is “big data,” according to the organizers of the TEDx event, “Big Data, Big Stories,” organized by UVM’s Complex Systems Center. And the effects, insights, problems and potential of all this information — the “big stories” — was the topic of the packed event’s 11 10-minute micro-lectures held at Fletcher Allen Health Care near campus.

Pattern recognition

In these “really data-rich worlds,” said UVM mathematician Peter Dodds — who organized the TEDx event along with his fellow mathematician Chris Danforth, roboticist Josh Bognard, and staffers Andi Elledge and Keri Toksu — something fundamental can change about how scientists do what they do.

When the data set gets big enough and the computers fast enough, Dodds said, instead of starting with a question, “there is a new way to approach things: which is simply that you have to go look for patterns, look for the shades, in these massive data sets.”

For example, in billions of Twitter tweets and blog posts, Dodds and his colleagues have found patterns of language that point to the rise and fall of the world’s mood. This discovery has allowed the scientists to create a near-real-time “hedonometer” — a happiness-measuring tool that can take the emotional pulse of places and groups of people around the world. Wednesday, not Monday, is the nadir of the workweek it seems. And the overall global “happiness signal” dropped, said Isabel Kloumann ’11 (one of Dodds's former students) during her TEDx talk, “corresponding to the time of the London riots breaking out.”

Call a robot

Who the scientist is may be changing too, under the storm of big data. In his talk, Mike Schmidt, a Cornell researcher, threw a clean curve of data points up on the screen and asked the audience to describe the equation that produced that pattern. The mathematically gifted in the group got it easily. “X squared,” someone shouted.

But as subsequent slides of data points got more complex, the audience was stumped. Into this kind of mess — or collections of data millions of times more messy — Schmidt would have researchers deploy a “new kind of artificial intelligence,” he says, “a robotic scientist” to fish out patterns from the seeming chaos.

As one example of this kind of silicon assistant, Schmidt has created a free software tool, Eureqa, that crunches raw experimental results and “distills out the fundamental mathematical properties of your data,” he says, “so that you come away with the model and deeper understanding of that data to help you ask the right questions.”

Ceci n’est pas une fish

If big data and fast computers are creating opportunity for new insights, the higher resolution and detail of the vast ocean of big data is also creating new challenges. Even headaches.

“With all this increased computing we are absolutely drowning in data,” said UVM’s Austin Troy, director of UVM’s Spatial Analysis Laboratory, in his TEDx talk, “and one of the types we are most drowning in is remote sensing data,” like that collected by satellites. Geographic images that once could only be resolved to a 30-meter-wide box labeled “forest,” can now be discerned as a specific maple tree with a broken branch.

In this case, the intelligence of computers can be the limiting factor rather than the breakthrough assistant. To explain, Troy put up side-by-side images of two bearded men. “I can tell within two seconds that that is George Carlin and that is Sigmund Freud,” he said, as the audience laughed. But for Troy to train a computer to recognize the difference would take “unbelievable amounts of time,” he said. Computers may be good at distilling mathematical approximations of data, but they don’t yet hold an old-fashioned candle to the human capacity for finding the gestalt.

In the same way, high-resolution images from space are providing an incredibly rich portrait of the planet — Troy showed a gorgeous image of the huge shadows of camels flowing across African desert and a tacky swimming pool in Nevada shaped like a tropical fish — but computers have been largely unable to pierce this raw data and find the camel or recognize the fish. Teaching computers to not work pixel by pixel, but, instead, to start to recognize the complex interplay of “shape, size, tone, pattern, texture, sight, and association,” Troy said, is one of the biggest challenges of big data.

Our satellites may be able to view every bumper-sticker on the planet and our computers may be able to complete calculations that would foil all of humanity, and yet, looked at another way, our computers are puny. All the computer storage in the world contains less information than is contained in your DNA. In one second, the 120 people gathered at the TEDxUVM lectures fire off as many neural impulses as all the computers in the world can perform operations.

