Strong Assumptions Research Articles

Data-driven economic MPC with asymptotic stability and strong duality verification using Hankel matrix

We consider the problem of dynamic regulation with an economic cost function to control unknown linear systems, in which improving the economic performance and guaranteeing the stability of economical optimal equilibrium point are control objectives. A data-driven economic MPC scheme is presented using measured input-output trajectories without a prior system identification step. Our method uses Hankel matrices which include one input-output data trajectory for prediction in economic MPC, while persistently exciting of the input generating the data is needed. One of the novelties of the presented framework is directly verifying the strong duality property from input-output trajectory with the general cost function, considered as the supply rate. This is used to find a Lyapunov function for data-driven economic MPC. Under the strong duality assumption, asymptotic stability of the economical optimal equilibrium point for the closed-loop system with terminal equality constraint is guaranteed. The proposed data-driven economic MPC approach needs only persistently exciting data trajectory along with an upper bound on the system order and need no model description and no online parameter estimation. The proposed scheme applicability compared to the existing model-based economic MPC and data-driven MPC is illustrated for continuous stirred tank reactor (CSTR) and a numerical example and the robustness of the proposed scheme is evaluated in the case of measurement noise, as well as nonlinear model for CSTR system.

Causal Inference About the Effects of Interventions From Observational Studies in Medical Journals

ImportanceMany medical journals, including JAMA, restrict the use of causal language to the reporting of randomized clinical trials. Although well-conducted randomized clinical trials remain the preferred approach for answering causal questions, methods for observational studies have advanced such that causal interpretations of the results of well-conducted observational studies may be possible when strong assumptions hold. Furthermore, observational studies may be the only practical source of information for answering some questions about the causal effects of medical or policy interventions, can support the study of interventions in populations and settings that reflect practice, and can help identify interventions for further experimental investigation. Identifying opportunities for the appropriate use of causal language when describing observational studies is important for communication in medical journals.ObservationsA structured approach to whether and how causal language may be used when describing observational studies would enhance the communication of research goals, support the assessment of assumptions and design and analytic choices, and allow for more clear and accurate interpretation of results. Building on the extensive literature on causal inference across diverse disciplines, we suggest a framework for observational studies that aim to provide evidence about the causal effects of interventions based on 6 core questions: what is the causal question; what quantity would, if known, answer the causal question; what is the study design; what causal assumptions are being made; how can the observed data be used to answer the causal question in principle and in practice; and is a causal interpretation of the analyses tenable?Conclusions and RelevanceAdoption of the proposed framework to identify when causal interpretation is appropriate in observational studies promises to facilitate better communication between authors, reviewers, editors, and readers. Practical implementation will require cooperation between editors, authors, and reviewers to operationalize the framework and evaluate its effect on the reporting of empirical research.

Unraveling the Non‐Homogeneous Dispersion Processes in Ocean and Coastal Circulations Using a Clustering Approach

AbstractDispersion processes in environmental flows have been traditionally studied under the strong assumption of homogeneous, isotropic and stationary turbulence. To overcome this limitation, we propose a new approach that combines autocorrelation analysis of simulated Lagrangian trajectories together with unsupervised clustering. To test the approach, we consider several dynamic scenarios around a coastal gulf, subject to different forcing, in order to compare our method with other approaches. Lagrangian trajectories forced by the varying coastal circulation exhibited different behaviors, looping and non‐looping paths, and produced a variety of Lagrangian autocorrelation functions. Our approach proves to be able to reveal spatio‐temporal variations in ocean dispersion processes without any a priori knowledge of the character of the trajectories. Clusters based on the autocorrelation functions are associated to different inhomogeneous dispersion processes. Finally, we propose a new stochastic model capable of predicting the different forms of autocorrelations.

Competing risks and multivariate outcomes in epidemiological and clinical trial research.

Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women's Health Initiative cohort and clinical trial data sets, and additional research needs will be described.

Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate

Abstract. Understanding the dynamics of slope instabilities is critical to mitigate the associated hazards, but their direct observation is often difficult due to their remote locations and their spontaneous nature. Seismology allows us to get unique information on these events, including on their dynamics. However, the link between the properties of these events (mass and kinematics) and the seismic signals generated is still poorly understood. We conducted a controlled rockfall experiment in the Riou Bourdoux torrent (southern French Alps) to try to better decipher those links. We deployed a dense seismic network and inferred the dynamics of the block from the reconstruction of the 3D trajectory from terrestrial and airborne high-resolution stereophotogrammetry. We propose a new approach based on machine learning to predict the mass and the velocity of each block. Our results show that we can predict those quantities with average errors of approximately 10 % for the velocity and 25 % for the mass. These accuracies are as good as or better than those obtained by other approaches, but our approach has the advantage in that it does not require the source to be localised, nor does it require a high-resolution velocity model or a strong assumption on the seismic wave attenuation model. Finally, the machine learning approach allows us to explore more widely the correlations between the features of the seismic signal generated by the rockfalls and their physical properties, and it might eventually lead to better constraints on the physical models in the future.

A transformer model for cause-specific hazard prediction

BackgroudModelling discrete-time cause-specific hazards in the presence of competing events and non-proportional hazards is a challenging task in many domains. Survival analysis in longitudinal cohorts often requires such models; notably when the data is gathered at discrete points in time and the predicted events display complex dynamics. Current models often rely on strong assumptions of proportional hazards, that is rarely verified in practice; or do not handle sequential data in a meaningful way. This study proposes a Transformer architecture for the prediction of cause-specific hazards in discrete-time competing risks. Contrary to Multilayer perceptrons that were already used for this task (DeepHit), the Transformer architecture is especially suited for handling complex relationships in sequential data, having displayed state-of-the-art performance in numerous tasks with few underlying assumptions on the task at hand.ResultsUsing synthetic datasets of 2000–50,000 patients, we showed that our Transformer model surpassed the CoxPH, PyDTS, and DeepHit models for the prediction of cause-specific hazard, especially when the proportional assumption did not hold. The error along simulated time outlined the ability of our model to anticipate the evolution of cause-specific hazards at later time steps where few events are observed. It was also superior to current models for prediction of dementia and other psychiatric conditions in the English longitudinal study of ageing cohort using the integrated brier score and the time-dependent concordance index. We also displayed the explainability of our model’s prediction using the integrated gradients method.ConclusionsOur model provided state-of-the-art prediction of cause-specific hazards, without adopting prior parametric assumptions on the hazard rates. It outperformed other models in non-proportional hazards settings for both the synthetic dataset and the longitudinal cohort study. We also observed that basic models such as CoxPH were more suited to extremely simple settings than deep learning models. Our model is therefore especially suited for survival analysis on longitudinal cohorts with complex dynamics of the covariate-to-outcome relationship, which are common in clinical practice. The integrated gradients provided the importance scores of input variables, which indicated variables guiding the model in its prediction. This model is ready to be utilized for time-to-event prediction in longitudinal cohorts.

Oversampling Framework Based on Sample Subspace Optimization with Accelerated Binary Particle Swarm Optimization for Imbalanced Classification

In response to the need to generate synthetic minority class samples to extend minority classes, the SMOTE-based oversampling methods have been favored for class-imbalanced classification. They usually generate unnecessary noise when training data are not well separated. Although filtering-based oversampling methods are recognized as effective solutions for addressing noise generation through employing specific noise filters based on instance selection methods to remove suspicious noise, they suffer from the following issues: a) noise filters heavily rely on strong assumptions, causing low robustness to different datasets; b) noise filters are specially designed for a specific oversampling method, and are not easily extended to others; and c) noise filters have a relatively high time consumption. To address noise generation while overcoming the above issues a)-c), an oversampling framework based on sample subspace optimization with accelerated binary particle swarm optimization (OF-SSO-ABPSO) is proposed. OF-SSO-ABPSO is a wrapping framework compatible with almost all the oversampling methods. First, in the framework, a SMOTE-based method is used to generate synthetic minority class samples. Second, a novel accelerated binary particle swarm optimization (ABPSO) algorithm with a new search space reduction strategy, a new particle update mechanism, and a new fitness function is proposed. Third, a novel ABPSO-based sample subspace optimization (SSO-ABPSO) method is proposed and used as a noise filter to remove suspicious noise from the training set and synthetic minority class samples. Experiments prove that, a) OF-SSO-ABPSO can improve 6 representative SMOTE variations by addressing noise generation, and b) OF-SSO-ABPSO outperforms 7 state-of-the-art filtering-based oversampling methods.

