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Relevant bibliographies by topics / Algorithmic decision systems

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Academic literature on the topic 'Algorithmic decision systems'

Author: Grafiati

Published: 24 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Algorithmic decision systems"

1

Herzog, Lisa. "Algorithmisches Entscheiden, Ambiguitätstoleranz und die Frage nach dem Sinn." Deutsche Zeitschrift für Philosophie 69, no. 2 (April 1, 2021): 197–213. http://dx.doi.org/10.1515/dzph-2021-0016.

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Abstract In more and more contexts, human decision-making is replaced by algorithmic decision-making. While promising to deliver efficient and objective decisions, algorithmic decision systems have specific weaknesses, some of which are particularly dangerous if data are collected and processed by profit-oriented companies. In this paper, I focus on two problems that are at the root of the logic of algorithmic decision-making: (1) (in)tolerance for ambiguity, and (2) instantiations of Campbell’s law, i. e. of indicators that are used for “social decision-making” being subject to “corruption pressures” and tending to “distort and corrupt” the underlying social processes. As a result, algorithmic decision-making can risk missing the point of the social practice in question. These problems are intertwined with problems of structural injustice; hence, if algorithms are to deliver on their promises of efficiency and objectivity, accountability and critical scrutiny are needed.

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Kleinberg, Jon, and Manish Raghavan. "Algorithmic monoculture and social welfare." Proceedings of the National Academy of Sciences 118, no. 22 (May 25, 2021): e2018340118. http://dx.doi.org/10.1073/pnas.2018340118.

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As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, lending, and other domains, concerns have been raised about the effects of algorithmic monoculture, in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. Here, we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents. Unexpected shocks are therefore not needed to expose the risks of monoculture; it can hurt accuracy even under “normal” operations and even for algorithms that are more accurate when used by only a single decision-maker. Our results rely on minimal assumptions and involve the development of a probabilistic framework for analyzing systems that use multiple noisy estimates of a set of alternatives.

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Zerilli, John, Alistair Knott, James Maclaurin, and Colin Gavaghan. "Algorithmic Decision-Making and the Control Problem." Minds and Machines 29, no. 4 (December 2019): 555–78. http://dx.doi.org/10.1007/s11023-019-09513-7.

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AbstractThe danger of human operators devolving responsibility to machines and failing to detect cases where they fail has been recognised for many years by industrial psychologists and engineers studying the human operators of complex machines. We call it “the control problem”, understood as the tendency of the human within a human–machine control loop to become complacent, over-reliant or unduly diffident when faced with the outputs of a reliable autonomous system. While the control problem has been investigated for some time, up to this point its manifestation in machine learning contexts has not received serious attention. This paper aims to fill that gap. We argue that, except in certain special circumstances, algorithmic decision tools should not be used in high-stakes or safety-critical decisions unless the systems concerned are significantly “better than human” in the relevant domain or subdomain of decision-making. More concretely, we recommend three strategies to address the control problem, the most promising of which involves a complementary (and potentially dynamic) coupling between highly proficient algorithmic tools and human agents working alongside one another. We also identify six key principles which all such human–machine systems should reflect in their design. These can serve as a framework both for assessing the viability of any such human–machine system as well as guiding the design and implementation of such systems generally.

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Green, Ben, and Yiling Chen. "Algorithm-in-the-Loop Decision Making." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 09 (April 3, 2020): 13663–64. http://dx.doi.org/10.1609/aaai.v34i09.7115.

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We introduce a new framework for conceiving of and studying algorithms that are deployed to aid human decision making: “algorithm-in-the-loop” systems. The algorithm-in-the-loop framework centers human decision making, providing a more precise lens for studying the social impacts of algorithmic decision making aids. We report on two experiments that evaluate algorithm-in-the-loop decision making and find significant limits to these systems.

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Rubel, Alan, Clinton Castro, and Adam Pham. "Algorithms, Agency, and Respect for Persons." Social Theory and Practice 46, no. 3 (2020): 547–72. http://dx.doi.org/10.5840/soctheorpract202062497.

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Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key ways in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one’s decision to take that action to an algorithm, thereby laundering one’s agency.

