pwc-archive/papers-with-abstracts huggingface dataset:
Prototype-Based Methods in Explainable AI and Emerging Opportunities in the Geosciences
Authors: Anushka Narayanan, Karianne J. Bergen
Date: 2024-10-22 00:00:00

Prototype-based methods are intrinsically interpretable XAI methods that produce predictions and explanations by comparing input data with a set of learned prototypical examples that are representative of the training data. In this work, we discuss a series of developments in the field of prototype-based XAI that show potential for scientific learning tasks, with a focus on the geosciences. We organize the prototype-based XAI literature into three themes: the development and visualization of prototypes, types of prototypes, and the use of prototypes in various learning tasks. We discuss how the authors use prototype-based methods, their novel contributions, and any limitations or challenges that may arise when adapting these methods for geoscientific learning tasks. We highlight differences between geoscientific data sets and the standard benchmarks used to develop XAI methods, and discuss how specific geoscientific applications may benefit from using or modifying existing prototype-based XAI techniques.
Stochastic Flow Matching for Resolving Small-Scale Physics
Authors: Stathi Fotiadis, Noah Brenowitz, Tomas Geffner, Yair Cohen, Michael Pritchard, Arash Vahdat, Morteza Mardani
Date: 2024-10-17 00:00:00

Conditioning diffusion and flow models have proven effective for super-resolving small-scale details in natural images.However, in physical sciences such as weather, super-resolving small-scale details poses significant challenges due to: (i) misalignment between input and output distributions (i.e., solutions to distinct partial differential equations (PDEs) follow different trajectories), (ii) multi-scale dynamics, deterministic dynamics at large scales vs. stochastic at small scales, and (iii) limited data, increasing the risk of overfitting. To address these challenges, we propose encoding the inputs to a latent base distribution that is closer to the target distribution, followed by flow matching to generate small-scale physics. The encoder captures the deterministic components, while flow matching adds stochastic small-scale details. To account for uncertainty in the deterministic part, we inject noise into the encoder output using an adaptive noise scaling mechanism, which is dynamically adjusted based on maximum-likelihood estimates of the encoder predictions. We conduct extensive experiments on both the real-world CWA weather dataset and the PDE-based Kolmogorov dataset, with the CWA task involving super-resolving the weather variables for the region of Taiwan from 25 km to 2 km scales. Our results show that the proposed stochastic flow matching (SFM) framework significantly outperforms existing methods such as conditional diffusion and flows.
Simultaneous Dimensionality Reduction for Extracting Useful Representations of Large Empirical Multimodal Datasets
Authors: Eslam Abdelaleem
Date: 2024-10-23 00:00:00
Tasks: Dimensionality Reduction

The quest for simplification in physics drives the exploration of concise mathematical representations for complex systems. This Dissertation focuses on the concept of dimensionality reduction as a means to obtain low-dimensional descriptions from high-dimensional data, facilitating comprehension and analysis. We address the challenges posed by real-world data that defy conventional assumptions, such as complex interactions within neural systems or high-dimensional dynamical systems. Leveraging insights from both theoretical physics and machine learning, this work unifies diverse reduction methods under a comprehensive framework, the Deep Variational Multivariate Information Bottleneck. This framework enables the design of tailored reduction algorithms based on specific research questions. We explore and assert the efficacy of simultaneous reduction approaches over their independent reduction counterparts, demonstrating their superiority in capturing covariation between multiple modalities, while requiring less data. We also introduced novel techniques, such as the Deep Variational Symmetric Information Bottleneck, for general nonlinear simultaneous reduction. We show that the same principle of simultaneous reduction is the key to efficient estimation of mutual information. We show that our new method is able to discover the coordinates of high-dimensional observations of dynamical systems. Through analytical investigations and empirical validations, we shed light on the intricacies of dimensionality reduction methods, paving the way for enhanced data analysis across various domains. We underscore the potential of these methodologies to extract meaningful insights from complex datasets, driving advancements in fundamental research and applied sciences. As these methods evolve, they promise to deepen our understanding of complex systems and inform more effective data analysis strategies.
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
Authors: Matthias Holzenkamp, Dongyu Lyu, Ulrich Kleinekathöfer, Peter Zaspel
Date: 2024-10-27 00:00:00
Tasks: Active Learning, GPR

