Estimating causal effects in panel data is a central problem in policy evaluation. Existing methods largely address retrospective questions of the form: what would have happened to a target unit under a different intervention during the observed panel? In many applications, however, decision-makers face prospective questions: what will happen to a target unit under an intervention it has not yet experienced, beyond the observed panel? This article develops a framework for answering such causal forecasting questions by integrating the retrospective counterfactual logic of synthetic-controls-based approaches with the extrapolative structure of multivariate time-series forecasting. Building on the latent factor models that justify unit-side regressions in synthetic controls, we impose low-rank temporal structure on the latent time factors to identify prospective causal forecast estimands. We operationalize this strategy through the Two-Way Synthetic Forecasting estimator, or TWSF, which learns cross-unit relationships from pre-treatment outcomes and combines them with a time-series model learned from treated donor trajectories under the intervention of interest. Under suitable conditions, we establish finite-sample forecasting error bounds that imply pointwise consistency and introduce an orthogonalized correction that yields asymptotic normality and thus enables pointwise inference. We extend the framework to fixed multi-step forecasting horizons through both direct and recursive procedures, each of which inherits analogous pointwise guarantees. We corroborate the theory with simulation studies and illustrate the practical utility of TWSF by studying the public-health impact of opening NFL stadiums during the 2020 season.

Territorial behavior can greatly accelerate decentralized agent dispersion on networks. This paper studies a network-agent dispersion problem in which m autonomous agents move in discrete time on a connected graph and seek a configuration in which no two agents occupy the same node. We focus on the dispersion case m = n, where successful configurations contain exactly one agent per node. In the baseline model, each agent follows a lazy random walk with a common laziness parameter p. This process defines a finite absorbing Markov chain, and the expected absorption time is used to measure dispersion efficiency. We introduce two local behavioral extensions: territorial behavior, in which an agent that is alone at a node claims that node and repels later arrivals, and directional bias, in which agents share a preferred direction of movement on paths and cycles. Exact calculations on three-agent path and cycle networks and Monte Carlo simulations on larger instances show that territorial behavior substantially reduces expected dispersion time, with larger relative reductions as network size increases. Directional bias alone has limited effect in most small-network cases, but when combined with territorial behavior it can produce large additional speedups. In particular, the simulations show reductions of 99.22% on L100 and 97.48% on C100 when all agents start from one node. These results show how simple local movement rules can strongly affect global dispersion time in decentralized networked multi-agent systems.

The Wasserstein median is a robust alternative to the Wasserstein barycenter for averaging probability measures, but exact empirical computation can be expensive. A natural metric-space Weiszfeld scheme updates the current candidate by solving a weighted Wasserstein barycenter problem at each outer iteration, producing a nested optimization problem. We propose a direct fixed-weight free-support solver that avoids this inner barycenter loop. At each iteration, the method solves exact optimal transport (OT) subproblems from the current candidate to the input measures, computes barycentric projections of the selected plans, and relocates each support atom to an inverse-distance-weighted average of its projected destinations. For a smoothed median objective, we show that this relocation is the exact minimizer of a tight majorization--minimization surrogate. This yields monotone descent for exact transport subproblems, convex-hull invariance, a finite-time best-residual rate, residual-to-gradient control under differentiability, and fixed-point and stationarity characterizations. We also give smoothing, stability, and resolution-consistency results clarifying the fixed-weight approximation. In exact-OT benchmarks, the direct solver attains median objectives close to tightly solved nested Weiszfeld baselines while using substantially fewer exact transport subproblems. Additional contamination, posterior aggregation, and image-prototype experiments show that the direct solver produces median summaries comparable to nested computation and less sensitive to outlying distributions than Wasserstein barycenters.