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Žliobaitė, Indrė. "Measuring discrimination in algorithmic decision making." Data Mining and Knowledge Discovery 31, no. 4 (March 31, 2017): 1060–89. http://dx.doi.org/10.1007/s10618-017-0506-1.

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BRKAN, Maja, and Grégory BONNET. "Legal and Technical Feasibility of the GDPR’s Quest for Explanation of Algorithmic Decisions: of Black Boxes, White Boxes and Fata Morganas." European Journal of Risk Regulation 11, no. 1 (March 2020): 18–50. http://dx.doi.org/10.1017/err.2020.10.

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Understanding of the causes and correlations for algorithmic decisions is currently one of the major challenges of computer science, addressed under an umbrella term “explainable AI (XAI)”. Being able to explain an AI-based system may help to make algorithmic decisions more satisfying and acceptable, to better control and update AI-based systems in case of failure, to build more accurate models, and to discover new knowledge directly or indirectly. On the legal side, the question whether the General Data Protection Regulation (GDPR) provides data subjects with the right to explanation in case of automated decision-making has equally been the subject of a heated doctrinal debate. While arguing that the right to explanation in the GDPR should be a result of interpretative analysis of several GDPR provisions jointly, the authors move this debate forward by discussing the technical and legal feasibility of the explanation of algorithmic decisions. Legal limits, in particular the secrecy of algorithms, as well as technical obstacles could potentially obstruct the practical implementation of this right. By adopting an interdisciplinary approach, the authors explore not only whether it is possible to translate the EU legal requirements for an explanation into the actual machine learning decision-making, but also whether those limitations can shape the way the legal right is used in practice.

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Shrestha, Yash Raj, and Yongjie Yang. "Fairness in Algorithmic Decision-Making: Applications in Multi-Winner Voting, Machine Learning, and Recommender Systems." Algorithms 12, no. 9 (September 18, 2019): 199. http://dx.doi.org/10.3390/a12090199.

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Algorithmic decision-making has become ubiquitous in our societal and economic lives. With more and more decisions being delegated to algorithms, we have also encountered increasing evidence of ethical issues with respect to biases and lack of fairness pertaining to algorithmic decision-making outcomes. Such outcomes may lead to detrimental consequences to minority groups in terms of gender, ethnicity, and race. As a response, recent research has shifted from design of algorithms that merely pursue purely optimal outcomes with respect to a fixed objective function into ones that also ensure additional fairness properties. In this study, we aim to provide a broad and accessible overview of the recent research endeavor aimed at introducing fairness into algorithms used in automated decision-making in three principle domains, namely, multi-winner voting, machine learning, and recommender systems. Even though these domains have developed separately from each other, they share commonality with respect to decision-making as an application, which requires evaluation of a given set of alternatives that needs to be ranked with respect to a clearly defined objective function. More specifically, these relate to tasks such as (1) collectively selecting a fixed number of winner (or potentially high valued) alternatives from a given initial set of alternatives; (2) clustering a given set of alternatives into disjoint groups based on various similarity measures; or (3) finding a consensus ranking of entire or a subset of given alternatives. To this end, we illustrate a multitude of fairness properties studied in these three streams of literature, discuss their commonalities and interrelationships, synthesize what we know so far, and provide a useful perspective for future research.

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Ahonen, Pertti, and Tero Erkkilä. "Transparency in algorithmic decision-making: Ideational tensions and conceptual shifts in Finland." Information Polity 25, no. 4 (December 4, 2020): 419–32. http://dx.doi.org/10.3233/ip-200259.

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This article uses a theoretical and methodological framework derived from the political theorist Quentin Skinner and the conceptual historian Reinhart Koselleck to examine ideational and conceptual tensions and shifts related to the transparency of algorithmic and other automatic governmental decision-making in Finland. Most of the research material comprises national and international official documents and semi-structured expert interviews. In Finland, the concepts of ‘algorithmic transparency’ and other ‘transparency of automatic decision-making’ are situated amongst a complex array of legal, ethical, political, policy-oriented, managerial, and technical semantic fields. From 2016 to 2019 Finland’s Deputy Ombudsman of Parliament and the Constitutional Committee of Parliament pinpointed issues in algorithmic and other automatic decision-making with the consequence that at the turn of 2019 and 2020, the Ministry of Justice started moving towards the preparation of new legislation to resolve these issues. In conclusion and as expected, Finland’s version of the Nordic tradition of the public sphere with established legal guarantees of public access to government documents indeed has both important enabling and constraining effects upon resolving the transparency issues.

