references.bib

@misc{UNComtrade2024,
  author       = {{United Nations Statistics Division}},
  title        = {{UN Comtrade Database: International Trade Statistics}},
  year         = {2024},
  howpublished = {\url{https://comtrade.un.org/}},
  note         = {Accessed: October 2025},
  institution  = {{United Nations}},
}

@article{webber10rbo,
author = {Webber, William and Moffat, Alistair and Zobel, Justin},
title = {A similarity measure for indefinite rankings},
year = {2010},
issue_date = {November 2010},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {28},
number = {4},
issn = {1046-8188},
url = {https://doi.org/10.1145/1852102.1852106},
doi = {10.1145/1852102.1852106},
abstract = {Ranked lists are encountered in research and daily life and it is often of interest to compare these lists even when they are incomplete or have only some members in common. An example is document rankings returned for the same query by different search engines. A measure of the similarity between incomplete rankings should handle nonconjointness, weight high ranks more heavily than low, and be monotonic with increasing depth of evaluation; but no measure satisfying all these criteria currently exists. In this article, we propose a new measure having these qualities, namely rank-biased overlap (RBO). The RBO measure is based on a simple probabilistic user model. It provides monotonicity by calculating, at a given depth of evaluation, a base score that is non-decreasing with additional evaluation, and a maximum score that is nonincreasing. An extrapolated score can be calculated between these bounds if a point estimate is required. RBO has a parameter which determines the strength of the weighting to top ranks. We extend RBO to handle tied ranks and rankings of different lengths. Finally, we give examples of the use of the measure in comparing the results produced by public search engines and in assessing retrieval systems in the laboratory.},
journal = {ACM Trans. Inf. Syst.},
month = nov,
articleno = {20},
numpages = {38},
keywords = {Rank correlation, probabilistic models, ranking}
}

@article{Landry_XGI_2023,
author = {Landry, Nicholas W. and Lucas, Maxime and Iacopini, Iacopo and Petri, Giovanni and Schwarze, Alice and Patania, Alice and Torres, Leo},
title = {{XGI: A Python package for higher-order interaction networks}},
doi = {10.21105/joss.05162},
journal = {Journal of Open Source Software},
publisher = {The Open Journal},
year = {2023},
month = may,
volume = {8},
number = {85},
pages = {5162},
url = {https://doi.org/10.21105/joss.05162},
}

@article{reuters2022,
  title = {Exclusive: Russia's attack on Ukraine halts half of world's neon output chips clouding outlook},
  author = {Reuters},
  year = {2022},
  note = {Online: \url{https://www.reuters.com/technology/exclusive-ukraine-halts-half-worlds-neon-output-chips-clouding-outlook-2022-03-11/}}
}

@article{affinitiv2022,
  title = {The Automotive Chip Shortage Crisis: An August 2022 Update},
  author = {Affinitiv},
  year = {2022},
  note = {Online: \url{https://www.affinitiv.com/blog/the-automotive-chip-shortage-crisis-an-august-2022-update/}}
}

@article{fierceelectronics2022,
  title = {Ukraine war could hurt supplies of neon, palladium needed for chips},
  author = {{Fierce Electronics}},
  year = {2022},
  note = {Online: \url{https://www.fierceelectronics.com/electronics/ukraine-war-could-hurt-supplies-neon-palladium-needed-chips}}
}

@misc{yangSocraticMethodSelfDiscovery2023,
  title = {The {{Socratic Method}} for {{Self-Discovery}} in {{Large Language Models}}},
  author = {Yang, Runzhe and Narasimhan},
  year = {2023},
  month = may,
  journal = {Princeton NLP},
  urldate = {2025-04-10},
}

@article{costello_lifewire_2025,
	title = {Where Is the i{P}hone Made?},
	journaltitle = {LifeWire: Tech for Humans},
    journal = {LifeWire: Tech for Humans},
	author = {Costello, Sam},
	date = {2025-04-14},
	publisher = {Dotdash Meredith},
        url = {https://www.lifewire.com/where-is-the-iphone-made-1999503}
}

@article{rogin_zahn_2024,
	title = {Hospitals nationwide grapple with {IV} fluid shortage caused by {H}urricane {H}elene},
	journaltitle = {PBS News},
    journal = {PBS News},
	author = {Rogin, Ali and Zahn, Harry},
	date = {2024-12-14},
	note = {Accessed Jun 7, 2025},
	publisher = {Public Broadcasting System},
        url = {https://www.pbs.org/newshour/show/hospitals-nationwide-grapple-with-iv-fluid-shortage-caused-by-hurricane-helene}
}

@article{jewett_2024,
	title = {{U.S.} Races to Replenish Storm-Battered Supplies of {IV} Fluids at Hospitals},
	journaltitle = {New York Times},
    journal = {New York Times},
	author = {Jewett, Christina},
	date = {2024-10-08},
	note = {Accessed Jun 7, 2025},
	publisher = {The New York Times Company},
        url = {https://www.nytimes.com/2024/10/09/health/hurricane-helene-iv-shortages.html}
}

@inproceedings{cheng_can_2021,
	location = {New York, {NY}, {USA}},
	title = {Can You Fake It Until You Make It? {I}mpacts of Differentially Private Synthetic Data on Downstream Classification Fairness},
	isbn = {978-1-4503-8309-7},
	url = {https://doi.org/10.1145/3442188.3445879},
	doi = {10.1145/3442188.3445879},
	series = {{FAccT} '21},
	shorttitle = {Can You Fake It Until You Make It?},
	pages = {149--160},
	booktitle = {Proceedings of the 2021 {ACM} Conference on Fairness, Accountability, and Transparency},
	publisher = {Association for Computing Machinery},
	author = {Cheng, Victoria and Suriyakumar, Vinith M. and Dullerud, Natalie and Joshi, Shalmali and Ghassemi, Marzyeh},
	urldate = {2021-03-14},
	date = {2021-03-03},
	year = 2021,
    address = {New York, {NY}, {USA}}
}

@inproceedings{jayaraman_evaluating_2019,
	location = {Santa Clara, {CA}, {USA}},
	title = {Evaluating Differentially Private Machine Learning in Practice},
	abstract = {Differential privacy is a strong notion for privacy that can be used to prove formal guarantees, in terms of a privacy budget, , about how much information is leaked by a mechanism. When used in privacy-preserving machine learning, the goal is typically to limit what can be inferred from the model about individual training records. However, the calibration of the privacy budget is not well understood. Implementations of privacy-preserving machine learning often select large values of in order to get acceptable utility of the model, with little understanding of the impact of such choices on meaningful privacy. Moreover, in scenarios where iterative learning procedures are used, relaxed definitions of differential privacy are often used which appear to reduce the needed privacy budget but present poorly understood trade-offs between privacy and utility. In this paper, we quantify the impact of these choices on privacy in experiments with logistic regression and neural network models. Our main finding is that there is no way to obtain privacy for free—relaxed definitions of differential privacy that reduce the amount of noise needed to improve utility also increase the measured privacy leakage. Current mechanisms for differentially private machine learning rarely offer acceptable utility-privacy trade-offs for complex learning tasks: settings that provide limited accuracy loss provide little effective privacy, and settings that provide strong privacy result in useless models.},
	pages = {18},
	booktitle = {{USENIX} Security},
	publisher = {Usenix},
	author = {Jayaraman, Bargav and Evans, David},
	date = {2019-08},
	langid = {english},
	keywords = {differential privacy, evaluation, machine learning, measurement, notes:obsidian, read},
	year = 2019,
}

@inproceedings{patki_synthetic_2016,
	title = {The {S}ynthetic {D}ata {V}ault},
	doi = {10.1109/DSAA.2016.49},
	abstract = {The goal of this paper is to build a system that automatically creates synthetic data to enable data science endeavors. To achieve this, we present the Synthetic Data Vault ({SDV}), a system that builds generative models of relational databases. We are able to sample from the model and create synthetic data, hence the name {SDV}. When implementing the {SDV}, we also developed an algorithm that computes statistics at the intersection of related database tables. We then used a state-of-the-art multivariate modeling approach to model this data. The {SDV} iterates through all possible relations, ultimately creating a model for the entire database. Once this model is computed, the same relational information allows the {SDV} to synthesize data by sampling from any part of the database. After building the {SDV}, we used it to generate synthetic data for five different publicly available datasets. We then published these datasets, and asked data scientists to develop predictive models for them as part of a crowdsourced experiment. By analyzing the outcomes, we show that synthetic data can successfully replace original data for data science. Our analysis indicates that there is no significant difference in the work produced by data scientists who used synthetic data as opposed to real data. We conclude that the {SDV} is a viable solution for synthetic data generation.},
	eventtitle = {2016 {IEEE} International Conference on Data Science and Advanced Analytics ({DSAA})},
	pages = {399--410},
	booktitle = {2016 {IEEE} International Conference on Data Science and Advanced Analytics ({DSAA})},
	author = {Patki, Neha and Wedge, Roy and Veeramachaneni, Kalyan},
	date = {2016-10},
	keywords = {Computational modeling, crowd sourcing, Data models, data science, Databases, Hidden Markov models, Numerical models, predictive modeling, Predictive models, Synthetic data generation},
	year = 2016,
}

@inproceedings{hittmeir_utility_2019,
	location = {New York, {NY}, {USA}},
	title = {On the Utility of Synthetic Data: {A}n Empirical Evaluation on Machine Learning Tasks},
	isbn = {978-1-4503-7164-3},
	url = {https://dl.acm.org/doi/10.1145/3339252.3339281},
	doi = {10.1145/3339252.3339281},
	series = {{ARES} '19},
	shorttitle = {On the Utility of Synthetic Data},
	pages = {1--6},
	booktitle = {Proceedings of the 14th International Conference on Availability, Reliability and Security},
	publisher = {Association for Computing Machinery},
	author = {Hittmeir, Markus and Ekelhart, Andreas and Mayer, Rudolf},
	urldate = {2023-04-06},
	date = {2019-08-26},
	year = 2019,
    address = {New York, {NY}, {USA}},
}