“I need to teach a computer to see objects,” Troy said, “and to think like me.” That could be a while.

No hurricane in a water molecule

But part of the promise of the big data revolution is looking for patterns and interactions that are beyond or alien to the human mind. And in these patterns may be hidden “a way to solve incredibly hard problems that we need to solve,” Peter Dodds said. Looking for the master variable that controls a hurricane's track, a sudden economic collapse, or an ecosystem breakdown is bound to fail, he thinks.

That’s because many of the most important problems we want to understand are driven by complex systems, “where there is no powerful central control,” Dodds said. Instead there are “lots of localized interactions giving rising to macroscopic behavior, and often the macroscopic behavior is disastrous, like crashes in the stock market or ecosystem crashes.”

“There is no hurricane in a water molecule,” he said, “there is no financial collapse in a dollar bill. It’s all in how these thing arise.” And how they arise may yield to the brute and tireless power of a computer in ways that the far-more-powerful and elegant human brain doesn’t take in.

“There are compelling reasons for understanding systems, and the reason we haven’t been able to do so for things like social systems and economics systems,” Dodds said, “is because we haven’t been able to describe them.”

But work like that of Rob Axtell, from George Mason University, is getting closer. His TEDx talk described a model of the U.S. economy with 150 million independent “agents,” each with complex — sometimes irrational (i.e., real world!) — rules of behavior representing individual people. By letting these agents all interact in computer simulations he is seeking a view of the larger macro-economy that emerges from millions of micro decisions.

“What we are finding in the last few years is that some things that we thought were beyond measure — like willpower,” or the economic value of nature, or the timing of “random” terrorist attacks, said UVM robotics expert Josh Bongard, “— are not.”

Getting hotter

Still, the fundamental unpredictability of some aspects of the future, like the weather beyond a few weeks, may be intractable, UVM mathematician and climate modeler Chris Danforth reminded the audience, invoking the great chaos theorist Edward Lorenz.

And yet Danforth — who served as the moderator of the TEDx event — says “the most important big data story,” is the one coming out of the huge pools of information going into long-term climate forecasts. (Remember: it’s very hard to know if it will rain next Tuesday, but very easy to know it will be colder in January than June.)

If “we get it wrong — or we don’t pay attention to what the models are telling us,” Danforth said, “we could end up in big trouble.”

The New York Times

Their findings suggest that our moods are driven in part by a shared underlying biological rhythm that transcends culture and environment. By analyzing the frequency with which words occurred in their massive database of tweets, they saw patterns including happy weekends, and a morning peak in mood followed by an afternoon decline—“the daily unraveling of the human mind,” Dodds calls it.  Other ‘happy” days often coincided with holidays, whereas especially unhappy days tended to coincide with unexpected events, such as the Japanese earthquake and tsunami. Their findings also hint at a global decline in mood starting in April 2009 that continues at least through the first half of 2011.

Peter Sheridan Dodds received a prestigious five-year $678,000 National Science Foundation (NSF) Faculty Early Career Development (CAREER) Award for his research entitled, "Explorations of Complex Social and Psychological Phenomena through Multiscale Online Sociological Experiments, Empirical Studies, and Theoretical Models." He is the twelfth UVM faculty member to receive a NSF Foundation CAREER Award given for research that equals the highest expectations of colleagues around the world.

To read the Science September 2011 Greg Miller article entitled, “Social Scientists Wade Into the Tweet Stream” visit: http://www.sciencemag.org/content/333/6051/1814.full

To read The New York Times article, “Twitter Study Tracks When We Are J” visit:
http://www.nytimes.com/2011/09/30/science/30twitter.html

For more on Twitter/Big Data:
http://www.sciencemag.org/site/multimedia/index.xhtml

For more information, contact: Peter Sheridan Dodds

On Sept. 26, President Barack Obama announced Bongard as one of 94 winners of the Presidential Early Career Award for Scientists and Engineers; he will be honored at a White House ceremony in October.

Bongard is only the second researcher in UVM history to receive the PECASE award, which provides $500,000 in research funds over several years.