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YÁÑEZ, J., J. MONTERO, and D. GÓMEZ. "AN ALGORITHMIC APPROACH TO PREFERENCE REPRESENTATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, supp02 (August 2008): 1–18. http://dx.doi.org/10.1142/s0218488508005455.

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In a previous paper, the authors proposed an alternative approach to classical dimension theory, based upon a general representation of strict preferences not being restricted to partial order sets. Without any relevant restriction, the proposed approach was conceived as a potential powerful tool for decision making problems where basic information has been modeled by means of valued binary preference relations. In fact, assuming that each decision maker is able to consistently manage intensity values for preferences is a strong assumption even when there are few alternatives being involved (if the number of alternatives is large, the same criticism applies to crisp preferences). Any representation tool, as the one proposed by the authors, will in principle play a key role in order to help decision makers to understand their preference structure. In this paper we introduce an alternative approach in order to avoid certain complexity issues of the initial proposal, allowing a close representation easier to be obtained in practice.

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Dissertations / Theses on the topic "Algorithmic decision systems"

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Böhnlein, Toni [Verfasser]. "Algorithmic Decision-Making in Multi-Agent Systems: Votes and Prices / Toni Böhnlein." München : Verlag Dr. Hut, 2018. http://d-nb.info/1164294113/34.

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Björklund, Pernilla. "The curious case of artificial intelligence : An analysis of the relationship between the EU medical device regulations and algorithmic decision systems used within the medical domain." Thesis, Uppsala universitet, Juridiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442122.

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The healthcare sector has become a key area for the development and application of new technology and, not least, Artificial Intelligence (AI). New reports are constantly being published about how this algorithm-based technology supports or performs various medical tasks. These illustrates the rapid development of AI that is taking place within healthcare and how algorithms are increasingly involved in systems and medical devices designed to support medical decision-making. The digital revolution and the advancement of AI technologies represent a step change in the way healthcare may be delivered, medical services coordinated and well-being supported. It could allow for easier and faster communication, earlier and more accurate diagnosing and better healthcare at lower costs. However, systems and devices relying on AI differs significantly from other, traditional, medical devices. AI algorithms are – by nature – complex and partly unpredictable. Additionally, varying levels of opacity has made it hard, sometimes impossible, to interpret and explain recommendations or decisions made by or with support from algorithmic decision systems. These characteristics of AI technology raise important technological, practical, ethical and regulatory issues. The objective of this thesis is to analyse the relationship between the EU regulation on medical devices (MDR) and algorithmic decision systems (ADS) used within the medical domain. The principal question is whether the MDR is enough to guarantee safe and robust ADS within the European healthcare sector or if complementary (or completely different) regulation is necessary. In essence, it will be argued that (i) while ADS are heavily reliant on the quality and representativeness of underlying datasets, there are no requirements with regard to the quality or composition of these datasets in the MDR, (ii) while it is believed that ADS will lead to historically unprecedented changes in healthcare , the regulation lacks guidance on how to manage novel risks and hazards, unique to ADS, and that (iii) as increasingly autonomous systems continue to challenge the existing perceptions of how safety and performance is best maintained, new mechanisms (for transparency, human control and accountability) must be incorporated in the systems. It will also be found that the ability of ADS to change after market certification, will eventually necessitate radical changes in the current regulation and a new regulatory paradigm might be needed.

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Fairley, Andrew. "Information systems for tactical decision making." Thesis, University of Liverpool, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.241479.

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Weingartner, Stephan G. "System development : an algorithmic approach." Virtual Press, 1987. http://liblink.bsu.edu/uhtbin/catkey/483077.

