About

I am an Assistant Professor in System Dynamics at MIT’s Sloan School of Management. My work uses quantitative behavioral models, assisted by the analysis of data, to study collective human behavior on a broad range of organization levels, from teams to cities. Recent research applications include collective decision-making, political polarization, scaling laws in cities, and bureaucracy in organizations. I received my PhD in Applied Mathematics from Northwestern University and was a fellow at the Santa Fe Institute.

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If you are looking for my interactive visualization of US Congress ideology, it’s here.

Media Coverage of Research

• Jan 2022: Nautilus reports on my social learner and collective decision paper.
• Sept 2021: PNAS News Feature quotes my interview on modeling political party polarization.
• May 2021: SIAM News reports on my work on how social learners affect collective decisions.
• April 2021: Big Think gives a concise and eloquent report on my “Falling through the cracks” paper.
• Oct 2020: Forbes discusses my “satisficing” voting model at length.
• Oct 2020: Wall Street Journal reports on my work on political party polarization as part of a larger piece about sociophysics, polarization, and media manipulation.
• Sept 2020: Complexity Podcast interviews me about my work on political polarization and social categories.
• Sept 2020: KSFR Radio interviewes about political party polarization.
• 2017: Northwestern News Network features my research on urban scaling laws.

New Publication Announcements

• Aug 2022: New pre-print, Scaling and the universality of function diversity across human organizations, appears in arXiv. We analyze data of US federal agencies, companies, and universites, and find the distinct number of functions (occupations or degrees offered) approximately scales with organization size to the power of 1/2. We discuss interpretations and potential theoretical frameworks that may explain these results.
• July 2022: New pre-print, Mathematical model bridges disparate timescales of lifelong learning, appears in arXiv. We develop a dynamical model for how the short (within work sessions) and long (lifelong skill development) timescales of learning interact with each other.
• May 2022: New pre-print perspective article, Collective Intelligence as Infrastructure for Reducing Broad Global Catastrophic Risks, appears in arXiv. We discuss how collective intelligence can be useful for shoring up our global catastrophic risk infrastructure.
• Aug 2021: New paper, Dynamical system model predicts when social learners impair collective performance appears in PNAS. We develop a general framework for how interaction of multiple social & psychological factors affects collective decisions. We predict a critical threshold for the proportion of those who follow others, above which an option may prevail regardless of merit. [Paper summary video]
• Aug 2021: New paper, Scaling of urban income inequality in the USA appears in Journal of the Royal Society Interface. With three former undergraduate research students, we analyze income distribution data in US cities in the urban scaling framework, and show that the poorest individuals do experience the increased return to scale for income. Our work suggest heterogeneous models are needed to form a more coherent understanding of urban scaling. [Non-paywall full text]
• April 2021: New preprint, When do social learners affect collective performance negatively? The predictions of a dynamical-system model appears on arXiv. We develop a general mathematical framework to model how the interaction of complex social and cognitive factors manifests in collective decision outcomes. Our model predicts a critical threshold of social learners (those who follow others instead of evaluating merit of the options on their own), above which the system destabilizes, and either option can prevail regardless of merit.
• Mar 2021: New paper, Falling through the cracks: Modeling the formation of social category boundaries appears in PLOS ONE. We predict boundaries between us and them on a continuum attribute using a dynamical system model. We find middle of the spectrum fall through the cracks of categorization, and validate with data on political independents.
• Aug 2020: New paper, Why are US parties so polarized? A “satisficing” dynamical model, appears in SIAM Review. We develop a model for the public’s voting behavior following satisficing decision making, and explain the disconnect between the predominantly moderate policy positions of the US public and the polarization of political parties. [Press release] [Animated summary of paper]
• July 2020: Present at IC2S2, about my work on modeling the formation of social categories. [Video]
• June 2020: Present at ACM Collective Intelligence conference, about my new opinion dynamics model for the effect of uninformed individuals on collective decisions. [Video]
• April 2020: New article, Misinformation about an outbreak like Covid-19 is important public health data, appears in STAT News. We discuss how gathering data on misinformation and human behavior in general, can be important for curbing the COVID-19 outbreak.
• Feb 2020: New paper, The interpretation of urban scaling analysis in time appears in Journal of the Royal Society Interface. We develop a theoretical framework to unify cross-sectional and temporal approaches in urban scaling. [Press release] [Preprint PDF]