@inproceedings{hittmeir_utility_2019-1,
	title = {Utility and Privacy Assessments of Synthetic Data for Regression Tasks},
	doi = {10.1109/BigData47090.2019.9005476},
	abstract = {With ever increasing capacity for collecting, storing, and processing of data, there is also a high demand for intelligent data analysis methods. While there have been impressive advances in machine learning and similar domains in recent years, this also gives rise to concerns regarding the protection of personal and otherwise sensitive data, especially if it is to be analysed by third parties. Besides anonymisation, which becomes challenging with high dimensional data, one approach for privacy-preserving data mining lies in the usage of synthetic data, which comes with the promise of protecting the users’ data and producing analysis results close to those achieved by using real data. In this paper, we analyse a number of different approaches for creating synthetic data, and study the utility of the created datasets for regression tasks, i.e. the prediction of a numeric value. We further investigate the similarity of real and synthetic data samples. Finally, we contribute to privacy assessments and measurements of the risk of attribute disclosure on synthetic data by extending an approach developed for categorical data.},
	eventtitle = {2019 {IEEE} International Conference on Big Data (Big Data)},
	pages = {5763--5772},
	booktitle = {2019 {IEEE} International Conference on Big Data (Big Data)},
	author = {Hittmeir, Markus and Ekelhart, Andreas and Mayer, Rudolf},
	date = {2019-12},
	keywords = {Attribute Disclosure Assessment, Data models, Data privacy, Machine learning, Machine Learning, Measurement, Privacy, Regression, Synthesizers, Synthetic Data, Task analysis, Tools},
	year = 2019,
}

@article{dankar_multi-dimensional_2022,
	title = {A Multi-Dimensional Evaluation of Synthetic Data Generators},
	volume = {10},
	issn = {2169-3536},
	doi = {10.1109/ACCESS.2022.3144765},
	pages = {11147--11158},
	journaltitle = {{IEEE} Access},
    journal = {{IEEE} Access},
	author = {Dankar, Fida K. and Ibrahim, Mahmoud K. and Ismail, Leila},
	date = {2022},
	note = {Conference Name: {IEEE} Access},
	keywords = {Correlation, Data models, Data utility, Generators, Machine learning, Measurement, Medical services, privacy enhancing technologies, Sociology, synthetic data generators},
	year = 2022,
}

@inproceedings{stadler_synthetic_2022,
	author = {Theresa Stadler and Bristena Oprisanu and Carmela Troncoso},
        title = {Synthetic Data {\textendash} Anonymisation {G}roundhog {D}ay},
        booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
        year = {2022},
        isbn = {978-1-939133-31-1},
        address = {Boston, MA},
        pages = {1451--1468},
        url = {https://www.usenix.org/conference/usenixsecurity22/presentation/stadler},
        publisher = {USENIX Association},
        month = aug
}

@inproceedings{ping_datasynthesizer_2017,
	location = {New York, {NY}, {USA}},
	title = {{DataSynthesizer}: {P}rivacy-Preserving Synthetic Datasets},
	isbn = {978-1-4503-5282-6},
	url = {https://dl.acm.org/doi/10.1145/3085504.3091117},
	doi = {10.1145/3085504.3091117},
	series = {{SSDBM} '17},
	shorttitle = {{DataSynthesizer}},
	abstract = {To facilitate collaboration over sensitive data, we present {DataSynthesizer}, a tool that takes a sensitive dataset as input and generates a structurally and statistically similar synthetic dataset with strong privacy guarantees. The data owners need not release their data, while potential collaborators can begin developing models and methods with some confidence that their results will work similarly on the real dataset. The distinguishing feature of {DataSynthesizer} is its usability --- the data owner does not have to specify any parameters to start generating and sharing data safely and effectively. {DataSynthesizer} consists of three high-level modules --- {DataDescriber}, {DataGenerator} and {ModelInspector}. The first, {DataDescriber}, investigates the data types, correlations and distributions of the attributes in the private dataset, and produces a data summary, adding noise to the distributions to preserve privacy. {DataGenerator} samples from the summary computed by {DataDescriber} and outputs synthetic data. {ModelInspector} shows an intuitive description of the data summary that was computed by {DataDescriber}, allowing the data owner to evaluate the accuracy of the summarization process and adjust any parameters, if desired. We describe {DataSynthesizer} and illustrate its use in an urban science context, where sharing sensitive, legally encumbered data between agencies and with outside collaborators is reported as the primary obstacle to data-driven governance. The code implementing all parts of this work is publicly available at https://github.com/{DataResponsibly}/{DataSynthesizer}.},
	pages = {1--5},
	booktitle = {Proceedings of the 29th International Conference on Scientific and Statistical Database Management},
	publisher = {Association for Computing Machinery},
	author = {Ping, Haoyue and Stoyanovich, Julia and Howe, Bill},
	urldate = {2023-04-18},
	date = {2017-06-27},
	year = 2017,
}

@article{boedihardjo_covariances_2022,
	title = {Covariance’s Loss is Privacy’s Gain: {C}omputationally Efficient, Private and Accurate Synthetic Data},
	issn = {1615-3383},
	url = {https://doi.org/10.1007/s10208-022-09591-7},
	doi = {10.1007/s10208-022-09591-7},
	shorttitle = {Covariance’s Loss is Privacy’s Gain},
	abstract = {The protection of private information is of vital importance in data-driven research, business and government. The conflict between privacy and utility has triggered intensive research in the computer science and statistics communities, who have developed a variety of methods for privacy-preserving data release. Among the main concepts that have emerged are anonymity and differential privacy. Today, another solution is gaining traction, synthetic data. However, the road to privacy is paved with {NP}-hard problems. In this paper, we focus on the {NP}-hard challenge to develop a synthetic data generation method that is computationally efficient, comes with provable privacy guarantees and rigorously quantifies data utility. We solve a relaxed version of this problem by studying a fundamental, but a first glance completely unrelated, problem in probability concerning the concept of covariance loss. Namely, we find a nearly optimal and constructive answer to the question how much information is lost when we take conditional expectation. Surprisingly, this excursion into theoretical probability produces mathematical techniques that allow us to derive constructive, approximately optimal solutions to difficult applied problems concerning microaggregation, privacy and synthetic data.},
	journaltitle = {Foundations of Computational Mathematics},
	shortjournal = {Found Comput Math},
	author = {Boedihardjo, March and Strohmer, Thomas and Vershynin, Roman},
	urldate = {2023-04-19},
	date = {2022-09-20},
	langid = {english},
	year = 2022,
}

@inproceedings{hittmeir_baseline_2020,
	location = {New York, {NY}, {USA}},
	title = {A Baseline for Attribute Disclosure Risk in Synthetic Data},
	isbn = {978-1-4503-7107-0},
	url = {https://dl.acm.org/doi/10.1145/3374664.3375722},
	doi = {10.1145/3374664.3375722},
	series = {{CODASPY} '20},
	abstract = {The generation of synthetic data is widely considered as viable method for alleviating privacy concerns and for reducing identification and attribute disclosure risk in micro-data. The records in a synthetic dataset are artificially created and thus do not directly relate to individuals in the original data in terms of a 1-to-1 correspondence. As a result, inferences about said individuals appear to be infeasible and, simultaneously, the utility of the data may be kept at a high level. In this paper, we challenge this belief by interpreting the standard attacker model for attribute disclosure as classification problem. We show how disclosure risk measures presented in recent publications may be compared to or even be reformulated as machine learning classification models. Our overall goal is to empirically analyze attribute disclosure risk in synthetic data and to discuss its close relationship to data utility. Moreover, we improve the baseline for attribute disclosure risk from the attacker's perspective by applying variants of the {RadiusNearestNeighbor} and the {EnsembleVote} classifier.},
	pages = {133--143},
	booktitle = {Proceedings of the Tenth {ACM} Conference on Data and Application Security and Privacy},
	publisher = {Association for Computing Machinery},
	author = {Hittmeir, Markus and Mayer, Rudolf and Ekelhart, Andreas},
	urldate = {2023-04-19},
	date = {2020-03-16},
	year = 2020,
}

@article{el_emam_seven_2020,
	title = {Seven Ways to Evaluate the Utility of Synthetic Data},
	volume = {18},
	issn = {1558-4046},
	doi = {10.1109/MSEC.2020.2992821},
	abstract = {Access to individual-level health data is going to be critical for managing the {COVID}-19 pandemic and enabling society to return to some form of (new) normal functioning. Broader data access is already starting to happen. At the same time, there has been growing alarm by the privacy community about the extent and manner of the level of data sharing that is going on with such sensitive information. In South Korea, broad data sharing has already resulted in some patients being reidentified and experiencing judgment and ridicule,1,2 and some governments have begun to reduce the amount of information being shared about {COVID}-19 cases.3-8 Data synthesis can provide a solution by enabling access to useful information while ensuring reasonable privacy protections.},
	pages = {56--59},
	number = {4},
	journaltitle = {{IEEE} Security \& Privacy},
	author = {El Emam, Khaled},
	date = {2020-07},
	note = {Conference Name: {IEEE} Security \& Privacy},
	keywords = {{COVID}-19, Data models, Data privacy, Information sharing, Public healthcare, Stability analysis, Viruses (medical)},
	year = 2020,
}

@inproceedings{ganev_robin_2022,
	title = {Robin {H}ood and {M}atthew Effects: {D}ifferential Privacy Has Disparate Impact on Synthetic Data},
	url = {https://proceedings.mlr.press/v162/ganev22a.html},
	shorttitle = {Robin {H}ood and {M}atthew Effects},
	eventtitle = {International Conference on Machine Learning},
	pages = {6944--6959},
	booktitle = {Proceedings of the 39th International Conference on Machine Learning},
	publisher = {{PMLR}},
	author = {Ganev, Georgi and Oprisanu, Bristena and Cristofaro, Emiliano De},
	urldate = {2023-04-19},
	date = {2022-06-28},
	langid = {english},
	note = {{ISSN}: 2640-3498},
    address = {Baltimore, MD},
    year = 2022,
}

@incollection{hutchison_pcps_2011,
	location = {Berlin, Heidelberg},
	title = {{PCPs} and the Hardness of Generating Private Synthetic Data},
	volume = {6597},
	isbn = {978-3-642-19570-9 978-3-642-19571-6},
	url = {http://link.springer.com/10.1007/978-3-642-19571-6_24},
	abstract = {Assuming the existence of one-way functions, we show that there is no polynomial-time, differentially private algorithm A that takes a database D ∈ (\{0, 1\}d)n and outputs a “synthetic database” D all of whose two-way marginals are approximately equal to those of D. (A two-way marginal is the fraction of database rows x ∈ \{0, 1\}d with a given pair of values in a given pair of columns.) This answers a question of Barak et al. ({PODS} ‘07), who gave an algorithm running in time poly(n, 2d).},
	pages = {400--416},
	booktitle = {Theory of Cryptography},
	publisher = {Springer Berlin Heidelberg},
	author = {Ullman, Jonathan and Vadhan, Salil},
	editor = {Ishai, Yuval},
	editorb = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard},
	editorbtype = {redactor},
	urldate = {2023-04-19},
	date = {2011},
	langid = {english},
	doi = {10.1007/978-3-642-19571-6_24},
	note = {Series Title: Lecture Notes in Computer Science},
	year = 2011,
}

