Papers

Classification systems shape how searchers find relevant objects. We study how revisions to classification architecture affect retrieval by changing the routes through which objects enter consideration. We theorize that creating a cross-cutting category can reduce classificatory distance and translation costs, whereas adding routes to an established system can increase route-selection and updating costs; category cleanup can restore contrast. Empirically, we exploit the 2013 creation and 2020 revision of Y02/Y04 green-technology codes in the Cooperative Patent Classification. We match treated patents to unchanged green patents within technology-cohort cells and estimate examiner- and applicant-specific effects on first-citation timing and citation volume. Category creation is associated with a 25% higher examiner first-citation hazard. In the mature system, additions are associated with 13-15% lower examiner hazards, while cleanup is associated with a 14% higher hazard and 12% higher citation volume. Effects are generally stronger for examiners, whose search task is more tightly coupled to patent classification. Timing and volume need not move together: creation and green-domain entry shift timing without detectable changes in volume, while cleanup increases both speed and recorded reach. Classification infrastructure may therefore change when relevant objects are found even when overall recorded attention is unchanged, and greater classificatory detail need not improve search efficiency.

We answer the question posed in the title with a nonlinear mixed-frequency vector autoregression, estimated with Bayesian additive regression trees. The model combines monthly macro-financial variables with quarterly bank lending survey data, and identifies the dynamic responses from high-frequency policy surprises. Sign asymmetry dominates; a tightening produces monetary policy transmission mostly in line with the theoretical predictions, whereas easing of any size produces mostly insignificant responses. Peak effects vary with initial conditions, while several common, predefined regime splits do not yield considerable differences in the propagation of the shocks.

This paper investigates spatial productivity convergence across Chinese provinces during structural transformation, examining how a shift in auonomous demand composition reshaped regional productivity dynamics. Using Panel Structural Vector Autoregressive modelling and province level data from 2001 to 2021, we analyse eastern, central, ad western regions across two sub-periods through the augmented Kaldor-Verdoorn law. A post-2011 spatial reversal emerges: inland regions exhibit stronger Verdoorn coefficients, while fiscal multipliers converge across coastal and inland areas, validating China's redistributive fiscal policies in narrowing regional productivity gaps. The Kaldor-Verdoorn coefficient remains robust throughout, while export effects on productivity collapse and government expenditure effects strenghten.

Ignoring unobserved heterogeneity in duration models biases parameter estimates and invalidates inference, but testing for it is non-regular: the null hypothesis lies on the boundary of the parameter space and some parameters are unidentified under the null. These features render standard asymptotic theory inapplicable. This paper develops an EM test for unobserved heterogeneity in censored Weibull duration models, building on the EM approach of Li, Chen, and Marriott (2009). The test statistic has an asymptotic null distribution equal to the square of max{0, N(0,1)}, hence critical values require neither simulation nor bootstrap, and the test accommodates covariate-dependent censoring of arbitrary form. Monte Carlo simulations compare the EM test with the likelihood ratio test (LRT) of Cho and White (2010), information matrix tests, and Lagrange multiplier tests. The EM test has empirical size close to the nominal level for sample sizes of 500 or more, where the LRT remains markedly conservative and the other tests over-reject. Its size-adjusted power is comparable to that of the LRT and higher than that of the other tests. Because size adjustment requires knowledge of the data-generating process and is unavailable in practice, the EM test attains higher power than the LRT in most designs as the tests would actually be applied. In an application to the Stanford Heart Transplant data, the EM test rejects homogeneity in every covariate specification, whereas the LRT's conclusion depends on a user-chosen set of admissible parameter values and on the specification.

Experimental evidence reveals that, in aggregating time preferences, a decision maker exhibits mild aversion to inequality across individuals' evaluations of a stream. Neither the utilitarian nor the maxmin aggregation rule can account for this finding. Building on principles concerning the comparability of intergenerational utilities and intergenerational fairness, this paper axiomatizes a family of aggregation rules that allow the decision maker to display varying degrees of inequality aversion. These rules fit well with experimental data.