Ethnic density is widely used in epidemiology and health geography as a contextual exposure, yet it is rarely examined as a measurement problem in its own right. Equivalent percentage values may represent different neighbourhood contexts across groups and places, particularly where migration, religion, language, household structure and socioeconomic conditions are spatially co-located. Using the harmonised Unified UK Census Data release, I analysed 239,023 small-area census data to examine ethnic density as an exploratory contextual co-occurrence construct. I estimated UK-wide mixed graphical models (MGM) for eight ethnic-density targets using 239,019 complete cases and 32 nodes per target-specific model. England-only spatial analyses then used k-nearest-neighbour Output Area centroids (k = 8) to estimate LISA and spatially adjusted residual networks. Ethnic density did not behave as a single contextual scalar. In the UK-wide MGM, the strongest retained target-neighbour edges differed across groups. Asian density was linked most strongly with Middle East/Asia-born share (0.59), Indian density with Hindu share (0.55), Pakistani density with Muslim share (0.47), Bangladeshi density with Muslim share (0.23), Black density with Africa-born share (0.42), and White density with Middle East/Asia-born share (0.35). England-only ethnic-density measures were strongly spatially autocorrelated, with Global Moran's I ranging from 0.57 for Mixed share to 0.90 for White share. After residualising against English region and local spatial lag, 64.3% to 96.4% of original target-node edges persisted across ethnic-density networks. Equivalent percentage values are not necessarily comparable across ethnic groups. This has implications for estimand definition, adjustment strategies, and the interpretation of ethnic density and other bundled contextual measures in urban health research.

Risk and decision models often combine sparse marginal information, precisely specified probability distributions, and dependence assumptions that are only partly justified. Probability bound analysis (PBA) represents epistemic uncertainty through probability boxes, but many applications assume independence or require dependence structures to be fully specified. We develop a dependence-sensitive PBA framework for black-box risk and decision models in which both marginal information and dependence information may be incomplete. The framework combines p-box parameters, precise-CDF parameters, and fixed quantities; incorporates specified dependence through copulas; and propagates unknown dependence through Fréchet-style admissible coupling sets. We also extend the construction to cross-dependence between imprecisely specified and precisely specified inputs. In an illustrative risk decision model, dependence assumptions materially affected output bounds and tail-risk summaries; analyses that ignored or simplified dependence produced narrower characterizations of plausible outcomes. The framework supports transparent uncertainty propagation when evidence is insufficient to justify either precise marginal distributions or a single dependence model.

The Small BAseline Subset technique provides remote measurements of ground displacement with high spatial resolution, making it a key tool for monitoring geophysical processes in hazard-prone areas. An effective analysis of this type of data requires reliable estimation of their second-order structure, which is difficult to achieve because the measurements are systematically missing over relatively large portions of the investigated areas. We tackle the problem from a functional data analysis perspective and treat the observations as partially observed functional data with two-dimensional domain. To properly characterize the data, we introduce the fragmented regime of partial observation, where parts of the curves are systematically missing across replicates. For this regime, we propose a novel method for covariance estimation, formulating the task as a matrix completion problem with Laplacian regularization. The estimator is nonparametric and free from stationarity or isotropy assumptions. Extensive simulations show that our method achieves consistently low estimation error across a range of covariance structures. Application to ground displacement data relative to the Phlegraean Fields demonstrates its ability to recover meaningful spatial dependence patterns, highlighting its potential for environmental risk assessment and monitoring.

Piecewise regression models are essential for identifying structural changes in longitudinal or spatial data across diverse scientific domains. While standard approaches often assume sharp, instantaneous transitions and single, non-hierarchical breakpoints, many real-world phenomena exhibit gradual, smoothed transitions that vary systematically across groups. We introduce smoothbp, an R package for fast, Bayesian hierarchical piecewise regression featuring logistic-smoothed transitions. By implementing a bespoke Metropolis-within-Gibbs sampler in Rust, smoothbp combines exact conjugate updates for linear terms with Hamiltonian Monte Carlo (HMC) transitions for non-linear location and sharpness parameters. smoothbp natively supports multiple change-points, random intercepts, random change-point timing, and structural covariates on all segment parameters. It also incorporates Kuo and Mallick (1998) spike-and-slab priors for automatic inference on the number of active breakpoints via the smoothbp_ss function. We document the sampler, validate parameter recovery and calibration through simulation-based calibration and interval-coverage studies, and contrast smoothbp against the existing software landscape across R, Python, Julia, and MATLAB, demonstrating its competitive efficiency against general-purpose probabilistic programming languages like brms and specialized packages like mcp.

