A fundamental challenge for modern economics is to understand what happens when actors in an economy are replaced with algorithms. Like rationality has enabled understanding of outcomes of classical economic actors, no-regret can enable the understanding of outcomes of algorithmic actors. This review article covers the classical computer science literature on no-regret algorithms to provide a foundation for an overview of the latest economics research on no-regret algorithms, focusing on the emerging topics of manipulation, statistical inference, and algorithmic collusion.

This paper introduces a contest-theoretic simplified model of triathlon as a sequential two-stage game. In Stage 1 (post-swim), participants decide whether to continue or withdraw from the contest, thereby generating an endogenous participation decision. In Stage 2 (bike-run), competition is represented as a Tullock contest in which swim drafting acts as a multiplicative shifter of quadratic effort costs. Closed-form equilibrium strategies are derived in the two-player case, and existence, uniqueness, and comparative statics are shown in the asymmetric n-player case. The continuation decision yields athlete-specific cutoff rules in swim drafting intensity and induces subgame-perfect equilibria (SPEs) with endogenous participation sets. The analysis relates swim drafting benefits, exposure, and group size to heterogeneous effective cost parameters and equilibrium efforts.

We examine behavioral axioms in decision theory that are satisfied approximately rather than exactly. We demonstrate that in key domains -- decisions under risk, uncertainty, and intertemporal choice -- behavior that \emph{almost} satisfies an axiom implies the existence of a utility function that is \emph{near} one that adheres to the standard theoretical representation (e.g., expected utility, or exponentially discounted utility). We explicitly quantify the distance between the utility that captures actual behavior and the ideal theoretical utility as a function of the measured deviation from the axiom. This result formally connects two distinct quantitative exercises: measuring empirical deviations from theory and utilizing approximate optimization. Effectively, we show that small deviations from behavioral axioms rationalize the use of standard models as valid approximations.

The equivalence between von Neumann's Minimax Theorem for zero-sum games and the LP Duality Theorem connects cornerstone problems of the two fields of game theory and optimization, respectively, and has been the subject of intense scrutiny for seven decades. Yet, as observed in this paper, the proof of the difficult direction of this equivalence is unsatisfactory: It does not assign distinct roles to the two players of the game, as is natural from the definition of a zero-sum game. In retrospect, a partial resolution to this predicament was provided in another brilliant paper of von Neumann, which reduced the assignment problem to zero-sum games. However, the underlying LP is highly specialized; all entries of its objective function vector are strictly positive, the constraint vector is all ones, and the constraint matrix is 0/1. We generalize von Neumann's result along two directions, each allowing negative entries in certain parts of the LP. Our reductions make explicit the roles of the two players of the reduced game, namely their maximin strategies are to play optimal solutions to the primal and dual LPs. Furthermore, unlike previous reductions, the value of the reduced game reveals the value of the given LP. Our generalizations encompass several basic economic scenarios.

Specifying the information structure in strategic environments is difficult for empirical researchers. We develop a test of information ordering that examines whether the true information structure is at least as informative as a proposed baseline. Using Bayes Correlated Equilibrium (BCE), we translate the ordering of information structures into testable moment inequalities and establish uniform asymptotic validity for our testing procedure. In an application to U.S. airline markets, we test whether hub airlines have informational advantages beyond cost and demand benefits. We reject the privileged information hypothesis, with rejections concentrated in large, competitive markets.

This paper examines the dynamic relationship between electoral polls and indicators of economic and financial uncertainty during the last two U.S. presidential elections (2020 and 2024). Using daily polling data on Donald Trump and measures such as the Aruoba-Diebold-Scotti Business Conditions Index, the 5-year Breakeven Inflation Rate, the Trade Policy Uncertainty index, and the VIX, we estimate conditional correlation models to capture time-varying interactions. The analysis reveals that in 2020, correlations between polls and uncertainty measures were highly dynamic and event-driven, reflecting the influence of exogenous shocks (COVID-19, oil price collapse) and political milestones (primaries, debates). In contrast, during the 2024 campaign, correlations remained close to zero, stable, and largely unresponsive to shocks, suggesting that entrenched polarization and non-economic events (e.g., assassination attempt, candidate changes) muted the economic channel. The study highlights how the interplay between voter sentiment, financial markets, and uncertainty varies across electoral contexts, offering a methodological contribution through the application of Dynamic Conditional Correlation models to political data and policy-relevant insights on the conditions under which economic fundamentals influence electoral dynamics.