A coalition that deviates once can profit many times when what it sells keeps working. In a threshold-encrypted mempool, a leading defense against maximal extractable value (MEV), a quorum of the decryption committee that sells its decryption capability to a front-runner exposes every later block that the capability still decrypts. We ask how large a penalty, such as slashable stake, deters this kind of collusion. In our repeated game, a single leak by any coalition in a monotone family of authorized coalitions (for example, any $k$ of the $n$ committee members) unlocks a set of future rounds, costs a one-time penalty, and ends the coalition's participation. We show that every dynamic deviation reduces to choosing a leak time, so deterrence holds if and only if each coalition's penalty covers the largest discounted value that a single leak reaches. Without discounting, over $T$ rounds of unit value full reuse needs a penalty of $T$ while binding each leak to its own round needs $1$, so no penalty that is constant in the horizon deters unbounded reuse; a reuse window of $w$ rounds costs at most $w$ times the largest per-round value. The cheapest profile of per-party stakes that deters every coalition solves a covering linear program. For blockchain design, per-epoch keys cut the required stake from the value of a key's lifetime to the value of one epoch; we calibrate the gap on Ethereum front-running data and place Ferveo and Shutter in the model. The analysis extends to sealed-bid auctions, multi-authority voting, and federated learning under a shared key.

This paper characterizes the global minimum of the continuously updating generalized method of moments (CU-GMM) objective in linear instrumental variables models. We allow optimal weighting matrices under heteroskedasticity, autocorrelation, or clustering. We show that the objective is a ratio of polynomials. For one endogenous regressor, stationary points of CU-GMM objective function are real eigenvalues of a companion matrix. Comparing their objective values with the value at infinity gives the global minimum, extending the classical eigenvector approach to limited information maximum likelihood. With multiple endogenous regressors, algebraic elimination and checks for real solutions identify the minimum among finitely many candidate objective values, including boundary values. Galois theory rules out general formulas by radicals even with one endogenous regressor and two instruments, while numerical root finding remains possible. Finding the global minimum allows us to compute overidentification and likelihood ratio tests, including the conditional likelihood ratio (CLR) test.

Before a flood, governments decide where to invest in protection; after it, how much of the loss to compensate. The European Union informs the first decision with a composite index that ranks every subnational unit by vulnerability. This paper tests the ranking by comparing Italian provinces struck by the same flood between 2005 and 2024. The published index does not order provinces by the losses they suffer, with or without controls for hazard, exposure and income ($\rho$ between -0.06 and 0.09). The failure lies in what it weights. As published, the index mostly measures economic development, while losses follow water: they arrive in recurring groups of provinces that share a river basin, and more than half of the ranking error lies between those groups. In a pre-registered test that rebuilds it and eight rivals at every forecast origin from 2010 to 2020, the index comes last on both questions an allocator asks. On where damage will land, its top ten provinces capture 16 per cent of later damage, less than a random draw; Italy's public flood hazard map, blind to losses, captures 44. On who loses more from the same water, its rank correlation with later losses is 0.03; its own components, reweighted on past losses, reach 0.42. Neither repair needs new data. The misranked loss is paid locally. Of EUR 12.3 billion recorded over 2013-2022, 85 per cent was not covered by national transfers, and prevention funding, scored the same way, does no better than chance. Because correlated losses cluster within borders, national pools in Europe need 39 per cent more reserve than cross-border pools of the same size. The index fails the same way across 1,142 European units, where the repairs are weaker.

Arctic product-mix auctions allow budget-constrained bidders to express preferences over multiple substitute goods. We give the first strongly polynomial algorithm to find the competitive equilibria when the auctioneer has separable convex piecewise-linear costs. It combines a strongly polynomial reduction to the costless Arctic auction with an existing strongly polynomial algorithm for costless Arctic auctions. We also give a polynomial-time Turing reduction from costless Arctic auctions to linear Fisher markets, and a strongly polynomial many-to-one reduction to linear Arrow-Debreu exchange markets. If a good is sold in positive quantity in two competitive equilibria, its price is the same in both.

Seasonal adjustment is fundamental to economic analysis, but uncertain because seasonal components are inherently latent. This article introduces a probabilistic model discovery method that decomposes a time series into seasonal and nonseasonal components. The method returns a posterior distribution over the structure and parameters of a seasonal component. In simulation studies, the method can improve point forecasts, interval predictions, and recovery of seasonal components relative to X-13ARIMA-SEATS. In a study of eight U.S. macroeconomic series during the COVID-19 recession, the method surfaces significant ex-ante uncertainty about current seasonal adjustments in real time, well before many X-13 revisions reach their eventual peaks.

