Hi! I’m a 4th year PhD candidate in Economics at MIT. I was previously at Oxford where I did my masters in Maths, and at Cambridge where I did my undergrad in Economics, graduating first in cohort. Here is a CV.

I work on game theory and economic theory. I’m interested in how information, learning, and beliefs shape equilibria + how they interact with more ‘non-standard’ aspects e.g., desire, attention, and memory. I’m motivated by applications to the economics of technology — I’d like to better understand incentives to develop new technologies, how these technologies shape welfare, and how we should regulate them. On the side I enjoy writing, pottery, and philosophy; I grew up in Singapore.

I expect to be on the job market this fall (2025/26)

Journal publications

  1. ‘Capability Accumulation and Conglomeratization in the Information Age’
    Journal of Economic Theory 210 (2023): 105647.
    (with Jun Chen & Matt Elliott)
    [pdf] [link]

    • A Simple explanation for why tech firms have expanded so quickly, into so many new industries.

Under revision

  1. ‘Attention Capture’
    (with Sivakorn Sanguanmoo; latest draft: Sep 24)
    [pdf] [ssrn] [arxiv]
    Revise & resubmit, Journal of Political Economy
    Talks: AEA 2025 meeting session on “Attention and Engagement on Platforms”, National University of Singapore, Singapore Management University

    • Unified analysis of how information captures attention.

  2. ‘Market Segmentation through Information’
    (with Matt Elliott, Andrea Galeotti, & Wenhao Li; latest draft: Feb 25)
    [pdf]
    Revise & resubmit, The Review of Economic Studies

    • Information is a powerful tool for shaping price competition + characterization of all outcomes when a platform controls both access and information.

  3. `An Economy of AI Agents’ (with Gillian Hadfield)
    Revising for NBER Handbook on the Economics of Transformative AI

    • Survey of recent developments and highlights open questions around how AI agents might interact with each other, shape markets and organizations, and attendant implications for policy.

Working papers (by last update)

  1. `Flexible Demand Manipulation’
    (with Yifan Dai; draft date: June 25)
    Talks: 2024 Berkeley/Columbia/Duke/MIT/Northwestern IO Theory Conference
    [pdf] [ssrn] [arxiv]

    • How do new persuasive/manipulative technologies (AI, rec systems, …) interact with market power?

  2. `Balanced Social Learning’ — preliminary, comments welcome!
    (with Ricky Li; latest draft: Apr 25)
    [pdf]

    • Tradeoff between speed (learning quickly) and breadth (learning broadly) + complete characterization of when and how balanced learning (= efficiency) is achieved.

  3. `Inertial Coordination Games’
    Extended abstract in 26th ACM Conference on Economics and Computation [EC’25]
    (with Ricky Li & Kei Uzui; draft date: March 25)
    [pdf] [arxiv]

    • Learning speeds determine whether shocks persist vs fizzle out: risk dominance iff posterior precisions grow sub-quadratically; otherwise shocks can propagate and generate self-fulfilling spirals.

  4. ‘Robust Technology Regulation’
    (with Sivakorn Sanguanmoo; latest draft: March 25)
    [pdf] [ssrn]
    Talks: AEA 2025 (‘Policy Implications of Transformative AI’ session), SITE 2025 (Dynamic Games, scheduled)

    • Adaptive regulatory sandboxes for risky R&D are (i) robust when the researcher’s learning process is chosen adversarially; (ii) dominant among robust mechanisms; and (iii) important — sans robustness, worst-case payoffs are unboundedly bad

  5. `Prices and Symmetries’ — new draft soon!!
    (with Pedro Martinez-Bruera; latest draft: Feb 25)
    [pdf] [ssrn] [slides]
    Talks: Network Science in Econ (Stanford)
    • Price interventions dominate quantities when shocks to the network of spillovers are sufficiently symmetric. Quantities dominate if they are sufficiently antisymmetric.

  6. `Informational Puts’
    Extended abstract in 25th ACM Conference on Economics and Computation [EC’24] (titled ‘Full Dynamic Implementation’)
    (with Sivakorn Sanguanmoo & Kei Uzui; latest draft: Dec 24)
    [pdf] [arxiv] [ssrn]
    Talks: Nuffield Oxford, Cambridge, EC’24, various Econometric Society meetings

    • How to achieve full implementation in dynamic supermodular games?

  7. ‘Delaying the Deviation’ (with Anna Merotto)
    [write us for a draft]

    • Randomizing small transfers across agents and across time (i) uniquely implements regime survival even when attacking is both payoff- and risk-dominant; and (ii) is sequentially optimal. Both kinds of randomization are necessary.

  8. `Persuasion and Optimal Stopping’
    (with Sivakorn Sanguanmoo & Weijie Zhong; latest draft: Dec 24)
    [pdf]
    Talks: Harvard EconCS seminar, SITE 2024 (dynamic games)

    • Unified analysis of optimal dynamic persuasion to implement joint distributions over actions, states, and stopping times. No intertemporal commitment is necessary.

  9. ‘Speed vs Resilience in Contagion’
    To be incorporated into a new paper `    Contagion over Time and Space’ with sparse continuous graphs and heterogeneous thresholds!
    (with Stephen Morris; draft date: Dec 22)
    [pdf] [ssrn] [slides]

    • Threshold contagion is faster in networks where it is harder to initiate contagion. Intermediate links are important bulwarks against contagion.

Selected work-in-progress (drafts coming soon!)

  1. `Memory Correlated Equilibrium’ (with Sivakorn Sanguanmoo)

    • A framework for modelling (various eqm. concepts & existence) and designing (revelation principle) correlated memory in extensive-form games. Applications to algorithmic cooperation, pricing, coordination.

  2. `Contagious Automation' (with Maryam Farboodi and Anchi Xia)
    • We analyze data-automation feedback loops: better data drives automation (via reinforcement learning) and better automation drives data accumulation (via equilibrium quantities). Global spillovers (via general equilibrium) and local spillovers (via transfer learning across similar tasks) drive the speed, direction, and limit of automation.

  3. `Human-AI Learning: Theory and Field Evidence from Interviews' (with Brian Jabarian) — data collected
    • How do human-AI (voice) conversations unfold, and how do the content and volume of transmitted info differ from human-human? We develop new theory and test it in field experiments where job applicants are randomized to face human vs AI interviewers.

  4. `Attentional Gravity’

    • Attention has geometry which shapes how we traverse through space (e.g., the internet), where we spend our time, and how our preferences change.

  5. `The Supply of Rationalizations’

    • How do we offer flow information to agents who have directional demand for beliefs and seek justifications for beliefs they want to hold for non-epistemic reasons?

  6. `Feasible Adapted Beliefs’ (with Sivakorn Sanguanmoo)

    • General principles for how information should be delivered when (i) the principal is learning over time; and (ii) the agent is learning from outside information.

  7. `Manipulated Competition’ (with Yifan Dai)

    • Complete characterization of optimal advertising plans to maximize producer or consumer surplus when firms compete on prices.