We study semi-supervised stochastic optimization when labeled data is scarce but predictions from pre-trained models are available. PPI and SVRG both reduce variance through control variates -- PPI uses predictions, SVRG uses reference gradients. We show they are mathematically equivalent and develop PPI-SVRG, which combines both. Our convergence bound decomposes into the standard SVRG rate plus an error floor from prediction uncertainty. The rate depends only on loss geometry; predictions affect only the neighborhood size. When predictions are perfect, we recover SVRG exactly. When predictions degrade, convergence remains stable but reaches a larger neighborhood. Experiments confirm the theory: PPI-SVRG reduces MSE by 43--52\% under label scarcity on mean estimation benchmarks and improves test accuracy by 2.7--2.9 percentage points on MNIST with only 10\% labeled data.

Matching mechanisms play a central role in operations management across diverse fields including education, healthcare, and online platforms. However, experimentally comparing a new matching algorithm against a status quo presents some fundamental challenges due to matching interference, where assigning a unit in one matching may preclude its assignment in the other. In this work, we take a design-based perspective to study the design of randomized experiments to compare two predetermined matching plans on a finite population, without imposing outcome or behavioral models. We introduce the notation of a disagreement set, which captures the difference between the two matching plans, and show that it admits a unique decomposition into disjoint alternating paths and cycles with useful structural properties. Based on these properties, we propose the Alternating Path Randomized Design, which sequentially randomizes along these paths and cycles to effectively manage interference. Within a minimax framework, we optimize the conditional randomization probability and show that, for long paths, the optimal choice converges to $\sqrt{2}-1$, minimizing worst-case variance. We establish the unbiasedness of the Horvitz-Thompson estimator and derive a finite-population Central Limit Theorem that accommodates complex and unstable path and cycle structures as the population grows. Furthermore, we extend the design to many-to-one matchings, where capacity constraints fundamentally alter the structure of the disagreement set. Using graph-theoretic tools, including finding augmenting paths and Euler-tour decomposition on an auxiliary unbalanced directed graph, we construct feasible alternating path and cycle decompositions that allow the design and inference results to carry over.

Higher education has expanded worldwide, with women outpacing men in many regions. While educational attainment is consistently linked to better physical health, its mental health effects - particularly for women - remain underexplored, and causal evidence is limited. We estimate the impact of college completion on depression among middle-aged women in South Korea, leveraging the 1993 higher education reform, which raised women's college attainment by 45 percentage points (pp) over the following decade. We use two nationally representative datasets to triangulate evidence, including the Korea National Health and Nutrition Examination Survey (KNHANES, 2007-2021) for physician-diagnosed depression, and the Korean Longitudinal Survey of Women and Families (KLoWF, 2007-2022) to validate findings using self-reports of depressive symptoms. We implement two-stage least squares (2SLS) with a birth-cohort instrument based on exposure to the reform (within 3 years of the cutoff in KNHANES and within 1 to 3 years in KLoWF). In KNHANES, college completion lowers physician-diagnosed depression by 2.4 pp, attenuating to 1.6 pp after adjusting for income, employment, and physical health. In KLoWF, college completion improves self-reported mental health. The weekly depressive-symptoms composite declines by 17.4 pp, attenuating to 16.4 pp after covariate adjustment. Placebo tests on unaffected cohorts yield null results. This study contributes to the growing quasi-experimental literature on education and mental health with convergent evidence across clinical diagnoses and self-reported depressive symptoms in South Korea. By focusing on college education in a non-Western setting, it extends the external validity of existing findings and highlights educational policy as a potential lever to reduce the burden of midlife depression among women.

The introduction of artificial intelligence (AI) agents into human group settings raises essential questions about how these novel participants influence cooperative social norms. While previous studies on human-AI cooperation have primarily focused on dyadic interactions, little is known about how integrating AI agents affects the emergence and maintenance of cooperative norms in small groups. This study addresses this gap through an online experiment using a repeated four-player Public Goods Game (PGG). Each group consisted of three human participants and one bot, which was framed either as human or AI and followed one of three predefined decision strategies: unconditional cooperation, conditional cooperation, or free-riding. In our sample of 236 participants, we found that reciprocal group dynamics and behavioural inertia primarily drove cooperation. These normative mechanisms operated identically across conditions, resulting in cooperation levels that did not differ significantly between human and AI labels. Furthermore, we found no evidence of differences in norm persistence in a follow-up Prisoner's Dilemma, or in participants' normative perceptions. Participants' behaviour followed the same normative logic across human and AI conditions, indicating that cooperation depended on group behaviour rather than partner identity. This supports a pattern of normative equivalence, in which the mechanisms that sustain cooperation function similarly in mixed human-AI and all human groups. These findings suggest that cooperative norms are flexible enough to extend to artificial agents, blurring the boundary between humans and AI in collective decision-making.