@misc{harrison_synthetic_2023,
	title = {Synthetic Data To Support {US}-{UK} Prize Challenge For Developing Privacy Enhancing Methods: {P}redicting Individual Infection Risk During A Pandemic},
	url = {https://dataverse.lib.virginia.edu/citation?persistentId=doi:10.18130/V3/ZOG1FF},
	shorttitle = {Synthetic Data To Support {US}-{UK} Prize Challenge For Developing Privacy Enhancing Methods},
	abstract = {Abstract. This document describes synthetically produced epidemic data to support the 2022 {US}-{UK} Prize Challenge focused on advancing privacy-enhancing technologies ({PETs}). Announced by the White House in December 2021, this challenge is part of a series of International Grand Challenges on Democracy-Affirming Technologies; see https://www.whitehouse.gov/ ostp/news-updates/2021/12/08/us-and-uk-to-partner-on-a-prize-challenges-toadvance-privacy-enhancing-technologies/ for more details. In this challenge, participants will attempt to predict the likelihood of an individual getting infected by a disease in a privacy-preserving manner. This task, while of high public health relevance, has been hampered by data availability. The challenge participants will be able to develop and demonstrate their proposed solutions using a synthetic dataset that we have specifically created for this challenge. The synthetic data is developed by integrating realistic data to produce a synthetic social contact network, along with a synthetic outbreak that is similar to the {COVID}-19 pandemic. This dataset, which is very detailed and realistic, serves as ground truth. In addition to the description of the synthetic data, this document also describes three centralized baselines that can be used to evaluate the performance of the proposed methods.},
	publisher = {University of Virginia Dataverse},
	author = {Harrison, Galen and Chen, Jiangzhuo and Mortveit, Henning and Hoops, Stefan and Porebski, Przemyslaw and Xie, Dawen and Wilson, Amanda and Bhattacharya, Parantapa and Vullikanti, Anil and Xiong, Li and Marathe, Madhav},
	editora = {Porebski, Przemyslaw},
	editoratype = {collaborator},
	urldate = {2023-04-19},
	date = {2023},
	langid = {english},
	doi = {10.18130/V3/ZOG1FF},
	note = {Type: dataset},
	year = 2023,
}

@article{thorve_high_2023,
	title = {High resolution synthetic residential energy use profiles for the {U}nited {S}tates},
	volume = {10},
	rights = {2023 The Author(s)},
	issn = {2052-4463},
	url = {https://www.nature.com/articles/s41597-022-01914-1},
	doi = {10.1038/s41597-022-01914-1},
	abstract = {Efficient energy consumption is crucial for achieving sustainable energy goals in the era of climate change and grid modernization. Thus, it is vital to understand how energy is consumed at finer resolutions such as household in order to plan demand-response events or analyze impacts of weather, electricity prices, electric vehicles, solar, and occupancy schedules on energy consumption. However, availability and access to detailed energy-use data, which would enable detailed studies, has been rare. In this paper, we release a unique, large-scale, digital-twin of residential energy-use dataset for the residential sector across the contiguous United States covering millions of households. The data comprise of hourly energy use profiles for synthetic households, disaggregated into Thermostatically Controlled Loads ({TCL}) and appliance use. The underlying framework is constructed using a bottom-up approach. Diverse open-source surveys and first principles models are used for end-use modeling. Extensive validation of the synthetic dataset has been conducted through comparisons with reported energy-use data. We present a detailed, open, high resolution, residential energy-use dataset for the United States.},
	pages = {76},
	number = {1},
	journaltitle = {Scientific Data},
	shortjournal = {Sci Data},
	author = {Thorve, Swapna and Baek, Young Yun and Swarup, Samarth and Mortveit, Henning and Marathe, Achla and Vullikanti, Anil and Marathe, Madhav},
	urldate = {2023-04-21},
	date = {2023-02-06},
	langid = {english},
	note = {Number: 1
Publisher: Nature Publishing Group},
	keywords = {Energy and behaviour, Energy modelling},
	year = 2023,
}

@article{venkatramanan_using_2018,
	title = {Using data-driven agent-based models for forecasting emerging infectious diseases},
	volume = {22},
	issn = {1755-4365},
	url = {https://www.sciencedirect.com/science/article/pii/S1755436517300221},
	doi = {10.1016/j.epidem.2017.02.010},
	series = {The {RAPIDD} Ebola Forecasting Challenge},
	abstract = {Producing timely, well-informed and reliable forecasts for an ongoing epidemic of an emerging infectious disease is a huge challenge. Epidemiologists and policy makers have to deal with poor data quality, limited understanding of the disease dynamics, rapidly changing social environment and the uncertainty on effects of various interventions in place. Under this setting, detailed computational models provide a comprehensive framework for integrating diverse data sources into a well-defined model of disease dynamics and social behavior, potentially leading to better understanding and actions. In this paper, we describe one such agent-based model framework developed for forecasting the 2014–2015 Ebola epidemic in Liberia, and subsequently used during the Ebola forecasting challenge. We describe the various components of the model, the calibration process and summarize the forecast performance across scenarios of the challenge. We conclude by highlighting how such a data-driven approach can be refined and adapted for future epidemics, and share the lessons learned over the course of the challenge.},
	pages = {43--49},
	journaltitle = {Epidemics},
	shortjournal = {Epidemics},
	author = {Venkatramanan, Srinivasan and Lewis, Bryan and Chen, Jiangzhuo and Higdon, Dave and Vullikanti, Anil and Marathe, Madhav},
	urldate = {2023-04-21},
	date = {2018-03-01},
	langid = {english},
	keywords = {Agent-based models, Bayesian calibration, Ebola, Emerging infectious diseases, Simulation optimization},
    year = 2018,
}

@article{rubin_statistical_1993,
	title = {Statistical Disclosure Limitation},
	volume = {9},
	pages = {461--468},
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@article{yang_differential_2012,
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	doi = {10.29012/jpc.v4i1.613},
	shorttitle = {Differential Privacy for Protecting Multi-dimensional Contingency Table Data},
	number = {1},
	journaltitle = {Journal of Privacy and Confidentiality},
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	author = {Yang, Xiaolin and Fienberg, Stephen E. and Rinaldo, Alessandro},
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	title = {{PrivBayes}: {P}rivate Data Release via {B}ayesian Networks},
	volume = {42},
	issn = {0362-5915},
	url = {https://dl.acm.org/doi/10.1145/3134428},
	doi = {10.1145/3134428},
	shorttitle = {{PrivBayes}},
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	number = {4},
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	shortjournal = {{ACM} Trans. Database Syst.},
	author = {Zhang, Jun and Cormode, Graham and Procopiuc, Cecilia M. and Srivastava, Divesh and Xiao, Xiaokui},
	urldate = {2023-05-02},
	date = {2017-10-27},
    year = 2017
}

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  address = {Berlin, Heidelberg},
  pages = {239--246},
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  isbn = {978-3-540-87471-3}
}

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	title = {The Algorithmic Foundations of Differential Privacy},
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	issn = {1551-305X, 1551-3068},
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	doi = {10.1561/0400000042},
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	author = {Dwork, Cynthia and Roth, Aaron},
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	langid = {english},
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@unpublished{samarth_swarup_generating_2017,
	location = {São Paulo, Brazil},
	title = {Generating Synthetic Populations for Social Modeling},
	url = {https://biocomplexity.virginia.edu/sites/default/files/staff/madhavtut2.pdf},
	type = {Technical Report},
	howpublished = {Technical Report},
	note = {{AAMAS}},
	author = {{Samarth Swarup} and {Madhav Marathe}},
	urldate = {2022-05-03},
	date = {2017},
    year = 2017,
}

@article{cohenPrivateNumbersPublic2022,
  title = {Private {{Numbers}} in {{Public Policy}}: {{Census}}, {{Differential Privacy}}, and {{Redistricting}}},
  shorttitle = {Private {{Numbers}} in {{Public Policy}}},
  author = {Cohen, Aloni and Duchin, Moon and Matthews, J. N. and Suwal, Bhushan},
  year = {2022},
  month = jun,
  journal = {Harvard Data Science Review},
  number = {Special Issue 2},
  issn = {2644-2353, 2688-8513},
  doi = {10.1162/99608f92.22fd8a0e},
  urldate = {2023-04-28},
  langid = {english},
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}

@inproceedings{chenGSWGANGradientSanitizedApproach2020,
  title = {{{GS-WGAN}}: {{A Gradient-Sanitized Approach}} for {{Learning Differentially Private Generators}}},
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  booktitle = {Advances in {{Neural Information Processing Systems}}},
  author = {Chen, Dingfan and Orekondy, Tribhuvanesh and Fritz, Mario},
  year = {2020},
  volume = {33},
  pages = {12673--12684},
  publisher = {{Curran Associates, Inc.}},
  address = {Vacouver, Canada},
  urldate = {2023-05-10},
}

@inproceedings{harderDPMERFDifferentiallyPrivate2021,
  title = {{{DP-MERF}}: {{Differentially Private Mean Embeddings}} with {{RandomFeatures}} for {{Practical Privacy-preserving Data Generation}}},
  shorttitle = {{{DP-MERF}}},
  booktitle = {Proceedings of {{The}} 24th {{International Conference}} on {{Artificial Intelligence}} and {{Statistics}}},
  author = {Harder, Frederik and Adamczewski, Kamil and Park, Mijung},
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  pages = {1819--1827},
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  urldate = {2023-05-09},
  langid = {english},
  address = {Online}
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@inproceedings{narayananRobustDeanonymizationLarge2008,
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}

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  author = {Ullman, Jonathan and Vadhan, Salil},
  editor = {Hutchison, David and Kanade, Takeo and Kittler, Josef and Kleinberg, Jon M. and Mattern, Friedemann and Mitchell, John C. and Naor, Moni and Nierstrasz, Oscar and Pandu Rangan, C. and Steffen, Bernhard and Sudan, Madhu and Terzopoulos, Demetri and Tygar, Doug and Vardi, Moshe Y. and Weikum, Gerhard and Ishai, Yuval},
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}

@article{mckennaOptimizingErrorHighdimensional2018,
  title = {Optimizing Error of High-Dimensional Statistical Queries under Differential Privacy},
  author = {McKenna, Ryan and Miklau, Gerome and Hay, Michael and Machanavajjhala, Ashwin},
  year = {2018},
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@inproceedings{mckennaGraphicalmodelBasedEstimation2019,
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  langid = {english},
  address = {Long Beach, CA}
}


@inproceedings{zhangPrivSynDifferentiallyPrivate2021,
  title = {\{\vphantom\}{{PrivSyn}}\vphantom\{\}: {{Differentially Private Data Synthesis}}},
  shorttitle = {\{\vphantom\}{{PrivSyn}}\vphantom\{\}},
  booktitle = {30th {{USENIX Security Symposium}} ({{USENIX Security}} 21)},
  author = {Zhang, Zhikun and Wang, Tianhao and Li, Ninghui and Honorio, Jean and Backes, Michael and He, Shibo and Chen, Jiming and Zhang, Yang},
  year = {2021},
  pages = {929--946},
  urldate = {2023-05-02},
  isbn = {978-1-939133-24-3},
  langid = {english},
}