Inspired by evolution

Bongard’s far-reaching work looks to nature for ideas. “The goal is to borrow ideas from neuroscience and evolution to help us build better and more intelligent robots,” he says.

 So far, scientists have had little success in building resilient machines that can continually perform behaviors that are fairly simple but require ongoing adaptation to changing conditions — like paving a road or cleaning up a toxic dump.

But Bongard is on a mission to make them.

“The prevailing approach to create such machines is to copy physiological and neurological systems observed in animals, and build them into robots,” Bongard notes. “This raises the issue however of what, from among the infinitude of existing biological structures, should be copied.”

Instead of guessing, Bongard has innovated systems in which computer programs copy the dynamics of biological evolution and replay them in a virtual space with numerous generations of synthetic creatures — something like a highly sophisticated video game.

The resulting algorithm yields ideas for robots that have optimized their neurological structures — and their behaviors and body plans — over many generations of being tested by virtual evolution, instead of human guesswork.

With these ideas in hand, Bongard and his students can then build actual robots in their workshop that are adaptable and capable of responding to novel challenges.

“My long-term goal is to give back to neuroscience and evolutionary biology, to give us a different tool to investigate: why does intelligence evolve?” Bongard says. “Under what conditions will intelligence evolve? Could we ever consider a machine to be intelligent, or is intelligence something limited to biological organisms?”

Presidential vision

Recognizing this kind of innovative work, the PECASE awards “embody the high priority the Obama Administration places on producing outstanding scientists and engineers to advance the Nation’s goals, tackle grand challenges, and contribute to the American economy,” the White House wrote in a press release.

In 1996, the National Science and Technology Council was commissioned by President Clinton to create a program that would support and honor outstanding scientists and engineers early in their research careers — from this council came the PECASE award.

Each year, more than a dozen federal departments and agencies nominate scientists and engineers whose early accomplishments “show the greatest promise for assuring America’s preeminence in science and engineering and contributing to the awarding agencies' missions,” the White House press office wrote.

“It is inspiring to see the innovative work being done by these scientists and engineers as they ramp up their careers — careers that I know will be not only personally rewarding but also invaluable to the Nation,” President Obama said in the White House release. “That so many of them are also devoting time to mentoring and other forms of community service speaks volumes about their potential for leadership, not only as scientists but as model citizens.”

An innovator

Bongard, an assistant professor of computer science in UVM’s College of Engineering and Mathematical Sciences, was one of 21 nominees presented by the National Science Foundation for the most recent round of awards.

Bongard’s research has received national and international attention, and has been featured in Wired magazine, the Boston Globe, The Voice of America, Popular Science, and many other outlets. He also received a fellowship from Microsoft Research in 2007 for research related to self-healing robots — one of five given nationwide. He was named by MIT as one of the world’s top innovators under 35.

Bongard will travel to Washington, D,C., Oct. 13-14, to receive the award and will attend three ceremonies cumulating with a recognition ceremony at the White House with President Obama.

“This award allows me to continue with my basic scientific research, but it also allows me to create tools that draw many people into my research beyond my graduate students,” Josh Bongard says. “Through this award, we’re developing a web interface that will allow people to perform evolutionary robotics experiments without having a background in evolution or robotics."

The study was published in the July 2011 issue of Science magazine and covered by Science Now:

The Taliban-backed suicide bombing that left 21 dead in a hotel in Kabul on Tuesday appeared to come out of nowhere. Insurgent attacks on coalition forces in Iraq and Afghanistan have also proved unpredictable, with weeks or even months between one burst of deadly fighting and the next. But according to a new study, attacks that seem sporadic in the beginning can begin to show a pattern as the aggressors refine their methods. The finding may provide a way for military leaders to gauge the timing of future attacks in a conflict, helping them allocate troops, weapons, and resources more safely and efficiently. The research may even lead to ways of anticipating such seemingly random events as suicide bombings.

Read more...

