[{"publication":"Proceedings of the 36th AAAI Conference on Artificial Intelligence","type":"preprint","status":"public","abstract":[{"text":"In online algorithm selection (OAS), instances of an algorithmic problem\r\nclass are presented to an agent one after another, and the agent has to quickly\r\nselect a presumably best algorithm from a fixed set of candidate algorithms.\r\nFor decision problems such as satisfiability (SAT), quality typically refers to\r\nthe algorithm's runtime. As the latter is known to exhibit a heavy-tail\r\ndistribution, an algorithm is normally stopped when exceeding a predefined\r\nupper time limit. As a consequence, machine learning methods used to optimize\r\nan algorithm selection strategy in a data-driven manner need to deal with\r\nright-censored samples, a problem that has received little attention in the\r\nliterature so far. In this work, we revisit multi-armed bandit algorithms for\r\nOAS and discuss their capability of dealing with the problem. Moreover, we\r\nadapt them towards runtime-oriented losses, allowing for partially censored\r\ndata while keeping a space- and time-complexity independent of the time\r\nhorizon. In an extensive experimental evaluation on an adapted version of the\r\nASlib benchmark, we demonstrate that theoretically well-founded methods based\r\non Thompson sampling perform specifically strong and improve in comparison to\r\nexisting methods.","lang":"eng"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"user_id":"38209","external_id":{"arxiv":["2109.06234"]},"_id":"30867","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"language":[{"iso":"eng"}],"citation":{"ieee":"A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm Selection under Censored Feedback,” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI, 2022.","chicago":"Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. “Machine Learning for Online Algorithm Selection under Censored Feedback.” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI, 2022.","ama":"Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection under Censored Feedback. <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. Published online 2022.","apa":"Tornede, A., Bengs, V., &#38; Hüllermeier, E. (2022). Machine Learning for Online Algorithm Selection under Censored Feedback. In <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>. AAAI.","short":"A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022).","mla":"Tornede, Alexander, et al. “Machine Learning for Online Algorithm Selection under Censored Feedback.” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence</i>, AAAI, 2022.","bibtex":"@article{Tornede_Bengs_Hüllermeier_2022, title={Machine Learning for Online Algorithm Selection under Censored Feedback}, journal={Proceedings of the 36th AAAI Conference on Artificial Intelligence}, publisher={AAAI}, author={Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}, year={2022} }"},"year":"2022","author":[{"first_name":"Alexander","id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede"},{"first_name":"Viktor","full_name":"Bengs, Viktor","id":"76599","last_name":"Bengs"},{"first_name":"Eyke","id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier"}],"date_created":"2022-04-12T11:58:56Z","date_updated":"2022-08-24T12:44:27Z","publisher":"AAAI","title":"Machine Learning for Online Algorithm Selection under Censored Feedback"},{"status":"public","abstract":[{"text":"The problem of selecting an algorithm that appears most suitable for a\r\nspecific instance of an algorithmic problem class, such as the Boolean\r\nsatisfiability problem, is called instance-specific algorithm selection. Over\r\nthe past decade, the problem has received considerable attention, resulting in\r\na number of different methods for algorithm selection. Although most of these\r\nmethods are based on machine learning, surprisingly little work has been done\r\non meta learning, that is, on taking advantage of the complementarity of\r\nexisting algorithm selection methods in order to combine them into a single\r\nsuperior