pwc-archive/papers-with-abstracts huggingface dataset:
A Lorentz-Equivariant Transformer for All of the LHC
Authors: Johann Brehmer, Víctor Bresó, Pim de Haan, Tilman Plehn, Huilin Qu, Jonas Spinner, Jesse Thaler
Date: 2024-11-01 00:00:00
Tasks: All

We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider. L-GATr represents data in a geometric algebra over space-time and is equivariant under Lorentz transformations. The underlying architecture is a versatile and scalable transformer, which is able to break symmetries if needed. We demonstrate the power of L-GATr for amplitude regression and jet classification, and then benchmark it as the first Lorentz-equivariant generative network. For all three LHC tasks, we find significant improvements over previous architectures.
Automated Global Analysis of Experimental Dynamics through Low-Dimensional Linear Embeddings
Authors: Samuel A. Moore, Brian P. Mann, Boyuan Chen
Date: 2024-11-01 00:00:00

Dynamical systems theory has long provided a foundation for understanding evolving phenomena across scientific domains. Yet, the application of this theory to complex real-world systems remains challenging due to issues in mathematical modeling, nonlinearity, and high dimensionality. In this work, we introduce a data-driven computational framework to derive low-dimensional linear models for nonlinear dynamical systems directly from raw experimental data. This framework enables global stability analysis through interpretable linear models that capture the underlying system structure. Our approach employs time-delay embedding, physics-informed deep autoencoders, and annealing-based regularization to identify novel low-dimensional coordinate representations, unlocking insights across a variety of simulated and previously unstudied experimental dynamical systems. These new coordinate representations enable accurate long-horizon predictions and automatic identification of intricate invariant sets while providing empirical stability guarantees. Our method offers a promising pathway to analyze complex dynamical behaviors across fields such as physics, climate science, and engineering, with broad implications for understanding nonlinear systems in the real world.
Are EEG functional networks really describing the brain? A comparison with other information-processing complex systems
Authors: Sofia Gil-Rodrigo, Raúl López-Martín, Görsev Yener, Jan R. Wiersema, Bahar Güntekin, Massimiliano Zanin
Date: 2024-11-03 00:00:00
Tasks: EEG

Functional networks representing human brain dynamics have become a standard tool in neuroscience, providing an accessible way of depicting the computation performed by the brain in healthy and pathological conditions. Yet, these networks share multiple characteristics with those representing other natural and man-made complex systems, leading to the question of whether they are actually capturing the uniqueness of the human brain. By resorting to a large set of data representing multiple financial, technological, social, and natural complex systems, and by relying on Deep Learning classification models, we show how they are highly similar. We specifically reach the conclusion that, under some general reconstruction methodological choices, it is as difficult to understand whether a network represents a human brain or a financial market, as to diagnose a major pathology. This suggests that functional networks are describing information processing mechanisms that are common across complex systems; but that are not currently defining the uniqueness of the human mind. We discuss the consequence of these findings for neuroscience and complexity science in general, and suggest future avenues for exploring this interesting topic.
An Efficient Hierarchical Preconditioner-Learner Architecture for Reconstructing Multi-scale Basis Functions of High-dimensional Subsurface Fluid Flow
Authors: Peiqi Li, Jie Chen
Date: 2024-11-01 00:00:00
Tasks: Computational Efficiency