According to the story, "Happy Words Trump Negativity in the English Language," the researchers used "overwhelming mathematical force" to analyze 361 billion words used in four enormous textual databases.

Dr. Margaret (Maggie) J. Eppstein from the UVM CEMS Department of Computer Science and Dr. Jeffrey D. Horbar, MD in the UVM Peds-Neonatology Department received the award for their research entitled, “Analysis of a collaborative worldwide network of neonatal ICUs”. 

Additional researchers include Dr. Donna M. Rizzo in the School of Engineering, Dr. Stuart A. Kauffman in the Department of Mathematics & Statistics and Biochemistry Departments, and consultant Ronald S. Burt, the Hobart W. Williams Professor of Sociology and Strategy from the Chicago Booth School of Business.

The research will look at the evolving interactions in the Vermont Oxford Network (VON) that impact health outcomes. VON was formed in 1990 as a not-for-profit.

Or at least that’s not too far off from what University of Vermont roboticist Josh Bongard has discovered, as he reports in the January 10 online edition of the Proceedings of the National Academy of Sciences.

In a first-of-its-kind experiment, Bongard created both simulated and actual robots that, like tadpoles becoming frogs, change their body forms while learning how to walk. And, over generations, his simulated robots also evolved, spending less time in “infant” tadpole-like forms and more time in “adult” four-legged forms.

These evolving populations of robots were able to learn to walk more rapidly than ones with fixed body forms. And, in their final form, the changing robots had developed a more robust gait -- better able to deal with, say, being knocked with a stick -- than the ones that had learned to walk using upright legs from the beginning.

“This paper shows that body change, morphological change, actually helps us design better robots,” Bongard says. “That’s never been attempted before.”

Robots are complex

Bongard’s research, supported by the National Science Foundation, is part of a wider venture called evolutionary robotics. “We have an engineering goal,” he says “to produce robots as quickly and consistently as possible.” In this experimental case: upright four-legged robots that can move themselves to a light source without falling over.

“But we don’t know how to program robots very well,” Bongard says, because robots are complex systems. In some ways, they are too much like people for people to easily understand them.

“They have lots of moving parts. And their brains, like our brains, have lots of distributed materials: there’s neurons and there’s sensors and motors and they’re all turning on and off in parallel,” Bongard says, “and the emergent behavior from the complex system which is a robot, is some useful task like clearing up a construction site or laying pavement for a new road.” Or at least that’s the goal.

But, so far, engineers have been largely unsuccessful at creating robots that can continually perform simple, yet adaptable, behaviors in unstructured or outdoor environments.

Which is why Bongard, an assistant professor in UVM’s College of Engineering and Mathematical Sciences, and other robotics experts have turned to computer programs to design robots and develop their behaviors -- rather than trying to program the robots’ behavior directly.

His new work may help.

To the light

Using a sophisticated computer simulation, Bongard unleashed a series of synthetic beasts that move about in a 3-dimensional space. “It looks like a modern video game,” he says. Each creature -- or, rather, generations of the creatures -- then run a software routine, called a genetic algorithm, that experiments with various motions until it develops a slither, shuffle, or walking gait -- based on its body plan -- that can get it to the light source without tipping over.

“The robots have 12 moving parts,” Bongard says. “They look like the simplified skeleton of a mammal: it’s got a jointed spine and then you have four sticks -- the legs -- sticking out.”

Some of the creatures begin flat to the ground, like tadpoles or, perhaps, snakes with legs; others have splayed legs, a bit like a lizard; and others ran the full set of simulations with upright legs, like mammals.

And why do the generations of robots that progress from slithering to wide legs and, finally, to upright legs, ultimately perform better, getting to the desired behavior faster?

“The snake and reptilian robots are, in essence, training wheels,” says Bongard, “they allow evolution to find motion patterns quicker, because those kinds of robots can’t fall over. So evolution only has to solve the movement problem, but not the balance problem, initially. Then gradually over time it’s able to tackle the balance problem after already solving the movement problem.”

