Repository for the Fall 2024 Computational Social Science Workshop
Time: 11:00 AM to 12:20 PM, Thursdays; Location: Kent 107; in present participation required (assessed by QR-code survey). Fall 2024 will focus on introducing students to faculty across the university involved in Computational Social Science research through sessions of curated lightning talks. No online participation is required except for the first week, week 1.
Note that Winter and Spring 2025 will be seminar-style, with a different (outside) speaker each week. Those quarters will involve required: 1) weekly reading associated with our speaker (like week 1 of this quarter); 2) posing an online question and answer about the reading (like week 1 of this quarter); and 3) attending in person (in 1155 E. 60th Street in Room 295).
For MACSS students or those enrolled in the workshop only, fill out this GitHub survey for the 2024/25 school year (even if you have filled out a similar form in the past). Note: If you don't fill this out, we cannot credit your participation in the workshop (and you will Fail).
Our lineup includes: Anjali Adukia (Harris), YC Leong (Psychology), Chenhao Tan (CS), Nick Feamster (CS).
Our lineup includes: Henry Dambanemuya, David Peterson, and Fabricio Vasselai.
Our lineup includes: Joshua Conrad Jackson (Booth), Sanjay Krishnan (CS), Marshini Chetty (CS), Wilma Bainbridge (Psych).
Our lineup includes:Ufuk Akcigit, Thibaut Lamadon, Max Tabord-Meehan and Eric Richert.
Our lineup includes: Jai Yu, Alexander Todorov, Edward Vogel, Jason N. MacLean
Speaker: Marc Berman is a Professor in the Department of Psychology and is involved in the Cognition, Social and Integrative Neuroscience programs. Understanding the relationship between individual psychological and neural processing and environmental factors lies at the heart of my research. In my lab we utilize brain imaging, behavioral experimentation, computational neuroscience and statistical models to quantify the person, the environment and their interactions. Marc received his B.S.E. in Industrial and Operations Engineering (IOE) from the University of Michigan and his Ph.D. in Psychology and IOE from the University of Michigan. He received post-doctoral training at the University of Toronto's Rotman Research Institute at Baycrest. Before arriving to Chicago he was an Assistant Professor of Psychology at the University of South Carolina.
Talk: Implicit racial biases are lower in more populous more diverse and less segregated US cities. Implicit biases - differential attitudes towards members of distinct groups - are pervasive in human societies and create inequities across many aspects of life. Recent research has revealed that implicit biases are generally driven by social contexts, but not whether they are systematically influenced by the ways that humans self-organize in cities. We leverage complex system modeling in the framework of urban scaling theory to predict differences in these biases between cities. Our model links spatial scales from city-wide infrastructure to individual psychology to predict that cities that are more populous, more diverse, and less segregated are less biased. We find empirical support for these predictions in U.S. cities with Implicit Association Test data spanning a decade from 2.7 million individuals and U.S. Census demographic data. Additionally, we find that changes in cities’ social environments precede changes in implicit biases at short time-scales, but this relationship is bi- directional at longer time-scales. We conclude that the social organization of cities may influence the strength of these biases.
Readings:
Please read the paper and post at least one question about the reading HERE by Wednesday, October 9 @ 11:59pm; and post five upvotes ("thumbs up") to others questions and one answer to someone else's question by Thursday, October 10 @ 10am.
James Evans is the Max Palevsky Professor of Sociology & Data Science at the University of Chicago, External Faculty at the Santa Fe Institute, and Visiting Faculty at Google. Evans’ research uses large-scale data, machine learning and generative AI to understand how collectives of humans and machines think and what they know. This involves inquiry into the emergence of ideas, shared patterns of reasoning, and processes of attention, communication, agreement, and certainty. Thinking and knowing collectives like science, modern large language models, the Web, or modern commercial enterprises involve complex networks of diverse human and machine intelligences, collaborating and competing to achieve overlapping aims. Evans’ work connects the interaction of these agents with the knowledge they produce and its value for themselves and the system. His work is supported by numerous federal agencies (NSF, NIH, DOD), foundations and philanthropies, has been published in Nature, Science, PNAS, and top social and computer science outlets, and has been covered by global news outlets from the New Yorker, Wall Street Journal, and New York Times to the Economist, Le Monde, and Die Zeit.
Simulating Subjects: The Promise and Peril of AI Stand-ins for Social Agents and Interactions. Large Language Models (LLMs), through their exposure to massive collections of online text, learn to reproduce the perspectives and linguistic styles of diverse social and cultural groups. This capability suggests a powerful social scientific application – the simulation of empirically realistic, culturally situated human subjects. Synthesizing recent research in artificial intelligence and computational social science, we outline a methodological foundation for simulating human subjects and their social interactions. We then identify nine characteristics of current models that are likely to impair realistic simulation human subjects, including atemporality, social acceptability bias, uniformity, and poverty of sensory experience. For each of these areas, we discuss promising approaches for overcoming their associated shortcomings. Given the rate of change of these models, we advocate for an ongoing methodological program on the simulation of human subjects that keeps pace with rapid technical progress.
Read the following
Plus ONE of the following 3
Post one question about the readings HERE by Wednesday, October 2 @ 11:59pm; and post five upvotes ("thumbs up") to others questions and one answer to someone else's question by Thursday, October 3 @ 10am.
This week only, propose one or more computational social scientists we can invite for our seminar series this year HERE, including a 1-2 sentence defense of why others might want to hear from them, by Wednesday, October 2 @ 11:59pm; and upvote ("thumbs up") five other proposals by Thursday, October 3 @ 5pm [HERE](). We will invite the top-most upranked people for the seminar over the course of the year!