The Experts below are selected from a list of 17235 Experts worldwide ranked by ideXlab platform
Piotr Skowron - One of the best experts on this subject based on the ideXlab platform.
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Multi-attribute Proportional Representation
Artificial Intelligence, 2018Co-Authors: Jérôme Lang, Piotr SkowronAbstract:Abstract We consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should have. We look for a set that fits as much as possible the desired distributions on all attributes. An example of application is the choice of members for a representative committee, where candidates are described by attributes such as gender, age and profession, and where we look for a committee that for each attribute offers a certain Representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. Another example of application is the selection of a common set of items to be used by a group of users, where items are labelled by attribute values. With a single attribute the problem collapses to the apportionment problem for party-list Proportional Representation systems (in such a case the value of the single attribute would be a political affiliation of a candidate). We study the properties of the associated subset selection rules, as well as their computational complexity.
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Multi-Attribute Proportional Representation
2016Co-Authors: Jérôme Lang, Piotr SkowronAbstract:We consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should fit. We look for a set that fits as much as possible the desired distributions on all attributes. Examples of applications include choosing members of a representative committee, where candidates are described by attributes such as sex, age and profession, and where we look for a committee that for each attribute offers a certain Representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. With a single attribute the problem boils down to the apportionment problem for party-list Proportional Representation systems (in such case the value of the single attribute is the political affiliation of a candidate). We study some properties of the associated subset selection rules, and address their computation.
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Multi-Attribute Proportional Representation
arXiv: Artificial Intelligence, 2015Co-Authors: Jérôme Lang, Piotr SkowronAbstract:We consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should have. We look for a set that fits as much as possible the desired distributions on all attributes. Examples of applications include choosing members of a representative committee, where candidates are described by attributes such as sex, age and profession, and where we look for a committee that for each attribute offers a certain Representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. With a single attribute the problem collapses to the apportionment problem for party-list Proportional Representation systems (in such case the value of the single attribute would be a political affiliation of a candidate). We study the properties of the associated subset selection rules, as well as their computation complexity.
Jérôme Lang - One of the best experts on this subject based on the ideXlab platform.
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Multi-attribute Proportional Representation
Artificial Intelligence, 2018Co-Authors: Jérôme Lang, Piotr SkowronAbstract:Abstract We consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should have. We look for a set that fits as much as possible the desired distributions on all attributes. An example of application is the choice of members for a representative committee, where candidates are described by attributes such as gender, age and profession, and where we look for a committee that for each attribute offers a certain Representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. Another example of application is the selection of a common set of items to be used by a group of users, where items are labelled by attribute values. With a single attribute the problem collapses to the apportionment problem for party-list Proportional Representation systems (in such a case the value of the single attribute would be a political affiliation of a candidate). We study the properties of the associated subset selection rules, as well as their computational complexity.
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Multi-Attribute Proportional Representation
2016Co-Authors: Jérôme Lang, Piotr SkowronAbstract:We consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should fit. We look for a set that fits as much as possible the desired distributions on all attributes. Examples of applications include choosing members of a representative committee, where candidates are described by attributes such as sex, age and profession, and where we look for a committee that for each attribute offers a certain Representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. With a single attribute the problem boils down to the apportionment problem for party-list Proportional Representation systems (in such case the value of the single attribute is the political affiliation of a candidate). We study some properties of the associated subset selection rules, and address their computation.
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Multi-Attribute Proportional Representation
arXiv: Artificial Intelligence, 2015Co-Authors: Jérôme Lang, Piotr SkowronAbstract:We consider the following problem in which a given number of items has to be chosen from a predefined set. Each item is described by a vector of attributes and for each attribute there is a desired distribution that the selected set should have. We look for a set that fits as much as possible the desired distributions on all attributes. Examples of applications include choosing members of a representative committee, where candidates are described by attributes such as sex, age and profession, and where we look for a committee that for each attribute offers a certain Representation, i.e., a single committee that contains a certain number of young and old people, certain number of men and women, certain number of people with different professions, etc. With a single attribute the problem collapses to the apportionment problem for party-list Proportional Representation systems (in such case the value of the single attribute would be a political affiliation of a candidate). We study the properties of the associated subset selection rules, as well as their computation complexity.
John Grundy - One of the best experts on this subject based on the ideXlab platform.
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ICWE - Merging intelligent API responses using a Proportional Representation approach
Lecture Notes in Computer Science, 2019Co-Authors: Tomohiro Ohtake, Alex Cummaudo, Mohamed Abdelrazek, Rajesh Vasa, John GrundyAbstract:Intelligent APIs, such as Google Cloud Vision or Amazon Rekognition, are becoming evermore pervasive and easily accessible to developers to build applications. Because of the stochastic nature that machine learning entails and disparate datasets used in their training, the output from different APIs varies over time, with low reliability in some cases when compared against each other. Merging multiple unreliable API responses from multiple vendors may increase the reliability of the overall response, and thus the reliability of the intelligent end-product. We introduce a novel methodology – inspired by the Proportional Representation used in electoral systems – to merge outputs of different intelligent computer vision APIs provided by multiple vendors. Experiments show that our method outperforms both naive merge methods and traditional Proportional Representation methods by 0.015 F-measure.