The Bayes space provides a Hilbert space structure for analysing probability density functions (PDFs), equipping them with a geometry that reflects their relative and constrained nature. A key tool in this framework is the centred logratio (clr) transformation, which establishes an isometric isomorphism between the Bayes space and (a subspace of) the classical $L^2$ space. This makes it possible to apply functional data analysis (FDA) techniques, particularly functional principal component analysis (FPCA), to both univariate and multivariate density data in the context of dimension reduction. For multivariate PDFs, embedding them in the Bayes space enables an orthogonal decomposition into independent and interactive components. Furthermore, the independent part can be decomposed into mutually orthogonal geometric marginals. This structure provides more profound insights into the sources of variation in multivariate densities. We show that this decomposition of the total variance is optimal in a PCA sense, impacting the interpretation of the eigenfunctions and scores resulting from FPCA. We demonstrate that applying FPCA directly to multivariate densities is equivalent in a certain sense to applying multivariate FPCA to their decomposed form, with the resulting eigenfunctions and scores decomposing accordingly. The unique decomposition based on these theoretical results is applied to housing and geological empirical data respectively, demonstrating the interpretability and practical value of this approach.

Predictive regression is a crucial tool for exploring return predictability. In this study, we introduce an efficient procedure for selecting and estimating active predictors and change points in structural break predictive regression. Our approach allows the number of change points to increase with the sample size and accommodates sparse active predictors that may be stationary or cointegrated. We begin by identifying the active predictors using a Sure Independence Canonical Screening (SICS) procedure. Next, we estimate the change points through a Ratio-Controlled Regression Screening (RCRS) method. Finally, we reduce redundancy by eliminating unnecessary breakpoints and predictors using information criteria (IC). This approach allows for consistent estimation and selection of true breakpoints and active predictors. Our simulations and empirical studies demonstrate that the proposed procedure performs effectively.

Mixed models are popular for the prediction of subject-specific repeated outcomes or center performance among many centers. When the goal is to identify extreme or poor outcomes, standard random effects predictions may, however, suffer from regression to the mean and underestimate values in the tail of their distribution. Optimally weighted random effect estimators have recently been proposed to mitigate this. Motivated by clinical settings where repeated outcomes may end in death, we extend that method to predict poor outcome defined as 'death or substandard repeated measures'. We start from joint models with shared random effects for the longitudinal and survival outcome and estimate their random effects by minimizing squared weighted prediction errors given available data on survival and repeated measures. As for mixed models, weights are chosen to more heavily penalize errors in the tails. We call the results WRaPs: Weighted Random effect Predictors. For basic models and a select set of weights analytical closed form solutions are derived from the usual joint model parameters. For the more complex setting, computational solutions are developed in rjags using MCMC methods within the Bayesian paradigm. We illustrate finite sample properties of the proposed method in Type I simulations with random intercept and slope; and apply the new approach to predict individual future outcomes and survival in a randomized study with glioblastoma patients.

Post-marketing pharmacovigilance relies on statistical signal detection methods to identify potential adverse drug reactions. The Weibull shape parameter (WSP) test concept exploits temporal information (electronic health records) to assess the hazard of an adverse event over time after drug initiation. A statistically significant deviation from constancy results in a signal. The WSP framework comprises a family of tests that differ with respect to the estimation approach (frequentist or Bayesian), the chosen time-to-event distribution (Weibull, double Weibull, power generalized Weibull) for hazard modeling, and test specification parameters. To facilitate practical application and encourage consideration of the WSP signal detection test in future research, we developed the R package WSPsignal. The package consolidates all functionalities required for WSP testing into a unified, open-source interface. It enables practitioners and researchers to apply default test specifications or perform simulation-based tuning to identify the optimal test for a given data scenario. We illustrate the package functionalities in two examples to follow along. In a large-sample setting (ca. 20 000 observations), a frequentist WSP test is considered. In a small-sample setting (ca. 1 000 observations), a Bayesian WSP test is chosen. The additional test specifications are optimized through simulation-based tuning.