algorithm selector. In this paper, we introduce the problem of meta\r\nalgorithm selection, which essentially asks for the best way to combine a given\r\nset of algorithm selectors. We present a general methodological framework for\r\nmeta algorithm selection as well as several concrete learning methods as\r\ninstantiations of this framework, essentially combining ideas of meta learning\r\nand ensemble learning. In an extensive experimental evaluation, we demonstrate\r\nthat ensembles of algorithm selectors can significantly outperform single\r\nalgorithm selectors and have the potential to form the new state of the art in\r\nalgorithm selection.","lang":"eng"}],"type":"preprint","publication":"Machine Learning","language":[{"iso":"eng"}],"user_id":"38209","department":[{"_id":"34"},{"_id":"7"},{"_id":"26"}],"project":[{"name":"SFB 901: SFB 901","_id":"1"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - B2: SFB 901 - Subproject B2"}],"external_id":{"arxiv":["2107.09414"]},"_id":"30865","citation":{"ieee":"A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection on a Meta Level,” <i>Machine Learning</i>. 2022.","chicago":"Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” <i>Machine Learning</i>, 2022.","ama":"Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection on a Meta Level. <i>Machine Learning</i>. Published online 2022.","mla":"Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” <i>Machine Learning</i>, 2022.","short":"A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning (2022).","bibtex":"@article{Tornede_Gehring_Tornede_Wever_Hüllermeier_2022, title={Algorithm Selection on a Meta Level}, journal={Machine Learning}, author={Tornede, Alexander and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2022} }","apa":"Tornede, A., Gehring, L., Tornede, T., Wever, M. D., &#38; Hüllermeier, E. (2022). Algorithm Selection on a Meta Level. In <i>Machine Learning</i>."},"year":"2022","title":"Algorithm Selection on a Meta Level","date_created":"2022-04-12T11:55:18Z","author":[{"first_name":"Alexander","id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede"},{"first_name":"Lukas","last_name":"Gehring","full_name":"Gehring, Lukas"},{"first_name":"Tanja","full_name":"Tornede, Tanja","id":"40795","last_name":"Tornede"},{"first_name":"Marcel Dominik","id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"date_updated":"2022-08-24T12:45:39Z"},{"citation":{"short":"A. Fuhrmann, R. Schoch, A. Rusam, M. Bosse, F. Flachmann, E. Moritzer, T. Hochrein, M. Bastian, Verbessertes Füllverhalten Im Spritzgießprozess Durch Schäumen von WPC: Bewertung Des Fließverhaltens Treibmittelbeladener Polymerschmelzen, Shaker, 2022.","bibtex":"@book{Fuhrmann_Schoch_Rusam_Bosse_Flachmann_Moritzer_Hochrein_Bastian_2022, title={Verbessertes Füllverhalten im Spritzgießprozess durch Schäumen von WPC: Bewertung des Fließverhaltens treibmittelbeladener Polymerschmelzen}, publisher={Shaker}, author={Fuhrmann, Anika and Schoch, Rebecca  and Rusam, Alexander and Bosse, Michael and Flachmann, Felix and Moritzer, Elmar and Hochrein, Thomas and Bastian, Martin}, year={2022} }","mla":"Fuhrmann, Anika, et al. <i>Verbessertes Füllverhalten Im Spritzgießprozess Durch Schäumen von WPC: Bewertung Des Fließverhaltens Treibmittelbeladener Polymerschmelzen</i>. Shaker, 2022.","apa":"Fuhrmann, A., Schoch, R., Rusam, A., Bosse, M., Flachmann, F., Moritzer, E., Hochrein, T., &#38; Bastian, M. (2022). <i>Verbessertes Füllverhalten im Spritzgießprozess durch Schäumen von WPC: Bewertung des Fließverhaltens treibmittelbeladener Polymerschmelzen</i>. Shaker.","ieee":"A. Fuhrmann <i>et al.</i>, <i>Verbessertes Füllverhalten im Spritzgießprozess durch Schäumen von WPC: Bewertung des Fließverhaltens treibmittelbeladener Polymerschmelzen</i>. Shaker, 2022.","chicago":"Fuhrmann, Anika, Rebecca  Schoch, Alexander Rusam, Michael Bosse, Felix Flachmann, Elmar Moritzer, Thomas Hochrein, and Martin Bastian. <i>Verbessertes Füllverhalten Im Spritzgießprozess Durch Schäumen von WPC: Bewertung Des Fließverhaltens Treibmittelbeladener Polymerschmelzen</i>. Shaker, 2022.","ama":"Fuhrmann A, Schoch R, Rusam A, et al. <i>Verbessertes Füllverhalten Im Spritzgießprozess Durch Schäumen von WPC: Bewertung Des Fließverhaltens Treibmittelbeladener Polymerschmelzen</i>. Shaker; 2022."