@article{vietriPrivateSyntheticData2022a,
  title = {Private Synthetic Data for Multitask Learning and Marginal Queries},
  author = {Vietri, Giuseppe and Archambeau, Cedric and Aydore, Sergul and Brown, William and Kearns, Michael and Roth, Aaron and Siva, Ankit and Tang, Shuai and Wu, Steven Z.},
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@inproceedings{aydoreDifferentiallyPrivateQuery2021a,
  title = {Differentially {{Private Query Release Through Adaptive Projection}}},
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  publisher = {{PMLR}},
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  urldate = {2023-05-09},
  langid = {english},
  address = {electronic}
}

@misc{xieDifferentiallyPrivateGenerative2018,
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    doi = {10.48550/arXiv.1802.06739},
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    urldate = {2023-05-09},
    date = {2018-02-19},
    year = {2018},
    eprinttype = {arxiv},
    eprint = {1802.06739 [cs, stat]},
    keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Cryptography and Security},
}

@inproceedings{torkzadehmahaniDPCGANDifferentiallyPrivate2019,
  title = {{{DP-CGAN}}: {{Differentially Private Synthetic Data}} and {{Label Generation}}},
  shorttitle = {{{DP-CGAN}}},
  booktitle = {2019 {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition Workshops}} ({{CVPRW}})},
  author = {Torkzadehmahani, Reihaneh and Kairouz, Peter and Paten, Benedict},
  year = {2019},
  month = jun,
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  publisher = {{IEEE}},
  address = {{Long Beach, CA, USA}},
  doi = {10.1109/CVPRW.2019.00018},
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  isbn = {978-1-72812-506-0},
  langid = {english},
}

@inproceedings{jordonPATEGANGeneratingSynthetic2018,
  title = {{{PATE-GAN}}: {{Generating Synthetic Data}} with {{Differential Privacy Guarantees}}},
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  author = {Jordon, James and Yoon, Jinsung and van der Schaar, Mihaela},
  year = {2019},
  publisher = {ICLR},
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  urldate = {2023-05-09},
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@misc{alzantotDifferentialPrivacySynthetic2019,
  title = {Differential {{Privacy Synthetic Data Generation}} Using {{WGANs}}},
  author = {Alzantot, Moustafa and Srivastava, Mani},
  year = {2019},
  file = {/Users/galenharrison/Zotero/storage/NYBXXMLH/Alzantot_Srivastava_2019_Differential Privacy Synthetic Data Generation using WGANs.pdf}
}

@misc{dingRetiringAdultNew2022,
  title = {Retiring {{Adult}}: {{New Datasets}} for {{Fair Machine Learning}}},
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@inproceedings{wuBoltonDifferentialPrivacy2017,
  title = {Bolt-on {{Differential Privacy}} for {{Scalable Stochastic Gradient Descent-based Analytics}}},
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}
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}

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  address={Honolulu, HI},
}
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@article{vanbreugelCanYouRely2023,
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  urldate = {2024-03-06},
  langid = {english},
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@article{sizikovaKnowledgebasedSilicoModels2023,
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@article{huMembershipInferenceAttacks2022,
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@inproceedings{yeomPrivacyRiskMachine2018,
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  shorttitle = {Privacy {{Risk}} in {{Machine Learning}}},
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  eventtitle = {2018 {{IEEE}} 31st {{Computer Security Foundations Symposium}} ({{CSF}})},
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@misc{duSystematicAssessmentTabular2024,
  title = {Systematic {{Assessment}} of {{Tabular Data Synthesis Algorithms}}},
  author = {Du, Yuntao and Li, Ninghui},
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  primaryclass = {cs},
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  urldate = {2024-04-16},
  archiveprefix = {arxiv},
  langid = {english},
}
@inproceedings{alaaHowFaithfulYour2022,
  title = {How {{Faithful}} Is Your {{Synthetic Data}}? {{Sample-level Metrics}} for {{Evaluating}} and {{Auditing Generative Models}}},
  shorttitle = {How {{Faithful}} Is Your {{Synthetic Data}}?},
  booktitle = {Proceedings of the 39th {{International Conference}} on {{Machine Learning}}},
  author = {Alaa, Ahmed and Breugel, Boris Van and Saveliev, Evgeny S. and van der Schaar, Mihaela},
  year = {2022},
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@article{chundawatUniversalMetricRobust2024,
  title = {A {{Universal Metric}} for {{Robust Evaluation}} of {{Synthetic Tabular Data}}},
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  year = {2024},
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@misc{arnoldReallyUsefulSynthetic2021,
  title = {Really {{Useful Synthetic Data}} -- {{A Framework}} to {{Evaluate}} the {{Quality}} of {{Differentially Private Synthetic Data}}},
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  year = {2021},
  month = oct,
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  primaryclass = {cs, stat},
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  archiveprefix = {arxiv},
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@article{hansenReimaginingSyntheticTabular2023,
  title = {Reimagining {{Synthetic Tabular Data Generation}} through {{Data-Centric AI}}: {{A Comprehensive Benchmark}}},
  shorttitle = {Reimagining {{Synthetic Tabular Data Generation}} through {{Data-Centric AI}}},
  author = {Hansen, Lasse and Seedat, Nabeel and {van der Schaar}, Mihaela and Petrovic, Andrija},
  year = {2023},
  month = dec,
  journal = {Advances in Neural Information Processing Systems},
  volume = {36},
  pages = {33781--33823},
  urldate = {2024-04-17},
  langid = {english},
}
@inproceedings{huSoKPrivacyPreservingData2023,
  title = {{{SoK}}: {{Privacy-Preserving Data Synthesis}}},
  shorttitle = {{{SoK}}},
  booktitle = {2024 {{IEEE Symposium}} on {{Security}} and {{Privacy}} ({{SP}})},
  author = {Hu, Yuzheng and Wu, Fan and Li, Qinbin and Long, Yunhui and Garrido, Gonzalo and Ge, Chang and Ding, Bolin and Forsyth, David and Li, Bo and Song, Dawn},
  year = {2023},
  month = oct,
  pages = {2--2},
  publisher = {IEEE Computer Society},
  issn = {2375-1207},
  doi = {10.1109/SP54263.2024.00002},
  urldate = {2024-04-16},
  isbn = {9798350331301},
  langid = {english},
}
@article{bowenComparativeStudyDifferentially2021,
  title = {Comparative {{Study}} of {{Differentially Private Synthetic Data Algorithms}} from the {{NIST PSCR Differential Privacy Synthetic Data Challenge}}},
  author = {Bowen, Claire McKay and Snoke, Joshua},
  year = {2021},
  month = feb,
  journal = {Journal of Privacy and Confidentiality},
  volume = {11},
  number = {1},
  issn = {2575-8527},
  doi = {10.29012/jpc.748},
  urldate = {2024-04-16},
  copyright = {http://creativecommons.org/licenses/by-nc-nd/4.0},
  langid = {english},
}

@misc{ganevInadequacySimilaritybasedPrivacy2023,
  title = {On the {{Inadequacy}} of {{Similarity-based Privacy Metrics}}: {{Reconstruction Attacks}} against ``{{Truly Anonymous Synthetic Data}}''},
  shorttitle = {On the {{Inadequacy}} of {{Similarity-based Privacy Metrics}}},
  author = {Ganev, Georgi and De Cristofaro, Emiliano},
  year = {2023},
  month = dec,
  number = {arXiv:2312.05114},
  eprint = {2312.05114},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2312.05114},
  urldate = {2024-04-17},
  archiveprefix = {arxiv},
}

