pwc-archive/papers-with-abstracts huggingface dataset:
The role of the Allee effect in common-pool resource and its sustainability
Authors: Chengyi Tu, Fabio Menegazzo, Paolo D'Odorico, Samir Suweis
Date: 2024-05-02 00:00:00
Tasks: Attribute, Management

The management of common-pool resources is a complex challenge due to the risk of overexploitation and the tragedy of the commons. A novel framework has been introduced to address this issue, focusing on the coevolutionary relationship between human behavior and common-pool resources within a human-environment system. However, the impact of the Allee effect on the coevolution and its resource sustainability is still unexplored. The Allee effect, a biological phenomenon characterized by a correlation between resource availability and growth rate, is a fundamental attribute of numerous natural resources. In this paper, we introduce two coevolutionary models of resource and strategy under replicator dynamics and knowledge feedback by applying the Allee effect to the common-pool resources within human-environment system. These models encapsulate various facets of resource dynamics and the players' behavior, such as resource growth function, the extraction rates, and the strategy update rules. We find that the Allee effect can induce bi-stability and critical transition, leading to either sustainable or unsustainable outcomes depending on the initial condition and parameter configuration. We demonstrate that knowledge feedback enhances the resilience and sustainability of the coevolving system, and these results advances the understanding of human-environment system and management of common-pool resources.
New Trends on the Systems Approach to Modeling SARS-CoV-2 Pandemics in a Globally Connected Planet
Authors: Giulia Bertaglia, Andrea Bondesan, Diletta Burini, Raluca Eftimie, Lorenzo Pareschi, Giuseppe Toscani
Date: 2024-05-01 00:00:00
Tasks: Decision Making

This paper presents a critical analysis of the literature and perspective research ideas for modeling the epidemics caused by the SARS-CoV-2 virus. It goes beyond deterministic population dynamics to consider several key complexity features of the system under consideration. In particular, the multiscale features of the dynamics from contagion to the subsequent dynamics of competition between the immune system and the proliferating virus. Other topics addressed in this work include the propagation of epidemics in a territory, taking into account local transportation networks, the heterogeneity of the population, and the study of social and economic problems in populations involved in the spread of epidemics. The overall content aims to show how new mathematical tools can be developed to address the above topics and how mathematical models and simulations can contribute to the decision making of crisis managers.
On the weight dynamics of learning networks
Authors: Nahal Sharafi, Christoph Martin, Sarah Hallerberg
Date: 2024-04-30 00:00:00
Tasks: regression, valid

Neural networks have become a widely adopted tool for tackling a variety of problems in machine learning and artificial intelligence. In this contribution we use the mathematical framework of local stability analysis to gain a deeper understanding of the learning dynamics of feed forward neural networks. Therefore, we derive equations for the tangent operator of the learning dynamics of three-layer networks learning regression tasks. The results are valid for an arbitrary numbers of nodes and arbitrary choices of activation functions. Applying the results to a network learning a regression task, we investigate numerically, how stability indicators relate to the final training-loss. Although the specific results vary with different choices of initial conditions and activation functions, we demonstrate that it is possible to predict the final training loss, by monitoring finite-time Lyapunov exponents or covariant Lyapunov vectors during the training process.
Pseudo-noise pulse-compression thermography: a powerful tool for time-domain thermography analysis
Authors: Marco Ricci, Rocco Zito, Stefano Laureti
Date: 2024-05-01 00:00:00