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merging intelligent api responses using a Proportional Representation approach
International Conference on Web Engineering, 2019Co-Authors: Tomohiro Ohtake, Alex Cummaudo, Mohamed Abdelrazek, Rajesh Vasa, John GrundyAbstract:Intelligent APIs, such as Google Cloud Vision or Amazon Rekognition, are becoming evermore pervasive and easily accessible to developers to build applications. Because of the stochastic nature that machine learning entails and disparate datasets used in their training, the output from different APIs varies over time, with low reliability in some cases when compared against each other. Merging multiple unreliable API responses from multiple vendors may increase the reliability of the overall response, and thus the reliability of the intelligent end-product. We introduce a novel methodology – inspired by the Proportional Representation used in electoral systems – to merge outputs of different intelligent computer vision APIs provided by multiple vendors. Experiments show that our method outperforms both naive merge methods and traditional Proportional Representation methods by 0.015 F-measure.
Tomohiro Ohtake - One of the best experts on this subject based on the ideXlab platform.
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ICWE - Merging intelligent API responses using a Proportional Representation approach
Lecture Notes in Computer Science, 2019Co-Authors: Tomohiro Ohtake, Alex Cummaudo, Mohamed Abdelrazek, Rajesh Vasa, John GrundyAbstract:Intelligent APIs, such as Google Cloud Vision or Amazon Rekognition, are becoming evermore pervasive and easily accessible to developers to build applications. Because of the stochastic nature that machine learning entails and disparate datasets used in their training, the output from different APIs varies over time, with low reliability in some cases when compared against each other. Merging multiple unreliable API responses from multiple vendors may increase the reliability of the overall response, and thus the reliability of the intelligent end-product. We introduce a novel methodology – inspired by the Proportional Representation used in electoral systems – to merge outputs of different intelligent computer vision APIs provided by multiple vendors. Experiments show that our method outperforms both naive merge methods and traditional Proportional Representation methods by 0.015 F-measure.
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merging intelligent api responses using a Proportional Representation approach
International Conference on Web Engineering, 2019Co-Authors: Tomohiro Ohtake, Alex Cummaudo, Mohamed Abdelrazek, Rajesh Vasa, John GrundyAbstract:Intelligent APIs, such as Google Cloud Vision or Amazon Rekognition, are becoming evermore pervasive and easily accessible to developers to build applications. Because of the stochastic nature that machine learning entails and disparate datasets used in their training, the output from different APIs varies over time, with low reliability in some cases when compared against each other. Merging multiple unreliable API responses from multiple vendors may increase the reliability of the overall response, and thus the reliability of the intelligent end-product. We introduce a novel methodology – inspired by the Proportional Representation used in electoral systems – to merge outputs of different intelligent computer vision APIs provided by multiple vendors. Experiments show that our method outperforms both naive merge methods and traditional Proportional Representation methods by 0.015 F-measure.
Anthony J. Mcgann - One of the best experts on this subject based on the ideXlab platform.
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Proportional Representation Within the Limits of Liberalism Alone
British Journal of Political Science, 2009Co-Authors: Eliora Van Der Hout, Anthony J. McgannAbstract:This article provides a justification of Proportional Representation (PR) in strictly liberal terms. Previous justifications of Proportional Representation have tended to be based either on its intuitive fairness to political parties, or on its being fair to social groups. The arguments of critics of PR, we argue, likewise rely on fairness to group identities. In contrast, our result shows that Proportionality is logically implied by liberal equality, that is, by the requirement that all individual voters be treated equally. Thus we provide a justification for PR in terms of the theory of voting, similar to May’s theorem for majority rule.
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Geographical Representation under Proportional Representation: The cases of Israel and the Netherlands
Electoral Studies, 2005Co-Authors: Michael Latner, Anthony J. McgannAbstract:It has frequently been argued that Proportional Representation leads to national politics with little or no regional Representation. We examine this in the case of the two most extreme cases of Proportional Representation, Israel and the Netherlands. We find that actually there are very distinct patterns of geographical Representation. Although central metropolitan areas are somewhat over-represented in the legislatures, so are the most peripheral regions. This is due to the fact that parties tend to choose representatives from the geographical regions where they expect to be electorally competitive. Furthermore, Proportional Representation does not necessarily lead to nationally competitive parties, as in Israel. We also consider the relationship between geographical and other aspects of descriptive Representation, such as gender and ethnicity. (c) 2005 Elsevier Ltd. All rights reserved.
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Equal Protection Implies Proportional Representation
2004Co-Authors: Eliora Van Der Hout, Anthony J. McgannAbstract:This paper shows that for a single-vote electoral system for a representative body to treat all voters and all parties equally, it must produce results essentially identical to list Proportional Representation (PR). Democratic theory has often been agnostic concerning representative institutions. Different institutions have been compared in terms of behavioral outcomes rather than axiomatic properties. Building on van der Hout et al.’s (2002) result, we show that for an electoral system to completely respect the principle of political equality, its results must be equivalent to those of list PR.
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Geographical Representation Under Proportional Representation: The Cases of Israel and the Netherlands
Center for the Study of Democracy, 2004Co-Authors: Michael Latner, Anthony J. McgannAbstract:It has frequently been argued that Proportional Representation lead to national politics with little or no regional Representation. We examine this in the case of the two most extreme cases of Proportional Representation, Israel and the Netherlands. We find that actually there are very distinct patterns of geographical Representation. Although central metropolitan areas are somewhat over-represented in the legislatures, so are the most peripheral regions. This is due to the fact that parties tend to choose representatives from the geographical regions where they expect to be electorally competitive. Furthermore, Proportional Representation does not necessarily lead to nationally competitive parties, as in Israel. We also consider the relationship between geographical and other aspects of descriptive Representation, such as gender and ethnicity.
