pwc-archive/papers-with-abstracts huggingface dataset:
Existence of $ε$-Nash Equilibria in Nonzero-Sum Borel Stochastic Games and Equilibria of Quantized Models
Authors: Naci Saldi, Gurdal Arslan, Serdar Yuksel
Date: 2024-11-16 00:00:00

Establishing the existence of exact or near Markov or stationary perfect Nash equilibria in nonzero-sum Markov games over Borel spaces remains a challenging problem, with few positive results to date. In this paper, we establish the existence of approximate Markov and stationary Nash equilibria for nonzero-sum stochastic games over Borel spaces, assuming only mild regularity conditions on the model. Our approach involves analyzing a quantized version of the game, for which we provide an explicit construction under both finite-horizon and discounted cost criteria. This work has significant implications for emerging applications such as multi-agent learning. Our results apply to both compact and non-compact state spaces. For the compact state space case, we first approximate the standard Borel model with a finite state-action model. Using the existence of Markov and stationary perfect Nash equilibria for these finite models under finite-horizon and discounted cost criteria, we demonstrate that these joint policies constitute approximate Markov and stationary perfect equilibria under mild continuity conditions on the one-stage costs and transition probabilities. For the non-compact state space case, we achieve similar results by first approximating the model with a compact-state model. Compared with previous results in the literature, which we comprehensively review, we provide more general and complementary conditions, along with explicit approximation models whose equilibria are $\epsilon$-equilibria for the original model.
Demonstrating Remote Synchronization: An Experimental Approach with Nonlinear Oscillators
Authors: Sanjeev Kumar Pandey, Neetish Patel
Date: 2024-11-16 00:00:00

This study investigates remote synchronization in arbitrary network clusters of coupled nonlinear oscillators, a phenomenon inspired by neural synchronization in the brain. Employing a multi-faceted approach encompassing analytical, numerical, and experimental methodologies, we leverage the Master Stability Function (MSF) to analyze network stability. We provide experimental evidence of remote synchronization between two clusters of nonlinear oscillators, where oscillators within each cluster are also remotely connected. This observation parallels the thalamus-mediated synchronization of neuronal populations in the brain. An electronic circuit testbed, supported by nonlinear ODE modeling and LT Spice simulation, was developed to validate our theoretical predictions. Future work will extend this investigation to encompass diverse network topologies and explore potential applications in neuroscience, communication networks, and power systems.
Self-Triggered Control in Artificial Pancreas
Authors: Debayani Ghosh, Sahaj Saxena, Navin Kumar
Date: 2024-11-16 00:00:00
Tasks: Management

The management of type 1 diabetes has been revolutionized by the artificial pancreas system (APS), which automates insulin delivery based on continuous glucose monitor (CGM). While conventional closed-loop systems rely on CGM data, which leads to higher energy consumption at the sensors and increased data redundancy in the underlying communication network. In contrast, this paper proposes a self-triggered control mechanism that can potentially achieve lower latency and energy efficiency. The model for the APS consists of a state and input-constrained dynamical system affected by exogenous meal disturbances. Our self-triggered mechanism relies on restricting the state evolution within the robust control invariant of such a system at all times. To that end, using tools from reachability, we associate a safe time interval with such invariant sets, which denotes the maximum time for which the invariant set remains invariant, even without transmission of CGM data at all times.
AC-Informed DC Optimal Transmission Switching Problems via Parameter Optimization
Authors: Babak Taheri, Daniel K. Molzahn
Date: 2024-11-15 00:00:00

Optimal Transmission Switching (OTS) problems minimize operational costs while treating both the transmission line energization statuses and generator setpoints as decision variables. The combination of nonlinearities from an AC power flow model and discrete variables associated with line statuses makes AC-OTS a computationally challenging Mixed-Integer Nonlinear Program (MINLP). To address these challenges, the DC power flow approximation is often used to obtain a DC-OTS formulation expressed as a Mixed-Integer Linear Program (MILP). However, this approximation often leads to suboptimal or infeasible switching decisions when evaluated with an AC power flow model. This paper proposes an enhanced DC-OTS formulation that leverages techniques for training machine learning models to optimize the DC power flow model's parameters. By optimally selecting parameter values that align flows in the DC power flow model with apparent power flows -- incorporating both real and reactive components -- from AC Optimal Power Flow (OPF) solutions, our method more accurately captures line congestion behavior. Integrating these optimized parameters into the DC-OTS formulation significantly improves the accuracy of switching decisions and reduces discrepancies between DC-OTS and AC-OTS solutions. We compare our optimized DC-OTS model against traditional OTS approaches, including DC-OTS, Linear Programming AC (LPAC)-OTS, and Quadratic Convex (QC)-OTS. Numerical results show that switching decisions from our model yield better performance when evaluated using an AC power flow model, with up to $44\%$ cost reductions in some cases.
Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling
Authors: Reyhaneh Taj
Date: 2024-11-13 00:00:00