Whether global warming is accelerating remains contested because internal variability and spatial heterogeneity can obscure changes in warming rates. Here we use a Bayesian hierarchical spatio-temporal model with structured spatial dependence to estimate local warming trajectories and acceleration, and apply the model to progressively truncated observations to infer when acceleration becomes detectable. We find that detectable acceleration emerges unevenly across the climate system, with the earliest high-confidence signals concentrated in selected high-latitude regions. Across retained grid cells, the proportion exceeding a 90% posterior probability of positive acceleration increases from 13.6% for 1970-1990 to 39.7% for 1970-2026, while the proportion exceeding a 50% threshold increases from 46.4% to 70.3%. These results show that spatial aggregation can delay detection by averaging regions where acceleration has already emerged with regions where it remains weak or uncertain. The framework provides a probabilistic diagnostic for identifying where warming is intensifying and when acceleration becomes statistically detectable.

Unlike traditional causal inference, which prospectively evaluates the effects of causes, apportioning causal responsibility requires a retrospective assessment to deduce the causes of an outcome that has already occurred. This paper proposes a quantitative framework for apportioning causal responsibility between two binary risk factors that jointly contribute to a realized adverse outcome. Ideally, knowing the individual's latent causal type, defined by the potential outcomes under all possible exposure combinations, would allow precise apportionment; however, these potential outcomes cannot be simultaneously observed. We therefore define the average causal responsibility of each risk factor as its expected responsibility over the distribution of latent causal types. Under the assumptions of no confounding and monotonicity, we establish nonparametric identification of this metric when the type-specific responsibilities satisfy a structural balance condition, and derive sharp bounds otherwise. We illustrate the proposed framework using the classic example of lung cancer attributable to smoking and asbestos exposures.

Anomalies in functional data arise from rare or distinct processes that deviate from the dominant data-generating mechanism. Detecting such departures is essential in applications where they may correspond to errors, structural changes, or other behavior of interest. This work introduces a Bayesian nonparametric approach for anomaly detection in multivariate functional data. We model functional data as an infinite mixture of multi-output Gaussian processes, with a finite and automatically determined number of mixture components obtained through slice sampling. Mean functions are represented using a wavelet basis and regularized through Besov priors to obtain a smooth and sparse representation of the data. Cross-functional dependence is captured using the intrinsic coregionalization model and we solve covariance kernel selection by introducing a Carlin-Chib product space step in the Markov Chain Monte Carlo algorithm. Within this model, anomalous observations are assigned to small mixture components without requiring prior specification of the number or nature of anomalies. We consider a semi-supervised setting, in which labels are available for 15% of the normal observations and a large class imbalance is present. The utility of our model is demonstrated on both univariate and multivariate functional data.

Continuous-time Markov Chains are widely used to model stochastic dynamical systems, but key summary quantities such as means and covariances are often intractable. While Monte Carlo sampling provides asymptotically exact estimates, it becomes computationally prohibitive when moments must be evaluated across many parameter values. We develop a simulation-based surrogate modeling framework that learns parameter-to-moment mappings from Monte Carlo-derived, noise-corrupted training targets, enabling efficient and accurate approximation across the parameter space. We show that Monte Carlo noise affects mean estimation primarily through additive variance, whereas covariance estimation is additionally impacted by bias arising from nonlinear transformations of empirical estimates. Using a stochastic Susceptible-Infected-Recovered model, we demonstrate that neural networks accurately learn both mean and covariance under fixed simulation budgets allocated to constructing the noisy training labels. We further characterize how to allocate computational resources between parameter-space coverage and Monte Carlo replication, showing that covariance estimation requires a balanced allocation to control both variance and bias, while mean estimation benefits more from increased parameter space coverage. Finally, we show that the learned moment mappings produce valid population-level quantities and perform well in downstream tasks such as whitening. These results highlight the importance of accounting for Monte Carlo noise in surrogate modeling and provide practical guidance for simulation-based learning in stochastic systems.