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The subject chosen to develop this thesis project on is developing an algorithm or methodology for system selection. The specific problem studied involves a procedure to determine anion computer system alternative is the best choice for a given user situation.The general problem to be addressed is the need for one to choose computing hardware, software, systems, or services in a -Logical approach from a user perspective, considering cost, performance and human factors. Most existing methods consider only cost and performance factors, combining these factors in ad hoc, subjective fashions to react: a selection decision. By not considering factors treat measure effectiveness and functionality of computer services for a user, existing methods ignore some of the most important measures of value to the user.In this work, a systematic and comprehensive approach to computer system selection has been developed. Also developed were methods for selecting and organizing various criteria.Also ways to assess the importance and value of different service attributes to a end-user are discussed.Finally, the feasibility of a systematic approach to computer system selection has been proven by establishing a general methodology and by proving it through a demonstration of a specific application.

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Manongga, D. H. F. "Using genetic algorithm-based methods for financial analysis." Thesis, University of East Anglia, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320950.

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Bacak, Hikmet Ozge. "Decision Making System Algorithm On Menopause Data Set." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12612471/index.pdf.

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Multiple-centered clustering method and decision making system algorithm on menopause data set depending on multiple-centered clustering are described in this study. This method consists of two stages. At the first stage, fuzzy C-means (FCM) clustering algorithm is applied on the data set under consideration with a high number of cluster centers. As the output of FCM, cluster centers and membership function values for each data member is calculated. At the second stage, original cluster centers obtained in the first stage are merged till the new numbers of clusters are reached. Merging process relies upon a &ldquo
similarity measure&rdquo
between clusters defined in the thesis. During the merging process, the cluster center coordinates do not change but the data members in these clusters are merged in a new cluster. As the output of this method, therefore, one obtains clusters which include many cluster centers. In the final part of this study, an application of the clustering algorithms &ndash
including the multiple centered clustering method &ndash
a decision making system is constructed using a special data on menopause treatment. The decisions are based on the clusterings created by the algorithms already discussed in the previous chapters of the thesis. A verification of the decision making system / v decision aid system is done by a team of experts from the Department of Department of Obstetrics and Gynecology of Hacettepe University under the guidance of Prof. Sinan Beksaç
.

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Wan, Min. "Decision diagram algorithms for logic and timed verification." Diss., [Riverside, Calif.] : University of California, Riverside, 2008. http://proquest.umi.com/pqdweb?index=0&did=1663077981&SrchMode=2&sid=1&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1268242250&clientId=48051.

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Thesis (Ph. D.)--University of California, Riverside, 2008.
Includes abstract. Title from first page of PDF file (viewed March 10, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 166-170). Also issued in print.

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Raboun, Oussama. "Multiple Criteria Spatial Risk Rating." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLED066.

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La thèse est motivée par une étude de cas intéressante liée à l’évaluation du risque nucléaire. Le cas d’étude consiste à évaluer l’impact d’un accident nucléaire survenu dans le milieu marin. Ce problème comporte des caractéristiques spatiales, différents enjeux économiques et environnementaux, des connaissances incomplètes sur les potentiels acteurs et un nombre élevé de scénarios d’accident possibles. Le cas d’étude a été résolu en utilisant différentes techniques d’analyse décisionnelle telles que la comparaison des loteries et les outils MCDA (Multiple Criteria Decision Analysis).Une nouvelle méthode de classification ordinale, nommée Dynamic-R, est née de cette thèse, visant à fournir une notation complète et convaincante. La méthode développée a fourni des résultats intéressants au cas d’étude et des propriétés théoriques très intéressantes qui sont présenté dans les chapitres 6 et 7 de ce manuscrit
The thesis is motivated by an interesting case study related to environmental risk assessment. The case study problem consists on assessing the impact of a nuclear accident taking place in the marine environment. This problem is characterized by spatial characteristics, different assets characterizing the spatial area, incomplete knowledge about the possible stakeholders, and a high number of possible accident scenarios. A first solution of the case study problem was proposed where different decision analysis techniques were used such as lotteries comparison, and MCDA (Multiple Criteria Decision Analysis) tools. A new MCDA rating method, named Dynamic-R, was born from this thesis, aiming at providing a complete and convincing rating. The developed method provided interesting results to the case study, and very interesting theoretical properties that will be presented in chapters 6 and 7 of this manuscript

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Chemla, Daniel, and Daniel Chemla. "Algorithms for optimizing shared mobility systems." Phd thesis, Université Paris-Est, 2012. http://pastel.archives-ouvertes.fr/pastel-00839521.