Scientific machine learning (SciML) represents a significant advancement in integrating machine learning (ML) with scientific methodologies. At the forefront of this development are Physics-Informed Neural Networks (PINNs), which offer a promising approach by incorporating physical laws directly into the learning process, thereby reducing the need for extensive datasets. However, when data is limited or the system becomes more complex, PINNs can face challenges, such as instability and difficulty in accurately fitting the training data. In this article, we explore the capabilities and limitations of the DeepXDE framework, a tool specifically designed for implementing PINNs, in addressing both forward and inverse problems related to dielectric properties. Using RC circuit models to represent dielectric materials in HVDC systems, we demonstrate the effectiveness of PINNs in analyzing and improving system performance. Additionally, we show that applying a logarithmic transformation to the current (ln(I)) significantly enhances the stability and accuracy of PINN predictions, especially in challenging scenarios with sparse data or complex models. In inverse mode, however, we faced challenges in estimating key system parameters, such as resistance and capacitance, in more complex scenarios with longer time domains. This highlights the potential for future work in improving PINNs through transformations or other methods to enhance performance in inverse problems. This article provides pedagogical insights for those looking to use PINNs in both forward and inverse modes, particularly within the DeepXDE framework.
Constrained composite Bayesian optimization for rational synthesis of polymeric particles
Authors: Fanjin Wang, Maryam Parhizkar, Anthony Harker, Mohan Edirisinghe
Date: 2024-11-06 00:00:00
Tasks: Bayesian Optimization

Polymeric nano- and micro-scale particles have critical roles in tackling critical healthcare and energy challenges with their miniature characteristics. However, tailoring their synthesis process to meet specific design targets has traditionally depended on domain expertise and costly trial-and-errors. Recently, modeling strategies, particularly Bayesian optimization (BO), have been proposed to aid materials discovery for maximized/minimized properties. Coming from practical demands, this study for the first time integrates constrained and composite Bayesian optimization (CCBO) to perform efficient target value optimization under black-box feasibility constraints and limited data for laboratory experimentation. Using a synthetic problem that simulates electrospraying, a model nanomanufacturing process, CCBO strategically avoided infeasible conditions and efficiently optimized particle production towards predefined size targets, surpassing standard BO pipelines and providing decisions comparable to human experts. Further laboratory experiments validated CCBO capability to guide the rational synthesis of poly(lactic-co-glycolic acid) (PLGA) particles with diameters of 300 nm and 3.0 $\mu$m via electrospraying. With minimal initial data and unknown experiment constraints, CCBO reached the design targets within 4 iterations. Overall, the CCBO approach presents a versatile and holistic optimization paradigm for next-generation target-driven particle synthesis empowered by artificial intelligence (AI).
Relating dynamics and structure of discrete and continuous time systems
Authors: Shuyun Jiao, David Waxman
Date: 2024-11-18 00:00:00

In numerous subjects there are instances of dynamical variables that change in discrete time. One such example is a population's size, which changes in discrete generations which we label by $n$ ($= 0, 1, 2, . ..$). There are some exactly soluble cases where the population's size at discrete time $n$, written $x_{n}$, can be related to a different problem, where change occurs in continuous time, $t$, and the population's size is then written as $x(t)$. The relation is that $x(t = n)$ precisely coincides with $x_{n}$. In such a case, we say the two models are 'equivalent', even though $x_{n}$ obeys a difference equation and $x(t)$ obeys a differential equation. A comparison of the difference equation and the differential equation allows us to make a correspondence between coefficients in these equations. This exposes structural similarities and sometimes appreciable structural differences of discrete and continuous time dynamics. We present cases of such equivalences for both time homogeneous and time inhomogeneous problems. Beyond this, when the equation that $x_{n}$ obeys is modified so there is no exact solution, we show how to construct an approximate continuous time solution that can capture rapid change, such as growth, of $x_{n}$. Additionally, in some discrete time problems, the solution has an oscillatory component that changes sign as $n \rightarrow n + 1$. We show that the way an equivalent continuous time solution replicates this oscillatory behaviour is to become complex. We give concrete examples of this 'complexification' of the continuous time solution, which is real at $t = 0,1,2,...$ but is complex away from the integers.
Integrated Ising Model with global inhibition for decision making
Authors: Olga Tapinova, Tal Finkelman, Tamar Reitich-Stolero, Rony Paz, Assaf Tal, Nir S. Gov
Date: 2024-11-17 00:00:00
Tasks: Decision Making