Uncertainty estimations for machine learning interatomic potentials (MLIPs) are crucial for quantifying model error and identifying informative training samples in active learning strategies. In this study, we evaluate uncertainty estimations of Gaussian process regression (GPR)-based MLIPs, including the predictive GPR standard deviation and ensemble-based uncertainties. We do this in terms of calibration and in terms of impact on model performance in an active learning scheme. We consider GPR models with Coulomb and Smooth Overlap of Atomic Positions (SOAP) representations as inputs to predict potential energy surfaces and excitation energies of molecules. Regarding calibration, we find that ensemble-based uncertainty estimations show already poor global calibration (e.g., averaged over the whole test set). In contrast, the GPR standard deviation shows good global calibration, but when grouping predictions by their uncertainty, we observe a systematical bias for predictions with high uncertainty. Although an increasing uncertainty correlates with an increasing bias, the bias is not captured quantitatively by the uncertainty. Therefore, the GPR standard deviation can be useful to identify predictions with a high bias and error but, without further knowledge, should not be interpreted as a quantitative measure for a potential error range. Selecting the samples with the highest GPR standard deviation from a fixed configuration space leads to a model that overemphasizes the borders of the configuration space represented in the fixed dataset. This may result in worse performance in more densely sampled areas but better generalization for extrapolation tasks.
Metamizer: a versatile neural optimizer for fast and accurate physics simulations
Authors: Nils Wandel, Stefan Schulz, Reinhard Klein
Date: 2024-10-10 00:00:00

Efficient physics simulations are essential for numerous applications, ranging from realistic cloth animations or smoke effects in video games, to analyzing pollutant dispersion in environmental sciences, to calculating vehicle drag coefficients in engineering applications. Unfortunately, analytical solutions to the underlying physical equations are rarely available, and numerical solutions require high computational resources. Latest developments in the field of physics-based Deep Learning have led to promising efficiency improvements but still suffer from limited generalization capabilities and low accuracy compared to numerical solvers. In this work, we introduce Metamizer, a novel neural optimizer that iteratively solves a wide range of physical systems with high accuracy by minimizing a physics-based loss function. To this end, our approach leverages a scale-invariant architecture that enhances gradient descent updates to accelerate convergence. Since the neural network itself acts as an optimizer, training this neural optimizer falls into the category of meta-optimization approaches. We demonstrate that Metamizer achieves unprecedented accuracy for deep learning based approaches - sometimes approaching machine precision - across multiple PDEs after training on the Laplace, advection-diffusion and incompressible Navier-Stokes equation as well as on cloth simulations. Remarkably, the model also generalizes to PDEs that were not covered during training such as the Poisson, wave and Burgers equation. Our results suggest that Metamizer could have a profound impact on future numerical solvers, paving the way for fast and accurate neural physics simulations without the need for retraining.
On the Robustness of Machine Learning Models in Predicting Thermodynamic Properties: a Case of Searching for New Quasicrystal Approximants
Authors: Fedor S. Avilov, Roman A. Eremin, Semen A. Budennyy, Innokentiy S. Humonen
Date: 2024-10-02 00:00:00

Despite an artificial intelligence-assisted modeling of disordered crystals is a widely used and well-tried method of new materials design, the issues of its robustness, reliability, and stability are still not resolved and even not discussed enough. To highlight it, in this work we composed a series of nested intermetallic approximants of quasicrystals datasets and trained various machine learning models on them correspondingly. Our qualitative and, what is more important, quantitative assessment of the difference in the predictions clearly shows that different reasonable changes in the training sample can lead to the completely different set of the predicted potentially new materials. We also showed the advantage of pre-training and proposed a simple yet effective trick of sequential training to increase stability.
Economic Integration of Africa in the 21st Century: Complex Network and Panel Regression Analysis
Authors: Tekilu Tadesse Choramo, Jemal Abafita, Yerali Gandica, Luis E C Rocha
Date: 2024-10-28 00:00:00

Global and regional integration has grown significantly in recent decades, boosting intra-African trade and positively impacting national economies through trade diversification and sustainable development. However, existing measures of economic integration often fail to capture the complex interactions among trading partners. This study addresses this gap by using complex network analysis and dynamic panel regression techniques to identify factors driving economic integration in Africa, based on data from 2002 to 2019. The results show that economic development, institutional quality, regional trade agreements, human capital, FDI, and infrastructure positively influence a country's position in the African trade network. Conversely, trade costs, the global financial crisis, and regional overlapping memberships negatively affect network based integration. Our findings suggest that enhancing a country's connectivity in the African trade network involves identifying key economic and institutional factors of trade partners and strategically focusing on continent-wide agreements rather than just regional ones to boost economic growth.
A Distributed Lag Approach to the Generalised Dynamic Factor Model (GDFM)
Authors: Philipp Gersing
Date: 2024-10-28 00:00:00
Tasks: regression

We provide estimation and inference for the Generalised Dynamic Factor Model (GDFM) under the assumption that the dynamic common component can be expressed in terms of a finite number of lags of contemporaneously pervasive factors. The proposed estimator is simply an OLS regression of the observed variables on factors extracted via static principal components and therefore avoids frequency domain techniques entirely.
Robust Network Targeting with Multiple Nash Equilibria
Authors: Guanyi Wang
Date: 2024-10-28 00:00:00
Tasks: counterfactual