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Bikes sharing systems have known a growing success all over the world. Several attempts have been made since the 1960s. The latest developments in ICT have enabled the system to become efficient. People can obtain real-time information about the position of the vehicles. More than 200 cities have already introduced the system and this trend keeps on with the launching of the NYC system in spring 2013. A new avatar of these means of transportation has arrived with the introduction of Autolib in Paris end of 2011.The objective of this thesis is to propose algorithms that may help to improve this system efficiency. Indeed, operating these systems induces several issues, one of which is the regulation problem. Regulation should ensures users that a right number of vehicles are present at any station anytime in order to fulfill the demand for both vehicles and parking racks. This regulation is often executed thanks to trucks that are travelling the city. This regulation issue is crucial since empty and full stations increase users' dissatisfaction. Finding the optimal strategy for regulating a network appears to be a difficult question. This thesis is divided into two parts. The first one deals with the "static" case. In this part, users' impact on the network is neglected. This is the case at night or when the system is closed. The operator faces a given repartition of the vehicles. He wants the repartition to match a target one that is known a priori. The one-truck and multiple-truck balancing problems are addressed in this thesis. For each one, an algorithm is proposed and tested on several instances. To deal with the "dynamic" case in which users interact with the system, a simulator has been developed. It is used to compare several strategies and to monitor redistribution by using trucks. Strategies not using trucks, but incentive policies are also tested: regularly updated prices are attached to stations to deter users from parking their vehicle at specified stations. At last, the question to find the best initial inventory is also addressed. It corresponds to the case when no truck are used within the day. Two local searches are presented and both aim at minimizing the total time lost by users in the system. The results obtained can be used as inputs for the target repartitions used in the first part. During my thesis, I participated to two EURO-ROADEF challenges, the 2010 edition proposed by EDF and the 2012 one by Google. In both case, my team reached the final phase. In 2010, our method was ranked fourth over all the participants and led to the publication of an article. In 2012, we ranked eighteenth over all the participants. Both works are added in the appendix

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Chung, Sai-ho, and 鍾世豪. "A multi-criterion genetic algorithm for supply chain collaboration." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B29357275.

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Books on the topic "Algorithmic decision systems"

1

Tijms, H. C. Stochastic models: An algorithmic approach. Chichester: Wiley, 1994.

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CAM: Algorithmen und Decision Support für die Fertigungssteuerung. Berlin: Springer-Verlag, 1989.

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A, Grundel Don, Murphey Robert, and Pardalos P. M. 1954-, eds. Theory and algorithms for cooperative systems. River Edge, NJ: World Scientific, 2004.

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Koukoudakis, Alexandros. Visualisation decision algorithm for temporal database management system. Manchester: UMIST, 1996.

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Chang, Hyeong Soo. Simulation-Based Algorithms for Markov Decision Processes. 2nd ed. London: Springer London, 2013.

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6

Zilio, Daniel C. Physical database design decision algorithms and concurrent reorganization for parallel database systems. Toronto: University of Toronto, Dept. of Computer Science, 1998.

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Gdanskiy, Nikolay. Fundamentals of the theory and algorithms on graphs. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/978686.

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The textbook describes the main theoretical principles of graph theory, the main tasks to be solved using graph structures, and General methods of their solution and specific algorithms, with estimates of their complexity. I covered a lot of the examples given questions to test knowledge and tasks for independent decisions. Along with the control tasks to verify the theoretical training provided practical assignments to develop programs to study topics of graph theory. Meets the requirements of Federal state educational standards of higher education of the last generation. Designed for undergraduate and graduate programs, studying information technology, for in-depth training in analysis and design of systems of complex structure. Also the guide can be useful to specialists of the IT sphere in the study of algorithmic aspects of graph theory.

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Varlamov, Oleg. 18 examples of mivar expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1248446.