This paper studies the endogenous informational evolution of dynamic economic systems. Building on Hurwicz's conceptual framework, such a system is defined as a dynamic joint adjustment process of resource and information allocation linked by recursive feedback. Economic theory gains precision by isolating particular margins, but when information production, disclosure, learning, incentives, actions, and future feasibility interact across these two processes, conclusions obtained by holding other margins fixed need not survive their joint determination. The paper develops a system-level dynamic equilibrium framework for this interaction. Holding particular links fixed yields familiar environments in dynamic mechanism design, information design, experimentation and learning, feedback and disclosure, and information acquisition. The analysis establishes three central failures relative to fixed or partially exogenous informational environments: informational efficiency under exogenous feasibility is generally incompatible with endogenous dynamic feasibility; incentive and disclosure design cannot generally be separated; and informational monotonicity can fail without implementation replicability. It also identifies the corresponding positive boundaries. Optimal disclosure may be interior or extreme; bang–bang disclosure arises under additional conditions, and a further specialization generates an endogenous transition from opacity to transparency. Information may therefore be optimally withheld even when its direct decision value is strictly positive. Extensions to private payoff-relevant information and limited commitment preserve the recursive structure.

We study how to allocate a good with positive externalities among agents in an information network, without monetary transfers. Each agent observes their own valuation and those of their neighbors. A principal seeks to allocate the good to the highest-valuation agent through a mechanism robust to agents' heterogeneous belief hierarchies, leveraging the network structure and the partial incentive alignment created by allocative externalities. We characterize the networks for which a robustly efficient mechanism exists: it exists if and only if at least two agents are connected to all others. Relaxing robustness to rule out weakly dominated strategies, one universally connected agent suffices. We generalize the results to a broader class of utility functions, show that robust welfare maximization is impossible unless the network is complete, and establish that an ex post incentive compatible mechanism that is efficient under truthful reports exists if and only if no agent is isolated.

We study information aggregation in the networked learning model introduced by Kearns, Roth, and Ryu (SODA 2026). There is a fixed distribution over $d$ features and a common label. Agents learn in topological order on a directed acyclic graph. Each observes a subset of the features and its parents' predictions, fits a linear predictor to minimize mean squared error, and passes only its prediction forward. The global predictor is the best linear predictor using all features. Kearns, Roth, and Ryu show that the output agent's error approaches the global predictor's error along sufficiently deep paths with suitable feature coverage, while insufficient depth can prevent aggregation even in large networks. In contrast to their main focus on a given graph and feature allocation, we consider the limits of the model under two settings. In the adaptive designer setting, a designer chooses the graph, feature allocation, and output agent knowing the distribution. In the oblivious designer setting, the designer fixes all three before an adversary chooses the distribution. Each agent observes one feature and receives predictions from a limited number of parents. We call the aggregation exact when the output agent matches the global predictor exactly. For $d\ge3$, we show that no finite depth guarantees exact aggregation for every distribution with one parent per agent, even when the designer knows the distribution. In contrast, two parents per agent suffice for exact aggregation even in the oblivious designer setting. A fixed graph, feature allocation, and output agent achieve this for every distribution at depth $O(d\log d)$. Knowing the distribution reduces the depth to $O(d)$. Both constructions use $O(d^2)$ agents, with a very large constant for two parents. We show the bounds on the depth and number of agents are all optimal up to constant factors.

