arXiv preprints from January 1, 2026 through September 9, 2026 — 17:18:05 EST
Empirical studies of peer effects often exploit conditional random assignment to peer groups within urns. We develop a GMM framework for estimation and inference in this setting. The framework separately identifies endogenous and contextual peer effects and nests tests of random peer-group assignment as a special case. It permits...
We ask whether COVID-19 lockdown stringency altered national Olympic performance between Rio 2016 and Tokyo 2020, using the Oxford Stringency Index and the 99 countries that won a medal in either edition. As in \citet{liu2024}, mean performance is unaffected: stringency is insignificant in every OLS and ANOVA specification. The...
We study the existence of a \emph{Regional Differential} in rugby sevens: whether, in tournaments where no competing team enjoys formal home status, some national sides systematically over- or under-perform depending on \emph{where} the event is staged. Using the universe of 2672 men's and women's matches from international rugby...
Research activity generates highly localized positive spillovers, yet in the U.S. it has become increasingly spatially misaligned with population and economic activity as people moved away from legacy cities where major research institutions remain anchored. Reallocating researchers toward population centers could broaden local access...
We design a method for measuring the risk preferences of agents in the deep past. The method combines a structural model of crop choice as a portfolio allocation with machine-learning prediction of expected crop returns, using historic agronomic and climate data. We estimate county-level risk preferences for the United States and...
We study how many observations are needed to determine the causal direction between two linearly related variables. Classical LiNGAM theory shows that independent non-Gaussian disturbances identify the direction, but does not quantify the difficulty when the causal effect is weak or the disturbances are nearly Gaussian. Let $β$ bound...
In Shapley-Scarf housing markets, Ma (1994) shows that top trading cycles (TTC) is the unique mechanism satisfying individual rationality (IR), Pareto efficiency (PE), and strategy-proofness. We ask what other mechanisms become possible when strategy-proofness is replaced by a weaker condition called non-obvious manipulability (NOM),...
Route and activity choice are connected levels of a common sequential mobility decision problem: activity choice determines what people do, where, and when, while route choice governs how they move between activities. This review develops a unified framework connecting transportation choice modeling with inverse reinforcement learning...
We propose a family of control variate estimators for variance reduction in design-based survey sampling and causal inference, with and without interference. In these settings, inverse probability weighting (IPW) estimators are widely used, but may have large variance when sampling, treatment, or exposure probabilities are small....
Neurofinance shows that financial valuation depends on evolving neural states, while temporal experience is itself state dependent. Yet intertemporal models typically treat time as exogenous and ask how delay affects valuation. This paper examines the converse question: can valuation-related neural dynamics generate subjective...
This paper proposes a metric approach to measuring the complexity of lotteries. Starting by observing that degenerate lotteries are the simplest choice alternatives, the complexity of a lottery is evaluated by its distance from the closest degenerate lottery. Equivalently, a lottery is complex when it is difficult to approximate it by...
We formulate the voter's strategic information acquisition to control the future self's action as a Bayesian persuasion problem. Our main result shows that acquiring information that is irrelevant to the voter's objective can be a worst-case optimal solution: it can reduce the possibility that the future self is swayed by additional...
Identifying and representing object instances such as cells or nuclei is a common task in microscopy image analysis. Established machine learning workflows typically use supervised detection or segmentation followed by feature extraction or classification, which requires manual annotations and treats instance segmentation and cell...
Componentwise weak convergence of signed synaptic kernels does not, by itself, determine the fast-synapse limit of a sparse threshold-reset network. Within a causal event protocol with clamped refractoriness and smooth positive-delay kernels, we construct two families whose excitatory and inhibitory measures converge weakly to $δ_0$...
Large language models can describe mechanisms, yet scalable post-training still depends on costly, manually curated biological reasoning traces. Here we show that cellular perturbation atlases can instead become reinforcement-learning environments, where measured gene responses provide computable rewards for biological reasoning. We...
Longitudinal symptom networks encode directed prediction across measurement occasions, but outgoing connectivity does not by itself identify which symptom should be modified, how strongly it should be changed, or how a perturbation would propagate to later symptoms. We introduce tSymPerturb, a temporal extension of SymPerturb for...
Lifelong learning requires acquiring new knowledge without erasing the old. Yet neural population codes for familiar stimuli and behaviors change over days and weeks. This coexistence of stable memory and changing internal codes may depend on how a learning system prevents forgetting. We therefore tested whether different...
In clinical motor imagery brain-computer interface (MI-BCI) decoding, cross-day transferability and online operation remain two critical challenges. Hypergraphs can improve transferability by capturing higher-order sample relationships, yet existing hypergraph-based methods for online emotion recognition neglect the cross-day benefits...
Born in London in 1953 and raised in Australia, Phil Bourne spent over four decades in science moving across scales: from crystal structures to the world's premier structural biology database; from scientific journals to national data policy; from molecules to institutions. The International Society for Computational Biology (ISCB)...
Global workspace theory explains conscious access as the broadcasting of selected information to the rest of the network, but it lacks a formal criterion for identifying the mechanism that enables this access. We propose that a global workspace is a mediator, namely, a subnetwork that receives activity from distributed systems,...
Characterising electrophysiological properties of cardiac tissue efficiently and accurately from spatially sparse intracardiac measurements is clinically important for localising ablation targets and improving arrhythmia treatment. We developed a graph neural network-based framework trained on synthetic electrogram signals on 2D flat...
The population structure of an inbred population of 781 people on Norfolk Island in the Pacific, 318 of which are descendants of the original Mutineers of the Bounty, is analyzed phenotypically using shape from stereo retinal fundus photographs. Three-dimensional optic nerve head (ONH) shape is reconstructed from stereo pairs by a...
Anticipated synchronization (AS), in which the receiver leads the sender and the phase lag is negative, can emerge in unidirectionally coupled dynamical systems when the receiver has faster internal dynamics than the sender. In cortical-like population models, AS and bistability between AS and delayed synchronization (DS) have been...
In this study, we develop a novel integrated framework that links the impact response with cellular dynamics using a live-neuron cell-integrated biomechanical full-body surrogate model. The impact event is simulated by allowing the surrogate model to fall from controlled seated release angles of 30-degree, 60-degree, and 90-degree....
Classical mathematical oncology employs deterministic mean-field models predicting that sufficiently high chemotherapy doses drive tumor density to zero. We show that this apparent cure is an artifact of neglecting demographic fluctuations. We construct a nonequilibrium stochastic PK-PD field theory combining a two-compartment...