Many policy problems involve designing individualized treatment allocation rules to maximize the equilibrium social welfare of interacting agents. Focusing on large-scale simultaneous decision games with strategic complementarities, we develop a method to estimate an optimal treatment allocation rule that is robust to the presence of multiple equilibria. Our approach remains agnostic about changes in the equilibrium selection mechanism under counterfactual policies, and we provide a closed-form expression for the boundary of the set-identified equilibrium outcomes. To address the incompleteness that arises when an equilibrium selection mechanism is not specified, we use the maximin welfare criterion to select a policy, and implement this policy using a greedy algorithm. We establish a performance guarantee for our method by deriving a welfare regret bound, which accounts for sampling uncertainty and the use of the greedy algorithm. We demonstrate our method with an application to the microfinance dataset of Banerjee et al. (2013).
Jacobian-free Efficient Pseudo-Likelihood (EPL) Algorithm
Authors: Takeshi Fukasawa
Date: 2024-10-26 00:00:00

This study proposes a simple procedure to compute Efficient Pseudo Likelihood (EPL) estimator proposed by Dearing and Blevins (2024) for estimating dynamic discrete games, without computing Jacobians of equilibrium constraints. EPL estimator is efficient, convergent, and computationally fast. However, the original algorithm requires deriving and coding the Jacobians, which are cumbersome and prone to coding mistakes especially when considering complicated models. The current study proposes to avoid the computation of Jacobians by combining the ideas of numerical derivatives (for computing Jacobian-vector products) and the Krylov method (for solving linear equations). It shows good computational performance of the proposed method by numerical experiments.
Testing the effects of an unobservable factor: Do marriage prospects affect college major choice?
Authors: Hayri Alper Arslan, Brantly Callaway, Tong Li
Date: 2024-10-25 00:00:00

Motivated by studying the effects of marriage prospects on students' college major choices, this paper develops a new econometric test for analyzing the effects of an unobservable factor in a setting where this factor potentially influences both agents' decisions and a binary outcome variable. Our test is built upon a flexible copula-based estimation procedure and leverages the ordered nature of latent utilities of the polychotomous choice model. Using the proposed method, we demonstrate that marriage prospects significantly influence the college major choices of college graduates participating in the National Longitudinal Study of Youth (97) Survey. Furthermore, we validate the robustness of our findings with alternative tests that use stated marriage expectation measures from our data, thereby demonstrating the applicability and validity of our testing procedure in real-life scenarios.
Knowledge and Freedom: Evidence on the Relationship Between Information and Paternalism
Authors: Max R. P. Grossmann
Date: 2024-10-28 00:00:00

When is autonomy granted to a decision-maker based on their knowledge, and if no autonomy is granted, what form will the intervention take? A parsimonious theoretical framework shows how policymakers can exploit decision-maker mistakes and use them as a justification for intervention. In two experiments, policymakers ("Choice Architects") can intervene in a choice faced by a decision-maker. We vary the amount of knowledge decision-makers possess about the choice. Full decision-maker knowledge causes more than a 60% reduction in intervention rates. Beliefs have a small, robust correlation with interventions on the intensive margin. Choice Architects disproportionately prefer to have decision-makers make informed decisions. Interveners are less likely to provide information. As theory predicts, the same applies to Choice Architects who believe that decision-maker mistakes align with their own preference. When Choice Architects are informed about the decision-maker's preference, this information is used to determine the imposed option. However, Choice Architects employ their own preference to a similar extent. A riskless option is causally more likely to be imposed, being correlated with but conceptually distinct from Choice Architects' own preference. This is a qualification to what has been termed "projective paternalism."
Beyond Baby Blues: The Child Penalty in Mental Health in Switzerland
Authors: Nora Bearth
Date: 2024-10-28 00:00:00

This paper investigates the mental health penalty for women after childbirth in Switzerland. Leveraging insurance data, we employ a staggered difference-in-difference research design. The findings reveal a substantial mental health penalty for women following the birth of their first child. Approximately four years after childbirth, there is a one percentage point (p.p.) increase in antidepressant prescriptions, representing a 50% increase compared to pre-birth levels. This increase rises to 1.7 p.p. (a 75% increase) six years postpartum. The mental health penalty is likely not only a direct consequence of giving birth but also a consequence of the changed life circumstances and time constraints that accompany it, as the penalty is rising over time and is higher for women who are employed.
Tests of thermal macroeconomic theory on simulated micro-economies
Authors: Yihang Luo, R. S. MacKay, Nick Chater
Date: 2024-10-27 00:00:00