@inproceedings{ganevRobinHoodMatthew2022,
  title = {Robin {{Hood}} and {{Matthew Effects}}: {{Differential Privacy Has Disparate Impact}} on {{Synthetic Data}}},
  shorttitle = {Robin {{Hood}} and {{Matthew Effects}}},
  booktitle = {Proceedings of the 39th {{International Conference}} on {{Machine Learning}}},
  author = {Ganev, Georgi and Oprisanu, Bristena and Cristofaro, Emiliano De},
  year = {2022},
  month = jun,
  pages = {6944--6959},
  publisher = {PMLR},
  issn = {2640-3498},
  urldate = {2023-04-19},
  langid = {english},
}
@article{giomiUnifiedFrameworkQuantifying2023,
  title = {A {{Unified Framework}} for {{Quantifying Privacy Risk}} in {{Synthetic Data}}},
  author = {Giomi, Matteo and Boenisch, Franziska and Wehmeyer, Christoph and Tasn{\'a}di, Borb{\'a}la},
  year = {2023},
  month = apr,
  journal = {Proceedings on Privacy Enhancing Technologies},
  volume = {2023},
  number = {2},
  pages = {312--328},
  issn = {2299-0984},
  doi = {10.56553/popets-2023-0055},
  urldate = {2024-04-17},
  copyright = {https://creativecommons.org/licenses/by/4.0/},
  langid = {english},
}
@inproceedings{harrisonJupyterLabRetrogradeContextual2024,
  title = {{{JupyterLab}} in {{Retrograde}}: {{Contextual Notifications That Highlight Fairness}} and {{Bias Issues}} for {{Data Scientists}}},
  shorttitle = {{{JupyterLab}} in {{Retrograde}}},
  booktitle = {Proceedings of the {{CHI Conference}} on {{Human Factors}} in {{Computing Systems}}},
  author = {Harrison, Galen and Bryson, Kevin and Bamba, Ahmad Emmanuel Balla and Dovichi, Luca and Binion, Aleksander Herrmann and Borem, Arthur and Ur, Blase},
  year = {2024},
  month = may,
  series = {{{CHI}} '24},
  pages = {1--19},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3613904.3642755},
  urldate = {2024-09-06},
  isbn = {9798400703300}
}
@inproceedings{hirschDesigningContestabilityInteraction2017,
  title = {Designing {{Contestability}}: {{Interaction Design}}, {{Machine Learning}}, and {{Mental Health}}},
  shorttitle = {Designing {{Contestability}}},
  booktitle = {Proceedings of the 2017 {{Conference}} on {{Designing Interactive Systems}}},
  author = {Hirsch, Tad and Merced, Kritzia and Narayanan, Shrikanth and Imel, Zac E. and Atkins, David C.},
  year = {2017},
  series = {{{DIS}} '17},
  pages = {95--99},
  publisher = {ACM},
  address = {New York, NY, USA},
  doi = {10.1145/3064663.3064703},
  urldate = {2019-02-16},
  isbn = {978-1-4503-4922-2}
}
@article{geigerMakingAlgorithmsPublic2024,
  title = {Making {{Algorithms Public}}: {{Reimagining Auditing}} from {{Matters}} of {{Fact}} to {{Matters}} of {{Concern}}},
  author = {Geiger, R Stuart and Tandon, Udayan and Gakhokidze, Anoolia and Song, Lian and Irani, Lilly},
  year = {2024},
  month = jan,
  journal = {International Journal of Communication},
  volume = {18},
  number = {2024},
  pages = {634--655},
  doi = {1932–8036/20240005},
  langid = {english}
}
@misc{shankarWhoValidatesValidators2024,
  title = {Who {{Validates}} the {{Validators}}? {{Aligning LLM-Assisted Evaluation}} of {{LLM Outputs}} with {{Human Preferences}}},
  shorttitle = {Who {{Validates}} the {{Validators}}?},
  author = {Shankar, Shreya and {Zamfirescu-Pereira}, J. D. and Hartmann, Bj{\"o}rn and Parameswaran, Aditya G. and Arawjo, Ian},
  year = {2024},
  month = apr,
  publisher = {arXiv},
  urldate = {2024-10-02},
  langid = {english}
}
@article{zhengJudgingLLMJudgeMTBench2023,
  title = {Judging {{LLM-as-a-Judge}} with {{MT-Bench}} and {{Chatbot Arena}}},
  author = {Zheng, Lianmin and Chiang, Wei-Lin and Sheng, Ying and Zhuang, Siyuan and Wu, Zhanghao and Zhuang, Yonghao and Lin, Zi and Li, Zhuohan and Li, Dacheng and Xing, Eric and Zhang, Hao and Gonzalez, Joseph E. and Stoica, Ion},
  year = {2023},
  month = dec,
  journal = {Advances in Neural Information Processing Systems},
  volume = {36},
  pages = {46595--46623},
  urldate = {2024-10-02},
  langid = {english}
}
@misc{ouyangTrainingLanguageModels2022,
  title = {Training Language Models to Follow Instructions with Human Feedback},
  author = {Ouyang, Long and Wu, Jeff and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll L. and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and Schulman, John and Hilton, Jacob and Kelton, Fraser and Miller, Luke and Simens, Maddie and Askell, Amanda and Welinder, Peter and Christiano, Paul and Leike, Jan and Lowe, Ryan},
  year = {2022},
  month = mar,
  number = {arXiv:2203.02155},
  eprint = {2203.02155},
  primaryclass = {cs},
  publisher = {arXiv},
  urldate = {2023-04-06},
  archiveprefix = {arXiv},
  langid = {english}
}
@misc{liSimpleEffectiveApproach2024,
  title = {A {{Simple}} but {{Effective Approach}} to {{Improve Structured Language Model Output}} for {{Information Extraction}}},
  author = {Li, Yinghao and Ramprasad, Rampi and Zhang, Chao},
  year = {2024},
  month = feb,
  number = {arXiv:2402.13364},
  eprint = {2402.13364},
  primaryclass = {cs},
  publisher = {arXiv},
  url = {https://arxiv.org/abs/2410.13341},
  urldate = {2024-11-07},
  archiveprefix = {arXiv},
  langid = {english}
}
@article{marxTechnologyEmergenceHazardous2010,
  title = {Technology: {{The Emergence}} of a {{Hazardous Concept}}},
  shorttitle = {Technology},
  author = {Marx, Leo},
  year = {2010},
  journal = {Technology and Culture},
  volume = {51},
  number = {3},
  pages = {561--577},
  publisher = {Johns Hopkins University Press},
  issn = {1097-3729},
  urldate = {2024-10-07}
}
@inproceedings{jacobsMeasurementFairness2021,
  title = {Measurement and {{Fairness}}},
  booktitle = {Proceedings of the 2021 {{ACM Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
  author = {Jacobs, Abigail Z. and Wallach, Hanna},
  year = {2021},
  month = mar,
  pages = {375--385},
  publisher = {ACM},
  address = {Virtual Event Canada},
  doi = {10.1145/3442188.3445901},
  urldate = {2023-03-14},
  isbn = {978-1-4503-8309-7},
  langid = {english}
}
@misc{wallachEvaluatingGenerativeAI2024,
  title = {Evaluating {{Generative AI Systems}} Is a {{Social Science Measurement Challenge}}},
  author = {Wallach, Hanna and Desai, Meera and Pangakis, Nicholas and Cooper, A. Feder and Wang, Angelina and Barocas, Solon and Chouldechova, Alexandra and Atalla, Chad and Blodgett, Su Lin and Corvi, Emily and Dow, P. Alex and {Garcia-Gathright}, Jean and Olteanu, Alexandra and Reed, Stefanie and Sheng, Emily and Vann, Dan and Vaughan, Jennifer Wortman and Vogel, Matthew and Washington, Hannah and Jacobs, Abigail Z.},
  year = {2024},
  month = nov,
  number = {arXiv:2411.10939},
  eprint = {2411.10939},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2411.10939},
  urldate = {2025-01-10},
  archiveprefix = {arXiv},
  langid = {english}
}
@misc{chouldechovaSharedStandardValid2024,
  title = {A {{Shared Standard}} for {{Valid Measurement}} of {{Generative AI Systems}}' {{Capabilities}}, {{Risks}}, and {{Impacts}}},
  author = {Chouldechova, Alexandra and Atalla, Chad and Barocas, Solon and Cooper, A. Feder and Corvi, Emily and Dow, P. Alex and {Garcia-Gathright}, Jean and Pangakis, Nicholas and Reed, Stefanie and Sheng, Emily and Vann, Dan and Vogel, Matthew and Washington, Hannah and Wallach, Hanna},
  year = {2024},
  month = dec,
  number = {arXiv:2412.01934},
  eprint = {2412.01934},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2412.01934},
  urldate = {2024-12-21},
  archiveprefix = {arXiv},
  langid = {english}
}

@misc{dowDimensionsGenerativeAI2024,
  title = {Dimensions of {{Generative AI Evaluation Design}}},
  author = {Dow, P. Alex and Vaughan, Jennifer Wortman and Barocas, Solon and Atalla, Chad and Chouldechova, Alexandra and Wallach, Hanna},
  year = {2024},
  month = nov,
  number = {arXiv:2411.12709},
  eprint = {2411.12709},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2411.12709},
  urldate = {2025-01-10},
  archiveprefix = {arXiv},
  langid = {english}
}
@misc{dornerLimitsScalableEvaluation2024,
  title = {Limits to Scalable Evaluation at the Frontier: {{LLM}} as {{Judge}} Won't Beat Twice the Data},
  shorttitle = {Limits to Scalable Evaluation at the Frontier},
  author = {Dorner, Florian E. and Nastl, Vivian Y. and Hardt, Moritz},
  year = {2024},
  month = oct,
  number = {arXiv:2410.13341},
  eprint = {2410.13341},
  primaryclass = {cs},
  publisher = {arXiv},
  url = {https://arxiv.org/abs/2410.13341},
  urldate = {2024-10-27},
  archiveprefix = {arXiv},
  langid = {english}
}
@misc{usgsMineralCommodities_2025,
    title={Mineral commodity summaries 2025 (ver. 1.2, March 2025): U.S. Geological Survey},
    shorttitle = {USGS Mineral Commodity Summaries 2025},
    author = {{U.S. Geological Survey}},
    year = {2025},
    month = mar,
    doi = {https://doi.org/10.3133/mcs2025},
    publisher = {U.S. Geological Survey},
    version = {1.2},
    pagecount = {212},
    urldate = {2025-06-24}
}

@misc{maurya2024ollama,
    title={Ollama: A Deep Dive into Running Large Language Models Locally(PART-1)},
    author = {Anoop Maurya},
    year = {2024},
    month = may,
    day = {14},
    publisher = {Medium},
    url={https://medium.com/@mauryaanoop3/ollama-a-deep-dive-into-running-large-language-models-locally-part-1-0a4b70b30982},
    urldate={2025-07-27}
}

@misc{srivastava2025,
    title={The Great LLM Debate: Are They Probabilistic or Stochastic?},
    author = {Rajesh Srivastava},
    year = {2025},
    month = jul,
    day = {22},
    publisher = {Medium},
    url={https://medium.com/@raj-srivastava/the-great-llm-debate-are-they-probabilistic-or-stochastic-3d1cd975994b},
    urldate={2025-07-27}
}

@misc{ollama_granite_2025,
    title={Ollama Model Library: granite3-dense},
    shorttitle = {granite3-dense},
    author = {Michael Chiang and Jeffrey Morgan},
    year = {2025},
    month = jul,
    publisher = {Ollama},
    url = {https://ollama.com/library/granite3-dense},
    urldate = {2025-06-27}
}

@misc{ollama_llama3.2_2025,
    title={Ollama Model Library: llama3.2},
    shorttitle = {llama3.2},
    author = {Michael Chiang and Jeffrey Morgan},
    year = {2025},
    month = jul,
    publisher = {Ollama},
    url = {https://ollama.com/library/llama3.2},
    urldate = {2025-06-27}
}

@misc{ollama_gemma3_2025,
    title={Ollama Model Library: gemma3},
    shorttitle = {gemma3},
    author = {Michael Chiang and Jeffrey Morgan},
    year = {2025},
    month = jul,
    publisher = {Ollama},
    url = {https://ollama.com/library/gemma3},
    urldate = {2025-06-27}
}

@misc{ollama_2025_website,
    title={Ollama: Get up and running with large language models.},
    author={Michael Chiang and Jeffrey Morgan},
    year = {2025},
    month = jul,
    publisher = {Ollama},
    url = {https://ollama.com},
    urldate = {2025-07-27}
}

@misc{ollama_library_2025,
    title={Ollama Model Library},
    author = {Michael Chiang and Jeffrey Morgan},
    year = {2025},
    month = jul,
    publisher = {Ollama},
    url = {https://ollama.com/library},
    urldate = {2025-07-27}
}

@misc{perplexity_2025,
     title={Introducing Perplexity Deep Research},
     author={Perplexity Team},
     year = {2025},
     month = feb,
     day = {14},
     publisher = {Perplexity.ai},
     url={https://www.perplexity.ai/hub/blog/introducing-perplexity-deep-research,
     urldate= {2025-08-05}}}

@article{aghaei2025potential,
  title   = {The Potential of Large Language Models in Supply Chain Management: Advancing Decision-Making, Efficiency, and Innovation},
  author  = {Aghaei, Raha and Kiaei, Ali A. and Boush, Mahnaz and Vahidi, Javad and Barzegar, Zeynab and Rofoosheh, Mahan},
  journal = {arXiv:2501.15411},
  year    = {2025},
  url     = {https://arxiv.org/abs/2501.15411},
  doi     = {10.48550/arXiv.2501.15411}
}