Applied researchers increasingly convert images into features with pretrained encoders, then use those features in a downstream prediction model. The encoder is often treated as an implementation detail. We show that it can instead be a consequential source of model uncertainty. We call this uncertainty representation risk: plausible pretrained encoders map the same images into different feature spaces and can yield sharply different out-of-sample conclusions from predictive performance. We compare ten modern and legacy frozen encoders across applications involving house prices, racehorse performance, breast-cancer histology, chest radiographs, continuous facial age, and rice disease. With common dimension control, heads, and group-safe splits, validation selects SigLIP 2 for houses, raising test $R^2$ from 0.396 for ResNet50 to 0.629, and DINOv2 for horses, raising $R^2$ from 0.029 to 0.105. No encoder is best in every task. Candidate procedures are constructed using training data and compared on a separate validation partition. The selected procedure reaches 0.658 for houses and 0.979 accuracy for pneumonia. Fixed-split gains are small for horses and rice, while repeated partitions reveal instability in horse feature union. Continuous age selects SigLIP 2 at 4.786 years MAE. The principal representation gaps persist with neural heads, similarly sized DINOv2 and ViT models, and limited adaptation. These results support a simple workflow: benchmark plausible representations, select on locked validation data, combine only when separate validation evidence justifies the additional cost, and report paired and split-level uncertainty. We implement this workflow in LOOKAGAIN-ML, the software package used to conduct the analyses in this paper.

Refugee integration is a central policy challenge for host countries, and where governments initially place refugees shapes their integration trajectories. Yet placement officers often have limited information about where each case is most likely to succeed. Algorithmic refugee matching uses administrative data, machine learning, and constrained optimization to recommend employment-optimized placements in real time as cases arrive, with human placement officers retaining final authority. Between January 2020 and June 2023, the Swiss State Secretariat for Migration randomly assigned about 2,000 refugee cases to receive a canton recommendation either algorithmically optimized for employment or drawn to approximate existing procedures, with placement officers and refugees blinded to assignment. The two arms used identical but separate canton and origin-group quotas, so gains reflect better refugee-canton matching rather than reallocation toward stronger labor markets. The trial began just before the COVID-19 pandemic shifted labor-market conditions. For the pre-registered primary outcome – the share of months employed during the first three years – the pooled intention-to-treat (ITT) estimate across the 2020-2023 placement cohorts was +2.2 percentage points (about 10% of the 22.3% control mean; 95% CI [+0.05, +4.33]), rising to +3.9 pp (about 17%; [+1.11, +6.68]) for the post-COVID 2022-2023 cohorts. Effects grew over time: at 36 months, the pooled ITT on the employment rate was +5.2 pp (about 11%; 95% CI [+1.10, +9.25]) – comparable to the gains from hundreds of hours of intensive language training. Overall, the results provide rare field evidence that AI-based decision support can improve high-stakes public-sector allocation, offering a scalable, low-cost way to raise refugee employment.

This paper analyzes the relative efficiency of horizontal and vertical organizational structures modeled as communication networks under limited managerial attention. In this framework, workers receive noisy signals about the state of the world and communicate reports to a decision-maker whose meeting time relatively more constrained. The clarity of each report depends endogenously on meeting lengths and the workers' heterogeneous communication and processing abilities. When workers possess identical communication abilities, the horizontal structure is always optimal because it allows the decision-maker to weight signals optimally without compounding transmission noise. Conversely, when workers have asymmetric communication abilities, the attention constraint of the decision-maker induces a structural switch. Under these constraints, the vertical structure becomes preferred because the decision-maker can delegate the time-intensive task of information aggregation to the superior communicator, balancing the loss from transmission noise against the premium on the decision-maker's scarce time. Under the assumption of homogeneous processing abilities I derive the analytical threshold for the n-worker model.

We study optimal dynamic contracts for long-term projects where a risk-averse agent exerts hidden effort to increase the drift of a Brownian process toward a com- pletion threshold. Relative to the first best, the optimal contract slows progress, makes success less likely, induces earlier termination, and narrows project scope. Allowing for costly project resets generates further distortions: resets may be excessive or insufficient depending on the residual effort required afterwards. With endogenous risk taking, optimal incentives introduce penalties for failure and reduce rewards for effort above a critical progress level, resulting in discontinuous and nonmonotonic effort in progress. In the baseline model, the optimal contract can be implemented with linear spot contracts that require no long-term commitment.

A principal relies on information held by a biased agent in decision-making. Without a prior over the circumstances that may arise, she designs a delegation rule to minimize the worst-case regret. Optimal delegation combines a default action, a fixed gap above it, and a higher discretionary interval with random boundaries. This structure separates two margins: whether the decision departs from the default and how much discretion the agent receives conditional on doing so. The default and gap limit excessive adjustment when little adaptation is warranted, while the higher interval preserves flexibility when larger adjustments are needed. Randomizing the discretionary interval's floor and ceiling hedges the principal's exposure across potential worst-case states. This rule is optimal among all stochastic incentive-compatible mechanisms.