},"year":"2022","publication_identifier":{"unknown":["9783844086676 "]},"title":"Verbessertes Füllverhalten im Spritzgießprozess durch Schäumen von WPC: Bewertung des Fließverhaltens treibmittelbeladener Polymerschmelzen","date_created":"2022-08-22T11:51:59Z","author":[{"first_name":"Anika","full_name":"Fuhrmann, Anika","last_name":"Fuhrmann"},{"first_name":"Rebecca ","full_name":"Schoch, Rebecca ","last_name":"Schoch"},{"full_name":"Rusam, Alexander","last_name":"Rusam","first_name":"Alexander"},{"first_name":"Michael","last_name":"Bosse","full_name":"Bosse, Michael"},{"orcid":"0000-0002-7651-7028","last_name":"Flachmann","full_name":"Flachmann, Felix","id":"38212","first_name":"Felix"},{"full_name":"Moritzer, Elmar","id":"20531","last_name":"Moritzer","first_name":"Elmar"},{"full_name":"Hochrein, Thomas","last_name":"Hochrein","first_name":"Thomas"},{"first_name":"Martin","last_name":"Bastian","full_name":"Bastian, Martin"}],"publisher":"Shaker","date_updated":"2022-08-24T07:16:33Z","status":"public","type":"report","language":[{"iso":"eng"}],"user_id":"38212","department":[{"_id":"321"},{"_id":"9"},{"_id":"367"},{"_id":"147"}],"_id":"33070"},{"type":"journal_article","publication":"Welding in the World","status":"public","abstract":[{"lang":"eng","text":"<jats:title>Abstract</jats:title><jats:p>Heated tool butt welding is a method often used for joining thermoplastics, especially when the components are made out of different materials. The quality of the connection between the components crucially depends on a suitable choice of the parameters of the welding process, such as heating time, temperature, and the precise way how the parts are then welded. Moreover, when different materials are to be joined, the parameter values need to be tailored to the specifics of the respective material. To this end, in this paper, three approaches to tailor the parameter values to optimize the quality of the connection are compared: a heuristic by Potente, statistical experimental design, and Bayesian optimization. With the suitability for practice in mind, a series of experiments are carried out with these approaches, and their capabilities of proposing well-performing parameter values are investigated. As a result, Bayesian optimization is found to yield peak performance, but the costs for optimization are substantial. In contrast, the Potente heuristic does not require any experimentation and recommends parameter values with competitive quality.</jats:p>"}],"user_id":"38209","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - B: SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - B2: SFB 901 - Subproject B2","_id":"10"}],"_id":"33090","language":[{"iso":"eng"}],"keyword":["Metals and Alloys","Mechanical Engineering","Mechanics of Materials"],"publication_status":"published","publication_identifier":{"issn":["0043-2288","1878-6669"]},"citation":{"chicago":"Gevers, Karina, Alexander Tornede, Marcel Dominik Wever, Volker Schöppner, and Eyke Hüllermeier. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” <i>Welding in the World</i>, 2022. <a href=\"https://doi.org/10.1007/s40194-022-01339-9\">https://doi.org/10.1007/s40194-022-01339-9</a>.","ieee":"K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials,” <i>Welding in the World</i>, 2022, doi: <a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>.","ama":"Gevers K, Tornede A, Wever MD, Schöppner V, Hüllermeier E. A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. <i>Welding in the World</i>. Published online 2022. doi:<a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>","bibtex":"@article{Gevers_Tornede_Wever_Schöppner_Hüllermeier_2022, title={A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials}, DOI={<a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>}, journal={Welding in the World}, publisher={Springer Science