@article{wang2025llms,
  title   = {LLMs for Supply Chain Management},
  author  = {Wang, Haojie and Jiang, Jiuyun and Hong, L. Jeff and Jiang, Guangxin},
  journal = {arXiv preprint arXiv:2505.18597},
  year    = {2025},
  url     = {https://arxiv.org/abs/2505.18597},
  doi     = {10.48550/arXiv.2505.18597}
}

@article{adenekan2025examining,
  title   = {Examining the integration of artificial intelligence in supply chain management during the transition from Industry 4.0 to Industry 6.0},
  author  = {Adenekan, Adeola},
  journal = {Frontiers in Artificial Intelligence},
  volume  = {4},
  year    = {2025},
  month   = {January},
  day     = {20},
  url     = {https://pmc.ncbi.nlm.nih.gov/articles/PMC11788849/},
  note    = {Published Online: Jan 20, 2025}
}

@article{wong2024enhancing,
  title   = {Enhancing Supply Chain Traceability through Blockchain and IoT Integration: A Comprehensive Review},
  author  = {Wong, Elton Kee Sheng and Ting, Huong Yong and Atanda, Abdulwahab Funsho},
  journal = {Green Intelligent Systems and Applications},
  volume  = {4},
  number  = {1},
  pages   = {11--28},
  year    = {2024},
  doi     = {10.53623/gisa.v4i1.355},
  url     = {https://tecnoscientifica.com/journal/gisa/article/view/355}
}

@article{rezki2024machine,
  title     = {Machine Learning for Proactive Supply Chain Risk Management: Predicting Delays and Enhancing Operational Efficiency},
  author    = {Rezki, Nisrine and Mansouri, Mohamed},
  journal   = {Management Systems in Production Engineering},
  volume    = {32},
  number    = {3},
  pages     = {345--356},
  year      = {2024},
  month     = {September},
  day       = {5},
  doi       = {10.2478/mspe-2024-0033},
  url       = {https://sciendo.com/article/10.2478/mspe-2024-0033},
  publisher = {Sciendo}
}

@article{fang2024potential,
  title   = {The Potential Application of Large Language Models in Pharmaceutical Supply Chain Management},
  author  = {Fang, Michael and others},
  journal = {PMC},
  year    = {2024},
  month   = {April},
  day     = {8},
  url     = {https://pmc.ncbi.nlm.nih.gov/articles/PMC11001215/}
}

@inproceedings{han2021technology,
  title  = {Technology Adoption in Dependency Networks: A Study of the Python Programming Language},
  author = {Han, Xintong and Xu, Lei},
  year   = {2021},
  month  = {November},
  day    = {24},
  url    = {https://leixu.org/Xu_TechAdoption.pdf},
  note   = {Working Paper}
}

@article{awasthi2024artificial,
  title   = {Artificial Intelligence in Supply Chain Management},
  author  = {Awasthi, Shyla},
  journal = {Journal of Student Research},
  volume  = {13},
  number  = {1},
  year    = {2024},
  month   = {February},
  day     = {29},
  doi     = {10.47611/jsrhs.v13i1.5996},
  url     = {https://www.jsr.org/hs/index.php/path/article/view/5996}
}

@misc{cambridge2016supply-2,
  title       = {Supply Chain AI Lab},
  author      = {{Institute for Manufacturing}},
  year        = {2016},
  institution = {University of Cambridge},
  url         = {https://www.ifm.eng.cam.ac.uk/research/supply-chain-ai-lab/},
  note        = {Accessed 2025}
}

@article{elbaz2021supply,
  title   = {Can supply chain risk management practices mitigate the disruption impacts on supply chains' resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era},
  author  = {El Baz, Jamal and Ruel, Salomée},
  journal = {International Journal of Production Economics},
  year    = {2021},
  month   = {March},
  url     = {https://pmc.ncbi.nlm.nih.gov/articles/PMC9759337/},
  note    = {Published online 2020 Oct 31}
}

@article{pham2023supply,
  title   = {Supply Chain Risk and its Impact on Performance: A Structured Literature Review},
  author  = {Pham, Thanh Hai and Testorelli, Raffaele and Verbano, Chiara},
  journal = {Journal of Industrial Engineering and Management},
  volume  = {16},
  number  = {2},
  pages   = {236--262},
  year    = {2023},
  doi     = {10.3926/jiem.4719},
  url     = {https://www.jiem.org/index.php/jiem/article/download/4719/1052}
}

@techreport{ibm2023quantum,
  title       = {Quantum computing in supply chain},
  author      = {{IBM Institute for Business Value}},
  year        = {2023},
  month       = {March},
  day         = {17},
  institution = {IBM},
  url         = {https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/quantum-computing-supplychain}
}

@techreport{nist2016cybersecurity,
  title       = {Cybersecurity Supply Chain Risk Management},
  author      = {{NIST Computer Security Resource Center}},
  year        = {2016},
  month       = {May},
  day         = {24},
  institution = {National Institute of Standards and Technology},
  url         = {https://csrc.nist.gov/projects/cyber-supply-chain-risk-management}
}

@online{mckinsey2024digital,
  title  = {Digital twins: The next frontier of factory optimization},
  author = {{McKinsey \& Company}},
  year   = {2024},
  month  = {January},
  day    = {10},
  url    = {https://www.mckinsey.com/capabilities/operations/our-insights/digital-twins-the-next-frontier-of-factory-optimization}
}

@online{maersk2024digital,
  title  = {Unlocking the true potential of digital twins in supply chains},
  author = {{A.P. Moller - Maersk}},
  year   = {2024},
  month  = {May},
  day    = {30},
  url    = {https://www.maersk.com/insights/digitalisation/2024/05/30/digital-twins-supply-chain}
}

@online{relex2025digital,
  title  = {Digital twins in supply chain management},
  author = {{RELEX Solutions}},
  year   = {2025},
  month  = {June},
  day    = {25},
  url    = {https://www.relexsolutions.com/resources/digital-twin-supply-chain/}
}

@online{viso2025computer,
  title  = {Popular applications of computer vision in manufacturing},
  author = {{Viso.ai}},
  year   = {2025},
  month  = {June},
  day    = {2},
  url    = {https://viso.ai/applications/computer-vision-in-manufacturing/}
}

@online{autmix2025computer,
  title  = {Computer vision for quality control},
  author = {{Autmix}},
  year   = {2025},
  month  = {March},
  day    = {28},
  url    = {https://autmix.com/en/blog/computer-vision-quality-control}
}

@online{macrometa2023edge,
  title  = {How Edge Computing Optimizes Supply Chain Management For eCommerce},
  author = {{Macrometa}},
  year   = {2023},
  month  = {April},
  day    = {20},
  url    = {https://www.macrometa.com/articles/how-edge-computing-optimizes-supply-chain-management-for-ecommerce}
}

@online{meegle2024edge,
  title  = {Edge Computing In Logistics},
  author = {{Meegle}},
  year   = {2024},
  month  = {December},
  day    = {11},
  url    = {https://www.meegle.com/en_us/topics/supply-chain/edge-computing-in-logistics}
}

@online{advatix2024edge,
  title  = {Edge Computing in Supply Chain: Real-Time Data Processing and Decision Making},
  author = {{Advatix}},
  year   = {2024},
  month  = {August},
  day    = {27},
  url    = {https://www.advatix.com/blog/edge-computing-in-supply-chain-real-time-data-processing-and-decision-making/}
}

@online{lumenalta2025rpa,
  title  = {7 benefits of robotic process automation in supply chain},
  author = {{Lumenalta}},
  year   = {2025},
  month  = {January},
  day    = {3},
  url    = {https://lumenalta.com/insights/7-benefits-of-robotic-process-automation-in-supply-chain}
}

@online{automationanywhere2024rpa,
  title  = {RPA in Supply Chain},
  author = {{Automation Anywhere}},
  year   = {2024},
  month  = {July},
  day    = {10},
  url    = {https://www.automationanywhere.com/solutions/manufacturing/rpa-in-supply-chain}
}

@online{upguard2025cyber,
  title  = {What is Cyber Supply Chain Risk Management?},
  author = {{UpGuard}},
  year   = {2025},
  month  = {June},
  day    = {24},
  url    = {https://www.upguard.com/blog/cyber-supply-chain-risk-management}
}

@online{techtarget2025quantum,
  title  = {How Quantum Computing Can Improve Supply Chains},
  author = {{TechTarget}},
  year   = {2025},
  month  = {April},
  day    = {17},
  url    = {https://www.techtarget.com/searchcio/tip/How-quantum-computing-can-improve-supply-chains}
}

@article{boger2025quantum,
  title   = {How Quantum Computing Will Transform Logistics},
  author  = {Boger, Yuval},
  journal = {Forbes},
  year    = {2025},
  month   = {January},
  day     = {23},
  url     = {https://www.forbes.com/councils/forbesbusinessdevelopmentcouncil/2025/01/23/how-quantum-computing-will-transform-logistics/}
}

@online{rice2024nlp,
  title  = {How Natural Language Processing is Streamlining Supply Chains},
  author = {Rice, [First Name]},
  year   = {2024},
  month  = {December},
  day    = {5},
  url    = {https://www.linkedin.com/pulse/how-natural-language-processing-streamlining-supply-chains-rice-tidue}
}

@online{packagex2025nlp,
  title  = {NLP and Logistics: Revolutionizing Trade \& Efficiency},
  author = {{PackageX}},
  year   = {2025},
  month  = {June},
  day    = {30},
  url    = {https://packagex.io/blog/nlp-and-logistics}
}

@online{numberanalytics2025nlp,
  title  = {Top 7 Ways Natural Language Processing Enhances Manufacturing},
  author = {Lee, Sarah},
  year   = {2025},
  month  = {March},
  day    = {27},
  url    = {https://www.numberanalytics.com/blog/nlp-manufacturing-efficiency}
}

@online{datex2024impact,
  title  = {The Impact of 5G on Warehousing and Supply Chain Operations},
  author = {{Datex Corp}},
  year   = {2024},
  month  = {May},
  day    = {29},
  url    = {https://www.datexcorp.com/impact-of-5g-on-warehousing/}
}

@online{denny2024how,
  title  = {How 5G Technology is Accelerating Real-Time Supply Chain Visibility},
  author = {Denny, Efrata},
  year   = {2024},
  month  = {September},
  day    = {23},
  url    = {https://www.linkedin.com/pulse/how-5g-technology-accelerating-real-time-supply-chain-efrata-denny-n5dwc}
}

@online{nsf2025supply,
  title  = {Supply Chain Sustainability and ESG Management},
  author = {{NSF International}},
  year   = {2025},
  url    = {https://www.nsf.org/sustainability/supply-chain-sustainability-esg-management}
}