Sound anything like how a human infant first learns to roll, then crawl, then cruise along the coffee table and, finally, walk?

“Yes,” says Bongard, “We’re copying nature, we’re copying evolution, we’re copying neural science when we’re building artificial brains into these robots.” But the key point is that his robots don’t only evolve their artificial brain -- the neural network controller -- but rather do so in continuous interaction with a changing body plan. A tadpole can’t kick its legs, because it doesn’t have any yet; it’s learning some things legless and others with legs.

And this may help to explain the most surprising -- and useful -- finding in Bongard’s study: the changing robots were not only faster in getting to the final goal, but afterward were more able to deal with new kinds of challenges that they hadn’t before faced, like efforts to tip them over.

Bongard is not exactly sure why this is, but he thinks it’s because controllers that evolved in the robots whose bodies changed over generations learned to maintain the desired behavior over a wider range of sensor-motor arrangements than controllers evolved in robots with fixed body plans. It seem that learning to walk while flat, squat, and then upright, gave the evolving robots resilience to stay upright when faced with new disruptions. Perhaps what a tadpole learns before it has legs makes it better able to use its legs once they grow.

“Realizing adaptive behavior in machines has to date focused on dynamic controllers, but static morphologies,” Bongard writes in his PNAS paper “This is an inheritance from traditional artificial intelligence in which computer programs were developed that had no body with which to affect, and be affected by, the world.”

“One thing that has been left out all this time is the obvious fact that in nature it’s not that the animal’s body stays fixed and its brain gets better over time,” he says, “in natural evolution animals bodies and brains are evolving together all the time.” A human infant, even if she knew how, couldn’t walk: her bones and joints aren’t up to the task until she starts to experience stress on the foot and ankle.

That hasn’t been done in robotics for an obvious reason: “it’s very hard to change a robot’s body,” Bongard says, “it’s much easier to change the programming inside its head.”

Lego proof

Still, Bongard gave it a try. After running 5000 simulations, each taking 30 hours on the parallel processors in UVM’s Vermont Advanced Computing Center -- “it would have taken 50 or 100 years on a single machine,” Bongard says—he took the task into the real world.

“We built a relatively simple robot, out of a couple of Lego Mindstorm kits, to demonstrate that you actually could do it,” he says. This physical robot is four-legged, like in the simulation, but the Lego creature wears a brace on its front and back legs. “The brace gradually tilts the robot,” as the controller searches for successful movement patterns, Bongard says, “so that the legs go from horizontal to vertical, from reptile to quadruped.

“While the brace is bending the legs, the controller is causing the robot to move around, so it’s able to move its legs, and bend its spine,” he says, “it’s squirming around like a reptile flat on the ground and then it gradually stands up until, at the end of this movement pattern, it’s walking like a coyote.”

“It’s a very simple prototype,” he says, “but it works; it’s a proof of concept.”

I am writing to inform you that I have decided to step down as Director of the Complex Systems Center, effective at the end of fall semester, 2010.   It’s been 4 years since I became Founding Director of the Complex Systems Center, and it has been an exciting time for me to lead the Center and help this transdisciplinary initiative gain strength to the point where it is now being supported as a campus-wide Spire of Excellence.

 I feel great satisfaction that the Complex Systems Initiative at UVM is now mature enough to be able to continue to thrive and grow without my leadership. Thus, while I was offered a place on the Complex Systems Steering Committee, I have decided that this is the right time for me to decline that position and to step down as Director, and allow others to take over leadership of the Complex Systems Initiative at UVM.   I’m quite excited at the prospect of having fewer things to organize and more time to devote to my own research in complex systems, in collaboration with many of you.   I will also continue to serve as Program Coordinator for the Certificate of Graduate Study in Complex Systems and Chair of the Complex Systems Curriculum Committee.

I truly appreciate the support I have felt from so many of you, and from the administration, in my efforts as Director of the Complex Systems Center; of course, the Center would be nothing without active and participating members, so we have really done this together. Thanks for your interest and involvement.  We now have a great Steering Committee to keep the Complex Systems Initiative moving forward and I very much look forward to participating with you in all the exciting activities that others will be organizing!