Humans and other organisms make decisions choosing between different options, with the aim to maximize the reward and minimize the cost. The main theoretical framework for modeling the decision-making process has been based on the highly successful drift-diffusion model, which is a simple tool for explaining many aspects of this process. However, new observations challenge this model. Recently, it was found that inhibitory tone increases during high cognitive load and situations of uncertainty, but the origin of this phenomenon is not understood. Motivated by this observation, we extend a recently developed model for decision making while animals move towards targets in real space. We introduce an integrated Ising-type model, that includes global inhibition, and use it to explore its role in decision-making. This model can explain how the brain may utilize inhibition to improve its decision-making accuracy. Compared to experimental results, this model suggests that the regime of the brain's decision-making activity is in proximity to a critical transition line between the ordered and disordered. Within the model, the critical region near the transition line has the advantageous property of enabling a significant decrease in error with a small increase in inhibition and also exhibits unique properties with respect to learning and memory decay.
Digital-Analog Quantum Machine Learning
Authors: Lucas Lamata
Date: 2024-11-16 00:00:00
Tasks: Quantum Machine Learning

Machine Learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and therefore improve their performance in a certain context or environment. In this way, many useful possibilities have been made accessible. However, dealing with an increasing amount of data poses difficulties for classical devices. Quantum systems may offer a way forward, possibly enabling to scale up machine learning calculations in certain contexts. On the other hand, quantum systems themselves are also hard to scale up, due to decoherence and the fragility of quantum superpositions. In the short and mid term, it has been evidenced that a quantum paradigm that combines evolution under large analog blocks with discrete quantum gates, may be fruitful to achieve new knowledge of classical and quantum systems with no need of having a fault-tolerant quantum computer. In this Perspective, we review some recent works that employ this digital-analog quantum paradigm to carry out efficient machine learning calculations with current quantum devices.
How quantum computing can enhance biomarker discovery
Authors: Frederik F. Flöther, Daniel Blankenberg, Maria Demidik, Karl Jansen, Raga Krishnakumar, Rajiv Krishnakumar, Nouamane Laanait, Laxmi Parida, Carl Saab, Filippo Utro
Date: 2024-11-15 00:00:00
Tasks: Time Series

Biomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very early indicators of a change in health status, for example for multi-factorial diseases, has been challenging. Discovery of such biomarkers stands to benefit significantly from advanced information processing and means to detect complex correlations, which quantum computing offers. In this perspective paper, quantum algorithms, particularly in machine learning, are mapped to key applications in biomarker discovery. The opportunities and challenges associated with the algorithms and applications are discussed. The analysis is structured according to different data types - multi-dimensional, time series, and erroneous data - and covers key data modalities in healthcare - electronic health records (EHRs), omics, and medical images. An outlook is provided concerning open research challenges.
Treatment Effect Estimators as Weighted Outcomes
Authors: Michael C. Knaus
Date: 2024-11-18 00:00:00

Estimators that weight observed outcomes to form effect estimates have a long tradition. Their outcome weights are widely used in established procedures, such as checking covariate balance, characterizing target populations, or detecting and managing extreme weights. This paper introduces a general framework for deriving such outcome weights. It establishes when and how numerical equivalence between an original estimator representation as moment condition and a unique weighted representation can be obtained. The framework is applied to derive novel outcome weights for the six seminal instances of double machine learning and generalized random forests, while recovering existing results for other estimators as special cases. The analysis highlights that implementation choices determine (i) the availability of outcome weights and (ii) their properties. Notably, standard implementations of partially linear regression-based estimators, like causal forests, employ outcome weights that do not sum to (minus) one in the (un)treated group, not fulfilling a property often considered desirable.
Feature Importance of Climate Vulnerability Indicators with Gradient Boosting across Five Global Cities
Authors: Lidia Cano Pecharroman, Melissa O. Tier, Elke U. Weber
Date: 2024-11-15 00:00:00
Tasks: Feature Importance

Efforts are needed to identify and measure both communities' exposure to climate hazards and the social vulnerabilities that interact with these hazards, but the science of validating hazard vulnerability indicators is still in its infancy. Progress is needed to improve: 1) the selection of variables that are used as proxies to represent hazard vulnerability; 2) the applicability and scale for which these indicators are intended, including their transnational applicability. We administered an international urban survey in Buenos Aires, Argentina; Johannesburg, South Africa; London, United Kingdom; New York City, United States; and Seoul, South Korea in order to collect data on exposure to various types of extreme weather events, socioeconomic characteristics commonly used as proxies for vulnerability (i.e., income, education level, gender, and age), and additional characteristics not often included in existing composite indices (i.e., queer identity, disability identity, non-dominant primary language, and self-perceptions of both discrimination and vulnerability to flood risk). We then use feature importance analysis with gradient-boosted decision trees to measure the importance that these variables have in predicting exposure to various types of extreme weather events. Our results show that non-traditional variables were more relevant to self-reported exposure to extreme weather events than traditionally employed variables such as income or age. Furthermore, differences in variable relevance across different types of hazards and across urban contexts suggest that vulnerability indicators need to be fit to context and should not be used in a one-size-fits-all fashion.
Monetary Incentives, Landowner Preferences: Estimating Cross-Elasticities in Farmland Conversion to Renewable Energy
Authors: Chad Fiechter, Binayak Kunwar, Guy Tchuente
Date: 2024-11-15 00:00:00

