pwc-archive/papers-with-abstracts huggingface dataset:
Equity Impacts of Public Transit Network Redesign with Shared Autonomous Mobility Services
Authors: Max T. M. Ng, Meredith Raymer, Hani S. Mahmassani, Omer Verbas, Taner Cokyasar
Date: 2025-01-03 00:00:00

This study examines the equity impacts of integrating shared autonomous mobility services (SAMS) into transit system redesign. Using the Greater Chicago area as a case study, we compare two optimization objectives in multimodal transit network redesign: minimizing total generalized costs (equity-agnostic) versus prioritizing service in low-income areas (equity-focused). We evaluate the achieved accessibility of clustered zones with redesigned transit networks under two objectives, compared to driving and the existing transit network. The transit access gaps across zones and between transit and driving are found to be generally reduced with the introduction of SAMS, but less so with the subsequent improved infrastructure under budget. Differential improvement in equity is seen across suburbs and areas of the city, reflecting the disparity in current transit access and improvement potential. In particular, SAMS bridges the transit access gaps in suburban and city areas currently underserved by transit. The City of Chicago, which is also disproportionately home to vulnerable populations, offers an avenue to improve vertical equity. These findings demonstrate that SAMS can enhance both horizontal and vertical equity in transit systems, particularly when equity is explicitly incorporated into the design objective.
Evaluation of Rail Decarbonization Alternatives: Framework and Application
Authors: Adrian Hernandez, Max TM Ng, Nazib Siddique, Pablo L. Durango-Cohen, Amgad Elgowainy, Hani S. Mahmassani, Michael Wang, Yan Zhou
Date: 2025-01-03 00:00:00

The Northwestern University Freight Rail Infrastructure and Energy Network Decarbonization (NUFRIEND) framework is a comprehensive industry-oriented tool for simulating the deployment of new energy technologies including biofuels, e-fuels, battery-electric, and hydrogen locomotives. By classifying fuel types into two categories based on deployment requirements, the associated optimal charging/fueling facility location and sizing problem are solved with a five-step framework. Life cycle analyses (LCA) and techno-economic analyses (TEA) are used to estimate carbon reduction, capital investments, cost of carbon reduction, and operational impacts, enabling sensitivity analysis with operational and technological parameters. The framework is illustrated on lower-carbon drop-in fuels as well as battery-electric technology deployments for US Eastern and Western Class I railroad networks. Drop-in fuel deployments are modeled as admixtures with diesel in existing locomotives, while battery-electric deployments are shown for varying technology penetration levels and locomotive ranges. When mixed in a 50 percent ratio with diesel, results show biodiesel's capacity to reduce emissions at 36 percent with a cost of 0.13 USD per kilogram of CO2 reduced, while e-fuels offer a 50 percent emissions reduction potential at a cost of 0.22 USD per kilogram of CO2 reduced. Battery-electric results for 50 percent deployment over all ton-miles highlight the value of future innovations in battery energy densities as scenarios assuming 800-mile range locomotives show an estimated emissions reduction of 46 percent with a cost of 0.06 USD per kilogram of CO2 reduced, compared to 16 percent emissions reduction at a cost of 0.11 USD per kilogram of CO2 reduced for 400-mile range locomotives.
Integrated Strategy for Urban Traffic Optimization: Prediction, Adaptive Signal Control, and Distributed Communication via Messaging
Authors: Ismail Zrigui, Samira Khoulji, Mohamed Larbi Kerkeb
Date: 2025-01-02 00:00:00
Tasks: Traffic Signal Control

