We are committed to advancing thought leadership and raising awareness of the critical issues shaping our world.
Through working papers, journal articles, and books, our faculty and collaborators share fresh insights to guide leadership practice, inform policy, and elevate public understanding.
A persistent puzzle throughout the cognitive and social sciences is how people manage to learn social conventions from the sparse and noisy behavioral data of diverse actors, without explicit instruction. Here, we show that the dominant theories…
Problem definition: Accurately modeling consumer behavior in energy operations is challenging due to uncertainty, behavioral heterogeneity, and limited empirical data-particularly in low-frequency, high-impact events. While generative AI trained…
Despite the large common net benefits of climate mitigation, broad-based political consensus for large-scale policy action remains elusive. We hypothesize that financial exposure to energy stocks central to the green transition can induce…
Antitrust laws mandate review of mergers and acquisitions (M&As) that exceed an asset size threshold based on accounting standards that exclude most intangible capital. We show that this exclusion leads to thousands of intangible-intensive M…
We discuss the conditions under which global imbalances, such as China being a large foreign creditor and the United States being a large foreign debtor, might also generate power imbalances. We highlight possible theoretical channels and…
We study whether AI methods applied to large-scale portfolio holdings data can improve financial regulation. We build a state-of-the-art, graph-based deep learning model tailored to security-level data on the holdings of financial intermediaries…
Governments use their countries’ economic strength from financial and trade relationships to achieve geopolitical and economic goals. We provide a model of the sources of geoeconomic power and how it is wielded. The source of this power is the…
Problem Definition: Bangladesh suffers from massive lead emissions from its circular Lead-Acid Battery (LAB)
industry. Informal smelting of scrap lead from Used Lead-Acid Batteries (ULAB) is especially emission-intensive…
Developing accurate clinical prediction models is often bottlenecked by the difficulty of generating meaningful predictive features from unstructured data. While electronic health records (EHRs) contain rich narrative information, extracting a…
Policymakers often distort goods markets to effect redistribution—for example, via price controls, differential taxation, or in-kind transfers. We investigate the optimality of such policies alongside the (optimally-designed) income tax. In our…
We study poverty minimization via direct transfers, framing this as a statistical learning problem while retaining the information constraints faced by real-world programs. Using nationally representative household consumption surveys from 23…
As increasingly capable agents are deployed, a central safety challenge is how to retain meaningful human control without modifying the underlying system. We study a minimal control interface in which an agent chooses whether to act autonomously…
This paper develops and estimates a structural model of the labor market for accountants that integrates forward-looking lifetime occupational choices with oligopsonistic employer demand. Using longitudinal resume data covering career transitions…
In collaboration with Feeding America, we aim to redesign Real-Time—a tool on its food sourcing and rescue platform, MealConnect—that facilitates the connection of ad-hoc, time-sensitive food donations to local agencies (e.g., meal programs)…
An enduring challenge in contagion theory is that the pathways contagions follow through social networks exhibit emergent complexities that are difficult to predict using network structure. Here, we address this challenge by developing a causal…
Over the past decade, the use of machine learning (ML) models in healthcare applications has rapidly increased. Despite high performance, modern ML models do not always capture patterns the end user requires. For example, a model may predict a…
Building on the canonical “bottleneck” model of Vickrey (1969), we show that carpooling and road pricing are highly complementary in addressing traffic congestion: they can be much more effective jointly than each one separately, and can improve…
Small business owners play a central role in all advanced economies. Nonetheless, they are an understudied occupational group politically, particularly compared to groups that represent smaller portions of the population (e.g., union members,…
Modern language-model deployments must often balance competing objectives—for example, helpfulness versus harmlessness, cost versus accuracy, and reward versus safety. We introduce Conformal Arbitrage, a post-hoc framework that…
As LLMs become the default interface for search, news, and everyday problem-solving, they may filter and frame political information before citizens ever confront it. Identifying and mitigating partisan “bias”—output with a systematic slant…
We investigate the ability of Vision Language Models (VLMs) to perform visual perspective taking using a novel set of visual tasks inspired by established human tests. Our approach leverages carefully controlled scenes, in which a single humanoid…
The growing popularity of generative artificial intelligence (AI) tools presents new challenges for data quality in online surveys and experiments. This study examines participants’ use of large language models to answer open-ended survey…
The likelihood that a deceased donor kidney will be used evolves during the allocation process. Transplant centers can either decline an organ offer for a single patient or for multiple patients at the same time. We…
Geoeconomics is the use of a country’s economic strength to exert influence on foreign entities to achieve geopolitical or economic goals. We discuss how concepts of power in the political science and economics literature can be used to guide…
Our events bring leaders together to promote dialogue, enhance understanding, and find solutions to complex challenges.