On modelling non‐performing loans in bank efficiency analysis

AbstractThis paper introduces a methodological framework for the examination of non‐performing loans (NPLs) as reverse outputs under the extended strong disposability assumption, which does not require NPLs to be jointly produced with net loans, as it is implied when they are modelled as undesirable outputs. A directional distance function model with reverse outputs is used and is compared with the models that treat NPLs as an undesirable output under the weak disposability and the constrained weak disposability assumptions with uniform and non‐uniform abatement factors. The model is applied at the case of European banks and for the sample to be representative the banks are chosen based on the European Banking Authority (EBA) stress test of 2021. The results indicate that the reverse output model have greater discriminatory power relative to all other models.

Causal interpretation of the hazard ratio in randomized clinical trials.

Although the hazard ratio has no straightforward causal interpretation, clinical trialists commonly use it as a measure of treatment effect. We review the definition and examples of causal estimands. We discuss the causal interpretation of the hazard ratio from a two-arm randomized clinical trial, and the implications of proportional hazards assumptions in the context of potential outcomes. We illustrate the application of these concepts in a synthetic model and in a model of the time-varying effects of COVID-19 vaccination. We define causal estimands as having either an individual-level or population-level interpretation. Difference-in-expectation estimands are both individual-level and population-level estimands, whereas without strong untestable assumptions the causal rate ratio and hazard ratio have only population-level interpretations. We caution users against making an incorrect individual-level interpretation, emphasizing that in general a hazard ratio does not on average change each individual's hazard by a factor. We discuss a potentially valid interpretation of the constant hazard ratio as a population-level causal effect under the proportional hazards assumption. We conclude that the population-level hazard ratio remains a useful estimand, but one must interpret it with appropriate attention to the underlying causal model. This is especially important for interpreting hazard ratios over time.

Better Foundations for Subjective Probability

ABSTRACT How do we ascribe subjective probability? In decision theory, this question is often addressed by representation theorems, going back to Ramsey (1926), which tell us how to define or measure subjective probability by observable preferences. However, standard representation theorems make strong rationality assumptions, in particular expected utility maximization. How do we ascribe subjective probability to agents which do not satisfy these strong rationality assumptions? I present a representation theorem with weak rationality assumptions which can be used to define or measure subjective probability for partly irrational agents.

Collider bias correction for multiple covariates in GWAS using robust multivariable Mendelian randomization.

Genome-wide association studies (GWAS) have identified many genetic loci associated with complex traits and diseases in the past 20 years. Multiple heritable covariates may be added into GWAS regression models to estimate direct effects of genetic variants on a focal trait, or to improve the power by accounting for environmental effects and other sources of trait variations. When one or more covariates are causally affected by both genetic variants and hidden confounders, adjusting for them in GWAS will produce biased estimation of SNP effects, known as collider bias. Several approaches have been developed to correct collider bias through estimating the bias by Mendelian randomization (MR). However, these methods work for only one covariate, some of which utilize MR methods with relatively strong assumptions, both of which may not hold in practice. In this paper, we extend the bias-correction approaches in two aspects: first we derive an analytical expression for the collider bias in the presence of multiple covariates, then we propose estimating the bias using a robust multivariable MR (MVMR) method based on constrained maximum likelihood (called MVMR-cML), allowing the presence of invalid instrumental variables (IVs) and correlated pleiotropy. We also established the estimation consistency and asymptotic normality of the new bias-corrected estimator. We conducted simulations to show that all methods mitigated collider bias under various scenarios. In real data analyses, we applied the methods to two GWAS examples, the first a GWAS of waist-hip ratio with adjustment for only one covariate, body-mass index (BMI), and the second a GWAS of BMI adjusting metabolomic principle components as multiple covariates, illustrating the effectiveness of bias correction.