and Business Media LLC}, author={Gevers, Karina and Tornede, Alexander and Wever, Marcel Dominik and Schöppner, Volker and Hüllermeier, Eyke}, year={2022} }","mla":"Gevers, Karina, et al. “A Comparison of Heuristic, Statistical, and Machine Learning Methods for Heated Tool Butt Welding of Two Different Materials.” <i>Welding in the World</i>, Springer Science and Business Media LLC, 2022, doi:<a href=\"https://doi.org/10.1007/s40194-022-01339-9\">10.1007/s40194-022-01339-9</a>.","short":"K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding in the World (2022).","apa":"Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., &#38; Hüllermeier, E. (2022). A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials. <i>Welding in the World</i>. <a href=\"https://doi.org/10.1007/s40194-022-01339-9\">https://doi.org/10.1007/s40194-022-01339-9</a>"},"year":"2022","author":[{"first_name":"Karina","full_name":"Gevers, Karina","id":"83151","last_name":"Gevers"},{"first_name":"Alexander","id":"38209","full_name":"Tornede, Alexander","last_name":"Tornede"},{"first_name":"Marcel Dominik","id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818"},{"first_name":"Volker","full_name":"Schöppner, Volker","id":"20530","last_name":"Schöppner"},{"last_name":"Hüllermeier","id":"48129","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_created":"2022-08-24T12:51:07Z","publisher":"Springer Science and Business Media LLC","date_updated":"2022-08-24T12:52:06Z","doi":"10.1007/s40194-022-01339-9","title":"A comparison of heuristic, statistical, and machine learning methods for heated tool butt welding of two different materials"},{"language":[{"iso":"eng"}],"department":[{"_id":"101"},{"_id":"655"}],"user_id":"47427","_id":"33150","external_id":{"arxiv":["2208.12094"]},"status":"public","abstract":[{"lang":"eng","text":"In this article, we build on previous work to present an optimization algorithm for nonlinearly constrained multi-objective optimization problems. The algorithm combines a surrogate-assisted derivative-free trust-region approach with the filter method known from single-objective optimization. Instead of the true objective and constraint functions, so-called fully linear models are employed and we show how to deal with the gradient inexactness in the composite step setting, adapted from single-objective optimization as well. Under standard assumptions, we prove convergence of a subset of iterates to a quasi-stationary point and if constraint qualifications hold, then the limit point is also a KKT-point of the multi-objective problem."}],"publication":"arXiv:2208.12094","type":"preprint","main_file_link":[{"open_access":"1","url":"https://arxiv.org/pdf/2208.12094"}],"title":"Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients","author":[{"first_name":"Manuel Bastian","last_name":"Berkemeier","id":"51701","full_name":"Berkemeier, Manuel Bastian"},{"first_name":"Sebastian","orcid":"0000-0002-3389-793X","last_name":"Peitz","id":"47427","full_name":"Peitz, Sebastian"}],"date_created":"2022-08-26T06:08:06Z","date_updated":"2022-08-26T06:12:10Z","oa":"1","citation":{"ama":"Berkemeier MB, Peitz S. Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients. <i>arXiv:220812094</i>. Published online 2022.","ieee":"M. B. Berkemeier and S. Peitz, “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients,” <i>arXiv:2208.12094</i>. 2022.","chicago":"Berkemeier, Manuel Bastian, and Sebastian Peitz. “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints Using Inexact Gradients.” <i>ArXiv:2208.12094</i>, 2022.","short":"M.B. Berkemeier, S. Peitz, ArXiv:2208.12094 (2022).","bibtex":"@article{Berkemeier_Peitz_2022, title={Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients}, journal={arXiv:2208.12094}, author={Berkemeier, Manuel Bastian and Peitz, Sebastian}, year={2022} }","mla":"Berkemeier, Manuel Bastian, and Sebastian Peitz. “Multi-Objective Trust-Region Filter Method for Nonlinear Constraints Using Inexact Gradients.” <i>ArXiv:2208.12094</i>, 2022.","apa":"Berkemeier, M. B., &#38; Peitz, S. (2022). Multi-Objective Trust-Region Filter Method for Nonlinear Constraints using Inexact Gradients. In <i>arXiv:2208.12094</i>."