When resources are scarce, will a population of AI agents coordinate in harmony, or descend into tribal chaos? Diverse decision-making AI from different developers is entering everyday devices -- from phones and medical devices to battlefield drones and cars -- and these AI agents typically compete for finite shared resources such as charging slots, relay bandwidth, and traffic priority. Yet their collective dynamics and hence risks to users and society are poorly understood. Here we study AI-agent populations as the first system of real agents in which four key variables governing collective behaviour can be independently toggled: nature (innate LLM diversity), nurture (individual reinforcement learning), culture (emergent tribe formation), and resource scarcity. We show empirically and mathematically that when resources are scarce, AI model diversity and reinforcement learning increase dangerous system overload, though tribe formation lessens this risk. Meanwhile, some individuals profit handsomely. When resources are abundant, the same ingredients drive overload to near zero, though tribe formation makes the overload slightly worse. The crossover is arithmetical: it is where opposing tribes that form spontaneously first fit inside the available capacity. More sophisticated AI-agent populations are not better: whether their sophistication helps or harms depends entirely on a single number -- the capacity-to-population ratio -- that is knowable before any AI-agent ships.

This paper derives new asymptotic results for the adaptive LASSO estimator in cointegrating regressions, allowing for uncertainty about whether the regressors are exact unit root processes. We study model selection probabilities, estimator consistency, and limiting distributions under standard and moving-parameter asymptotics. We further derive uniform convergence rates and the fastest local-to-zero rates detectable by the estimator under conservative and consistent tuning. For consistent tuning, we construct confidence regions that are easy to implement, uniformly valid over the parameter space, and achieve sure asymptotic coverage without requiring knowledge or estimation of local-to-unity or long-run covariance parameters. Simulation results reveal that the finite-sample distribution of the adaptive LASSO estimator can deviate substantially from the oracle property, whereas moving-parameter asymptotics provide much more accurate approximations. Consequently, in addition to being infeasible in applications due to their dependence on non-estimable nuisance parameters, oracle-based confidence regions are often too small to achieve adequate coverage in empirically relevant scenarios with small but non-zero coefficients. In contrast, the proposed confidence regions are always feasible and deliver reliable coverage across the parameter space. An empirical application to predicting the U.S. unemployment rate illustrates their practical usefulness for quantifying uncertainty around adaptive LASSO estimates.

A principal uses payments conditioned on stochastic outcomes of a team project to elicit costly effort from the team members. We develop a multi-agent generalization of a classic first-order approach to contract optimization by leveraging methods from network games. The main results characterize the optimal allocation of incentive pay across agents and outcomes. Incentive optimality requires equalizing, across agents, a product of (i) individual productivity (ii) organizational centrality and (iii) responsiveness to monetary incentives. We specialize the model to explore several applied questions, including whether compensation should reward individual ability or collaborativeness and how the strength of complementarities shapes pay dispersion.

Gallice and Monzón (2019) present a natural environment that sustains full co-operation in one-shot social dilemmas among a finite number of self-interested agents. They demonstrate that in a sequential public goods game, where agents lack knowledge of their position in the sequence but can observe some predecessors' actions, full contribution emerges in equilibrium due to agents' incentive to induce potential successors to follow suit. In this study, we aim to test the theoretical predictions of this model through an economic experiment. We conducted three treatments, varying the amount of information about past actions that a subject can observe, as well as their positional awareness. Through rigorous structural econometric analysis, we found that approximately 25% of the subjects behaved in line with the theoretical predictions. However, we also observed the presence of alternative behavioural types among the remaining subjects. The majority were classified as conditional co-operators, showing a willingness to cooperate based on others' actions. Some subjects exhibited altruistic tendencies, while only a small minority engaged in free-riding behaviour.

We generalize the model of Gallice and Monzon (2019) to incorporate a public goods game with groups, position uncertainty, and observational learning. Contributions are simultaneous within groups, but groups play sequentially based on their observation of an incomplete sample of past contributions. We show that full cooperation between and within groups is possible with self-interested players on a fixed horizon. Position uncertainty implies the existence of an equilibrium where groups of players conditionally cooperate in the hope of influencing further groups. Conditional cooperation implies that each group member is pivotal, so that efficient simultaneous provision within groups is an equilibrium.