Pulse-compression is a correlation-based measurement technique successfully used in many NDE applications to increase the SNR in the presence of huge noise, strong signal attenuation or when high excitation levels must be avoided. In thermography, the pulse-compression approach was firstly introduced in 2005 by Mulavesaala and co-workers, and then further developed by Mandelis and co-authors that applied to thermography the concept of the thermal-wave radar developed for photothermal measurements. Since then, many measurement schemes and applications have been reported in the literature by several groups by using various heating sources, coded excitation signals, and processing algorithms. The variety of such techniques is known as pulse-compression thermography or thermal-wave radar imaging. Even despite the continuous improvement of these techniques during these years, the advantages of using a correlation-based approach in thermography are still not fully exploited and recognized by the community. This is because up to now the reconstructed thermograms' time sequences after pulse-compression were affected by the so-called sidelobes. This is a severe drawback since it hampers an easy interpretation of the data and their comparison with other thermography techniques. To overcome this issue and unleash the full potential of the approach, this paper shows how it is possible to implement a pulse-compression thermography procedure capable of suppressing any sidelobe by using a pseudo-noise excitation and a proper processing algorithm.
Misspecification of Multiple Scattering in Scalar Wave Fields and its Impact in Ultrasound Tomography
Authors: Eduardo Pérez, Sebastian Semper, Sayako Kodera, Florian Römer, Giovanni Del Galdo
Date: 2024-05-02 00:00:00

In this work, we investigate the localization of targets in the presence of multiple scattering. We focus on the often omitted scenario in which measurement data is affected by multiple scattering, and a simpler model is employed in the estimation. We study the impact of such model mismatch by means of the Misspecified Cram\'er-Rao Bound (MCRB). In numerical simulations inspired by tomographic inspection in ultrasound nondestructive testing, the MCRB is shown to correctly describe the estimation variance of localization parameters under misspecification of the wave propagation model. We provide extensive discussion on the utility of the MCRB in the practical task of verifying whether a chosen misspecified model is suitable for localization based on the properties of the maximum likelihood estimator and the nuanced distinction between bias and parameter space differences. Finally, we highlight that careful interpretation is needed whenever employing the classical CRB in the presence of mismatch through numerical examples based on the Born approximation and other simplified propagation models stemming from it.
On the Role of Reflectarrays for Interplanetary Links
Authors: Eray Guven, Pablo Camacho, Elham Baladi, Gunes Karabulut Kurt
Date: 2024-05-01 00:00:00

Interplanetary links (IPL) serve as crucial enablers for space exploration, facilitating secure and adaptable space missions. An integrated IPL with inter-satellite communication (IP-ISL) establishes a unified deep space network, expanding coverage and reducing atmospheric losses. The challenges, including irregularities in charged density, hardware impairments, and hidden celestial body brightness are analyzed with a reflectarray-based IP-ISL between Earth and Moon orbiters. It is observed that $10^{-8}$ order severe hardware impairments with intense solar plasma density drops an ideal system's spectral efficiency (SE) from $\sim\!38~\textrm{(bit/s)/Hz}$ down to $0~\textrm{(bit/s)/Hz}$. An ideal full angle of arrival fluctuation recovery with full steering range achieves $\sim\!20~\textrm{(bit/s)/Hz}$ gain and a limited beamsteering with a numerical reflectarray design achieves at least $\sim\!1~\textrm{(bit/s)/Hz}$ gain in severe hardware impairment cases.
On Quantum Ambiguity and Potential Exponential Computational Speed-Ups to Solving Dynamic Asset Pricing Models
Authors: Eric Ghysels, Jack Morgan
Date: 2024-05-02 00:00:00
Tasks: Model Selection

We formulate quantum computing solutions to a large class of dynamic nonlinear asset pricing models using algorithms, in theory exponentially more efficient than classical ones, which leverage the quantum properties of superposition and entanglement. The equilibrium asset pricing solution is a quantum state. We introduce quantum decision-theoretic foundations of ambiguity and model/parameter uncertainty to deal with model selection.
Asymptotic Properties of the Distributional Synthetic Controls
Authors: Lu Zhang, Xiaomeng Zhang, Xinyu Zhang
Date: 2024-05-02 00:00:00
Tasks: Causal Inference

