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Authors seek to define ‘Hamilton’ secret superpower
LAWRENCE – What is the essential quality that has made “Hamilton” one of the most successful Broadway musicals ever? That was the question Paul Laird, University of Kansas professor of musicology, wanted to explore in his new book, “Dueling Grounds: Revolution and Revelation in the Musical Hamilton” (Oxford University Press).
New research model reveals how contagion spreads among network of connected people
LAWRENCE — Misinformation and disease have one major theme in common: They can spread easily. A new paper by a University of Kansas professor of economics links the two even further by postulating a single model to study both cases. The new model and associated algorithms can be used to understand potential consequences of policies designed to control or spread contagion among a network of connected people.
Full stories below.
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Contact: Rick Hellman, KU News Service, 785-864-8852, [email protected], @RickHellman
Authors seek to define ‘Hamilton’ secret superpower
LAWRENCE – What is the essential quality that has made “Hamilton” one of the most successful Broadway musicals ever?
That was the question Paul Laird, University of Kansas professor of musicology, and his co-author/editor, Mary Jo Lodge, associate professor of theater at Lafayette College, asked themselves and their colleagues a couple of years ago.
Their answer, spelled out in the form of the new book “Dueling Grounds: Revolution and Revelation in the Musical Hamilton” (Oxford University Press), is that liminality – that condition of being on the border between two disparate things – is key to understanding the play’s awesome power.
“Almost any way you look at the show, there are two counter-ideas rubbing up against each other,” Laird said. “You have these conservative and liberal viewpoints being expressed, talking and singing, rap and pop songs, and, of course, the Black and white racial subtext.
“It’s shown in the costumes, which are 18th century from the neck down and 21st century from the neck up. That helps us see the performers’ faces much more than if they were wearing powdered wigs.
“And then there is the remarkable, liminal role of the ensemble, which transforms itself into any number of forms. The dancers are used brilliantly. They’re onstage almost the whole time as soldiers, as people on the street, or simply providing a physical reaction to what has happened on stage. At one point, during the duel scene where Burr shoots Hamilton, one even becomes the bullet, tracing its path through the air with her hand. I have never seen such fluidity.”
That’s saying something, as Laird is a longtime musicologist and author of several books and scholarly articles on composers Leonard Bernstein, Stephen Schwartz and others. Bernstein makes a brief appearance in the chapter of the “Dueling Grounds” that Laird wrote, about pre-“Hamilton” musicals set during the American Revolution or during the country’s early years.
The 1976 show “1600 Pennsylvania Avenue,” with book and lyrics by Alan Jay Lerner and music by Bernstein, was a monumental flop, running for just seven performances, Laird writes, “because of a flawed book and its strong criticism of racial problems in the United States, not a message that resonated with the audience during the Bicentennial.”
More successful, in Laird’s estimation, was “1776,” with music and lyrics by Sherman Edwards and book by Peter Stone (1969 Broadway, 1972 film).
Not that it holds a candle to “Hamilton” artistically or commercially. Hardly anything does. For Laird and his co-authors, it’s right up there or even beyond “West Side Story,” “South Pacific” or “Fiddler on the Roof.”
That does not mean, however, that “Dueling Grounds” is unalloyed in its praise for creator Lin-Manuel Miranda’s masterpiece, according to Laird. One issue is that Miranda wanted the show, based on Ron Chernow’s biography “Alexander Hamilton,” to stand up to historical scrutiny, a difficult task for a musical that must entertain and cannot be filled with too many cast members or tell too complex a story. Various authors in “Dueling Grounds” point out where “Hamilton” falls short of the mark as history, especially factually and in omitting the actual representation of the real lives that people of color lived during the period. Another area of controversy in “Hamilton” is in the limited portrayal of the female characters. They perform some of the best songs, but, as author Stacy Wolf wrote in the book, “… the three women in the musical occupy the most conventional and stereotypical roles of muse, wife, and whore.” In an age when representation of people of color and women has become a major concern, one interested in social justice must ask such questions, but, as Laird noted, “Hamilton” occupies a commercial medium, and “compromises made for the sake of entertainment make it difficult to satisfy fully every constituency.”