Many operational AI systems depend on large-scale human annotation to detect rare but consequential events (e.g., fraud, defects, and medical abnormalities). When positives are rare, the prevalence effect induces systematic cognitive biases that inflate misses and can propagate through the AI lifecycle via biased training labels. We analyze prior experimental evidence and run a field experiment on DiagnosUs, a medical crowdsourcing platform, in which we hold the true prevalence in the unlabeled stream fixed (20% blasts) while varying (i) the prevalence of positives in the gold-standard feedback stream (20% vs. 50%) and (ii) the response interface (binary labels vs. elicited probabilities). We then post-process probabilistic labels using a linear-in-log-odds recalibration approach at the worker and crowd levels, and train convolutional neural networks on the resulting labels. Balanced feedback and probabilistic elicitation reduce rare-event misses, and pipeline-level recalibration substantially improves both classification performance and probabilistic calibration; these gains carry through to downstream CNN reliability out of sample.

This paper considers the problem of estimating the variance of a sum of a triangular array of random vectors with heterogeneous means. When random vectors exhibit two-way cluster dependence or weak dependence, standard variance estimators designed under homogeneous means can underestimate the true variance, which results in subsequent tests being oversized. To restore validity, this paper proposes a simple conservative variance estimator robust to heterogeneous means and shows its asymptotic validity.

We study optimization problems in which a linear functional is maximized over probability measures that are dominated by a given measure according to an integral stochastic order in an arbitrary dimension. We show that the following four properties are equivalent for any such order: (i) the test function cone is closed under pointwise minimum, (ii) the value function is affine, (iii) the solution correspondence has a convex graph with decomposable extreme points, and (iv) every ordered pair of measures admits an order-preserving coupling. As corollaries, we derive the extreme and exposed point properties involving integral stochastic orders such as multidimensional mean-preserving spreads and stochastic dominance. Applying these results, we generalize Blackwell's theorem by completely characterizing the comparisons of experiments that admit two equivalent descriptions -- through instrumental values and through information technologies. We also show that these results immediately yield new insights into information design, mechanism design, and decision theory.

Design-based simulations - procedures that hold realized outcomes fixed and generate variation by resampling treatment assignment or shocks - are widely used in both methodological and applied work to assess inference procedures. This paper studies the extent to which such simulations are informative about inference validity. Focusing on shift-share designs, we show that standard simulations that fix outcomes and resample shocks may rely on a data-generating process that is not aligned with the true one. In particular, these simulations confound true treatment effects with error dependence, potentially overstating inference distortions due to spatial correlation. We propose alternative simulation designs that circumvent this problem and illustrate their use in prominent empirical applications. Our results highlight that the usefulness of design-based simulations depends critically on how closely the simulated data-generating process aligns with the true one.

When outcome data are expensive or onerous to collect, scientists increasingly substitute predictions from machine learning and AI models for unlabeled cases, a process which has consequences for downstream statistical inference. While recent methods provide valid uncertainty quantification under independent sampling, real-world applications involve missing at random (MAR) labeling and spatial dependence. For inference in this setting, we propose a doubly robust estimator with cross-fit nuisances. We show that cross-fitting induces fold-level correlation that distorts spatial variance estimators, producing unstable or overly conservative confidence intervals. To address this, we propose a jackknife spatial heteroscedasticity and autocorrelation consistent (HAC) variance correction that separates spatial dependence from fold-induced noise. Under standard identification and dependence conditions, the resulting intervals are asymptotically valid. Simulations and benchmark datasets show substantial improvement in finite-sample calibration, particularly under MAR labeling and clustered sampling.

Geopolitics is shaped by trade and borders. We develop a general-equilibrium model in which both are endogenously determined in a linear world. Their interaction rationalizes geopolitical outcomes that cannot be obtained when either trade or borders are treated as exogenous. This unified and tractable framework is used to study political economy, security, and ideology within and across states.

Decentralized autonomous organizations (DAOs) are designed to disperse control, yet recent evidence shows that effective governance is often concentrated in a small number of participants. This note studies one simple mechanism behind that pattern. Because decentralized governance is monitor-intensive, rising proposal flow may eventually outpace the capacity of broad-based participation. Using a DAO--quarter panel, I estimate a fixed-effects kink model with DAO and quarter fixed effects and find a statistically significant decline in the marginal responsiveness of active voters once proposal activity crosses an interior threshold. I then study realized voting concentration using kink specifications with data-driven cutoffs. Across specifications, decentralization gains do not persist indefinitely once governance workload becomes sufficiently high, and load-based measures show especially clear evidence of a transition toward more concentrated realized control. The results provide reduced-form evidence consistent with a ``too big to monitor'' mechanism in DAO governance: when proposal flow grows faster than broad participation can keep up, effective control may drift toward a smaller set of highly active participants.

