Qualitative Analysis

Predicting the Future: Harnessing the Power of Probabilistic Judgements Through Forecasting Tournaments

April 29, 2025
by Christian Caballero. From the threat of nuclear war to rogue superintelligent AI to future pandemics and climate catastrophes, the world faces risks that are both urgent and deeply uncertain. These risks are where traditional data-driven models fall short—there’s often no historical precedent, no baseline data, and no clear way to simulate a future world. In cases like this, how can we anticipate the future? Forecasting tournaments offer one answer, harnessing the wisdom of crowds to generate probabilistic estimates of uncertain future events. By incentivizing accuracy through structured competition and deliberation, these tournaments have produced aggregate predictions of future events that outperform well-calibrated statistical models and teams of experts. As they continue to develop and expand into more domains, they also raise urgent questions about bias, access, and whose knowledge gets to shape our collective sensemaking of the future.

Seyi Olojo

Data Science Fellow 2021-2022
School of Information

Seyi is a PhD Student in the School of Information and is a member of the Algorithmic Fairness and Opacity Group. Her research broadly explores the problem space of digital memory, specifically the social discourse surrounding algorithms, ethics, and engagement. Additionally, her work often explores histories of quantification and the politics of categories within emerging technologies. She uses a mixed methods approach to research; this includes ethnography, interviews, grounded theory, surveys, data analysis and values-based design. Here at the D-lab, she leads the qualitative...

Sahiba Chopra

Data Science Fellow 2024-2025
Haas School of Business

I'm a PhD student in the Management and Organizations (Macro) group at Berkeley Haas. I have a diverse professional background, primarily as a data scientist across numerous industries, including fintech, cleantech, and media. I hold a BA in Economics from the University of Maryland, an MS in Applied Economics from the University of San Francisco, and an MS in Business Administration from UC Berkeley.

My research focuses on the intersection of inequality, technology, and the labor market. I am particularly interested in understanding how to reduce inequality in...

Why Data Disaggregation Matters: Exploring the Diversity of Asian American Economic Outcomes Using Public Use Microdata Sample (PUMS) Data

February 11, 2025
by Taesoo Song. Asian Americans are often overlooked in discussions of racial inequality due to their high average socioeconomic attainment. Many academic and policy researchers treat Asians as a single racial category in their analysis. However, this broad categorization can mask significant within-group disparities, leaving many disadvantaged individuals without access to vital resources and policy support. Song emphasizes the importance of data disaggregation in revealing Asian American inequalities, particularly in areas like income and homeownership, and demonstrates how breaking down these categories can lead to more targeted and effective policy solutions.

Field Experiments in Corporations

January 28, 2025
by Yue Lin. How do social science researchers conduct field experiments with private actors? Yue Lin provides a brief overview of the recent developments in political economy and management strategy, with a focus on filing field experiments within private corporations. Unlike conventional targets like individuals and government agencies, private companies are an emergent sweet spot for scholars to test for important theories, such as sustainability, censorship, and market behavior. After comparing the strengths and weaknesses of this powerful yet nascent method, Lin brainstorms some practical solutions to improve the success rate of field experimental studies. She aims to introduce a new methodological tool in a nascent research field and shed some light on improving experimental quality while adhering to ethical standards.

Looking Ahead: How Adolescents’ Consideration of Future Consequences Shapes Their Developmental Outcomes

March 25, 2025
by Elaine Luo. Adolescents constantly balance immediate impulses with long-term goals. Our research explored how adolescents differ in their tendency to think about immediate versus future consequences, and how these differences relate to academic performance, stress, and perceived life chances. Using Latent Profile Analysis, we identified three distinct groups: Indifferent (low consideration overall), Future-Focused (prioritizing future outcomes), and Dual-Focused (high consideration of both immediate and future outcomes). Results indicated the Dual-Focused adolescents had higher academic achievement, whereas the Future-Focused group perceived the most positive life prospects. A discussion on practical implications and future research direction for supporting balanced decision-making among adolescents is also provided.

Claudia von Vacano, Ph.D.

Founding Executive Director, P.I., Research Director, FSRDC

Dr. Claudia von Vacano is the Founding Executive Director and Senior Research Associate of D-Lab and Digital Humanities at Berkeley and is on the boards of the Social Science Matrix and Berkeley Center for New Media. She has worked in policy and educational administration since 2000, and at the UC Office of the President and UC Berkeley since 2008. She received a Master’s degree from Stanford University in Learning, Design, and Technology. Her doctorate is in Policy, Organizations, Measurement, and Evaluation from UC Berkeley. Her expertise is in organizational theory and...

Fritz_X_DargesBlue42… Who Are You?

January 14, 2025
by Jonathan Pérez. Reflecting on the complexities of the human experience is paramount to conducting research. Jonathan Pérez, through his exploration of a conspiracy subreddit, reflects on his experience trying to find the human behind the datum. Jonathan critiques the harmful effects of dehumanizing rhetoric and the researcher’s responsibility to navigate ethical implications. In doing so, he establishes three guiding rules to support researchers seeking to humanize their analysis: 1) a researcher must always find the story behind the data; 2) a researcher must protect themselves; 3) a researcher must still humanize participants (even those who perpetuate harmful narratives).

What are Time Series Made of?

December 10, 2024
by Bruno Smaniotto. Trend-cycle decompositions are statistical tools that help us understand the different components of Time Series – Trend, Cycle, Seasonal, and Error. In this blog post, we will provide an introduction to these methods, focusing on the intuition behind the definition of the different components, providing real-life examples and discussing applications.

Language Models in Mental Health Conversations – How Empathetic Are They Really?

December 3, 2024
by Sohail Khan. Language models are becoming integral to daily life as trusted sources of advice. While their utility has expanded from simple tasks like text summarization to more complex interactions, the empathetic quality of their responses is crucial. This article explores methods to assess the emotional appropriateness of these models, using metrics such as BLEU, ROUGE, and Sentence Transformers. By analyzing models like LLaMA in mental health dialogues, we learn that while they suffer through traditional word-based metrics, LLaMA's performance in capturing empathy through semantic similarity is promising. In addition, we must advocate for continuous monitoring to ensure these models support their users' mental well-being effectively.