Modeling subsurface fluid flow in porous media is crucial for applications such as oil and gas exploration. However, the inherent heterogeneity and multi-scale characteristics of these systems pose significant challenges in accurately reconstructing fluid flow behaviors. To address this issue, we proposed Fourier Preconditioner-based Hierarchical Multiscale Net (FP-HMsNet), an efficient hierarchical preconditioner-learner architecture that combines Fourier Neural Operators (FNO) with multi-scale neural networks to reconstruct multi-scale basis functions of high-dimensional subsurface fluid flow. Using a dataset comprising 102,757 training samples, 34,252 validation samples, and 34,254 test samples, we ensured the reliability and generalization capability of the model. Experimental results showed that FP-HMsNet achieved an MSE of 0.0036, an MAE of 0.0375, and an R2 of 0.9716 on the testing set, significantly outperforming existing models and demonstrating exceptional accuracy and generalization ability. Additionally, robustness tests revealed that the model maintained stability under various levels of noise interference. Ablation studies confirmed the critical contribution of the preconditioner and multi-scale pathways to the model's performance. Compared to current models, FP-HMsNet not only achieved lower errors and higher accuracy but also demonstrated faster convergence and improved computational efficiency, establishing itself as the state-of-the-art (SOTA) approach. This model offers a novel method for efficient and accurate subsurface fluid flow modeling, with promising potential for more complex real-world applications.
Eco-evolutionary constraints for the endemicity of rapidly evolving viruses
Authors: David Soriano-Paños
Date: 2024-11-04 00:00:00

Antigenic escape constitutes the main mechanism allowing rapidly evolving viruses to achieve endemicity. Beyond granting immune escape, empirical evidence also suggests that mutations of viruses might increase their inter-host transmissibility. While both mechanisms are well-studied individually, their combined effects on viral endemicity remain to be explored. Here we propose a minimal eco-evolutionary framework to simulate epidemic outbreaks generated by pathogens evolving both their transmissibility and immune escape. Our findings uncover a very rich phenomenology arising from the complex interplay between both evolutionary pathways and the underlying contagion dynamics. We first show that contagions at the population level constrain the effective evolution of the virus, accelerating the increase in transmissibility in the first epidemic wave while favoring antigenic variation in the transition to the endemic phase. Our results also reveal that accounting for both evolutionary pathways changes the features of the viruses more prone to become endemic. While chances for endemicity increase with infectiousness of the wild-type variant for viruses not evolving their transmissibility, a non-monotonic behavior is observed when the latter mechanism is included, favoring less transmissible viruses and impairing those ones with intermediate infectiousness.
Identifiability analysis of vaccination decision-making dynamics
Authors: Azadeh Aghaeeyan, Mark A. Lewis, Pouria Ramazi
Date: 2024-11-01 00:00:00
Tasks: Decision Making

Variations in individuals' perceptions of vaccination and decision-making processes can give rise to poor vaccination coverage. The future vaccination promotion programs will benefit from understanding this heterogeneity amongst groups within a population and, accordingly, tailoring the communication strategies. Motivated by this, we developed a mechanistic model consisting of a system of ordinary differential equations that categorizes individuals based on two factors: (i) perceived payoff gains for vaccination and (ii} decision-making strategies where we assumed that individuals may behave as either myopic rationalists, going for a dose of vaccine if doing so maximizes their perceived payoff gain, or success-based learners, waiting to observe feedback on vaccination before deciding. We then investigated the global identifiability of group proportions and perceived payoff gains, that is, the possibility of globally retrieving these parameters by observing the error-free cumulative proportion of vaccinated individuals over time. To do so, for each group, we assumed a piecewise constant payoff gain and, for each time interval, obtained the so-called generalized input-output equation. We then proved the global identifiability of these parameters under certain conditions. Global identifiability opens the door to reliable estimations of the group proportions and their perceived payoffs.
Coupling quantum-like cognition with the neuronal networks within generalized probability theory
Authors: Andrei Khrennikov, Masanao Ozawa, Felix Benninger, Oded Shor
Date: 2024-10-29 00:00:00