This work introduces an integrated approach to optimizing urban traffic by combining predictive modeling of vehicle flow, adaptive traffic signal control, and a modular integration architecture through distributed messaging. Using real-time data from various sensors, the system anticipates traffic fluctuations and dynamically adjusts signal phase durations to minimize delays and improve traffic flow. This proactive adjustment, supported by algorithms inspired by simulated annealing and reinforcement learning, also enhances energy efficiency, reduces pollutant emissions, and responds effectively to unexpected events (adverse weather, accidents, or temporary gatherings). Preliminary simulations conducted in a realistic urban environment demonstrate a significant reduction in average waiting times. Future developments include incorporating data from connected vehicles, integrating new modes of transport, and continuously refining predictive models to address the growing challenges of urban mobility.
Fuzzy Model Identification and Self Learning with Smooth Compositions
Authors: Ebrahim Navid Sadjadi, Jesus Garcia, Jose M. Molina, Akbar Hashemi Borzabadi, Monireh Asadi Abchouyeh
Date: 2024-12-31 00:00:00
Tasks: Chemical Process, Self-Learning

This paper develops a smooth model identification and self-learning strategy for dynamic systems taking into account possible parameter variations and uncertainties. We have tried to solve the problem such that the model follows the changes and variations in the system on a continuous and smooth surface. Running the model to adaptively gain the optimum values of the parameters on a smooth surface would facilitate further improvements in the application of other derivative based optimization control algorithms such as MPC or robust control algorithms to achieve a combined modeling-control scheme. Compared to the earlier works on the smooth fuzzy modeling structures, we could reach a desired trade-off between the model optimality and the computational load. The proposed method has been evaluated on a test problem as well as the non-linear dynamic of a chemical process.
Slow spatial migration can help eradicate cooperative antimicrobial resistance in time-varying environments
Authors: Lluís Hernández-Navarro, Kenneth Distefano, Uwe C. Täuber, Mauro Mobilia
Date: 2025-01-03 00:00:00

Antimicrobial resistance (AMR) is a global threat and combating its spread is of paramount importance. AMR often results from a cooperative behaviour with shared protection against drugs. Microbial communities generally evolve in volatile environments and spatial structures. Migration, fluctuations, and environmental variability thus have significant impacts on AMR, whose maintenance in static environments is generally promoted by migration. Here, we demonstrate that this picture changes dramatically in time-fluctuating spatially structured environments. To this end, we consider a two-dimensional metapopulation model consisting of demes in which drug-resistant and sensitive cells evolve in a time-changing environment in the presence of a toxin against which protection can be shared. Cells migrate between neighbouring demes and hence connect them. When the environment varies neither too quickly nor too slowly, the dynamics is characterised by bottlenecks causing fluctuation-driven local extinctions, a mechanism countered by migration that rescues AMR. Through simulations and mathematical analysis, we investigate how migration and environmental variability influence the probability of resistance eradication. We determine the near-optimal conditions for the fluctuation-driven AMR eradication, and show that slow but non-zero migration speeds up the clearance of resistance and can enhance its eradication probability. We discuss our study's impact on laboratory-controlled experiments.
Modeling measurements for quantitative imaging subsurface targets
Authors: Arnold Kim, Chrysoula Tsogka
Date: 2025-01-06 00:00:00

We study ground-penetrating synthetic aperture radar measurements of scattering by targets located below a rough air-soil interface. By considering the inherent space/angle limitations of this imaging modality, we introduce a simplified model for measurements. This model assumes (i) first-order interactions between the target and the air-soil interface, (ii) scattering by the target below a flat air-soil interface, and (iii) a point target model. Using the method of fundamental solutions to simulate two-dimensional simulations of scalar waves for the direct scattering problem, we systematically study each of these data modeling assumptions. To test and validate these assumptions, we apply principal component analysis to approximately remove ground bounce signals from measurements and then apply Kirchhoff migration on that processed data to produce images. We show that images using this modeled data are nearly identical to those that use simulated measurements from the full direct scattering problem. In that way, we show that this model contains the essential information contained in measurements. Consequently, it provides a theoretical framework for understanding how inherent space/angle limitations affect subsurface imaging systems.
Identifying the Hidden Nexus between Benford Law Establishment in Stock Market and Market Efficiency: An Empirical Investigation
Authors: M. R. Sarkandiz
Date: 2025-01-05 00:00:00
Tasks: Time Series