Sincerely,

Maggie Eppstein

Promoting collaborative, interdisciplinary complex systems research across UVM is central to the CSC mission and contributes to a natural partnership with the Transportation Research Center.  “One of the things that makes the complex systems approach so powerful is that it can work in many different domains and the insights gained in one domain can transfer to very different domains, ” states Dr. Eppstein. “We want to continue to integrate and synergize with UVM’s unique areas of strength, such as the TRC.”

The value of that collaboration is evident in TRC’s Signature Project #5, “Multi-Scale Model of the U.S. Transportation Energy Market for Policy Assessment.”  For this project, Dr. Eppstein and her colleagues are using agent-based modeling to explore factors that could influence sales of plug-in hybrid electric vehicles (PHEVs).  By examining model sensitivities to potential leverage points, the research team can identify policies and procedures that could be most effective in promoting PHEV market penetration. 

Results indicate that unless agents can estimate and make known differences in lifetime fuel costs across vehicle types, PHEVs are not likely to significantly penetrate the market.  “If policymakers want PHEVs to catch on, they need to find a way to make this comparison easy for consumers, such as through information required on vehicle stickers,” Dr. Eppstein concludes. This work was presented at the 2^nd Annual Complexity in Business Conference in November and is currently in journal peer review.

Led by Drs. Paul Hines and Chris Danforth, two faculty in the UVM Complex Systems Center and assistant professors in the College of Engineering and Mathematical Sciences (CEMS), the UVM-IBM project uses complex systems modeling approaches to reduce the risk of large blackouts caused by cascading failure in the electricity infrastructure. The research will focus on the development of new methods, based on complex systems concepts and high-performance computing, for estimating and reducing cascading failure risks -- invaluable information as Vermont actively develops the nation's first, statewide smart grid infrastructure.

"Implementation of a statewide Smart Grid holds tremendous promise to improve the daily lives of Vermonters," said Leahy, "The research conducted by UVM and IBM through this public-private partnership will improve the reliability of the power grid and help anticipate some of the unforeseen challenges posed by this new technology. Vermont is poised to lead the pack on Smart Grid adoption and this funding will help solidify that position."

"IBM's Vermont site has a long history of success working with UVM and we're pleased that this collaboration has now been extended to include IBM Research, said Janette Bombardier, director, IBM Vermont site operations and senior location executive. "IBM has used systems modeling and monitoring to improve energy use at its Vermont facility, and this research will focus on how to apply similar techniques to create a more stable and reliable electric grid for Vermont and nationwide."

"The growing UVM-IBM partnership in complex systems approaches and high performance computing on issues critical to the State of Vermont and our nation showcases excellence in both institutions," said UVM Vice President for Research Domenico Grasso, "I sincerely thank Senator Leahy for his support of this important research and look forward to our faculty's continued collaborations with IBM."

IBM will leverage the team's development of computationally efficient complex systems of systems approaches and Hybrid High Performance Computing (HPC) implementations, to compliment its "Smarter Energy" research agenda. The computationally rigorous field of complex systems is extremely well-suited to this research -- simulating complex interactions of technological, human, and environmental systems, among others. Complex systems was recently identified by UVM as one of three transdisciplinary "Spires of Excellence" for university-wide strategic investment.

Professor Hines moved to the UVM College of Engineering and Mathematical Sciences in 2007 after earning degrees in engineering and public policy from Carnegie-Mellon University. He has been active in Vermont energy systems education efforts and, in addition to his position at UVM, holds an adjunct position with Vermont Law School. Professor Danforth, Hines' co-investigator, moved to UVM from the University of Maryland and is an expert in the modeling of chaotic interactions. Danforth is engaged in complex systems modeling studies ranging from the "emotional state of the blogosphere" to blood coagulation and is also a national leader in math and climate system education both at UVM and through the Governor's Institutes of Vermont (GIV) program for Vermont high schools.