The past few years have seen a surge in the application of quantum theory methodologies and quantum-like modeling in fields such as cognition, psychology, and decision-making. Despite the success of this approach in explaining various psychological phenomena such as order, conjunction, disjunction, and response replicability effects there remains a potential dissatisfaction due to its lack of clear connection to neurophysiological processes in the brain. Currently, it remains a phenomenological approach. In this paper, we develop a quantum-like representation of networks of communicating neurons. This representation is not based on standard quantum theory but on generalized probability theory (GPT), with a focus on the operational measurement framework. Specifically, we use a version of GPT that relies on ordered linear state spaces rather than the traditional complex Hilbert spaces. A network of communicating neurons is modeled as a weighted directed graph, which is encoded by its weight matrix. The state space of these weight matrices is embedded within the GPT framework, incorporating effect observables and state updates within the theory of measurement instruments a critical aspect of this model. This GPT based approach successfully reproduces key quantum-like effects, such as order, non-repeatability, and disjunction effects (commonly associated with decision interference). Moreover, this framework supports quantum-like modeling in medical diagnostics for neurological conditions such as depression and epilepsy. While this paper focuses primarily on cognition and neuronal networks, the proposed formalism and methodology can be directly applied to a wide range of biological and social networks.
Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
Authors: Akash Kundu, Leopoldo Sarra
Date: 2024-10-31 00:00:00
Tasks: GPU, Program Synthesis, reinforcement-learning, Reinforcement Learning

Designing quantum circuits for specific tasks is challenging due to the exponential growth of the state space. We introduce gadget reinforcement learning (GRL), which integrates reinforcement learning with program synthesis to automatically generate and incorporate composite gates (gadgets) into the action space. This enhances the exploration of parameterized quantum circuits (PQCs) for complex tasks like approximating ground states of quantum Hamiltonians, an NP-hard problem. We evaluate GRL using the transverse field Ising model under typical computational budgets (e.g., 2- 3 days of GPU runtime). Our results show improved accuracy, hardware compatibility and scalability. GRL exhibits robust performance as the size and complexity of the problem increases, even with constrained computational resources. By integrating gadget extraction, GRL facilitates the discovery of reusable circuit components tailored for specific hardware, bridging the gap between algorithmic design and practical implementation. This makes GRL a versatile framework for optimizing quantum circuits with applications in hardware-specific optimizations and variational quantum algorithms. The code is available at: https://github.com/Aqasch/Gadget_RL
Identification and Inference in General Bunching Designs
Authors: Myunghyun Song
Date: 2024-11-06 00:00:00
Tasks: counterfactual

This paper develops an econometric framework and tools for the identification and inference of a structural parameter in general bunching designs. We present point and partial identification results, which generalize previous approaches in the literature. The key assumption for point identification is the analyticity of the counterfactual density, which defines a broader class of distributions than many commonly used parametric families. In the partial identification approach, the analyticity condition is relaxed and various inequality restrictions can be incorporated. Both of our identification approaches allow for observed covariates in the model, which has previously been permitted only in limited ways. These covariates allow us to account for observable factors that influence decisions regarding the running variable. We provide a suite of counterfactual estimation and inference methods, termed the generalized polynomial strategy. Our method restores the merits of the original polynomial strategy proposed by Chetty et al. (2011) while addressing several weaknesses in the widespread practice. The efficacy of the proposed method is demonstrated compared to the polynomial estimator in a series of Monte Carlo studies within the augmented isoelastic model. We revisit the data used in Saez (2010) and find substantially different results relative to those from the polynomial strategy.
Randomly Assigned First Differences?
Authors: Clément de Chaisemartin, Ziteng Lei
Date: 2024-11-05 00:00:00
Tasks: regression

We consider treatment-effect estimation using a first-difference regression of an outcome evolution $\Delta Y$ on a treatment evolution $\Delta D$. Under a causal model in levels, the residual of the first-difference regression is a function of the period-one treatment $D_{1}$. Then, researchers should test if $\Delta D$ and $D_{1}$ are correlated: if they are, the first-difference regression may suffer from an omitted variable bias. To solve it, researchers may control for $E(\Delta D|D_{1})$. We apply these results to regressions of US industries' employment evolutions on the evolution of their Chinese imports, estimated on the data of \cite{acemoglu2016import}. $\Delta D$ and $D_{1}$ are strongly correlated. Controlling for $E(\Delta D|D_{1})$ halves the estimated effect of Chinese imports.
Robust Market Interventions
Authors: Andrea Galeotti, Benjamin Golub, Sanjeev Goyal, Eduard Talamàs, Omer Tamuz
Date: 2024-11-05 00:00:00