Benford's law, or the law of the first significant digit, has been subjected to numerous studies due to its unique applications in financial fields, especially accounting and auditing. However, studies that addressed the law's establishment in the stock markets generally concluded that stock prices do not comply with the underlying distribution. The present research, emphasizing data randomness as the underlying assumption of Benford's law, has conducted an empirical investigation of the Warsaw Stock Exchange. The outcomes demonstrated that since stock prices are not distributed randomly, the law cannot be held in the stock market. Besides, the Chi-square goodness-of-fit test also supported the obtained results. Moreover, it is discussed that the lack of randomness originated from market inefficiency. In other words, violating the efficient market hypothesis has caused the time series non-randomness and the failure to establish Benford's law.
Revealed Social Networks
Authors: Christopher P. Chambers, Yusufcan Masatlioglu, Christopher Turansick
Date: 2025-01-05 00:00:00

The linear-in-means model is the standard empirical model of peer effects. Using choice data and exogenous group variation, we first develop a revealed preference style test for the linear-in-means model. This test is formulated as a linear program and can be interpreted as a no money pump condition with an additional incentive compatibility constraint. We then study the identification properties of the linear-in-means model. A key takeaway from our analysis is that there is a close relationship between the dimension of the outcome variable and the identifiability of the model. Importantly, when the outcome variable is one-dimensional, failures of identification are generic. On the other hand, when the outcome variable is multi-dimensional, we provide natural conditions under which identification is generic.
Estimating Discrete Choice Demand Models with Sparse Market-Product Shocks
Authors: Zhentong Lu, Kenichi Shimizu
Date: 2025-01-04 00:00:00
Tasks: counterfactual

We propose a new approach to estimating the random coefficient logit demand model for differentiated products when the vector of market-product level shocks is sparse. Assuming sparsity, we establish nonparametric identification of the distribution of random coefficients and demand shocks under mild conditions. Then we develop a Bayesian procedure, which exploits the sparsity structure using shrinkage priors, to conduct inference about the model parameters and counterfactual quantities. Comparing to the standard BLP (Berry, Levinsohn, & Pakes, 1995) method, our approach does not require demand inversion or instrumental variables (IVs), thus provides a compelling alternative when IVs are not available or their validity is questionable. Monte Carlo simulations validate our theoretical findings and demonstrate the effectiveness of our approach, while empirical applications reveal evidence of sparse demand shocks in well-known datasets.
Prediction with Differential Covariate Classification: Illustrated by Racial/Ethnic Classification in Medical Risk Assessment
Authors: Charles F. Manski, John Mullahy, Atheendar S. Venkataramani
Date: 2025-01-04 00:00:00
Tasks: Classification, Decision Making

A common practice in evidence-based decision-making uses estimates of conditional probabilities P(y|x) obtained from research studies to predict outcomes y on the basis of observed covariates x. Given this information, decisions are then based on the predicted outcomes. Researchers commonly assume that the predictors used in the generation of the evidence are the same as those used in applying the evidence: i.e., the meaning of x in the two circumstances is the same. This may not be the case in real-world settings. Across a wide-range of settings, ranging from clinical practice or education policy, demographic attributes (e.g., age, race, ethnicity) are often classified differently in research studies than in decision settings. This paper studies identification in such settings. We propose a formal framework for prediction with what we term differential covariate classification (DCC). Using this framework, we analyze partial identification of probabilistic predictions and assess how various assumptions influence the identification regions. We apply the findings to a range of settings, focusing mainly on differential classification of individuals' race and ethnicity in clinical medicine. We find that bounds on P(y|x) can be wide, and the information needed to narrow them available only in special cases. These findings highlight an important problem in using evidence in decision making, a problem that has not yet been fully appreciated in debates on classification in public policy and medicine.
Instrumental Variables with Time-Varying Exposure: New Estimates of Revascularization Effects on Quality of Life
Authors: Joshua D. Angrist, Bruno Ferman, Carol Gao, Peter Hull, Otavio L. Tecchio, Robert W. Yeh
Date: 2025-01-03 00:00:00
Tasks: Selection bias