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Many years of research on mivar technologies of logical artificial intelligence have allowed us to create a new powerful, versatile and fast tool, which is called "multidimensional open gnoseological active net" — "multidimensional open gnoseological active net: MOGAN". This tool allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format, and it can be used to model cause-and-effect relationships in different subject areas and create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with "Big Knowledge". The reader, after studying this tutorial, you will be able to create mivar expert system with the help of CASMI Wi!Mi. Designed for students, bachelors, masters and postgraduate students studying artificial intelligence methods, as well as for users, experts and specialists, creating a system of information processing and management, mivar models, expert systems, automated control systems, systems of decision support and Recommender systems.

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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.

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Antony, Richard T. Principles of data fusion automation. Boston: Artech House, 1995.

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Book chapters on the topic "Algorithmic decision systems"

1

Prestwich, Steve D., S. Armagan Tarim, Roberto Rossi, and Brahim Hnich. "Neuroevolutionary Inventory Control in Multi-Echelon Systems." In Algorithmic Decision Theory, 402–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04428-1_35.

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Kumova, Bora İ., and Hüseyin Çakır. "Algorithmic Decision of Syllogisms." In Trends in Applied Intelligent Systems, 28–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13025-0_4.

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Rubel, Alan, Adam Pham, and Clinton Castro. "Agency Laundering and Algorithmic Decision Systems." In Information in Contemporary Society, 590–98. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15742-5_56.

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De Stefano, Valerio. "Algorithmic Bosses and What to Do About Them: Automation, Artificial Intelligence and Labour Protection." In Studies in Systems, Decision and Control, 65–86. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45340-4_7.

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Nohel, Jan. "Possibilities of Raster Mathematical Algorithmic Models Utilization as an Information Support of Military Decision Making Process." In Modelling and Simulation for Autonomous Systems, 553–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14984-0_41.

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Lindelauf, Roy. "Nuclear Deterrence in the Algorithmic Age: Game Theory Revisited." In NL ARMS, 421–36. The Hague: T.M.C. Asser Press, 2020. http://dx.doi.org/10.1007/978-94-6265-419-8_22.

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AbstractCommonly used game and decision theoretic models fail to explain the empirics of deterrence. This has unjustly led many theorists to criticize the (rationality and other) assumptions underpinning of such models. No serious game theorist will contend that his theoretic model will possibly take account of all the peculiarities involved in decision making and therefore be an accurate model of such situations. Games are an aid to thinking about some of the aspects of the broader situation. Game theory models prescribe what a decision maker ought to do in a given situation, not what a decision maker actually does. To maintain nuclear strategic stability, it is of paramount importance to understand the dynamical interplay between all players involved in decision making processes with regard to nuclear strategy. History has shown some progress in understanding nuclear deterrence by the use of initial game- and decision theoretic models to alleviate the burden of human cognitive biases. Since it is highly likely that (semi-)autonomous systems will in some way participate in the future nuclear strategic landscape, combined with the fact that the nuclear deterrent decision-cycle will also be based on algorithmic analysis, rational deterrence theory is and should be an integral element of strategic thinking about nuclear deterrence. That, or it might as well be game over.

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Chennaoui, Adil, and Marc Paquet. "Planning System for Emergency Services." In Algorithmic Decision Theory, 130–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41575-3_10.

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Junges, Sebastian, Hazem Torfah, and Sanjit A. Seshia. "Runtime Monitors for Markov Decision Processes." In Computer Aided Verification, 553–76. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_26.

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AbstractWe investigate the problem of monitoring partially observable systems with nondeterministic and probabilistic dynamics. In such systems, every state may be associated with a risk, e.g., the probability of an imminent crash. During runtime, we obtain partial information about the system state in form of observations. The monitor uses this information to estimate the risk of the (unobservable) current system state. Our results are threefold. First, we show that extensions of state estimation approaches do not scale due the combination of nondeterminism and probabilities. While exploiting a geometric interpretation of the state estimates improves the practical runtime, this cannot prevent an exponential memory blowup. Second, we present a tractable algorithm based on model checking conditional reachability probabilities. Third, we provide prototypical implementations and manifest the applicability of our algorithms to a range of benchmarks. The results highlight the possibilities and boundaries of our novel algorithms.