Sequential tests that allow continuous monitoring are common in A/B experimentation. Power calculations for these tests require simulations that are hard to scale across many metrics on an experimentation platform. Instead, a common sizing heuristic inflates the fixed-sample size until the marginal rejection probability at the planned endpoint reaches $1-β$. This last-point rule is conservative because always-valid (AV) power is the probability of a boundary crossing at any time during the run, not at the endpoint alone. We give a closed-form correction factor $k^(α, β, t_0)$ expressed in elementary functions and the bivariate normal CDF, where $t_0 = m/n_z$ is the burn-in fraction. The closed-form approximation depends on the boundary only through its value and slope at the planned endpoint and can be evaluated for any smooth concave boundary. We work out three cases: the confidence sequences of Waudby-Smith et al. (2023) and Maharaj et al. (2023), and the mixture sequential probability ratio test of Johari et al. (2022). Setting the total sample size to $k^ \cdot n_z$, where $n_z$ is the fixed-sample size for allocation ratio $r$, hits empirical power within approximately 3 percentage points of target in Gaussian simulations. The correction factor depends on the allocation ratio $r$ only through $t_0 = m/n_z(r)$. We study sensitivity to the burn-in parameter and show that the correction saves 8--20% of the last-point sample budget across the operating range.

For $α\in(0,2)$, the generalized energy distance and the Gini covariance statistic are based on kernels of the form $(x,y)\mapsto \|x-y\|^α$, where $\|\cdot\|$ denotes the norm in a real separable Hilbert space. This paper investigates the boundary regime $α\downarrow 0$. After suitable normalization, the corresponding energy distance converges to a logarithmic energy distance involving the kernel $(x,y)\mapsto\log\|x-y\|$. We establish that the resulting logarithmic energy distance retains the fundamental characterization property of ordinary energy distances in separable Hilbert spaces and derive a representation in terms of Gaussian-kernel maximum mean discrepancies. Motivated by this representation, we introduce a logarithmic Gini covariance for the $k$-sample problem and investigate its structural and asymptotic properties. In particular, we derive a representation in terms of pairwise logarithmic energy distances, establish a characterization theorem for equality of distributions, develop asymptotic null and alternative theory for the corresponding empirical statistic, and discuss permutation-based implementation. The logarithmic framework reveals a new boundary phenomenon within the family of energy-type statistics and provides connections with kernel methods, functional data analysis, and high-dimensional inference.

The probabilistic reading of the cumulative accuracy profile (CAP) has a long industry lineage. Falkenstein, Boral and Carty (2000) state, in discrete form, that the default rate at a score percentile equals the portfolio average rate times the local slope of the power curve; van der Burgt (2008, 2019) formalizes this as the continuous identity $p(D\mid x) = p_D\, dy/dx$ and imports the continuous form as a working fact; Tasche (2009) analyzes the resulting calibration method; Voloshyn and Voloshyn (2023) substitute Bayes' theorem, $f(x\mid D)=p(D\mid x) f(x)/p_D$, into the area integral and write the Gini as a functional of the calibration curve. The slope itself is already in the lineage (van der Burgt's $dy/dx$ is the ratio of the two cumulative differentials), but it enters as a cited working fact, never as Bayes' theorem. We make that identification explicit and draw out its consequences. First, the CAP slope is Bayes' theorem in cumulative coordinates: the standardized PD it recovers is the posterior probability rescaled by the prior. The weight of the paper then falls on two results this reading unlocks. The odds form places the weight of evidence (the log of the likelihood ratio, i.e. the Bayes factor) and the information value inside one geometry (the weight of evidence at a point is the log of the ratio of the "bad" and "good" CAP slopes). The accuracy ratio, Somers' $D_{xy}$, and the Gini $(2A-1)/(1-p_D)$ are revealed as one number computed three ways. Run in comparison mode (realized outcomes against model claims), the same identity recovers the reliability diagram in cumulative coordinates, with the sign of the gap between the empirical and model-implied Gini coefficients as a calibration diagnostic. A worked five-band example carries every identity in discrete form, and a kernel-density example extends them to the continuous case.