When can interventions in markets be designed to increase surplus robustly -- i.e., with high probability -- accounting for uncertainty due to imprecise information about economic primitives? In a setting with many strategic firms, each possessing some market power, we present conditions for such interventions to exist. The key condition, recoverable structure, requires large-scale complementarities among families of products. The analysis works by decomposing the incidence of interventions in terms of principal components of a Slutsky matrix. Under recoverable structure, a noisy signal of this matrix reveals enough about these principal components to design robust interventions. Our results demonstrate the usefulness of spectral methods for analyzing imperfectly observed strategic interactions with many agents.
Beyond the Traditional VIX: A Novel Approach to Identifying Uncertainty Shocks in Financial Markets
Authors: Ayush Jha, Abootaleb Shirvani, Svetlozar T. Rachev, Frank J. Fabozzi
Date: 2024-11-05 00:00:00
Tasks: Time Series

We introduce a new identification strategy for uncertainty shocks to explain macroeconomic volatility in financial markets. The Chicago Board Options Exchange Volatility Index (VIX) measures market expectations of future volatility, but traditional methods based on second-moment shocks and time-varying volatility of the VIX often fail to capture the non-Gaussian, heavy-tailed nature of asset returns. To address this, we construct a revised VIX by fitting a double-subordinated Normal Inverse Gaussian Levy process to S&P 500 option prices, providing a more comprehensive measure of volatility that reflects the extreme movements and heavy tails observed in financial data. Using an axiomatic approach, we introduce a general family of risk-reward ratios, computed with our revised VIX and fitted over a fractional time series to more accurately identify uncertainty shocks in financial markets.
Does Regression Produce Representative Causal Rankings?
Authors: Apoorva Lal
Date: 2024-11-04 00:00:00
Tasks: regression

We examine the challenges in ranking multiple treatments based on their estimated effects when using linear regression or its popular double-machine-learning variant, the Partially Linear Model (PLM), in the presence of treatment effect heterogeneity. We demonstrate by example that overlap-weighting performed by linear models like PLM can produce Weighted Average Treatment Effects (WATE) that have rankings that are inconsistent with the rankings of the underlying Average Treatment Effects (ATE). We define this as ranking reversals and derive a necessary and sufficient condition for ranking reversals under the PLM. We conclude with several simulation studies conditions under which ranking reversals occur.
On the Asymptotic Properties of Debiased Machine Learning Estimators
Authors: Amilcar Velez
Date: 2024-11-04 00:00:00

This paper studies the properties of debiased machine learning (DML) estimators under a novel asymptotic framework, offering insights for improving the performance of these estimators in applications. DML is an estimation method suited to economic models where the parameter of interest depends on unknown nuisance functions that must be estimated. It requires weaker conditions than previous methods while still ensuring standard asymptotic properties. Existing theoretical results do not distinguish between two alternative versions of DML estimators, DML1 and DML2. Under a new asymptotic framework, this paper demonstrates that DML2 asymptotically dominates DML1 in terms of bias and mean squared error, formalizing a previous conjecture based on simulation results regarding their relative performance. Additionally, this paper provides guidance for improving the performance of DML2 in applications.
Estimating Nonseparable Selection Models: A Functional Contraction Approach
Authors: Fan Wu, Yi Xin
Date: 2024-11-04 00:00:00

We propose a novel method for estimating nonseparable selection models. We show that, given the selection rule and the observed selected outcome distribution, the potential outcome distribution can be characterized as the fixed point of an operator, which we prove to be a functional contraction. We propose a two-step semiparametric maximum likelihood estimator to estimate the selection model and the potential outcome distribution. The consistency and asymptotic normality of the estimator are established. Our approach performs well in Monte Carlo simulations and is applicable in a variety of empirical settings where only a selected sample of outcomes is observed. Examples include consumer demand models with only transaction prices, auctions with incomplete bid data, and Roy models with data on accepted wages.
Changes-In-Changes For Discrete Treatment
Authors: Onil Boussim
Date: 2024-11-03 00:00:00