As an alternative to synthetic control, the distributional Synthetic Control (DSC) proposed by Gunsilius (2023) provides estimates for quantile treatment effect and thus enabling researchers to comprehensively understand the impact of interventions in causal inference. But the asymptotic properties of DSC have not been built. In this paper, we first establish the DSC estimator's asymptotic optimality in the essence that the treatment effect estimator given by DSC achieves the lowest possible squared prediction error among all potential estimators from averaging quantiles of control units. We then establish the convergence rate of the DSC weights. A significant aspect of our research is that we find the DSC synthesis forms an optimal weighted average, particularly in situations where it is impractical to perfectly fit the treated unit's quantiles through the weighted average of the control units' quantiles. Simulation results verify our theoretical insights.
Estimating Heterogeneous Treatment Effects with Item-Level Outcome Data: Insights from Item Response Theory
Authors: Joshua B. Gilbert, Zachary Himmelsbach, James Soland, Mridul Joshi, Benjamin W. Domingue
Date: 2024-04-30 00:00:00
Tasks: Causal Inference

Analyses of heterogeneous treatment effects (HTE) are common in applied causal inference research. However, when outcomes are latent variables assessed via psychometric instruments such as educational tests, standard methods ignore the potential HTE that may exist among the individual items of the outcome measure. Failing to account for "item-level" HTE (IL-HTE) can lead to both underestimated standard errors and identification challenges in the estimation of treatment-by-covariate interaction effects. We demonstrate how Item Response Theory (IRT) models that estimate a treatment effect for each assessment item can both address these challenges and provide new insights into HTE generally. This study articulates the theoretical rationale for the IL-HTE model and demonstrates its practical value using 75 datasets from 48 randomized controlled trials containing 5.8 million item responses in economics, education, and health research. Our results show that the IL-HTE model reveals item-level variation masked by single-number scores, provides more meaningful standard errors in many settings, allows for estimates of the generalizability of causal effects to untested items, resolves identification problems in the estimation of interaction effects, and provides estimates of standardized treatment effect sizes corrected for attenuation due to measurement error.
Modelling user behavior towards smartphones and wearable technologies: A bibliometric study and brief literature review
Authors: Maral Jamalova
Date: 2024-05-02 00:00:00
Tasks: Articles

The study uses bibliometric as well as content analysis to determine the current situation regarding the application of technology adoption models (i.e., the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, and Innovation Diffusion Theory) to the smartphone market that also includes smart wearables. Hereby the author would like to determine the connection between smartphone usage and adoption models and enrich literature by defining state-of-the-art tendencies and approaches. To achieve the goal, the author applied a two-stage approach: in the first stage, 213 articles were analyzed using Citation and Bibliographic coupling tools in VOSviewer (1.6.20). The papers were selected from the Scopus database and the search of the papers was conducted in the fields of Economics, Business, and Computer technologies. In the second stage, the author conducted a brief literature review of the most influential papers. The results illustrate the situation regarding the implementation of different models in the case of smartphone adoption. Content analyses of the most influential papers were applied to explain and enrich the results of bibliometric analyses as well as determine research gaps and future research development.
Does Financial Literacy Impact Investment Participation and Retirement Planning in Japan?
Authors: Yi Jiang, Shohei Shimizu
Date: 2024-05-02 00:00:00
Tasks: Causal Discovery, Causal Inference

By employing causal discovery method, the Fast Causal Inference (FCI) model to analyze data from the 2022 "Financial Literacy Survey," we explore the causal relationships between financial literacy and financial activities, specifically investment participation and retirement planning. Our findings indicate that increasing financial literacy may not directly boost engagement in financial investments or retirement planning in Japan, which underscores the necessity for alternative strategies to motivate financial activities among Japanese households. This research offers valuable insights for policymakers focused on improving financial well-being by advancing the use of causal discovery algorithms in understanding financial behaviors.
Racial and Ethnic Disparities in Mortgage Lending: New Evidence from Expanded HMDA Data
Authors: Sean Lewis-Faupel, Nicholas Tenev
Date: 2024-05-01 00:00:00
Tasks: Credit score