In this paper, we test predictions of a new theory of macroeconomics, called "thermal macroeconomics." The theory aims to apply the mathematical structure of classical thermodynamics, including analogues of temperature and entropy, to predict aspects of the aggregate behaviour of populations of economic agents without analyzing their detailed interactions. We test the theory by comparing its predictions with the behaviour of a variety of simulated micro-economies in which goods and money can be exchanged between agents, confirming the predictions of the theory. The paper serves also to illustrate and make more tangible the predictions of thermal macroeconomics.
Democratising Agricultural Commodity Price Forecasting: The AGRICAF Approach
Authors: Rotem Zelingher
Date: 2024-10-27 00:00:00

Ensuring food security is a critical global challenge, particularly for low-income countries where food prices impact the access to nutritious food. The volatility of global agricultural commodity (AC) prices exacerbates food insecurity, with international trade restrictions and market disruptions further complicating the situation. Despite online platforms for monitoring food prices, there is a significant gap in providing detailed explanations and forecasts accessible to non-specialists. To address this, we propose the Agricultural Commodity Analysis and Forecasts (AGRICAF) methodology, integrating explainable machine learning (XML) and econometric techniques to analyse and forecast global agricultural commodity prices up to one year ahead, dynamically adapting to different forecast horizons. This innovative integration allows us to model complex interactions and dynamics while providing clear, interpretable results. This paper demonstrates how AGRICAF can be used, applying it to three major agricultural commodities - maize, soybean, and wheat - and explaining how different factors impact prices across various months and forecast horizons. By facilitating access to accurate and interpretable medium-term forecasts of AC prices, AGRICAF can contribute to developing a fair and sustainable food system.
Strategic Control of Facial Expressions by the Fed Chair
Authors: Hunter Ng
Date: 2024-10-26 00:00:00

This article investigates whether the Federal Reserve Chair strategically controls facial expressions during FOMC press conferences and how these nonverbal cues affect financial markets. I use facial recognition technology on videos of press conferences from April 2011 to December 2020 to quantify changes in the Chair's nonverbal signals. Results show that facial expressions serve as a separate public signal, distinct from verbal content. Using deepfakes, I find that the same facial expressions expressed by different Fed Chairs are interpreted differentially. As their tenure increases, negative expressions become more frequent, eliciting adverse market reactions. Furthermore, the markets interpretation of these expressions evolves over time, suggesting that investors process facial cues with dual-processing finite-state Markov memory. In line with the Fed's goals of transparency and non-volatility, I find that Fed Chairs do not strategically control their expressions.
New evaluation tool for predicting disability pension risk among Finnish public sector employees
Authors: Petra Sohlman, Risto Louhi, Janne Salonen
Date: 2024-10-25 00:00:00

Using unique research data, we investigate disability retirement risk under the statutory public sector pension scheme in Finland. The statistical analysis yields two indicators: risk for upcoming permanent disability pension and critical duration of sickness absence days for public sector occupations. Statistical analysis is based on logistic regression model where the outcome is the disability pension, using sickness benefit spells and other individual background information as covariates. The results underline the importance of minimizing the sickness spells and their duration to the risk and reveal differences in risk across occupations. We conclude that the proposed risk model is a promising tool which can help employers and the pension industry in preventing permanent disability.
On the limits of informationally efficient stock markets: New insights from a chartist-fundamentalist model
Authors: Laura Gardini, Davide Radi, Noemi Schmitt, Iryna Sushko, Frank Westerhoff
Date: 2024-10-28 00:00:00

We utilize a chartist-fundamentalist model to examine the limits of informationally efficient stock markets. In our model, chartists are permanently active in the stock market, while fundamentalists trade only when their mispricing-dependent trading signals are strong. Our findings indicate the possible coexistence of two distinct regimes. Depending on the initial conditions, the stock market may exhibit either constant or oscillatory mispricing. Constant mispricing occurs when chartists remain the sole active speculators, causing the stock price to converge toward a nonfundamental value. Conversely, the stock price oscillates around its fundamental value when fundamentalists repeatedly enter and exit the market. Exogenous shocks result in intricate regime-switching dynamics.
Motivated Reasoning and the Political Economy of Climate Change Inaction
Authors: Philipp Denter
Date: 2024-10-28 00:00:00

Two office-driven politicians compete in an election by proposing policies. There are two possible states of the world: climate change is either mild, with no lasting effect on welfare if addressed properly, or severe, leading to reduced welfare even with appropriate measures. Voters receive signals about the state but may interpret them in a non-Bayesian way, holding motivated beliefs. An equilibrium always exists where voters ignore signals suggesting severe consequences, causing politicians to propose policies for mild climate change--even when they know otherwise. If severe climate change leads to only moderate welfare losses, an efficient equilibrium also exists. In this equilibrium, voters trust politicians to choose the optimal policies, implying voters choose to trust their signals, which in turn encourages optimal policy choices by politicians. The model highlights the role of political rhetoric and trust in government, and a first glance at the data reveals patterns consistent with the models predictions.