This paper generalizes the changes-in-changes (CIC) model to handle discrete treatments with more than two categories, extending the binary case of Athey and Imbens (2006). While the original CIC model is well-suited for binary treatments, it cannot accommodate multi-category discrete treatments often found in economic and policy settings. Although recent work has extended CIC to continuous treatments, there remains a gap for multi-category discrete treatments. I introduce a generalized CIC model that adapts the rank invariance assumption to multiple treatment levels, allowing for robust modeling while capturing the distinct effects of varying treatment intensities.
Empirical Welfare Analysis with Hedonic Budget Constraints
Authors: Debopam Bhattacharya, Ekaterina Oparina, Qianya Xu
Date: 2024-11-01 00:00:00

We analyze demand settings where heterogeneous consumers maximize utility for product attributes subject to a nonlinear budget constraint. We develop nonparametric methods for welfare-analysis of interventions that change the constraint. Two new findings are Roy's identity for smooth, nonlinear budgets, which yields a Partial Differential Equation system, and a Slutsky-like symmetry condition for demand. Under scalar unobserved heterogeneity and single-crossing preferences, the coefficient functions in the PDEs are nonparametrically identified, and under symmetry, lead to path-independent, money-metric welfare. We illustrate our methods with welfare evaluation of a hypothetical change in relationship between property rent and neighborhood school-quality using British microdata.
Inference in a Stationary/Nonstationary Autoregressive Time-Varying-Parameter Model
Authors: Donald W. K. Andrews, Ming Li
Date: 2024-11-01 00:00:00
Tasks: Unity

This paper considers nonparametric estimation and inference in first-order autoregressive (AR(1)) models with deterministically time-varying parameters. A key feature of the proposed approach is to allow for time-varying stationarity in some time periods, time-varying nonstationarity (i.e., unit root or local-to-unit root behavior) in other periods, and smooth transitions between the two. The estimation of the AR parameter at any time point is based on a local least squares regression method, where the relevant initial condition is endogenous. We obtain limit distributions for the AR parameter estimator and t-statistic at a given point $\tau$ in time when the parameter exhibits unit root, local-to-unity, or stationary/stationary-like behavior at time $\tau$. These results are used to construct confidence intervals and median-unbiased interval estimators for the AR parameter at any specified point in time. The confidence intervals have correct asymptotic coverage probabilities with the coverage holding uniformly over stationary and nonstationary behavior of the observations.
The ET Interview: Professor Joel L. Horowitz
Authors: Sokbae Lee
Date: 2024-10-31 00:00:00
Tasks: Econometrics

Joel L. Horowitz has made profound contributions to many areas in econometrics and statistics. These include bootstrap methods, semiparametric and nonparametric estimation, specification testing, nonparametric instrumental variables estimation, high-dimensional models, functional data analysis, and shape restrictions, among others. Originally trained as a physicist, Joel made a pivotal transition to econometrics, greatly benefiting our profession. Throughout his career, he has collaborated extensively with a diverse range of coauthors, including students, departmental colleagues, and scholars from around the globe. Joel was born in 1941 in Pasadena, California. He attended Stanford for his undergraduate studies and obtained his Ph.D. in physics from Cornell in 1967. He has been Charles E. and Emma H. Morrison Professor of Economics at Northwestern University since 2001. Prior to that, he was a faculty member at the University of Iowa (1982-2001). He has served as a co-editor of Econometric Theory (1992-2000) and Econometrica (2000-2004). He is a Fellow of the Econometric Society and of the American Statistical Association, and an elected member of the International Statistical Institute. The majority of this interview took place in London during June 2022.