@online{nsf2025key,
  title  = {Key Strategies for Effective Supply Chain ESG Reporting},
  author = {{NSF International}},
  year   = {2025},
  month  = {January},
  day    = {15},
  url    = {https://www.nsf.org/knowledge-library/key-strategies-effective-supply-chain-esg-reporting}
}

@online{coupa2025esg,
  title  = {ESG in Supply Chains: Moving From Risk to Opportunity},
  author = {{Coupa}},
  year   = {2025},
  month  = {April},
  day    = {21},
  url    = {https://www.coupa.com/blog/supply-chain-esg/}
}

@article{georgetown2024role,
  title  = {The Role of AI in Developing Resilient Supply Chains},
  author = {{Georgetown Journal of International Affairs}},
  year   = {2024},
  month  = {February},
  day    = {5},
  url    = {https://gjia.georgetown.edu/2024/02/05/the-role-of-ai-in-developing-resilient-supply-chains/}
}

@article{mdpi2022benefit,
  title   = {A Benefit Dependency Network for Shadow Information Technology Adoption, Based on Practitioners' Viewpoints},
  author  = {[Authors Not Specified]},
  journal = {Informatics},
  volume  = {9},
  number  = {4},
  pages   = {95},
  year    = {2022},
  month   = {November},
  day     = {24},
  doi     = {10.3390/informatics9040095},
  url     = {https://www.mdpi.com/2227-9709/9/4/95}
}

@misc{wholechain2019blockchain,
  title  = {Blockchain Based Supply Chain Traceability},
  author = {{Wholechain}},
  year   = {2019},
  url    = {https://wholechain.com}
}

@online{ibm2024ai,
  title  = {What Is AI in Supply Chain?},
  author = {{IBM}},
  year   = {2024},
  month  = {May},
  day    = {20},
  url    = {https://www.ibm.com/think/topics/ai-supply-chain}
}

@online{perfectplanner2024role,
  title  = {The Role of Machine Learning in Supply Chain Risk Management},
  author = {{Perfect Planner}},
  year   = {2024},
  month  = {May},
  day    = {9},
  url    = {https://perfectplanner.io/machine-learning/}
}

@article{li2023optiguide,
  title={Large Language Models for Supply Chain Optimization},
  author={Li, Beibin and Mellou, Konstantina and Zhang, Bo and Pathuri, Jeevan and Menache, Ishai},
  journal={arXiv:2307.03875},
  year={2023},
  month={July},
  day={13},
  url={https://arxiv.org/pdf/2307.03875.pdf}
}

@inproceedings{google2015automated,
  title	= {Automated Decomposition of Build Targets},
  author	= {Mohsen Vakilian and Raluca Sauciuc and J. David Morgenthaler and Vahab Mirrokni},
  year	= {2015},
  URL	= {http://conferences.computer.org/icse/2015/content/papers/1934a123.pdf},
  booktitle	= {Proceedings of the 37th International Conference on Software Engineering},
  pages	= {123--133}
}

@article{wikipedia2020dependency,
  title={Dependency network},
  author={{Wikipedia Contributors}},
  journal={Wikipedia},
  year={2020},
  month={September},
  day={22},
  note = {Online: \url{https://en.wikipedia.org/wiki/Dependency_network}}
}

@techreport{argonne2015critical,
  title={Analysis of Critical Infrastructure Dependencies and Interdependencies},
  author={Petit, Frederic and Verner, Duane and Brannegan, Dan and Buehring, William and Dickinson, Douglas and Guziel, Kibaek and Haffenden, Robert and Phillips, Joseph and Peerenboom, James},
  institution={Argonne National Laboratory},
  year={2015},
  month={June},
  note={Online: \url{https://publications.anl.gov/anlpubs/2015/06/111906.pdf}}
}

@misc{cisa2024infrastructure,
  title={Infrastructure Dependency Primer},
  author={{Cybersecurity and Infrastructure Security Agency}},
  year={2024},
  note = {Online: \url{https://www.cisa.gov/topics/critical-infrastructure-security-and-resilience/resilience-services/infrastructure-dependency-primer}}
}

@online{newswire2024mapping,
  title={Mapping Your Supply Chains: A Guide to Risk Prioritization and Action},
  author={{CIRAS}},
  year={2024},
  month={July},
  day={2},
  note = {Online: \url{https://newswire.ciras.iastate.edu/2024/07/02/mapping-your-supply-chains-a-guide-to-risk-prioritization-and-action/}}
}

@article{df-alliance2023supply,
  title={Supply Chain Mapping: Importance \& Benefits},
  author={{DF Alliance}},
  year={2023},
  month={September},
  day={28},
  note = {Online: \url{https://www.df-alliance.com/blog/supply-chain-mapping-what-it-is--why-it-s-important}}
}

@online{ibm2023supply,
  title={What Is Supply Chain Risk Management?},
  author={{IBM}},
  year={2023},
  month={December},
  day={2},
  note = {Online: \url{https://www.ibm.com/think/topics/supply-chain-risk-management}}
}

@online{wikipedia2019supply,
  title={Supply chain risk management},
  author={{Wikipedia Contributors}},
  year={2019},
  month={November},
  day={5},
  note = {Online: \url{https://en.wikipedia.org/wiki/Supply_chain_risk_management}}
}

@online{netsuite2024supply,
  title={Supply Chain Traceability: Benefits \& Implementation Blueprint},
  author={Schwartz, Lisa},
  year={2024},
  month={October},
  day={29},
  note = {Online: \url{https://www.netsuite.com/portal/resource/articles/erp/supply-chain-traceability.shtml}}
}

@techreport{nist2024supply,
  title={Supply Chain Traceability: Manufacturing Meta-Framework},
  author={Pease, Michael and Wallace, Evan and Reed, Harvey and Martin, Vivian L and Granata, Steve},
  institution={National Institute of Standards and Technology},
  year={2024},
  month={September},
  day={27},
  number={NIST IR 8536 ipd},
  url={https://nvlpubs.nist.gov/nistpubs/ir/2024/NIST.IR.8536.ipd.pdf},
  note = {Online: \url{https://nvlpubs.nist.gov/nistpubs/ir/2024/NIST.IR.8536.ipd.pdf}}
}

@online{linkedin2024llm,
  title={The Role of Large Language Models (LLMs) in Supply Chain Management},
  author={Raper, Jason},
  year={2024},
  month={September},
  day={22},
  note = {Online: \url{https://www.linkedin.com/pulse/role-large-language-models-llms-supply-chain-management-jason-raper-tk2hc}}
}

@article{maryniak2024chatgpt,
  title={ChatGPT in supply chain management--a research model},
  author={Maryniak, Anna and Pogorzelec-Glaser, Katarzyna},
  journal={Zeszyty Naukowe Politechniki {\'S}ląskiej. Organizacja i Zarządzanie},
  number={203},
  year={2024}
}

@online{pacificdataintegrators2024transformative,
  title={Transformative Use Cases in Supply Chain Management},
  author={{Pacific Data Integrators}},
  year={2024},
  month={October},
  day={4},
  note = {Online: \url{https://www.pacificdataintegrators.com/blogs/unlocking-the-potential-of-llms}}
}

@online{cambridge2016supply,
  title={Predicting Hidden Supply Network Dependencies},
  author={{Institute for Manufacturing}},
  institution={University of Cambridge},
  year={2016},
  note = {Online: \url{https://www.ifm.eng.cam.ac.uk/research/supply-chain-ai-lab/current-themes/supply-chain-link-prediction/}}
}

@article{pmc2008differential,
  title={Differential dependency network analysis to identify condition-specific topological changes in biological networks},
  author={Zhang, Xinyu and Zhao, Xing-Ming and He, Kun and Lu, Le and Cao, Yiwei and Liu, Jingdong and Hao, Jin-Kao and Liu, Zhi-Ping and Chen, Luonan},
  journal={Bioinformatics},
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  number={4},
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  year={2008},
  month={December},
  day={26},
  url={https://pmc.ncbi.nlm.nih.gov/articles/PMC2642641/}
}

@online{mills2023ai,
  title={AI in Knowledge Synthesis: Turning Information into Insight},
  author={Mills, Olivier},
  year={2023},
  month={May},
  day={9},
  note = {Online: \url{https://oliviermills.com/articles/ai-knowledge-synthesis-turning-information-insight}}
}

@Article{arxiv2024automating,
  author    = {Susnjak, Teo and Hwang, Peter and Reyes, Napoleon and Barczak, Andre L. C. and McIntosh, Timothy and Ranathunga, Surangika},
  journal   = {ACM Transactions on Knowledge Discovery from Data},
  title     = {Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning},
  year      = {2025},
  issn      = {1556-472X},
  month     = mar,
  number    = {3},
  pages     = {1--39},
  volume    = {19},
  doi       = {10.1145/3715964},
  publisher = {Association for Computing Machinery (ACM)},
}


@article{shah2025roleprompting,
  title={Mastering role prompting: How to get the best responses from LLMs},
  author={Drishti Shah},
  journal={Portkey},
  year={2025},
  month={March},
  day={3},
  note = {Online: \url{https://portkey.ai/blog/role-prompting-for-llms}}
}

@techreport{riggs2025,  
  author       = {Powers‐Riggs, Aidan and Hart, Brian and Funaiole, Matthew P. and Thomsett, Scott and Zieleniewski, Simon and Rose, Tim},  
  title        = {Beyond Rare Earths: China’s Growing Threat to Gallium Supply Chains},  
  institution  = {Center for Strategic and International Studies},  
  year         = {2025},  
  month        = {July},  
  type         = {CSIS Brief},  
  note         = {Based on data from Rovjok},  
  url          = {https://www.csis.org/analysis/beyond-rare-earths-chinas-growing-threat-gallium-supply-chains}  
}  

@article{kong2024better,
  title={Better Zero-Shot Reasoning with Role-Play Prompting},
  author={Aobo Kong and Shiwan Zhao and Hao Chen and Qicheng Li and Yong Qin and Ruiqi Sun and Xin Zhou and Enzhi Wang and Xiaohang Dong},
  journal={arXiv preprint arXiv:2308.07702},
  year={2024},
  url={https://arxiv.org/abs/2308.07702}
}

@article{Wang2014II-RFID,
  author = {Ke-Sheng Wang and Gang Jin and Yan-Chen Wang and Wenbin Wang},
  title = {Intelligent and integrated RFID (II-RFID) system for improving traceability in manufacturing},
  journal = {Advances in Manufacturing},
  volume = {2},
  number = {2},
  pages = {138--147},
  year = {2014},
  doi = {10.1007/s40436-014-0053-6},
  publisher = {Springer},
}