This paper investigates gaps in access to and the cost of housing credit by race and ethnicity using the near universe of U.S. mortgage applications. Our data contain borrower creditworthiness variables that have historically been absent from industry-wide application data and that are likely to affect application approval and loan pricing. We find large unconditional disparities in approval and pricing between racial and ethnic groups. After conditioning on key elements of observable borrower creditworthiness, these disparities are smaller but remain economically meaningful. Sensitivity analysis indicates that omitted factors as predictive of approval/pricing and race/ethnicity as credit score can explain some of the pricing disparities but cannot explain the approval disparities. Taken together, our results suggest that credit score, income, and down payment requirements significantly contribute to disparities in mortgage access and affordability but that other systemic barriers are also responsible for a large share of disparate outcomes in the mortgage market.
The value of non-traditional credentials in the labor market
Authors: Susan Athey, Emil Palikot
Date: 2024-04-30 00:00:00

This study investigates the labor market value of credentials obtained from Massive Open Online Courses (MOOCs) and shared on business networking platforms. We conducted a randomized experiment involving more than 800,000 learners, primarily from developing countries and without college degrees, who completed technology or business-related courses on the Coursera platform between September 2022 and March 2023. The intervention targeted learners who had recently completed their courses, encouraging them to share their credentials and simplifying the sharing process. One year after the intervention, we collected data from LinkedIn profiles of approximately 40,000 experimental subjects. We find that the intervention leads to an increase of 17 percentage points for credential sharing. Further, learners in the treatment group were 6\% more likely to report new employment within a year, with an 8\% increase in jobs related to their certificates. This effect was more pronounced among LinkedIn users with lower baseline employability. Across the entire sample, the treated group received a higher number of certificate views, indicating an increased interest in their profiles. These results suggest that facilitating credential sharing and reminding learners of the value of skill signaling can yield significant gains. When the experiment is viewed as an encouragement design for credential sharing, we can estimate the local average treatment effect (LATE) of credential sharing (that is, the impact of credential sharing on the workers induced to share by the intervention) for the outcome of getting a job. The LATE estimates are imprecise but large in magnitude; they suggest that credential sharing more than doubles the baseline probability of getting a new job in scope for the credential.
Blockchain Price vs. Quantity Controls
Authors: Abdoulaye Ndiaye
Date: 2024-04-30 00:00:00

This paper studies the optimal transaction fee mechanisms for blockchains, focusing on the distinction between price-based ($\mathcal{P}$) and quantity-based ($\mathcal{Q}$) controls. By analyzing factors such as demand uncertainty, validator costs, cryptocurrency price fluctuations, price elasticity of demand, and levels of decentralization, we establish criteria that determine the selection of transaction fee mechanisms. We present a model framed around a Nash bargaining game, exploring how blockchain designers and validators negotiate fee structures to balance network welfare with profitability. Our findings suggest that the choice between $\mathcal{P}$ and $\mathcal{Q}$ mechanisms depends critically on the blockchain's specific technical and economic features. The study concludes that no single mechanism suits all contexts and highlights the potential for hybrid approaches that adaptively combine features of both $\mathcal{P}$ and $\mathcal{Q}$ to meet varying demands and market conditions.
Conceiving Naturally After IVF: the effect of assisted reproduction on obstetric interventions and child health at birth
Authors: Fabio I. Martinenghi, Xian Zhang, Luk Rombauts, Georgina M. Chambers
Date: 2024-04-30 00:00:00