Urban traffic regulation policies are increasingly used to address congestion, emissions, and accessibility in cities, yet their impacts are difficult to assess due to the socio-technical complexity of urban mobility systems. Recent advances in data availability and computational power enable new forms of model-driven, simulation-based decision support for transportation policy design. This paper proposes a novel simulation paradigm for the ex-ante evaluation of direct and indirect impacts, spanning traffic conditions, transportation-related effects and economic accessibility. The approach integrates a multi-layer urban mobility model combining a physical layer of mobility flows and emissions with a social layer capturing behavioral responses and adaptation to policy changes. Real-world data are used to instantiate the current as-is scenario, while policy alternatives and behavioral assumptions are encoded as model parameters to generate multiple what-if scenarios. The framework supports systematic comparison across scenarios by analyzing variations in simulated outcomes induced by policy interventions. The proposed approach is illustrated through a case study that aims to assess the impacts of the introduction of broad urban traffic restriction schemes. Results demonstrate the framework's ability to explore alternative regulatory designs and user responses, supporting informed and anticipatory evaluation of urban traffic policies.

Chiappori et al. (2025) study several indices of assortativeness in matching, including the aggregate likelihood ratio and the odds ratio. We provide a counterexample showing that their axiomatization of the aggregate likelihood ratio is not valid as stated. We identify the exact class of indices characterized by the axioms in Chiappori et al. (2025). We then show that the axiomatization of the aggregate likelihood ratio can be recovered by adding new axioms. In addition, we point out errors in the axiomatizations of other measures in Chiappori et al. (2025). Finally, we offer a generalization of the odds ratio from two-type markets to multi-type markets.

Almost all countries in the world require banks to report suspicious transactions to national authorities. The reports are known as suspicious transaction or activity reports (we use the former term) and are intended to help authorities detect and prosecute money laundering. In this paper, we investigate the relationship between suspicious transaction reports and convictions for money laundering in the European Union. We use publicly available data from Europol, the World Bank, the International Monetary Fund, and the European Sourcebook of Crime and Criminal Justice Statistics. To analyze the data, we employ a log-transformation and fit pooled (i.e., ordinary least squares) and fixed effects regression models. The fixed effects models, in particular, allow us to control for unobserved country-specific confounders (e.g., different laws regarding when and how reports should be filed). Initial results indicate that the number of suspicious transaction reports and convictions for money laundering in a country follow a sub-linear power law. Thus, while more reports may lead to more convictions, their marginal effect decreases with their amount. The relationship is robust to control variables such as the size of shadow economies and police forces. However, when we include time as a control, the relationship disappears in the fixed effects models. This suggests that the relationship is spurious rather than causal, driven by cross-country differences and a common time trend. In turn, a country cannot, ceteris paribus and with statistical confidence, expect that an increase in suspicious transaction reports will drive an increase in convictions. Our results have important implications for international anti-money laundering efforts and policies. (...)

For linear regression models with cross-section or panel data, it is natural to assume that the disturbances are clustered in two dimensions. However, the finite-sample properties of two-way cluster-robust tests and confidence intervals are often poor. We discuss several ways to improve inference with two-way clustering. Two of these are existing methods for avoiding, or at least ameliorating, the problem of undefined standard errors when a cluster-robust variance matrix estimator (CRVE) is not positive definite. One is a new method that always avoids the problem. More importantly, we propose a family of new two-way CRVEs based on the cluster jackknife and prove that they yield valid inferences asymptotically. Simulations for models with two-way fixed effects suggest that, in many cases, the cluster-jackknife CRVE combined with our new method yields surprisingly accurate inferences. We provide a software package, twowayjack for Stata, that implements our recommended variance estimator.

This paper studies identification and estimation in semiparametric logit models when social networks are endogenous. In many applications, unobserved individual traits shape both the outcome of interest and the formation of social ties, so standard logit specifications, including those augmented with common network controls, can be biased. I show how network data can be used to address this endogeneity without imposing a parametric structure on the link formation process. Although the outcome equation is semiparametric in this social component and the network formation process is left unspecified, the logistic distribution assumption is crucial for identification. I show that slope parameters are point identified by pairwise comparisons of agents who share identical network formation behavior. I propose feasible estimators based on matching agents using network similarity measures and establish their consistency and asymptotic normality. Monte Carlo simulations demonstrate good finite-sample performance, and an empirical application to microfinance adoption demonstrates that accounting for endogenous network formation materially affects estimated covariate effects.