Enhancing stability and interpretability in the study of strength behavior for coarse-grained soils

Coarse-grained soils have been commonly employed as filling materials in engineering projects, and comprehending the mechanical properties of these soils is crucial for ensuring project safety. Currently, artificial intelligence (AI) plays a significant role in advancing these fields. However, the inherent lack of stability and interpretability of AI models constrains their potential for providing substantial assistance. To solve these shortcomings, this study investigates the mechanical properties of coarse-grained soils and proposes an approach that integrates a stable learning module with neural networks to enhance the stability of an AI model. The proposed method addresses the challenge of relaxing the strong assumption of independent and identically distributed (i.i.d.) data. In addition, it improves the stability of the prediction of a relationship between the deviatoric stress and axial strain. The proposed method is verified by experiments. The experimental results confirm that the proposed method performs well on real-world data samples that deviate from the i.i.d. assumption. Moreover, the proposed method enhances the interpretability of the AI model and facilitates the identification of crucial features for assessing the strength behavior of coarse-grained soils. This feature of interpretability is invaluable for practical engineering applications.

X-Wing

X-Wing is a hybrid key-encapsulation mechanism based on X25519 and ML-KEM-768. It is designed to be the sensible choice for most applications. The concrete choice of X25519 and ML-KEM-768 allows X-Wing to achieve improved efficiency compared to using a generic KEM combiner. In this paper, we introduce the X-Wing hybrid KEM construction and provide a proof of security. We show (1) that X-Wing is a classically IND-CCA secure KEM if the strong Diffie-Hellman assumption holds in the X25519 nominal group, and (2) that X-Wing is a post-quantum IND-CCA secure KEM if ML-KEM-768 is itself an IND-CCA secure KEM and SHA3-256 is secure when used as a pseudorandom function. The first result is proved in the ROM, whereas the second one holds in the standard model. Loosely speaking, this means X-Wing is secure if either X25519 or ML-KEM-768 is secure. We stress that these security guarantees and optimizations are only possible due to the concrete choices that were made, and it may not apply in the general case.

Variational inference: uncertainty quantification in additive models

AbstractMarkov chain Monte Carlo (MCMC)-based simulation approaches are by far the most common method in Bayesian inference to access the posterior distribution. Recently, motivated by successes in machine learning, variational inference (VI) has gained in interest in statistics since it promises a computationally efficient alternative to MCMC enabling approximate access to the posterior. Classical approaches such as mean-field VI (MFVI), however, are based on the strong mean-field assumption for the approximate posterior where parameters or parameter blocks are assumed to be mutually independent. As a consequence, parameter uncertainties are often underestimated and alternatives such as semi-implicit VI (SIVI) have been suggested to avoid the mean-field assumption and to improve uncertainty estimates. SIVI uses a hierarchical construction of the variational parameters to restore parameter dependencies and relies on a highly flexible implicit mixing distribution whose probability density function is not analytic but samples can be taken via a stochastic procedure. With this paper, we investigate how different forms of VI perform in semiparametric additive regression models as one of the most important fields of application of Bayesian inference in statistics. A particular focus is on the ability of the rivalling approaches to quantify uncertainty, especially with correlated covariates that are likely to aggravate the difficulties of simplifying VI assumptions. Moreover, we propose a method, where we combine both advantages of MFVI and SIVI and compare its performance. The different VI approaches are studied in comparison with MCMC in simulations and an application to tree height models of douglas fir based on a large-scale forestry data set.