A growing share of the world's population is being born via assisted reproductive technology (ART), including in-vitro fertilisation (IVF). However, two concerns persist. First, ART pregnancies correlate with predictors of poor outcomes at birth--and it is unclear whether this relationship is causal. Second, the emotional and financial costs associated with ART-use might exacerbate defensive medical behaviour, where physicians intervene more than necessary to reduce the risk of adverse medical outcomes and litigation. We address the challenge of identifying the pure effect of ART-use on both maternal and infant outcomes at birth by leveraging exogenous variation in the success of ART cycles. We compare the obstetric outcomes for ART-conceived births with those of spontaneously-conceived births after a failed ART treatment. Moreover, we flexibly adjust for key confounders using double machine learning. We do this using clinical registry ART data and administrative maternal and infant data from New South Wales (NSW) between 2009-2017. We find that ART slightly decreases the risk of obstetric interventions, lowering the risk of a caesarean section and increasing the rate of spontaneous labour (+3.5 p.p.). Moreover, we find that ART has a statistically and clinically insignificant effect on infant health outcomes. Keywords: Fertility, Assisted reproduction, IVF, Caesarean Section, Obstetric, Infertility. JEL classification: I10, I12, I19.
Dynamic opinion updating with endogenous networks
Authors: Ugo Bolletta, Paolo Pin
Date: 2024-05-02 00:00:00

Polarization is a well-documented phenomenon across a wide range of social issues. However, prevailing theories often compartmentalize the examination of herding behavior and opinion convergence within different contexts. In this study, we delve into the micro-foundations of how individuals strategically select reference groups, offering insight into a dynamic process where both individual opinions and the network evolve simultaneously. We base our model on two parameters: people's direct benefit from connections and their adaptability in adjusting their opinions. Our research highlights which conditions impede the network from achieving complete connectivity, resulting in enduring polarization. Notably, our model also reveals that polarization can transiently emerge during the transition towards consensus. We explore the connection between these scenarios and a critical network metric: the initial diameter, under specific conditions related to the initial distribution of opinions.
A Taste for Variety
Authors: Galit Ashkenazi-Golan, Dominik Karos, Ehud Lehrer
Date: 2024-05-01 00:00:00

A decision maker repeatedly chooses one of a finite set of actions. In each period, the decision maker's payoff depends on fixed basic payoff of the chosen action and the frequency with which the action has been chosen in the past. We analyze optimal strategies associated with three types of evaluations of infinite payoffs: discounted present value, the limit inferior, and the limit superior of the partial averages. We show that when the first two are the evaluation schemes, a stationary strategy can always achieve the best possible outcome. However, for the latter evaluation scheme, a stationary strategy can achieve the best outcome only if all actions that are chosen with strictly positive frequency by an optimal stationary strategy have the same basic payoff.
Remote Sensing Data Assimilation with a Chained Hydrologic-hydraulic Model for Flood Forecasting
Authors: Thanh Huy Nguyen, Andrea Piacentini, Sophie Ricci, Ludovic Cassan, Simon Munier, Quentin Bonassies, Raquel Rodriguez-Suquet
Date: 2024-05-01 00:00:00
Tasks: Friction

A chained hydrologic-hydraulic model is implemented using predicted runoff from a large-scale hydrologic model (namely ISBA-CTRIP) as inputs to local hydrodynamic models (TELEMAC-2D) to issue forecasts of water level and flood extent. The uncertainties in the hydrological forcing and in friction parameters are reduced by an Ensemble Kalman Filter that jointly assimilates in-situ water levels and flood extent maps derived from remote sensing observations. The data assimilation framework is cycled in a real-time forecasting configuration. A cycle consists of a reanalysis and a forecast phase. Over the analysis, observations up to the present are assimilated. An ensemble is then initialized from the last analyzed states and issued forecasts for next 36 hr. Three strategies of forcing data for this forecast are investigated: (i) using CTRIP runoff for reanalysis and forecast, (ii) using observed discharge for analysis, then CTRIP runoff for forecast and (iii) using observed discharge for reanalysis and keep a persistent discharge value for forecast. It was shown that the data assimilation strategy provides a reliable reanalysis in hindcast mode. The combination of observed discharge and CTRIP runoff provides the most accurate results. For all strategies, the quality of the forecast decreases as the lead time increases. When the errors in CTRIP forcing are non-stationary, the forecast capability may be reduced. This work demonstrates that the forcing provided by a hydrologic model, while imperfect, can be efficiently used as input to a hydraulic model to issue reanalysis and forecasts, thanks to the assimilation of in-situ and remote sensing observations.