Laird said the editors tried to make the book as current as possible, given that it was completed in the midst of Broadway’s COVID-19 pandemic shutdown. The premiere of the filmed version of “Hamilton” on the Disney + streaming service, for example, is addressed in the conclusion, as is the “Hamilton” company’s reaction to the George Floyd murder case and Black Lives Matter movement.
Not that this will be the last word. There is still plenty of room left for additional scholarly and critical perspectives on “Hamilton,” Laird and Lodge wrote. The editors had three times as many submissions as they could include in “Dueling Grounds,” forcing them to leave additional good material on the cutting-room floor.
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Contact: Jon Niccum, KU News Service, 785-864-7633, [email protected]
New research model reveals how contagion spreads among network of connected people
LAWRENCE — What do misinformation and disease have in common? They spread easily.
But a new paper titled “Control and spread of contagion in networks” links the two even further by postulating a single model to study both cases. The new model and associated algorithms can be used to understand potential consequences of policies designed to control or spread contagion among a network of connected people.
“We started off thinking about how to model misinformation using decentralized and interdependent decision-making models from game theory,” said Tarun Sabarwal, professor of economics at the University of Kansas and director of the Center for Analytical Research in Economics.
“In games with strategic complements, there is a natural incentive for people to move in the same direction as others. That’s the underlying foundation, and you see this all over the place. Whether using the same technology to collaborate with others or believing the same things that our friends believe, people have a tendency to coordinate with their colleagues and friends.”
Co-written with John Higgins, a KU senior in economics, their model and analysis are applicable to many situations, ranging from regime change to runs on hand sanitizer during a pandemic. But, obviously, two current issues with the most pertinence involve the dispersal of fake news and COVID-19.
Sabarwal explains that if someone wants to sow discord within a country through misinformation, they only need to convince a relatively small minority of citizens to believe an inflammatory piece of information, and this can be enough to influence a larger percentage. The more people convinced initially, the greater the potential sweep of misinformation. It’s the same for infectious diseases.
“We expected it to go in this direction, but the speed with which things escalated with virality was very surprising,” Sabarwal said.
“Under reasonable assumptions, if contagion has spread to about 40% of the network, then it’s almost impossible to stop. Even if people around you are not infected, you will still get infected because of the aggregate effect flowing through communal interaction.”
Sabarwal models the proliferation of an action using a network coordination game that is generalized to include a tractable measure of aggregated virality to make it more realistic. These new algorithms study the spread of contagion in scale-free networks with 1,000 players using millions of Monte Carlo simulations.
“One new aspect of our work is an explicit inclusion of an aggregated virality impact, based on the number of non-neighbors who are infected,” he said.
Sabarwal emphasizes this is often seen in business, epidemiology and computer science. For instance, marketing-type models ask, “Which people do I need to flip so that I can maximize others who will buy the product?” In epidemiology, researchers will look at the probability of infecting something, then they set up a differential equation to see how this will evolve over time. In computer science, they will study misinformation while also investigating the person-to-person effect.
“We are saying that in social networks, in addition to what my neighbors are doing, I may be affected by global conditions in the network. In the case of misinformation, for example, even if all my neighbors are saying ‘no,’ if many other people are saying ‘yes’ on Twitter or Facebook, that has an additional impact on me. For infectious diseases, even if people around me are not infected, I may be at higher risk due to travel of unrelated people or interaction with others in anonymous communal markets. Our model captures this effect in a natural manner.”
What makes this trickier for contemporary society is the role of aggregated virality in social media/social networks.
“Earlier, you could share something with the whole country through a newspaper or TV. These media have built-in checks and balances so that not everything you say is heard at the national level,” said Sabarwal, who has been at KU since 2008.
“In social networks, you send whatever you want to send, and it goes wherever it wants to go. Moreover, the platforms incentivize greater information sharing among more people. A well-coordinated misinformation campaign in this setting can have broad and serious societal consequences.”
Sabarwal hopes his model provides more people with new research tools to analyze additional situations.
Also, the professor noted, the model can be read “both ways.”
He said, “You can read it in terms of, ‘What should we be doing to prevent the spread of something negative?’ But you can also look at it in terms of, ‘What should we be doing to increase the spread of something positive?’”
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