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Aspnes, James, Eric Blais, Murat Demirbas, Ryan O’Donnell, Atri Rudra, and Steve Uurtamo. "k + Decision Trees." In Algorithms for Sensor Systems, 74–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16988-5_7.

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Shin, Yong-Jun, Eunho Cho, and Doo-Hwan Bae. "PASTA: An Efficient Proactive Adaptation Approach Based on Statistical Model Checking for Self-Adaptive Systems." In Fundamental Approaches to Software Engineering, 292–312. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71500-7_15.

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AbstractProactive adaptation, in which the adaptation for a system’s reliable goal achievement is performed by predicting changes in the environment, is considered as an effective alternative to reactive adaptation, in which adaptation is performed after observing changes. When predicting the environmental changes, the prediction may be uncertain, so it is necessary to verify and confirm an adaptation’s consequences before execution. To resolve the uncertainty, probabilistic model checking (PMC) has been utilized for verification of adaptation tactics’ effects on the goal of a self-adaptive system (SAS). However, PMC-based approaches have limitations on the state-explosion problem of complex SAS model verification and the modeling languages supported by the model checkers. In this paper, to overcome the limitations of the PMC-based approaches, we propose an efficient Proactive Adaptation approach based on STAtistical model checking (PASTA). Our approach allows SASs to mitigate the uncertainty of the future environment, faster than the PMC-based approach, by producing statistically sufficient samples for verification of adaptation tactics based on statistical model checking (SMC) algorithms. We provide algorithmic processes, a reference architecture, and an open-source implementation skeleton of PASTA for engineers to apply it for SAS development. We evaluate PASTA on two SASs using actual data and show that PASTA is efficient comparing to the PMC-based approach. We also provide a comparative analysis of the advantages and disadvantages of PMC- and SMC-based proactive adaptation to guide engineers’ decision-making for SAS development.

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Conference papers on the topic "Algorithmic decision systems"

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Wang, Ruotong, F. Maxwell Harper, and Haiyi Zhu. "Factors Influencing Perceived Fairness in Algorithmic Decision-Making." In CHI '20: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3313831.3376813.

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Blair, Kathryn, Pil Hansen, and Lora Oehlberg. "Participatory Art for Public Exploration of Algorithmic Decision-Making." In DIS '21: Designing Interactive Systems Conference 2021. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3468002.3468235.

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Aysolmaz, Banu, Deniz Iren, and Nancy Dau. "Preventing Algorithmic Bias in the Development of Algorithmic Decision-Making Systems: A Delphi Study." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2020. http://dx.doi.org/10.24251/hicss.2020.648.

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Rajeshkanna, A., and K. Arunesh. "ID3 Decision Tree Classification: An Algorithmic Perspective based on Error rate." In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). IEEE, 2020. http://dx.doi.org/10.1109/icesc48915.2020.9155578.

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Gercekovich, D. A., O. Yu Basharina, I. S. Shilnikova, E. Yu Gorbachevskaya, and S. A. Gorsky. "Information and algorithmic support of a multi-level integrated system for the investment strategies formation." In 3rd International Workshop on Information, Computation, and Control Systems for Distributed Environments 2021. Crossref, 2021. http://dx.doi.org/10.47350/iccs-de.2021.06.

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The article summarizes the accumulated practical experience of the authors in the development of algorithms for the formation of investment strategies. For this purpose, the optimization of the studied parameters, information support of investment activities, verification, monitoring and adjustment in the testing mode and the subsequent practical application of the described tools are considered. The system is based on the main provisions of the Markowitz portfolio theory. The analytical block of the Information System Portfolio Investor includes Profitability-Risk model; empirical models of optimal complexity; hybrid predictive model systems; the principle of combining (integrating) both models and forecasts, as well as decision rules; optimization of the training sample length (modified Markowitz model); optimization of the frequency of monitoring and adjusting the composition of the investment portfolio. The principles of design and development of the information block of the system, its replenishment and functioning are described in detail. All the above listed components of the algorithmic content of the investment decision making system are described sequentially. The system modules have been successfully tested on a wide class of financial instruments: ordinary shares, preferred shares, government and corporate bonds, exchange commodities, stock, commodity, industry and bond indices, exchange-traded investment funds and real estate funds. The implemented Markowitz model with a dynamic database of historical data can significantly increase the efficiency of investment decisions, which is facilitated by taking into account the characteristics of both the markets under study and the corresponding financial instruments.