This study examines the impact of monetary factors on the conversion of farmland to renewable energy generation, specifically solar and wind, in the context of expanding U.S. energy production. We propose a new econometric method that accounts for the diverse circumstances of landowners, including their unordered alternative land use options, non-monetary benefits from farming, and the influence of local regulations. We demonstrate that identifying the cross elasticity of landowners' farming income in relation to the conversion of farmland to renewable energy requires an understanding of their preferences. By utilizing county legislation that we assume to be shaped by land-use preferences, we estimate the cross-elasticities of farming income. Our findings indicate that monetary incentives may only influence landowners' decisions in areas with potential for future residential development, underscoring the importance of considering both preferences and regulatory contexts.
Direct Fractional Auction
Authors: Dmitriy Taubman, Boris Gleyzer, Ke Yang, Farhad Rassekh
Date: 2024-11-18 00:00:00

This paper designs a market algorithm for fractional ownership of an indivisible asset. It provides an efficient market mechanism, named Direct Fractional Auction (DFA) that offers valuable assets to both small and large investors who can become partial owners of such assets. Additionally, it introduces procedures and algorithms with DFA to determine the optimal winning combinations of unaffiliated bidders. DFA, on the one hand, transfers the partial ownership to the winners and, on the other, redirects the proceeds from the auctions to the sellers. We show that the DFA algorithm works more efficiently than the Greedy algorithm in maximizing the seller's value. We also demonstrate the possibility of reducing the complexity of the problem using the "pruning" method for data pre-processing.
The Impact of the General Data Protection Regulation (GDPR) on Online Usage Behavior
Authors: Klaus M. Miller, Julia Schmitt, Bernd Skiera
Date: 2024-11-18 00:00:00

Privacy regulations often necessitate a balance between safeguarding consumer privacy and preventing economic losses for firms that utilize consumer data. However, little empirical evidence exists on how such laws affect firm performance. This study aims to fill that gap by quantifying the impact of the European Union's General Data Protection Regulation (GDPR) on online usage behavior over time. We analyzed data from 6,286 websites across 24 industries, covering 10 months before and 18 months after the GDPR's enactment in 2018. Employing a generalized synthetic control estimator, we isolated the short- and long-term effects of the GDPR on user behavior. Our results show that the GDPR negatively affected online usage per website on average; specifically, weekly visits decreased by 4.88% in the first 3 months and 10.02% after 18 months post-enactment. At the 18-month mark, these declines translated into average revenue losses of about USD 7 million for e-commerce websites and nearly USD 2.5 million for ad-based websites. Nonetheless, the GDPR's impact varied across website size, industry, and user origin, with some large websites and industries benefiting from the regulation. Notably, the largest 10% of websites pre-GDPR suffered less, suggesting that the GDPR has increased market concentration.
Disagreement Spillovers
Authors: Giampaolo Bonomi
Date: 2024-11-17 00:00:00

As US political party leaders increasingly take stances both on economic and cultural (i.e., social policy) issues, the economic views of opposite cultural groups are growing apart. This paper explores a novel explanation for this phenomenon. I provide experimental evidence that adding social policy content to a policy message pushes those disagreeing with the social policy to disagree also with the economic content of the message. As my results suggest regular deviations from Bayesian explanations, I propose a model of identity-based belief updating that predicts the main regularities found in the experiment. Finally, I shed light on opinion leaders' incentives to strengthen the association between social policy and economic policy views.
Air Pollution and Under-5 Child Mortality: Evidence from China's Coal Power Plant Phase-out Policy
Authors: X. Liu, H. Yu
Date: 2024-11-16 00:00:00

This paper evaluates the impact of a mandatory shutdown policy of small-capacity coal power plants during China's $11^{th}$ 5-Year Plan period (2006--2010) on under-5 mortality. We collect capacity and location information on 2181 coal power plants that operated during 2000--2010 and compile a unique data set that combines coal power plants, county-level under-5 mortality and socioeconomic variables, high spatial resolution data of PM$_{2.5}$ and SO$_2$ and meteorological conditions. We model the impacts of air pollution on under-5 mortality using the IV-Lasso method, with distance-weighted sums of retired capacity and high-altitude wind status as instrumental variable candidates for air pollution. Our estimates imply that the phase-out policy saved around 46,000 lives during the $11^{th}$ 5-Year Plan period. We also find heterogeneity in the policy effects across regions. (\textit{JEL} I10, I18, P25, P28, Q52, Q53, Q58)