References of "Hartmann, Stephan"
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See detailReliable Methods of Judgement Aggregation
Hartmann, Stephan; Pigozzi, Gabriella UL; Sprenger, Jan

in Journal of Logic & Computation (2010), 20(2), 603617

The aggregation of consistent individual judgements on logically interconnected propositions into a collective judgement on the same propositions has recently drawn much attention. Seemingly reasonable ... [more ▼]

The aggregation of consistent individual judgements on logically interconnected propositions into a collective judgement on the same propositions has recently drawn much attention. Seemingly reasonable aggregation procedures, such as propositionwise majority voting, cannot ensure an equally consistent collective conclusion. The literature on judgement aggregation refers to such a problem as the discursive dilemma. In this article we assume that the decision which the group is trying to reach is factually right or wrong. Hence, we address the question of how good various approaches are at selecting the right conclusion. We focus on two approaches: distance-based procedures and a Bayesian analysis. They correspond to group-internal and group external decision making, respectively. We compare those methods in a probabilistic model whose assumptions are subsequently relaxed. Our findings have two general implications for judgement aggregation problems: first, in a voting procedure, reasons should carry higher weight than the conclusion, and second, considering members of an advisory board to be highly competent is a better strategy than discounting their advice. [less ▲]

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See detailJudgment Aggregation and the Problem of Truth-Tracking
Pigozzi, Gabriella UL; Hartmann, Stephan

in Proceedings of the 11th conference on Theoretical aspects of rationality and knowledge (2007)

The problem of the aggregation of consistent individual judgments on logically interconnected propositions into a collective judgment on the same propositions has recently drawn much attention. The ... [more ▼]

The problem of the aggregation of consistent individual judgments on logically interconnected propositions into a collective judgment on the same propositions has recently drawn much attention. The difficulty lies in the fact that a seemingly reasonable aggregation procedure, such as propositionwise majority voting, cannot ensure an equally consistent collective outcome. The literature on judgment aggregation refers to such dilemmas as the doctrinal paradox. Three procedures have been proposed in order to overcome the paradox: the premise-based and conclusion-based procedures on the one hand, and the fusion approach on the other hand. In this paper we assume that the decision which the group is trying to reach is factually right or wrong. Hence, the question is how good the fusion approach is in tracking the truth, and how it compares with the premise-based and conclusion-based procedures. We address these questions in a probabilistic framework and show that belief fusion does especially well for individuals with a middling competence of hitting the truth of a proposition. [less ▲]

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See detailAggregation in Multi-Agent Systems and the Problem of Truth-Tracking
Pigozzi, Gabriella UL; Hartmann, Stephan

Poster (2007)

One of the major problems that artificial intelligence needs to tackle is the combination of different and potentially conflicting sources of information. Examples are multi-sensor fusion, database ... [more ▼]

One of the major problems that artificial intelligence needs to tackle is the combination of different and potentially conflicting sources of information. Examples are multi-sensor fusion, database integration and expert systems development. In this paper we are interested in the aggregation of propositional logic-based information, a problem recently addressed in the literature on information fusion. It has applications in multiagent systems that aim at aggregating the distributed agent-based knowledge into an (ideally) unique set of propositions. We consider a group of autonomous agents who individually hold a logically consistent set of propositions. Each set of propositions represents an agent's beliefs on issues on which the group has to make a collective decision. To make the collective decision, several aggregation procedures have been proposed in the literature. Assuming that all propositions in question are factually right or wrong, we ask how good belief fusion is as a truth tracker. Will it single out the true set of propositions? And how does information fusion compare with other aggregation procedures? We address these questions in a probabilistic framework and show that information fusion does especially well for agents with a middling competence of hitting the truth of an individual proposition. [less ▲]

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See detailMerging judgments and the problem of truth-tracking
Pigozzi, Gabriella UL; Hartmann, Stephan

Scientific Conference (2006, December 07)

Detailed reference viewed: 38 (0 UL)