We analyze distributions of historic S&P500 multi-day returns, for the number of days of accumulation from 20 to 120. With the increase of the number of days of accumulation, we observe clear tempering of power-law tails toward a seemingly finite value. To explain this phenomenon, we employ a model that produces a "capped Inverse Gamma" stationary (steady-state) distribution for stochastic volatility which, in turn, produces a "tempered Student-t" distribution for returns. We then employ Jones-Faddy-like symmetry breaking mechanism that produces a "tempered Skew-t" distribution. This distribution provides rather good fits to the distributions of accumulated multi-day S&P500 returns, which exhibit symmetry breaking between gains and losses -- as reflected by positive mean and negative skew. Tempered Skew-t fits are also consistent with near perfect linear dependence on the number of days of accumulation of the mean values and, even more so, of the variances (mean squared realized volatility) of the distributions.

This paper presents a reproducible synthetic benchmark comparing a computational planner, an agent-based market, and a hybrid meta-market within a common simulated economy. The benchmark incorporates input-output production networks, heterogeneous firms, capacity constraints, endogenous prices, welfare metrics, structural shocks, adversarial stress testing, and information-reporting experiments. Across training, holdout, and adversarial scenarios, the planner consistently achieves lower welfare losses than the decentralized alternatives. The main contribution is methodological rather than ideological. While the benchmark demonstrates a falsifiable framework for comparing economic coordination mechanisms, it does not establish the empirical superiority of planning. Several design choices mechanically favor the planner, including informational asymmetries, incomplete market representation, and simplified institutional assumptions. The results should therefore be interpreted as validation of a synthetic experimental architecture and as a prototype for future research. The paper concludes by outlining a validation agenda based on empirical calibration, structural holdouts, sensitivity analysis, uncertainty quantification, mechanism-design tests, and independent replication.

An integrated and extendable approach for stress-testing loan portfolios is presented, which includes both a loan production component and a credit risk component. In this approach, we simulate a completed portfolio using realistic loan parameters and distributional assumptions. Thereafter, we generate the uncertain cash flow history of these loans within a multistate probabilistic framework. We illustrate our approach using a simulation-based study, though the approach can be fit to real-world data. Such a simulation-based approach is ideal for stress-testing since it allows for evaluating a range of conditions. From these completed loans, we compute portfolio-level credit risk metrics, e.g., default and loss rates. Stress scenarios are introduced by varying the loan parameters accordingly within a broader Monte Carlo setup, thereby resulting in a range of portfolios. A classical approach to stress-testing does not typically integrate loan production or embed the correlation structure amongst risk metrics. In our approach, we integrate the forecasting of risk metrics with receipt-generation. Given data, the loan parameters within our extendable approach can be dynamically modelled as functions of input variables using any applicable technique. Overall, our approach can render predictions that are more dynamic and flexibly tuned, which can enhance stress-testing practices within any bank.

This paper complements and extends Doldi, Frittelli and Maggis, Collective completeness and pricing-hedging duality, Math. Finan. Econ. 19, 757-784 (2025), by studying collective pricing and hedging when admissible risk exchanges form a finitely generated convex cone. The collective First Fundamental Theorem of Asset Pricing and the collective pricing-hedging duality are extended to this setting. A key contribution is a closedness result showing that no collective arbitrage implies the closedness of the aggregate feasibility cone combining infinite-dimensional trading opportunities with finite-dimensional exchanges. The paper also proves that no-collective-arbitrage prices for vectors of contingent claims form a relatively open convex set. Finally, strong collective replicability is introduced and shown to be equivalent to price uniqueness. This leads to an enhanced collective Second Fundamental Theorem of Asset Pricing, providing equivalent characterizations of collective completeness and strong collective completeness in terms of the uniqueness of the collective equivalent martingale measure. We highlight that several core aspects of the theory are substantially altered when exchanges belong to a convex cone rather than a vector space.