AI advice increasingly enters human decision workflows before costly information acquisition and final action. This paper studies how such advice affects decision quality, measured by the gross utility of the final action. Advice shifts beliefs before the decision maker chooses an acquisition experiment with uniformly posterior-separable (UPS) costs. The analysis characterizes gross effects through the continuation gross value generated by net-optimal acquisition. Convexity of this value function is necessary and sufficient for every AI signal to weakly improve gross utility at every prior, while nonconvexity allows a binary signal to cause gross harm. The criterion gives tight state-space results. Every AI signal is weakly gross-improving in binary-state problems under arbitrary actions, payoffs, and UPS costs. A minimal three-state problem can generate gross harm through concentrated shutdown, where valuable acquisition stops. For classification problems with Shannon acquisition costs, the criterion becomes an active-set slope test. It guarantees weak gross improvement for homogeneous classification and all three-state weighted classification problems, while a minimal four-state failure arises from attention dilution, where acquisition shifts away from high-stake distinctions. Along a Blackwell-increasing mixture path, gross utility can first decrease and then increase. With endogenous interpretation, cheaper interpretation can reduce gross utility.

Existing studies consistently find that home batteries and electric vehicles optimized against real-time electricity prices create new load peaks in local distribution grids, because all assets respond to the same price signal in sync. However, all but one of the 18 studies reviewed here treat wholesale prices as exogenous. This paper argues that treating prices as non-responsive biases the result: in reality, charging in low-price hours raises the wholesale price, which dampens the incentive to charge. I test this by simulating households with rooftop solar and home batteries under a spot-based retail tariff, calibrated to German data for 2025 with a model of equilibrium wholesale prices. With exogenous prices, my results confirm the finding from the previous literature: synchronous charging pushes the coincident import peak of the local grid to 83% above its no-battery level. With endogenous prices, the same fleet leaves the peak 6% below the no-battery benchmark. In other words, treating prices as endogenous reduces the coincident peak at high battery penetration nearly by half. This finding is robust across alternative price functions and exogenous price paths, different measures of the coincident peak, 25 scenario variations and three historical years. I conclude that new local peaks remain possible, particularly in grids where battery deployment runs ahead of the national average, but the risk and magnitude are considerably smaller than the existing literature suggests.

We give a finite simplex theorem. When the number of states equals the number of source signals and the source posterior likelihood-ratio vectors form a simplex, Blackwell dominance is characterized by a universal barycentric-coordinate condition. We also give an explicit countable-state, countable-signal diagnostic-monitor theorem with a closed-form universal criterion and a closed-form garbling; tensorization yields dominance at every sample size. Finally, we provide a three-state, two-signal counterexample to the sufficiency conjectured by Mu, Pomatto, Strack, and Tamuz for their many-state moment-generating-function conditions for large-sample Blackwell dominance. The pair satisfies strict comparisons on both parameter domains, all ordered Kullback-Leibler inequalities, bounded likelihood ratios and pairwise genericity, but dominance fails at every positive sample size. The finite theorem, obstruction, and continuum inequalities are verified in Lean 4.30 with Mathlib; the countable diagnostic proof is given analytically.

Price stability remains a pillar in monetary policy practices and carries a special importance within monetary unions. Mainstream economics tried to leverage price stability using price indices and several metrics to shed light on specific dynamics and optimal macroeconomic levels. The wide availability of data led researchers to consider the study of systems using Random Matrix Theory, based on inner correlation patterns. This aims to enhance the multivariate analysis by removing noisy patterns from the signal and improve data quality for further inferences. This work considers the collection of monthly inflation indices in the Eurozone as a system of prices to analyze its eigenvalues' statistical and asymptotic properties and uncover inner country-level insights. Results confirm the system cannot assumed to be randomly generated, and the data exhibit noise-dominated patterns, due to small and persistent variations at the country-level. The latter make the inter-country correlations more dynamic and the separation of the signal from the noise quiet difficult. Findings identified two countries as distorting inflation dynamics besides three other distinct, regional-based groups of countries. Variability sources might stem from economic episodes fueling inflation spikes in some countries, as well as methodological aspects used to ensure data quality and representativeness in the European Union. Despite being complex, the system demonstrates a certain stability, in terms of self-organization; while large monthly fluctuations cannot be considered as rare events, but part of the data-generating process.