The ISCHEMIA Trial randomly assigned patients with ischemic heart disease to an invasive treatment strategy centered on revascularization with a control group assigned non-invasive medical therapy. As is common in such ``strategy trials,'' many participants assigned to treatment remained untreated while many assigned to control crossed over into treatment. Intention-to-treat (ITT) analyses of strategy trials preserve randomization-based comparisons, but ITT effects are diluted by non-compliance. Conventional per-protocol analyses that condition on treatment received are likely biased by discarding random assignment. In trials where compliance choices are made shortly after assignment, instrumental variables (IV) methods solve both problems -- recovering an undiluted average causal effect of treatment for treated subjects who comply with trial protocol. In ISCHEMIA, however, some controls were revascularized as long as five years after random assignment. This paper extends the IV framework for strategy trials, allowing for such dynamic non-random compliance behavior. IV estimates of long-run revascularization effects on quality of life are markedly larger than previously reported ITT and per-protocol estimates. We also show how to estimate complier characteristics in a dynamic-treatment setting. These estimates reveal increasing selection bias in naive time-varying per-protocol estimates of revascularization effects. Compliers have baseline health similar to that of the study population, while control-group crossovers are far sicker.
When Do Voters Stop Caring? Estimating the Shape of Voter Utility Function
Authors: Aleksandra Conevska, Can Mutlu
Date: 2025-01-06 00:00:00
Tasks: Form

In this paper, we address a longstanding puzzle over the functional form that better approximates voter's political utility. Though it has become the norm in the literature to represent the voters' political utility with concave loss functions, for decades scholars have underscored this assumption's potential shortcomings. Yet there exists little to no evidence to support one functional form assumption over another. We fill this gap by first identifying electoral settings where the different functional forms generate divergent predictions about voter behavior. Then, we assess which functional form better matches observed voter and abstention behavior using Cast Vote Record (CVR) data that captures the anonymized ballots of millions of voters in the 2020 U.S. general election. Our findings indicate that concave loss functions fail to predict voting and abstention behavior and it is the reverse S-shaped loss functions, such as the Gaussian function, that better match the observed voter behavior.
Environmental Policy in General Equilibrium under Market Power and Price Discrimination
Authors: Tengjiao Chen, Daniel H. Karney
Date: 2025-01-06 00:00:00

This study constructs a novel analytical general equilibrium model to compare environmental policies in a setting where oligopolistic energy firms engage in third-degree price discrimination across residential consumers and industrial firms. Closed-form solutions demonstrate the impact on prices and quantities. The resulting welfare change is decomposed across three distortions: output, price discrimination, and externality. This study finds that the output distortion and price discrimination welfare effects generally move in opposite directions under policies such as an emission tax or a two-part instrument. Numerical analysis compares policies and finds scenarios where the output distortion and price discrimination welfare changes fully offset and thus leaves the net welfare gain of the externality correction. In this way, environmental policy can be designed to mitigate output distortion welfare concerns when firms have market power.
Characterizing Measurement Error in the German Socio-Economic Panel Using Linked Survey and Administrative Data
Authors: Nico Thurow
Date: 2025-01-06 00:00:00
Tasks: Survey