},"year":"2022"},{"publisher":"Springer Science and Business Media LLC","date_updated":"2022-08-29T08:38:12Z","author":[{"first_name":"Margarete","last_name":"Redlin","id":"135","full_name":"Redlin, Margarete"}],"date_created":"2022-08-29T06:49:33Z","title":"Differences in NPI strategies against COVID-19","doi":"10.1007/s11149-022-09452-9","publication_identifier":{"issn":["0922-680X","1573-0468"]},"publication_status":"published","year":"2022","citation":{"apa":"Redlin, M. (2022). Differences in NPI strategies against COVID-19. <i>Journal of Regulatory Economics</i>. <a href=\"https://doi.org/10.1007/s11149-022-09452-9\">https://doi.org/10.1007/s11149-022-09452-9</a>","short":"M. Redlin, Journal of Regulatory Economics (2022).","mla":"Redlin, Margarete. “Differences in NPI Strategies against COVID-19.” <i>Journal of Regulatory Economics</i>, Springer Science and Business Media LLC, 2022, doi:<a href=\"https://doi.org/10.1007/s11149-022-09452-9\">10.1007/s11149-022-09452-9</a>.","bibtex":"@article{Redlin_2022, title={Differences in NPI strategies against COVID-19}, DOI={<a href=\"https://doi.org/10.1007/s11149-022-09452-9\">10.1007/s11149-022-09452-9</a>}, journal={Journal of Regulatory Economics}, publisher={Springer Science and Business Media LLC}, author={Redlin, Margarete}, year={2022} }","chicago":"Redlin, Margarete. “Differences in NPI Strategies against COVID-19.” <i>Journal of Regulatory Economics</i>, 2022. <a href=\"https://doi.org/10.1007/s11149-022-09452-9\">https://doi.org/10.1007/s11149-022-09452-9</a>.","ieee":"M. Redlin, “Differences in NPI strategies against COVID-19,” <i>Journal of Regulatory Economics</i>, 2022, doi: <a href=\"https://doi.org/10.1007/s11149-022-09452-9\">10.1007/s11149-022-09452-9</a>.","ama":"Redlin M. Differences in NPI strategies against COVID-19. <i>Journal of Regulatory Economics</i>. Published online 2022. doi:<a href=\"https://doi.org/10.1007/s11149-022-09452-9\">10.1007/s11149-022-09452-9</a>"},"_id":"33221","department":[{"_id":"475"},{"_id":"200"},{"_id":"202"}],"user_id":"14931","keyword":["Economics and Econometrics"],"language":[{"iso":"eng"}],"publication":"Journal of Regulatory Economics","type":"journal_article","abstract":[{"lang":"eng","text":"<jats:title>Abstract</jats:title><jats:p>Non-pharmaceutical interventions are an effective strategy to prevent and control COVID-19 transmission in the community. However, the timing and stringency to which these measures have been implemented varied between countries and regions. The differences in stringency can only to a limited extent be explained by the number of infections and the prevailing vaccination strategies. Our study aims to shed more light on the lockdown strategies and to identify the determinants underlying the differences between countries on regional, economic, institutional, and political level. Based on daily panel data for 173 countries and the period from January 2020 to October 2021 we find significant regional differences in lockdown strategies. Further, more prosperous countries implemented milder restrictions but responded more quickly, while poorer countries introduced more stringent measures but had a longer response time. Finally, democratic regimes and stronger manifested institutions alleviated and slowed down the introduction of lockdown measures.</jats:p>"}],"status":"public"},{"doi":"10.1186/s12651-022-00319-2","title":"Modelling artificial intelligence in economics","volume":56,"date_created":"2022-08-29T06:43:37Z","author":[{"first_name":"Thomas","id":"186","full_name":"Gries, Thomas","last_name":"Gries"},{"last_name":"Naudé","full_name":"Naudé, Wim","first_name":"Wim"}],"date_updated":"2022-08-30T07:37:57Z","publisher":"Springer Science and Business Media LLC","intvolume":"        56","citation":{"apa":"Gries, T., &#38; Naudé, W. (2022). Modelling artificial intelligence in economics. <i>Journal for Labour Market Research</i>, <i>56</i>(1), Article 12. <a