Initiation mechanism of arcing generated in RF capacitively coupled plasma

In our previous study, we established an arcing generation and measurement system and we observed prior light emission before arcing current development. However, we briefly analyzed those light emissions with strong assumptions without detailed experiment evaluations and thus, the investigation of the formation mechanism in the initiation phase with detailed experiment evaluations has yet to be conducted. In this work, we investigated the initiation mechanism of arcing generated on an arcing inducing probe (AIP) in a radio frequency capacitively coupled plasma (CCP) environment. Here, the AIP is an aluminum rod covered by anodized film and its tip edge is partially stripped to localize arcing on this edge. We measured emission light, voltage, and current waveforms induced by arcing. The spatiotemporal image of the emission light revealed that the tip glow is the brightest intensity and has longest lifetime during arcing, meaning that it is the primary process in whole arcing process. The current waveform induced by arcing corresponds to the time evolution of the tip glow and estimations revealed that the electron emission is the predominant component of the current formation. Furthermore, snapshot images with AIPs having enlarged stripping area exhibited that arcing occurs at the boundary between the alnuminum and anodized film (dielectric), where charging of ions from the CCP on the film surface can induce high-electric field. In addition, we found that the energy relaxation length of emitted electrons for collisions with Ar atoms, which are the background gas, is much larger than the tip glow diameter, meaning that the electon-Ar collision cannot maintain tip glow. This result supports additional source of atoms to sustain the tip glow such as the surface evaporation from arcing spot, of which evidence was speculated our previous study. We estimated minimum aluminum vapor density and surface temperature, which is sufficiently high enough to induce surface vaporization. Combining those experiment results and estimations, that are electron emission, high surface temperature, and surface evaporation, we can speculate that the initiation mechanism of arcing near dielectric surface in radio-frequency CCP environment is the thermionic emission and surface evaporation from arcing spot.

Hybrid Approach Proves Effective in Multiwell Forecasting in Unconventionals

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3855422, “RGNet for Multiwell Forecasting in Unconventional Reservoirs,” by Zhenyu Guo, SPE, and Sathish Sankaran, SPE, Xecta Digital Labs, and Ying Li, The University of Tulsa. The paper has not been peer reviewed. _ The aim of the complete paper is to propose a hybrid approach that combines physics with data-driven approaches for efficient and accurate forecasting of the performance of unconventional wells under codevelopment. The method the authors propose is the reservoir graph network (RGNet) model. By reducing system complexity while maintaining fundamental physics, the model provides an efficient and accurate way to model, history-match, and predict unconventional wells. Compared with a full-physics model that takes from hours to days to run, the described model only takes from seconds to minutes. Performance Analysis of Unconventional Reservoirs Developing unconventional reservoirs is a complex process involving well targeting, timing, spacing, and completion design for horizontal wells with hydraulic fractures. Among commonly used methods, decline curve analysis (DCA) can execute quick well-performance analysis without considering the complex physics in unconventional reservoirs piece by piece. The convenient aspect of DCA is that practitioners can obtain forecast results quickly by providing historical production data. However, because DCA has some strong assumptions on operational conditions and does not incorporate the key physics of the flow, it may fail to predict accurately in many situations. As a simple, fast tool for analyzing the capacity of a reservoir, rate transient analysis (RTA) has been used to model and forecast unconventional wells. Both pressure and rate data are considered to generate solutions that are more credible than those generated by DCA, where only rate information is used. The drawback of RTA is related to significant assumptions used to derive analytical solutions (e.g., homogeneous reservoir and simple planar fractures with uniform properties). Moreover, it is not an easy task to acquire some of the information required by RTA. Full-physics reservoir simulation models are used as the most-rigorous approach to understand dynamics of unconventional reservoirs because they allow for different complexities for modeling unconventionals. However, because it takes tremendous effort for information gathering, geological/fracture modeling, and history matching, applying this approach to large-scale reservoirs is not tractable. Based on the idea of diffusive time of flight (DTOF), the RGNet model was developed for reservoir modeling, history matching, forecasting, and optimization. It can model multiple wells with communications. In this work, the authors incorporate pressure dependency of pore volumes and transmissibility into RGNet while modeling unconventional reservoirs. Methodology The basic idea of the described model comes from the DTOF transformation that converts the 3D diffusivity problem into a 1D problem. The complete paper provides several equations that enable this conversion.

