D. Cornilly, Giovanni Puccetti, L. Rüschendorf, S. Vanduffel
We study a synchronization problem with multiple instances. First, we show that the problem we consider can be formulated as the problem of finding an intra-column rearrangement for multiple matrices (which reflect problem instances) such that the row sums across the various matrices show minimum variability. To obtain the optimal rearrangement, we introduce the Block Swapping Algorithm (BSA) and a further customization of it that we label as the Customized Block Swapping Algorithm (Cust BSA). A numerical study shows that the two algorithms we propose yield high-quality solutions and also deal efficiently with high-dimensional set-ups.
{"title":"On a Synchronization Problem With Multiple Instances","authors":"D. Cornilly, Giovanni Puccetti, L. Rüschendorf, S. Vanduffel","doi":"10.2139/ssrn.3681186","DOIUrl":"https://doi.org/10.2139/ssrn.3681186","url":null,"abstract":"We study a synchronization problem with multiple instances. First, we show that the problem we consider can be formulated as the problem of finding an intra-column rearrangement for multiple matrices (which reflect problem instances) such that the row sums across the various matrices show minimum variability. To obtain the optimal rearrangement, we introduce the Block Swapping Algorithm (BSA) and a further customization of it that we label as the Customized Block Swapping Algorithm (Cust BSA). A numerical study shows that the two algorithms we propose yield high-quality solutions and also deal efficiently with high-dimensional set-ups.","PeriodicalId":254950,"journal":{"name":"DecisionSciRN: Algorithmic Decision-Making (Sub-Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130960077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automated decision-making (ADM) in the public sector creates a wide range of issues that require public law analysis. A precondition of such analysis is the existential question of whether mechanisms for enforcing public law norms will continue to be effective in the era of the digital state. This article considers one institutional manifestation of that fundamental question: how public law errors in ADM systems are evidenced in judicial review proceedings. Our analysis of the nature of proving error in ADM systems reveals that this emergent mode of administration will likely have a range of impacts on contemporary judicial review evidence practices — we identify seven potential effects. This exploration also exposes how current scholarship is operating on a deficient account of the role of evidence in public law adjudication. In this sense, our thesis reveals how advancements in digital government expose the frailties and limitations of our existing understanding of public law.
{"title":"Judicial Review Evidence in the Era of the Digital State","authors":"Joe Tomlinson, Katy Sheridan, Adam Harkens","doi":"10.2139/ssrn.3615312","DOIUrl":"https://doi.org/10.2139/ssrn.3615312","url":null,"abstract":"Automated decision-making (ADM) in the public sector creates a wide range of issues that require public law analysis. A precondition of such analysis is the existential question of whether mechanisms for enforcing public law norms will continue to be effective in the era of the digital state. This article considers one institutional manifestation of that fundamental question: how public law errors in ADM systems are evidenced in judicial review proceedings. Our analysis of the nature of proving error in ADM systems reveals that this emergent mode of administration will likely have a range of impacts on contemporary judicial review evidence practices — we identify seven potential effects. This exploration also exposes how current scholarship is operating on a deficient account of the role of evidence in public law adjudication. In this sense, our thesis reveals how advancements in digital government expose the frailties and limitations of our existing understanding of public law.","PeriodicalId":254950,"journal":{"name":"DecisionSciRN: Algorithmic Decision-Making (Sub-Topic)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114929026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this research in progress we present the initial stage of a large ethnographic study at a healthcare network in the US. Our goal is to understand how healthcare organizations in the US use algorithms to improve efficiency (cost saving) and effectiveness (quality) of healthcare. Our preliminary findings illustrate that at the national level, algorithms might be detrimental to healthcare quality because they do not consider (and differentiate) contextual issues such as social and cultural (local) settings. At the practice (hospital/physician) level, they help managing the tradeoff between following national “best practices” and accommodating needs of special patients or particular situations, because hospital-based algorithms can be over-ridden by clinicians. We conclude that, while more data needs to be collected, a responsible use of algorithms requires their constant supervision and their application with respect to specific social and cultural settings.
{"title":"Algorithmic Decision-making in the US Healthcare Industry","authors":"Marco Marabelli, S. Newell, Xinru Page","doi":"10.2139/ssrn.3262379","DOIUrl":"https://doi.org/10.2139/ssrn.3262379","url":null,"abstract":"In this research in progress we present the initial stage of a large ethnographic study at a healthcare network in the US. Our goal is to understand how healthcare organizations in the US use algorithms to improve efficiency (cost saving) and effectiveness (quality) of healthcare. Our preliminary findings illustrate that at the national level, algorithms might be detrimental to healthcare quality because they do not consider (and differentiate) contextual issues such as social and cultural (local) settings. At the practice (hospital/physician) level, they help managing the tradeoff between following national “best practices” and accommodating needs of special patients or particular situations, because hospital-based algorithms can be over-ridden by clinicians. We conclude that, while more data needs to be collected, a responsible use of algorithms requires their constant supervision and their application with respect to specific social and cultural settings.","PeriodicalId":254950,"journal":{"name":"DecisionSciRN: Algorithmic Decision-Making (Sub-Topic)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127652576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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