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Ferrero, Federico, and Adriana Gewerc Barujel. "Algorithmic Driven Decision-Making Systems in Education: Analyzing Bias from the Sociocultural Perspective." In 2019 XIV Latin American Conference on Learning Technologies (LACLO). IEEE, 2019. http://dx.doi.org/10.1109/laclo49268.2019.00038.

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Edwards, James A., and Uzi Vishkin. "Linking parallel algorithmic thinking to many-core memory systems and speedups for boosted decision trees." In MEMSYS '18: The International Symposium on Memory Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240302.3240321.

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Veale, Michael, Max Van Kleek, and Reuben Binns. "Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making." In CHI '18: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3173574.3174014.

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Kumar, Akshat. "Multiagent Decision Making and Learning in Urban Environments." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/895.

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Our increasingly interconnected urban environments provide several opportunities to deploy intelligent agents---from self-driving cars, ships to aerial drones---that promise to radically improve productivity and safety. Achieving coordination among agents in such urban settings presents several algorithmic challenges---ability to scale to thousands of agents, addressing uncertainty, and partial observability in the environment. In addition, accurate domain models need to be learned from data that is often noisy and available only at an aggregate level. In this paper, I will overview some of our recent contributions towards developing planning and reinforcement learning strategies to address several such challenges present in large-scale urban multiagent systems.

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"INVESTIGATIONS ON OBJECT-CENTERED ROUTING IN DYNAMIC ENVIRONMENTS: ALGORITHMIC FRAMEWORK AND INITIAL NUMERICAL RESULTS - Support for Distributed Decision Making in Transport Systems." In 9th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002379502250230.

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Reports on the topic "Algorithmic decision systems"

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Ludwig, Jens, and Sendhil Mullainathan. Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System. Cambridge, MA: National Bureau of Economic Research, September 2021. http://dx.doi.org/10.3386/w29267.

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Buchanan, Ben. The AI Triad and What It Means for National Security Strategy. Center for Security and Emerging Technology, August 2020. http://dx.doi.org/10.51593/20200021.

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One sentence summarizes the complexities of modern artificial intelligence: Machine learning systems use computing power to execute algorithms that learn from data. This AI triad of computing power, algorithms, and data offers a framework for decision-making in national security policy.

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Lagutin, Andrey, and Tatyana Sidorina. SYSTEM OF FORMATION OF PROFESSIONAL AND PERSONAL SELF-GOVERNMENT AMONG CADETS OF MILITARY INSTITUTES. Science and Innovation Center Publishing House, December 2020. http://dx.doi.org/10.12731/self-government.

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When carrying out professional activities, officers of the VNG of the Russian Federation are often in difficult, stressful, emotionally stressful situations associated with the use of weapons as a particularly dangerous means of destruction. The right to use a weapon by an officer makes him responsible for its use. And therefore requires the officer to make a balanced optimal decision, which is associated with the risk and transience of events, and in which no mistake can be made, since the price of it can be someone's life. It is at such a moment that it is important that the officer has stable skills in making a decision on the use of weapons, and this requires skills not only in managing subordinates or the situation,but in managing himself. The complication of the military-professional activity, manifested in the need to develop the ability to quickly and accurately make command decisions, exacerbating the problem of social responsibility of an officer who has the management of unit that leads to an understanding of his singular personal and professional responsibility, as the ability to govern themselves makes it possible to achieve a positive result of the Department for the DBA. This characterizes the need for a commander to have the ability to manage himself, as a "system" that manages others. Forming skills of self-control, patience, compassion, having mastered algorithms of making managerial decisions, the cycle of implementing managerial functions, etc., a person comes to the belief: "before effectively managing others, it is necessary to learn how to manage yourself." The required level of personal and professional maturity can be formed in a person as a result of purposeful self-management, which determines the special role of professional and personal self-management in the training of future officers.

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