The impact of excess body weight on employment outcomes: A systematic review of the evidence

BackgroundExcess body weight has been recognised as an important factor in influencing labour market outcomes. Several hypotheses explain the causal effect of excess body weight on employment outcomes, including productivity, labour supply, and discrimination. In this review, we provide a systematic synthesis of the evidence on the causal impact of excess body weight on labour market outcomes worldwide. MethodsWe searched Econ Lit, and Web of Science databases for relevant studies published from 1st Jan 2010 to 20th Jan 2023. Studies were included if they were either longitudinal analysis, pooled cross-sectional or cross-sectional studies if they used instrumental variable methodology based on Mendelian Randomisation. Only studies with measures of body weight and employment outcomes were included. ResultsThe number of potentially relevant studies constituted 4,321 hits. A total of 59 studies met the inclusion criteria and were qualitatively reviewed by the authors. Most of the included studies were conducted in the USA (N=18), followed by the UK (N=9), Germany (N=6), Finland (N=4), and non-EU countries (N=22). Evidence from the included studies suggests that the effect of excess weight differs by gender, ethnicity, country, and time period. White women with excess weight in the USA, the UK, Germany, Canada, and in the EU (multi-country analyses) are less likely to be employed, and when employed they face lower wages compared to normal weight counterparts. For men there is no effect of excess weight on employment outcomes or the magnitude of the effect is much smaller or even positive in some cases. ConclusionsThis review has shown that despite ample research on the relationship between excess weight and employment status and wages, robust causal evidence of the effects of excess weight on employment outcomes remains scarce and relies significantly on strong statistical and theoretical assumptions. Further research into these relationships outside of USA and Western Europe context is needed.

Local Independence Testing for Point Processes.

Constraint-based causal structure learning for point processes require empirical tests of local independence. Existing tests require strong model assumptions, e.g., that the true data generating model is a Hawkes process with no latent confounders. Even when restricting attention to Hawkes processes, latent confounders are a major technical difficulty because a marginalized process will generally not be a Hawkes process itself. We introduce an expansion similar to Volterra expansions as a tool to represent marginalized intensities. Our main theoretical result is that such expansions can approximate the true marginalized intensity arbitrarily well. Based on this, we propose a test of local independence and investigate its properties in real and simulated data.

Analyzing Queueing Problems via Bandits With Linear Reward & Nonlinear Workload Fairness

Queueing models serve as important building blocks in many networking applications such as task scheduling in mobile edge computing nodes, traffic scheduling in networks, congestion control in Internet, etc. However, queueing theory often needs to make strong assumptions about the arrival process or service rewards at each queue. In addition, fairness in serving workload among all queues is of great importance in many applications. In this paper, we address how to optimize resource allocation among multiple queues with a fairness guarantee and without any a priori knowledge of these queues' parameters. To characterize queues with unknown parameters and the fairness requirement, we formulate an online learning model with a varying and continuous action space, as well as a nonlinear utility objective. We design an online learning algorithm to tackle the problem. We prove that our algorithm has a regret upper bound of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\sqrt{T}\log T)$</tex-math></inline-formula> and our model has a regret lower bound of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Omega (\sqrt{T})$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$T$</tex-math></inline-formula> stands for the number of decision rounds. The asymptotic closeness of upper and lower bounds guarantees their near tightness and our algorithm's near optimality. We discuss our model's real-world applications in mobile edge computing, wireless networks, and crowdsourcing, and conduct simulations to validate our algorithm's effectiveness.

Citizen monitoring in environmental disclosure: An economics perspective

Criticism is mounting that market-led and state-led initiatives for environmental impact disclosure are too limited in scope and that they rest on too strong assumptions about the quality and impartiality of monitoring and enforcement, with resulting insufficient effect on environmental sustainability. It has been proposed that citizen monitoring may contribute to counteract this void. However, to our knowledge, policy analysis in general and economics in particular has not paid much attention to this role of citizen monitoring. This paper aims to bridge that gap from an economics lens, by exploring the dynamics of disclosing local environmental impact and the potential role of citizen monitoring in environmental policy. To this end, the paper addresses monopolistic versus pluralistic environmental disclosure, letting citizen monitoring represent the latter. The study uses the mining industry as an illustrative case, because of that sector's particular transparency challenges in international value chains, typically with strong negative local environmental impact. It is shown how pluralistic information provision such as citizen monitoring can contribute to incentivizing more reliable information provision, especially in countries with weak state institutions, which is particularly important in the case of high-risk environmental impact. The findings should be of use for shaping environmental policy, providing valuable insights for both policymakers and scholars.