In school choice, a lottery number is often used by the matching mechanism to break ties when there are more students who prefer the same school than the number of seats available. There has been growing theoretical and empirical interest in understanding the impact of revealing the lottery number to students. In practice, in recent years, the NYC Public Schools started revealing the lottery number to students to improve transparency. Theoretical findings from prior literature also suggest that revealing the lottery number strictly improves the number of matches under the deferred acceptance algorithm. However, these theoretical results are based on the over-simplifying assumption that all students share the same preference ranking for schools. In this work, we relax this assumption and allow students to have heterogeneous preference rankings. Under a game-theoretic model where student strategies form a Bayesian Nash equilibrium, we characterize scenarios where revealing the lottery number can either improve or worsen the matching outcome, measured by two metrics of match rate and social welfare. We further consider revealing partial information about the lottery, and demonstrate non-monotonic effects in the amount of information available to students. These results together illustrate complex tradeoffs induced by the lottery revealing policy.

We test whether a multivariate vector X conforms to a specified distribution F, a problem in copula modelling and density forecasting. The Rosenblatt transform reduces it to a test of uniformity, but depends on an arbitrary coordinate ordering that strongly affects power under dependence. We study order randomization: applying the transform under many random orderings and merging the evidence with dependence-robust rules. Reordering conserves the total Mahalanobis signal energy and merely redistributes it, so one ordering is a lucky or unlucky draw. In simulations we observe significant gains in calibrated power over both the expected single random ordering and order-invariant references. Two ingredients are essential: a two-sided base statistic, and a re-estimating parametric bootstrap that restores level under an estimated null and unlocks the gain. The calibrated pooled tests are robust to the departure's shape; no order-invariant reference we compare is: the symmetric-root test collapses on diffuse departures, while the shape-flat chi-squared test trails on concentrated ones. We apply it to a Gaussian foreign-exchange risk model over a decade of daily data on nine currencies, where it detects episodes such as Brexit and COVID. Though we focus on Gaussian nulls, the procedure extends to any null whose conditional distributions can be computed and simulated from.

A seller and a buyer with independent private values can trade only at a posted price. We determine the worst case of this mechanism exactly: the best posted price always guarantees a $\beta_*=0.738024\ldots$ fraction of first-best welfare, where $\beta_*$ is given in closed form by the root of an explicit equation, the worst-case buyer is unique up to scaling, and the worst case is never attained. This closes the gap $[0.7292,0.73805]$ left by work from SODA 2016 through two STOC 2023 papers and AAAI 2026. The proof is an explicit certificate of optimality: after one change of variables, the gap between the optimum and the value of any buyer is a sum of nonnegative integrals, as in a linear-programming dual, and equality identifies the worst-case shape. The certificate also gives the complete tradeoff between gains from trade and the seller's initial welfare: when first-best gains from trade are a fraction $\kappa$ of initial seller welfare, the exact worst-case fraction $\rho(\kappa)$ of gains obtained by the best price satisfies $\rho(\kappa)\sim2/\log(1/\kappa)$ as $\kappa\to0$. The worst-case buyer's survival function has two constant segments joined by an explicit nonexponential curve, and the worst case is approached through a vanishing atom escaping to infinity. The same constant is the exact guarantee of dominant-strategy mechanisms with individual rationality and strong budget balance in every realization. For two units with increasing submodular valuations, an explicit finite instance has ratio below $0.7290804$, so two units are strictly harder than one. Fixing the buyer, we characterize the least probability a random common price needs to guarantee a given ratio against every seller; bounding this quantity over all buyers would determine the exact two-unit constant. Within an explicit family the worst ratio is $0.729080\ldots$, conjectured to be the two-unit constant.

