People demonstrate against the price rise of Metro tickets, in downtown Santiago, Chile, in October 2019. The protests against the Metro ticket price rise saw the closure of all suburban lines in the city. File Photo by Alberto Pena/EPA-EFE
July 21 (UPI) -- After a series of demonstrations and riots rippled across Chilean society in 2019, a team of researchers in Chile and Britain, including economists, mathematicians and physicists, decided to find out if social unrest follows predictable patterns.
For their study, the results of which were published Tuesday in the journal Chaos, scientists combined epidemic models with analytical tools adapted from the physics of disorder.
Adopting the perspectives of social scientists and economists, researchers used their new model to analyze the trajectory of the 2019 social unrest in Chile.
The findings showed the spread of riots today involve highly dynamic processes. According to the study's authors, traditional epidemic models are less able to predict the spread of upheaval than they were several decades ago.
For more than a century, scientists have been using epidemiological mathematical models to study the spread of diseases.
"In the 1970s, this type of methodology was used to understand the dynamics of riots that occurred in U.S. cities in the 1960s," study co-author Jocelyn Olivari Narea said in a news release.
"More recently, it was used to model French rioting events in 2005," said Narea, an assistant professor at Adolfo Ibáñez University in Chile.
One of the most popular mathematical models used to predict the spread of disease is called the SIR epidemiological model. The model separates a population into three groups: susceptible, infectious and recovered individuals.
"Within a rioting context, someone 'susceptible' is a potential rioter, an 'infected individual' is an active rioter, and a 'recovered person' is one that stopped rioting," said study co-author Katia Vogt-Geisse, professor of mathematical biology at Adolfo Ibáñez University. "Rioting spreads when effective contact between an active rioter and a potential rioter occurs."
While studying the inner workings of the SIR epidemiological model, researchers realized the model's mathematics is based on what are called Hamiltonian mechanics. The same mathematical structures define Newton's laws of physics.
"This allowed us to apply well-known tools of the physics of chaos to show that within the presence of an external force, the dynamics become very rich," said co-author Sergio Rica Mery, professor of physics at Universidad Adolfo Ibáñez.
"The external force that we included in the model represents the occasional trigger that increases rioting activity," Ibáñez said.
When the models were tweaked to account for rioting triggers, researchers found the trajectory of the subsequent social disruption was highly influenced by the number of potential rioters and active rioters.
Despite the dynamic processes that have dictated the spread of social unrest during the 21st century, the researchers suggest epidemiological models can still be tweaked and updated to predict the spread of riots and upheaval.
"While you might think that the study of disease transmission and problems of a social nature vary greatly, our work shows epidemiological models of the most simple SIR type, enriched by triggers and tools of the physics of chaos, can describe rioting activities well," Vogt-Geisse said.
"The external force that we included in the model represents the occasional trigger that increases rioting activity," Ibáñez said.
When the models were tweaked to account for rioting triggers, researchers found the trajectory of the subsequent social disruption was highly influenced by the number of potential rioters and active rioters.
Despite the dynamic processes that have dictated the spread of social unrest during the 21st century, the researchers suggest epidemiological models can still be tweaked and updated to predict the spread of riots and upheaval.
"While you might think that the study of disease transmission and problems of a social nature vary greatly, our work shows epidemiological models of the most simple SIR type, enriched by triggers and tools of the physics of chaos, can describe rioting activities well," Vogt-Geisse said.
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Can social unrest, riot dynamics be modeled?
Exploring episodes of social unrest and rioting, discovering a way to model its spread
WHEREVER RIOT COPS APPEAR SO DO RIOTS
IMAGE: THE DYNAMICS OF SERIOUS RIOTING EVENTS DURING THE 2019 CHILEAN SOCIAL UNREST. view more
CREDIT: SERGIO RICA MERY
WASHINGTON, July 21, 2020 -- Episodes of social unrest rippled throughout Chile in 2019 and disrupted the daily routines of many citizens. Researchers specializing in economics, mathematics and physics in Chile and the U.K. banded together to explore the surprising social dynamics people were experiencing.
To do this, they combined well-known epidemic models with tools from the physics of chaos and interpreted their findings through the lens of social science as economics.
In the journal Chaos, from AIP Publishing, the team reports that social media is changing the rules of the game, and previously applied epidemic-like models, on their own, may no longer be enough to explain current rioting dynamics. Using epidemiological mathematical models to understand the spread of infectious diseases dates back more than 100 years.
"In the 1970s, this type of methodology was used to understand the dynamics of riots that occurred in U.S. cities in the 1960s," said Jocelyn Olivari Narea, co-author and an assistant professor at Adolfo Ibáñez University in Chile. "More recently, it was used to model French rioting events in 2005."
From a mathematical point of view, the team's work is based on the SIR epidemiological model, known for modeling infectious disease spread. This technique separates the population into susceptible, infectious and recovered individuals.
"Within a rioting context, someone 'susceptible' is a potential rioter, an 'infected individual' is an active rioter, and a 'recovered person' is one that stopped rioting," explained co-author Katia Vogt-Geisse. "Rioting spreads when effective contact between an active rioter and a potential rioter occurs."
They discovered that the SIR model uses Hamiltonian mechanics for mathematics, just like Newton's laws for physics.
"This allowed us to apply well-known tools of the physics of chaos to show that within the presence of an external force, the dynamics become very rich," said co-author Sergio Rica Mery. "The external force that we included in the model represents the occasional trigger that increases rioting activity."
When including such triggers, the team found the way a sequence of events occurs varies greatly based on the initial number of potential rioters and active rioters.
"Even the sequence of rioting events can be chaotic," Rica Mery said. "Rich dynamics reveal the complexity involved in making predictions of rioting activity."
The team's work comes at a timely moment as social unrest is becoming more common --even within the context of the current pandemic.
"We just saw episodes of rioting in Minnesota due to racial unrest and how it ended up spreading to various locations within the U.S. and even abroad," Olivari Narea said.
The team pointed out it was surprising that the idea of disease spread can be well applied to rioting activity spread to obtain a good fit of rioting activity data.
"While you might think that the study of disease transmission and problems of a social nature vary greatly, our work shows epidemiological models of the most simple SIR type, enriched by triggers and tools of the physics of chaos, can describe rioting activities well," Vogt-Geisse said.
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The article, "The anatomy of the 2019 Chilean social unrest," is authored by Paulina Caroca, Carlos Cartes, Toby P. Davies, Jocelyn Olivari, Sergio Rica and Katia Vogt-Geisse. It will appear in Chaos, July 21, 2020 (DOI: 10.1063/5.0006307). After that date, it can be accessed at https:/
ABOUT THE JOURNAL
Chaos is devoted to increasing the understanding of nonlinear phenomena in all areas of science and engineering and describing their manifestations in a manner comprehensible to researchers from a broad spectrum of disciplines. See https:/
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