This paper studies the macroeconomic dynamics of climate policy in a multi-sector dynamic general equilibrium model with renewable and non-renewable energy, sector-specific capital adjustment frictions, household energy demand, and endogenous fossil resource dynamics. The central mechanism is that decarbonization requires reallocating energy use and installed capital: fossil energy demand can contract immediately, while renewable capacity and abatement adjust only gradually. The analysis delivers four results. First, gradual policy implementation sharply reduces transition costs: relative to immediate implementation, gradual emissions caps improve welfare by 2.26 percentage points under comprehensive regulation and by 5.06 percentage points under firm-only regulation. Second, renewable energy subsidies and non-renewable energy taxes support renewable capital accumulation and reduce, but do not eliminate, the welfare cost of front-loaded tightening. Third, sectoral coverage changes the welfare ranking across implementation speeds. Firm-only regulation performs better under gradual implementation because it shields utility-relevant household energy services, but becomes nearly as costly as the carbon-price-only transition under immediate implementation. Fourth, endogenous fossil exploration and stock-dependent extraction costs transmit climate policy into lower extraction, fewer discoveries, and a declining shadow value of reserves, providing a structural mechanism for stranded fossil assets. The results show that deep decarbonization can be achieved at substantially lower macroeconomic cost when policy manages the speed and incidence of energy-capital reallocation.

Using average vehicle speed data in 10-minute increments at the Traffic Message Channel (TMC) location level, along with precise crash timing and location information, we analyze driving behavior around five Florida stadiums before and after NFL and NBA regular season games from 2015 to 2019. We find no evidence of emotional driving following NBA games, but strong and consistent effects following NFL games, concentrated in predicted-close games that end in disappointing home-team losses -- combining high pre-game suspense with negative outcome valence. These games are associated with significant increases in average vehicle speed within 3 km of stadiums during the first post-game hour, dissipating with increasing time and distance from the stadium. Average vehicle speed increases by up to 3 mph relative to predicted-close games that ended in a win -- an effect several times larger than the typical game day versus non-game day speed differential. Overall, our results highlight how the combination of sustained suspense and negative outcome valence in close sporting contests can spill over into risky post-game driving behavior, underscoring the behavioral and public safety implications of affective cues in large-scale sporting events.

Previous studies conclude that intermediaries account for a large share of exports. Using Spanish firm-level data, we show that many firms classified as intermediaries are either manufacturer-owned export arms that ship their parent firms' products or vertically integrated firms that control design, production, and distribution and predominantly export goods sold under their own brands. Once we exclude these export arms and vertically integrated firms, the share of intermediaries in exports in our sample falls by about 70%. We also show that pure intermediaries differ markedly from export arms and vertically integrated firms along key firm and export dimensions.

Some firms export their own products directly, others rely on intermediary firms to export on their behalf, and still others both export their own products and intermediate exports for other producers. To explain this heterogeneity, we develop a model in which firms differ along two dimensions: manufacturing capability and commercial capability. Manufacturing capability lowers the marginal cost of producing a variety, whereas commercial capability lowers the variable cost of reaching foreign customers. Different combinations of these capabilities generate the different types of firms observed in export markets: direct exporters, indirect exporters, pure intermediaries, and hybrid firms. The model predicts that commercially capable intermediaries are matched with more manufacturing-capable producers, and that more commercially capable intermediaries export a broader set of varieties. We provide suggestive evidence for these predictions using Spanish firm-level export data.

Effective collaborative data entry and transparency are foundational for building robust databases and high-quality data synthesis. Yet researchers often face inconsistent data entries, inadvertently introducing errors, misreadings, and inconsistencies that compromise data integrity. Despite the growing use of open-source tools, many still rely on inefficient formats or costly commercial platforms, while fewer adopt complex open-source solutions. These inefficiencies slow workflows and hinder researchers' ability to build foundational databases for synthesis research, including meta-analyses. To address this, we developed CollaboratoR, a customizable R package that automates data validation and aggregation, ensuring consistency and transparency and adhering to FAIR data principles, while optionally using Google Sheets for collaborative data entry and GitHub for version control. CollaboratoR fills the gap between ad-hoc spreadsheets and complex systems for data extraction in meta-analyses. Data are entered into shared Google Sheets, validated, and pushed to GitHub for version control, then re-validated after verification to ensure accuracy before finalizing. Tested in two case studies, plant competition and avian interaction databases, CollaboratoR proved effective at managing large collaborative datasets. In both, automated validation flagged common entry and formatting issues early, improving traceability and reducing time spent on post-hoc cleaning. This framework applies across disciplines where data synthesis informs data-driven decision-making, such as social science, ecology, and medical and pharmaceutical research. Ultimately, CollaboratoR offers guidance for efficient, transparent, and reproducible collaborative data management, enhancing research synthesis across fields and industries alike.