@inproceedings{Lick2023Collaborative,
  author = {Lick, Jonas and Wohlers, Benjamin and Sahrhage, Philip and Schreckenberg, Felix and Klöckner, Stefan and Enzberg, Sebastian and Kühn, Alexander and Dumitrescu, Roman},
  title = {Integrating Domain Expertise and Artificial Intelligence for Effective Supply Chain Management Planning Tasks: A Collaborative Approach},
  booktitle = {Artificial Intelligence, Social Computing and Wearable Technologies. AHFE (2023) International Conference},
  editor = {Waldemar Karwowski and Tareq Ahram},
  publisher = {AHFE International, USA},
  volume = {113},
  year = {2023},
  doi = {10.54941/ahfe1004185},
  organization = {AHFE Open Access}
}

@misc{Guan2024RiskInterdependency,
  author = {Li Guan and Alireza Abbasi and Michael J. Ryan and José M. Merigó},
  title = {An Integrated Risk Interdependency Network-Driven Multi-Criteria Decision-Making Model for Project Risk Assessment},
  howpublished = {Available at SSRN},
  year = {2024},
  url = {https://ssrn.com/abstract=4715244},
  doi = {10.2139/ssrn.4715244},
  note = {SSRN Working Paper}
}

@article{juttner2003supply,
  title={Supply chain risk management: outlining an agenda for future research},
  author={J{\"u}ttner, Uta and Peck, Helen and Christopher, Martin},
  journal={International Journal of Logistics: research and applications},
  volume={6},
  number={4},
  pages={197--210},
  year={2003},
  publisher={Taylor \& Francis}
}

@article{li2025supply,
  author    = {Li, Qingrui},
  title     = {Supply Chain Risk Management: Theory and Practice},
  journal   = {Scientific Journal of Economics and Management Research},
  volume    = {7},
  number    = {1},
  pages     = {136--147},
  year      = {2025},
  doi       = {10.54691/9zbm4p87},
  url       = {https://doi.org/10.54691/9zbm4p87}
}

@article{dziczek2022chipcrisis,
  author    = {Dziczek, Kristin},
  title     = {Why the Automotive Chip Crisis Isn't Over (Yet)},
  journal   = {Chicago Fed Letter},
  number    = {473},
  year      = {2022},
  month     = {October},
  institution = {Federal Reserve Bank of Chicago},
  url       = {https://www.chicagofed.org/publications/chicago-fed-letter/2022/473}
}

@article{ZHU2025100234,
title = {Exploring cascading failures in supply chain risk management: A systematic review, 2013-2024},
journal = {Journal of Safety Science and Resilience},
pages = {100234},
year = {2025},
issn = {2666-4496},
doi = {https://doi.org/10.1016/j.jnlssr.2025.100234},
url = {https://www.sciencedirect.com/science/article/pii/S2666449625000684},
author = {{Xiao-xin} Zhu and Jiahui Zhu and David Regan and {Zhi-min} Wen},
keywords = {Supply chain network, Cascading failure, Risk management, Literature review}
}

@article{optimizing2024analytics,
  author    = {Ibrahim Adedeji Adeniran and Christianah Pelumi Efunniyi and Olajide Soji Osundare and Angela Omozele Abhulimen},
  title     = {Optimizing logistics and supply chain management through advanced analytics},
  journal   = {International Journal of Scientific Research \& Engineering Trends},
  year      = {2024},
  month     = {August},
  url       = {https://srrjournals.com/ijsret/content/optimizing-logistics-and-supply-chain-management-through-advanced-analytics-insights}
}

@article{che2025efficient,
  author    = {Che, M. and Wei, Y. and Yan, H.},
  title     = {Efficient algorithms for Tucker decomposition via approximate matrix multiplication},
  journal   = {Advances in Computational Mathematics},
  volume    = {51},
  pages     = {20},
  year      = {2025},
  doi       = {10.1007/s10444-025-10232-0},
  url       = {https://doi.org/10.1007/s10444-025-10232-0}
}

@article{liu2025finite,
  title={Finite mixtures of functional graphical models: Uncovering heterogeneous dependencies in high-dimensional data},
  author={Liu, Qihai and Lee, Kevin H and Kang, Hyun Bin},
  journal={PloS one},
  volume={20},
  number={1},
  pages={e0316458},
  year={2025},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{wasi2025gnnscm,
  author    = {Azmine Toushik Wasi and MD Shafikul Islam and Adipto Raihan Aki and Mahathir Mohammad Bappy},
  title     = {Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks},
  journal   = {arXiv preprint arXiv:2411.08550},
  year      = {2025},
  url       = {https://arxiv.org/abs/2411.08550v2}
}

@article{zhang2025multiagent,
  author    = {Yao Zhang and Zaixi Shang and Silpan Patel and Mikel Zuniga},
  title     = {From Unstructured Communication to Intelligent RAG: Multi-Agent Automation for Supply Chain Knowledge Bases},
  journal   = {arXiv preprint arXiv:2506.17484},
  year      = {2025},
  url       = {https://arxiv.org/abs/2506.17484}
}

@article{mattegunta2025scv,
  author    = {Mattegunta, V.K.P.},
  title     = {Supply Chain Visibility Platforms: A Framework for Enhanced Transparency in Global Supply Networks},
  journal   = {European Journal of Computer Science and Information Technology},
  volume    = {13},
  number    = {11},
  pages     = {114--130},
  year      = {2025}
}

@misc{ibm_granite_3_1_2024,
  title={Granite 3.1 Language Model},
  author={IBM Granite Team},
  year={2024},
  howpublished={\url{https://github.com/ibm-granite/granite-3.1-language-models}},
  note={Model run locally using Ollama (https://ollama.com/)}
}

@misc{meta_llama_3_2_2024,
  title={Llama 3.2 Language Model},
  author={Meta AI},
  year={2024},
  howpublished={\url{https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct}},
  note={Model run locally using Ollama (https://ollama.com/)}
}

@article{gemma_2025,
  title={Gemma 3},
  author={Gemma Team, Google DeepMind},
  year={2025},
  url={https://arxiv.org/abs/2503.19786},
  note={Model run locally using Ollama (https://ollama.com/)}
}

@article{le2021,
  title={The impact of factors on information sharing: An application of meta-analysis},
  author={Le, Thi Kieu Trang and Nguyen, Mai and Ngo, Ha Minh},
  journal={PLoS One},
  volume={16},
  number={12},
  pages={e0260653},
  year={2021},
  publisher={Public Library of Science San Francisco, CA USA},
  doi={10.1371/journal.pone.0260653}
}

@article{sharma2022,
  title={Complexity in a multinational enterprise's global supply chain and its international business performance: A bane or a boon?},
  author={Sharma, Amalesh and Adhikary, Amit and Borah, Sourav Bikash},
  journal={PLoS One},
  volume={17},
  number={3},
  pages={e0264947},
  year={2022},
  publisher={Public Library of Science San Francisco, CA USA},
  doi={10.1371/journal.pone.0264947}
}

@article{swink2023,
  title={Supply chain visibility types and contextual characteristics: A literature‐based synthesis},
  author={Swink, Morgan and Jacobs, Brian W},
  journal={Journal of Business Logistics},
  volume={44},
  number={3},
  pages={336--380},
  year={2023},
  publisher={Wiley Online Library},
  doi={10.1111/jbl.12366}
}

@article{baldwin2023hidden,
  title={Hidden Exposure: Measuring US Supply Chain Reliance},
  author={Baldwin, Richard and Freeman, Rebecca and Theodorakopoulos, Angelos},
  journal={Brookings Papers on Economic Activity},
  volume={2023},
  number={Fall},
  pages={79--134},
  year={2023},
  publisher={The Brookings Institution},
  doi={10.1353/eca.2024.a916483},
  url={https://www.brookings.edu/wp-content/uploads/2023/09/Baldwin-et-al-session_16820-BPEA-FA23_WEB.pdf}
}

@article{colon2022systemic,
  title={Systemic risks in supply chains: a need for system-level governance},
  author={Colon, C{\'e}lian and Hochrainer-Stigler, Stefan},
  journal={Supply Chain Management: An International Journal},
  volume={28},
  number={4},
  pages={568--584},
  year={2022},
  publisher={Emerald Publishing Limited},
  doi={10.1108/SCM-03-2022-0101},
  url={https://www.emerald.com/insight/content/doi/10.1108/SCM-03-2022-0101/full/html}
}

@techreport{shapiro1998bottom,
  title={Bottom-up vs. Top-down Approaches to Supply Chain Management and Modeling},
  author={Shapiro, Jeremy F.},
  year={1998},
  month={April},
  institution={Massachusetts Institute of Technology, Sloan School of Management},
  type={Working Paper},
  number={4017},
  series={Sloan WP},
  address={Cambridge, MA},
  url={https://dspace.mit.edu/handle/1721.1/2710},
  note={Available at MIT DSpace}
}

@article{hasan2022demand,
  title={Demand and Supply Disruptions During the Covid-19 Crisis on Firm Performance},
  author={Hasan, Mohammad Morshedur and Rahman, Rashidah Abdul and Hossain, Saad Zaki and Abedin, Mohammad Ziaul and Darda, Md Aslam},
  journal={Sustainability},
  volume={14},
  number={24},
  pages={16573},
  year={2022},
  publisher={MDPI},
  doi={10.3390/su142416573}
}

@article{chakraborty2022empirical,
  title={Empirical evidence from the semiconductor shortage during COVID-19},
  author={Chakraborty, Tanmoy and Jaef, Rudolfs Nils Friedrich},
  journal={Economics Letters},
  volume={213},
  pages={110371},
  year={2022},
  publisher={Elsevier},
  doi={10.1016/j.econlet.2022.110371}
}

@article{tam2022understanding,
  title={Understanding systemic disruption from the Covid-19-induced semiconductor shortage for the auto industry},
  author={Tam, Paulina S and Sood, Sahil and Johnston, Christopher},
  journal={OMEGA - The International Journal of Management Science},
  volume={113},
  pages={102689},
  year={2022},
  publisher={Elsevier},
  doi={10.1016/j.omega.2022.102689}
}

@article{zhang2024reshaping,
  title={Reshaping heavy rare earth supply chains amidst China's stringent environmental regulations},
  author={Zhang, Yunfei and Liu, Kai and Tukker, Arnold and Zheng, Heming and Shen, Lei},
  journal={Nature Communications},
  volume={15},
  pages={598},
  year={2024},
  publisher={Nature Portfolio},
  doi={10.1038/s41467-024-44888-5}
}

@article{hu2025technological,
  title={Technological risks disrupting trade stability in the global lithium supply chain},
  author={Hu, Ruohan and Liu, Kai and Tukker, Arnold and Bijl, David L and Kuipers, Koen and Shen, Lei},
  journal={Nature Communications},
  volume={16},
  pages={5071},
  year={2025},
  publisher={Nature Portfolio},
  doi={10.1038/s41467-025-05701-2}
}

@inproceedings{jaimez2021towards,
  title={Towards a Multi-Agent System Architecture for Supply Chain Management},
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