We study whether Fintech disrupts the banking sector by intensifying competition for scarce deposits funds and raising deposit rates. Using difference-in-difference estimation around the exogenous removal of marketplace platform investing restrictions by US states, we show the cost of deposits increase by approximately 11.5% within small financial institutions. However, these price changes are effective in preventing a drain of liquidity. Size and geographical diversification through branch networks can mitigate the effects of Fintech competition by sourcing deposits from less competitive markets. The findings highlight the unintended consequences of the growing Fintech sector on banks and offer policy insights for regulators and managers into the ongoing development and impact of technology on the banking sector.

Field experiments (A/B tests) are often the most credible benchmark for methods (algorithms) in societal systems, but their cost and latency bottleneck rapid methodological progress. LLM-based persona simulation offers a cheap synthetic alternative, yet it is unclear whether replacing humans with personas preserves the benchmark interface that adaptive methods optimize against. We prove an if-and-only-if characterization: when (i) methods observe only the aggregate outcome (aggregate-only observation) and (ii) evaluation depends only on the submitted artifact and not on the method's identity or provenance (method-blind evaluation), swapping humans for personas is just panel change from the method's point of view, indistinguishable from changing the evaluation population (e.g., New York to Jakarta). Furthermore, we move from validity to usefulness: we define an information-theoretic discriminability of the induced aggregate channel and show that making persona benchmarking as decision-relevant as a field experiment is fundamentally a sample-size question, yielding explicit bounds on the number of independent persona evaluations required to reliably distinguish meaningfully different methods at a chosen resolution.

This paper reexamines the effects of the Latin Monetary Union (LMU) - a 19th century agreement among several European countries to standardize their currencies through a bimetallic system based on fixed gold and silver content - on trade. Unlike previous studies, this paper adopts the latest advances in gravity modeling and a more rigorous approach to defining the control group by accounting for the diversity of currency regimes during the early years of the LMU. My findings suggest that the LMU had a positive effect on trade between its members until the early 1870s, when bimetallism was still considered a viable monetary system. These effects then faded, converging to zero. Results are robust to the inclusion of additional potential confounders, the use of various samples spanning different countries and trade data sources, and alternative methodological choices.

Policy learning algorithms are widely used in areas such as personalized medicine and advertising to develop individualized treatment regimes. However, most methods force a decision even when predictions are uncertain, which is risky in high-stakes settings. We study policy learning with abstention, where a policy may defer to a safe default or an expert. When a policy abstains, it receives a small additive reward on top of the value of a random guess. We propose a two-stage learner that first identifies a set of near-optimal policies and then constructs an abstention rule from their disagreements. We establish fast O(1/n)-type regret guarantees when propensities are known, and extend these guarantees to the unknown-propensity case via a doubly robust (DR) objective. We further show that abstention is a versatile tool with direct applications to other core problems in policy learning: it yields improved guarantees under margin conditions without the common realizability assumption, connects to distributionally robust policy learning by hedging against small data shifts, and supports safe policy improvement by ensuring improvement over a baseline policy with high probability.

Does greater connectivity enhance the value of public information? I study a networked beauty contest game where agents balance adaptation to the fundamental with local coordination. The analysis reveals a stark non-monotonicity: while public disclosure improves welfare when interactions are uniform, regardless of their intensity, it can be detrimental in core-periphery structures. This welfare loss stems from a distortion driven by the core, where core agents over-respond to a noisy public signal, forcing peripheral neighbors to absorb this volatility to maintain alignment. These findings suggest that standard transparency policies can backfire in tiered markets where dominant hubs propagate excess volatility.

This paper studies how the timing of NGO activism shapes its effectiveness in influencing corporate behavior. Using data on 2,500 campaigns targeting U.S. firms, we show that campaigns timed at annual general meetings (AGMs) generate large visibility gains but little contemporaneous influence, while campaigns launched before the AGM significantly increase shareholder proposal success and improve firms' environmental and social performance. We develop a dynamic model in which NGOs trade off awareness building and credibility formation, generating a lifecycle in activism from visibility-seeking to influence-oriented engagement. Therefore, NGOs' objectives evolve endogenously to coordinate stakeholder pressure and shape corporate behavior.

We provide a comprehensive examination of the predictive performance of panel forecasting methods based on individual, pooling, fixed effects, and empirical Bayes estimation, and propose optimal weights for forecast combination schemes. We consider linear panel data models, allowing for weakly exogenous regressors and correlated heterogeneity. We quantify the gains from exploiting panel data and demonstrate how forecasting performance depends on the degree of parameter heterogeneity, whether such heterogeneity is correlated with the regressors, the goodness of fit of the model, and the dimensions of the data. Monte Carlo simulations and empirical applications to house prices and CPI inflation show that empirical Bayes and forecast combination methods perform best overall and rarely produce the least accurate forecasts for individual series.