Identifying what individual neurons encode in higher-order visual cortex is an open problem. Responses resist intuitive parameterization, and the deep-network embeddings used in their place are black boxes. Here, we introduce NEURRATOR, a framework that decodes spiking activity into free-form natural-language narration of the viewed scene at single-neuron resolution. A learned encoder maps spike trains from arbitrary subsets of simultaneously-recorded neurons into the patch-embedding space of a frozen CLIP, from which a multimodal language model and sparse autoencoder generates and validates a description with no language-side training. Applied to Neuropixel recordings of mouse visual cortex during natural-movie viewing, NEURRATOR narrates from thousands of neurons, singular cortical regions, local populations, or from a molecularly-defined cell-types. We use this property to (i) quantify how decoding fidelity scales with population size and cortical region, and (ii) "neurrate", in plain language, what individual neurons and genetically-tagged inhibitory cell-types contribute to visual representation. This recasts cell identity from a classification target into a functional probe of the visual system, providing a new unit of biological insights in neural systems.

Forecasting epidemic trajectories is important for public health decision-making, but no single model is consistently reliable across epidemic phases and forecasting settings. We evaluate Multi-Armed Bandit (MAB)-inspired adaptive weighting strategies for combining epidemic forecasting models when component-model performance changes over time. Using U.S. COVID-19 incidence data from three epidemic waves, we compare UCB, EXP3, and epsilon-greedy weighting rules under fixed short-window and growing calibration windows, with both deterministic and stochastic ensemble variants. The model pool includes SIR, SEIR, GLM, Gompertz, Richards, ARIMA, random walk with drift, simple exponential smoothing, Holt's linear trend method, and exponential growth. Adaptive ensembles are compared with individual models and with naive, unweighted, and inverse-WIS weighted ensemble benchmarks. Forecast performance is assessed using RMSE, weighted interval score (WIS), 95% prediction-interval coverage, and mean 95% prediction-interval width. Across waves, calibration windows, and forecast horizons, EXP3Stoch, EXP3Det, and EPSStoch achieved the lowest mean forecast WIS. The main gains were in probabilistic forecast quality, especially WIS and interval coverage, rather than uniformly lower point forecast error. Simple benchmarks, including the unweighted and inverse-WIS ensembles, remained competitive in several settings. These results suggest that MAB-inspired adaptive weighting is a useful complementary tool for epidemic forecasting, especially when model skill is time-varying and forecast uncertainty is substantial.

Nitrous oxide (N2O) emissions from water resource recovery facilities (WRRFs) fluctuate over time and can arise from multiple microbial pathways, making source attribution and full-scale prediction difficult. The difficulty is compounded by the high dimensionality of activated sludge microbiomes, whose complex and dynamic community structure can obscure relationships with N2O emission patterns. This study evaluated whether interpretable, low-dimensional representations of activated sludge microbiomes can be correlated with N2O emission states. Temporal 16S rRNA gene amplicon profiles and N2O emission metrics were collected from two full-scale WRRFs in Switzerland. Genus-level relative-abundance profiles were summarized using archetypal analysis (AA), which represents each sample as a convex combination of a small number of interpretable community profiles. In both WRRFs, three archetypes captured most explainable variation in community composition (63%--73%) and defined a simplex state space in which samples clustered near vertices and edges, indicating that community compositions were organized around distinct archetypal states and their mixtures. Without using emission labels while training, the archetypal state space aligned strongly with binary N2O emission states: high-emission observations in both plants concentrated around a specific archetype, and temporal trajectories showed consistent high weights of this archetype during high-emission periods. Functional summaries suggested site-specific but pathway-relevant interpretations of the high-N2O archetype. Temperature further structured the archetypal state space, indicating seasonal forcing of microbiome configurations associated with elevated N2O. Overall, AA provides an interpretable framework to track microbiome regime shifts and may support operational tracking of high-N2O emission states in full-scale WRRFs.