Through three UVM “Town Hall” meetings and a campus-wide Faculty survey, the working group identified at least 52 UVM faculty who self-describe their research as all or partly in complex systems and at least another 50 research-active faculty who expressed strong interest in collaborating with complex systems researchers.  This high level of interest in Complex Systems, coupled with the existing strengths of the UVM Complex Systems Center, helped the working group to write a strong proposal for Complex Systems as a UVM Transdisciplinary Spire of Excellence.

 After extensive internal and external review, President Fogel, Provost Knodell, and Vice President for Research Grasso selected Complex Systems as one of three UVM Spires of Excellence, and on May 21, 2010 this was endorsed by the UVM Board of Trustees. 

In September nominations and applications were invited for membership on the TRI Steering Committees. More than 30 nominations and applications of the highest caliber were received. The administration sought input from Faculty Senate leadership, the Council of Deans, and the University Distinguished Professors. A joint Faculty Senate/Administration committee made the final selection decisions and in October, Steering Committee members were named. The Steering Committee including Professors Peter Dodds, Joshua Bongard, Jason Bates, Christopher Danforth, Christopher Koliba and David Novak will help guide the implementation phase of the UVM Spire of Excellence in Complex Systems.

In addition, the Provost has announced recruitment for 30 faculty positions, with up to 10 of them in support of the Complex Systems Spire. Job postings for these positions are listed under the Complex Systems Center “Open Faculty Positions” link.

One of Lashua's current initiatives, for example, involves the transformation of a 99-acre tree farm into a public recreational facility with involvement by the State of Vermont, a local non-profit, the Village of Essex Junction, the Town of Essex, multiple youth soccer organizations, and area residents.

Town managers, Koliba writes, are often "caught between a host of public and private actors with interests defined by the narrowness and expansiveness of their concerns." Koliba, associate professor of Community Development and Applied Economics, authored the book with help from Asim Zia, assistant professor in CDAE, and Jack W. Meek, professor of public administration at the University of La Verne. The book illustrates how important skill sets of oversight, resource provision, negotiation and bargaining, facilitation, collaboration and systems thinking are within the landscape of public administration today.

"There is an intuitive component to the job, and interpersonal and people skills are definitely important," says Lashua. "But without these skill sets, the rest of it becomes a lot harder, and it's far more difficult to leverage the full effectiveness of these governance networks."

The complex nature of cross-sector alignments

"The case we make early on in the book is that governance networks have always been there," Koliba says. "You can go back to the founding fathers and their dilemmas around divided government and the three branches of government -- that's a network." But since the early days of American democracy, government networks have multiplied, Koliba says.

The book fills a gap in the existing knowledge and literature on how these complex networks affect contemporary policy implementation. In essence, Koliba says, there's been a shift from a unitary government offering services to a much more complicated, polycentric network organized around governance.

In the book, Koliba identifies four major governance network configurations: public-private partnerships; grant-contract arrangements; regulatory sub-systems; and interest group coalitions. When these networks intermingle, he says, complicated cross-sector alignments emerge, raising tricky questions about the role of the private sector working with, and performing, public sector tasks. These partnerships have caused "sector blurring" -- an erosion of the distinction between the public and private sector actors -- and raise questions about a trend toward running government like a business.

"There hasn't been a lot of discussion about what the implications of doing that are," says Koliba. "Where's accountability in there? What about the corporate responsibility piece? When do businesses have a responsibility to the common good? And so there's an isomorphism to sector blurring: how do the characteristics and traits of one actor in the network affect the characteristics and shapes of the other? So it's a two-way street; the membrane is permeable both ways."

Many of the concepts in the book are aligned with the mission of the MPA program, which is to produce effective public policy makers like Lashua. "Our mission is to prepare public administrators and policy analysts to understand complex governance systems," says Koliba, a mission that he says aligns with the tradition of collaborative democratic government that's the hallmark of Vermont communities.