Crowd in the Machine

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Human activities rarely happen in isolation. Traffic flow, commuting, social media sharing, and even crisis evacuations are all examples of collective behaviors that shape society. The theme “Crowd in the Machine: People, Mobility & Complex Systems” studies these dynamics using simulations, models, and data analysis to uncover order within complex systems.

From smarter urban transport to crowd safety, from epidemiology to online networks, this theme demonstrates how computation can explain and improve large-scale interactions. Students learn to design tools that make sense of collective patterns, helping cities and communities become safer, more efficient, and more resilient.

ARADA, Marian G., MIT 2013
A microsimulation study on the effects of infrastructure developments and traffic policies in the Roundabout Junction in Upper Bicutan, Taguig City, Philippines
ARRIOLA, Johaira Mae T., MSCS 2018
Multi-agent simulation of pursuit-evasion in dynamic graphs
DOGELIO, Emirrah Hazelle T., BSCS 2014
Pedestrian traffic simulation in Lopez Avenue Los Baños, Laguna
IGNACIO, Michael Jade R., BSCS 2013
Quantitative comparison of Social Force Model and Social Comparison Theory in microsimulating crowd dynamics
LEAÑO, Rovin T., BSCS 2020
Multi-agent simulation of fake news proliferation in social media
NGOHO, Louie Vincent A., BSCS 2009
Measuring the dynamics of a large crowd pedestrian traffic using a machine vision system
PALOMO, Rose Shane R., BSCS 2014
A simulation of student movement under normal conditions in UPLB Physical Sciences Building
REY, John Carlo R., BSCS 2014
A multi-agent simulation of crowd egress dynamics from the New UPLB Administration Building
TATARO, Merly F., MIT 2013
A microsimulation study on the effects of traffic policies in the Roundabout Junction in Upper Bicutan, Taguig City, Philippines
TECSON, Christian Dan N., BSCS 2015
A simulation of student movement under panic conditions in PhySci

Students are encouraged to contribute to this theme by designing simulations, building predictive models, or analyzing real-world datasets that capture the dynamics of people and mobility. Whether one is curious about how traffic jams form, how information spreads, or how large groups respond to stress, the student work can shed light on the complexity of collective behavior. By joining this theme, the student helps advance computational insights that guide smarter policies, safer cities, and more resilient communities.