This manuscript is an English translation and extended version of a paper originally published in Japanese (2022). Driven by remarkable advances in remote sensing and big data processing, spatial technologies are increasingly leveraged in economic research. While satellite nighttime light intensity is widely recognized for tracking macroeconomic parameters - such as regional GDP, employment, and population - less attention has been paid to fine-grained spatial processing methodologies for local tourism economies. This study first details a raster processing technique applied to nightlight imagery. It then focuses on Yuzawa Town (Uonuma District, Niigata Prefecture), analyzing the local economy and sports/tourist attractions. Specifically, I investigate the spatio-temporal relationships between tourist arrivals and various local statistical datasets. Furthermore, this paper highlights how nightlight data can capture intra-municipal economic dynamics that remain undetectable through standard macroeconomic indicators.

We study a fixed-normalization class of symmetric bimatrix games generated by a common kernel and show that it admits efficient approximation despite exact symmetric-equilibrium computation remaining PPAD-hard. For every rational game in the class, a single auxiliary zero-sum saddle computation yields a rational symmetric \(1/5\)-approximate Nash equilibrium in polynomial time. We also give exact recognition and unique kernel recovery, and an exact polynomial-time post-processing algorithm that minimizes regret along the segment joining the selected saddle strategies. For arbitrary rational square games, we formulate the nearest common-kernel projection as a linear program with an explicit dual certificate, obtaining a certified \((1/5+2\eta^*)\)-approximation guarantee. A separate selector result proves a sharp regret-to-uniformity constant for the full-subset construction. The results provide a tractable and certifiable structured-game regime and clarify which common-kernel reduction architectures cannot support certain fine-grained approximation-hardness objectives.

We propose a new $L_2$-type test for white noise which allows the dimension $p$ of the time series to either (i) be a fixed constant, or (ii) diverge with the sample size $n$. The proposed test statistic exhibits an interesting phase transition, following two different regimes of behavior: $p$ is fixed, and $p\rightarrow\infty$. Because identification of the operable regime is difficult, if not impossible in practice, we devise a novel adaptive bootstrap method to construct unified testing procedure across different phases. Numerical experiments confirm the good finite sample performance of the proposed adaptive $L_2$-type test in comparison to the existing methods in the literature. The proposed testing procedure has been implemented in R package HDTSA.

We often learn from multiple sources that convey information in different ways. How informative is it to know the source of a signal, and how is this informativeness shaped by the distribution of sources? We extend the standard (binary-state, binary-signal) Blackwell experiment model by introducing a commonly known distribution over signaling schemes, representing the distribution of information sources. We compare learning under two information models: source-aware, where decision makers observe a signaling scheme and its realization (e.g., raw reviews, search results), and source-blind, where only the signal realization is observed (e.g., aggregate ratings, LLM-generated summaries). We show that a mean-preserving spread in the distribution of signaling schemes translates into Blackwell dominance for source-aware decision makers, implying they are "risk-loving" in information sources. In contrast, it has no impact on source-blind decision makers. When learning from repeated draws of signaling schemes, source-blind decision makers learn more slowly. However, as long as the average signaling scheme is $\varepsilon$ away from being completely uninformative, source-blind learning can match source-aware learning by using at most $O(1/\varepsilon)$ times more data.

This paper develops a theory of accommodation traps in workplace hierarchies. Workers with weak outside options face a higher relational cost of appearing unavailable, resistant, or difficult, and so adopt accommodative communication (extra deference, softened boundaries, visible flexibility) to preserve the employment relationship. This short-run strategy is also an informative signal: a friction-minimizing supervisor rationally infers that an accommodating worker is less likely to resist, and assigns that worker the invisible, low-promotability work. The signal harms the worker who sends it. Unlike agents in ratchet-effect models, who conceal information that would invite heavier demands, dependent workers reveal their low refusal cost, because the relational value of accommodating rises with dependence. The resulting inequality requires no productivity difference and no taste for discrimination, and the supervisor's beliefs are correct, so correcting beliefs cannot remove it. When visible work builds outside options and invisible work erodes them, the allocation becomes self-reinforcing, and even workers who start out identical are locked into unequal roles with probability one. The equilibrium is privately rational for every party yet socially inefficient whenever diminishing returns to outside options outweigh the supervisor's friction cost. Attaching recognition to invisible work, rotating it by rule, or limiting managerial discretion each weakens or breaks the link from communication to assignment that sustains the trap.