This paper exploits the linkage of German administrative social security data (GER: Integrierte Erwerbsbiografien) and survey data from the socio-economic panel (GER: Sozio-\"okonomisches Panel, SOEP) for the characterization of measurement error in metrics quantifying individual-specific labor earnings in Germany. We find that survey participants' decision whether to consent to linkage is non-random based on observables. In that sense, the studied sample does not constitute a random sample of SOEP. Measurement error is not classical: we observe underreporting of income on average, autocorrelation, and non-zero correlation with the true signal and other observable characteristics. In levels, calculated reliability ratios above 0.94 hint at a relaitvely small attenuation bias in simple linear univariate regressions with earnings as the explanatory variable. For changes in income, i.e. first differences, the bias from measurement error is exacerbated.
The Explore of Knowledge Management Dynamic Capabilities, AI-Driven Knowledge Sharing, Knowledge-Based Organizational Support, and Organizational Learning on Job Performance: Evidence from Chinese Technological Companies
Authors: Jun Cui
Date: 2025-01-05 00:00:00
Tasks: Management

Drawing upon Resource-Based Theory (RBT) and the Knowledge-Based View (KBV), this study investigates the impact of Knowledge-Based Organizational Support (KOS), AI-Driven Knowledge Sharing (KS), Organizational Learning (OL), and Knowledge Management Dynamic Capabilities (KMDC) on Organizational Performance (OP) in Chinese firms. In particular, this research explores the relationships among these factors, alongside control variables such as education level, staff skills, and technological innovation, to provide a comprehensive understanding of their influence on performance management. While recent studies on organizational performance have predominantly concentrated on digital business strategies and high-level decision-making, limited attention has been given to the role of digital maturity, workplace activities, and communication-related dynamics. This study addresses these gaps by consolidating critical factors that contribute to overarching job performance within organizations. Moreover, to empirically test the proposed hypotheses, data were collected from 129 valid questionnaires completed by employees across various Chinese firms. The research employed confirmatory factor analysis (CFA) to validate the measurement constructs and structural equation modeling (SEM) to evaluate the hypothesized relationships. The findings reveal several significant insights: (1) KOS, KS with AI, KMDC, and OL each have a direct positive effect on OP, emphasizing their critical roles in enhancing organizational outcomes. (2) Control variables, including education level, staff skills, and technological innovation, significantly moderate the relationships between KOS, KS with AI, KMDC, OL, and OP, further amplifying their impact.
Strategic Investment to Mitigate Transition Risks
Authors: Jiayue Zhang, Tony S. Wirjanto, Lysa Porth, Ken Seng Tan
Date: 2025-01-04 00:00:00
Tasks: Navigate

This paper investigates strategic investments needed to mitigate transition risks, particularly focusing on sectors significantly impacted by the shift to a low-carbon economy. It emphasizes the importance of tailored sector-specific strategies and the role of government interventions, such as carbon taxes and subsidies, in shaping corporate behavior. In providing a multi-period framework, this paper evaluates the economic and operational trade-offs companies face under four various decarbonization scenarios: immediate, quick, slow, and no transitions. The analysis provides practical insights for both policymakers and business leaders, demonstrating how regulatory frameworks and strategic investments can be aligned to manage transition risks while optimizing long-term sustainability effectively. The findings contribute to a deeper understanding of the economic impacts of regulatory policies and offer a comprehensive framework to navigate the complexities of transitioning to a low-carbon economy.
The Effect of Capital Share on Income Inequality: Identifying the Time Patterns
Authors: Oğuzhan Akgün, Ezgi Özsöğüt
Date: 2025-01-04 00:00:00

This study explores the link between the capital share and income inequality over the past four decades across 56 countries. Calculating the capital share from national accounts alongside top income share data from the World Inequality Database, which is based on the Distributional National Accounts methodology, we ensure the consistency in the theory and measurement. Employing a structural econometric approach, we account for heterogeneous and time-varying transmission coefficients from the capital share to personal income inequality. Our findings reveal that a one percentage point (pp) increase in the capital share raises the income share of the top 5% by 0.17 pp on average. Advanced economies show a stable transmission coefficient with rising capital and labor income inequality, while emerging economies experience an increasing transmission coefficient alongside growing capital income inequality. In contrast, a third group exhibits a declining transmission coefficient and rising labor income inequality. Overall, changes in the capital share account for approximately 50% of the rise in income inequality, underscoring its pivotal role over the last four decades.