href=\"https://doi.org/10.1186/s12651-022-00319-2\">https://doi.org/10.1186/s12651-022-00319-2</a>","bibtex":"@article{Gries_Naudé_2022, title={Modelling artificial intelligence in economics}, volume={56}, DOI={<a href=\"https://doi.org/10.1186/s12651-022-00319-2\">10.1186/s12651-022-00319-2</a>}, number={112}, journal={Journal for Labour Market Research}, publisher={Springer Science and Business Media LLC}, author={Gries, Thomas and Naudé, Wim}, year={2022} }","mla":"Gries, Thomas, and Wim Naudé. “Modelling Artificial Intelligence in Economics.” <i>Journal for Labour Market Research</i>, vol. 56, no. 1, 12, Springer Science and Business Media LLC, 2022, doi:<a href=\"https://doi.org/10.1186/s12651-022-00319-2\">10.1186/s12651-022-00319-2</a>.","short":"T. Gries, W. Naudé, Journal for Labour Market Research 56 (2022).","chicago":"Gries, Thomas, and Wim Naudé. “Modelling Artificial Intelligence in Economics.” <i>Journal for Labour Market Research</i> 56, no. 1 (2022). <a href=\"https://doi.org/10.1186/s12651-022-00319-2\">https://doi.org/10.1186/s12651-022-00319-2</a>.","ieee":"T. Gries and W. Naudé, “Modelling artificial intelligence in economics,” <i>Journal for Labour Market Research</i>, vol. 56, no. 1, Art. no. 12, 2022, doi: <a href=\"https://doi.org/10.1186/s12651-022-00319-2\">10.1186/s12651-022-00319-2</a>.","ama":"Gries T, Naudé W. Modelling artificial intelligence in economics. <i>Journal for Labour Market Research</i>. 2022;56(1). doi:<a href=\"https://doi.org/10.1186/s12651-022-00319-2\">10.1186/s12651-022-00319-2</a>"},"year":"2022","issue":"1","publication_identifier":{"issn":["2510-5019","2510-5027"]},"publication_status":"published","language":[{"iso":"eng"}],"keyword":["General Medicine"],"article_number":"12","department":[{"_id":"475"},{"_id":"200"},{"_id":"202"}],"user_id":"135","_id":"33220","status":"public","abstract":[{"text":"<jats:title>Abstract</jats:title><jats:p>We provide a partial equilibrium model wherein AI provides abilities combined with human skills to provide an aggregate intermediate service good. We use the model to find that the extent of automation through AI will be greater if (a) the economy is relatively abundant in sophisticated programs and machine abilities compared to human skills; (b) the economy hosts a relatively large number of AI-providing firms and experts; and (c) the task-specific productivity of AI services is relatively high compared to the task-specific productivity of general labor and labor skills. We also illustrate that the contribution of AI to aggregate productive labor service depends not only on the amount of AI services available but on the endogenous number of automated tasks, the relative productivity of standard and IT-related labor, and the substitutability of tasks. These determinants also affect the income distribution between the two kinds of labor. We derive several empirical implications and identify possible future extensions.</jats:p>","lang":"eng"}],"publication":"Journal for Labour Market Research","type":"journal_article"},{"year":"2022","citation":{"ama":"Daymude JJ, Richa AW, Scheideler C. Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems. In: Aspnes J, Michail O, eds. <i>1st Symposium on Algorithmic Foundations of Dynamic Networks, SAND 2022, March 28-30, 2022, Virtual Conference</i>. Vol 221. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2022:12:1–12:19. doi:<a href=\"https://doi.org/10.4230/LIPIcs.SAND.2022.12\">10.4230/LIPIcs.SAND.2022.12</a>","chicago":"Daymude, Joshua J., Andréa W. Richa, and Christian Scheideler. “Local Mutual Exclusion for Dynamic, Anonymous, Bounded Memory Message Passing Systems.” In <i>1st Symposium on Algorithmic Foundations of Dynamic Networks, SAND 2022, March 28-30, 2022, Virtual Conference</i>, edited by James Aspnes and Othon Michail, 221:12:1–12:19. LIPIcs. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. <a href=\"https://doi.org/10.4230/LIPIcs.SAND.2022.12\">https://doi.org/10.4230/LIPIcs.SAND.2022.12</a>.","ieee":"J. J. Daymude, A. W. Richa, and C. 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