Stephan Hartmann 0001

dblp:41/5344-1 · DBLP profile ↗
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14ranked-venue papers
6as first author
9since 2021 · last 2024
0000-0001-8676-2177ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 12 · 5 first-author · 9 since 2021Applied, interdisciplinary, general and emerging computing · 12 · 5 first-author · 9 since 2021Theory of computation · 2 · 1 first-author
YearPublicationVenuePosition
2024 A New Posterior Probability-Based Measure of Coherence
Stephan Hartmann 0001, Borut Trpin
CogSci1
2024 Novel Predictions for Boundedly Rational Agents: A Bayesian Analysis
Rafael Fuchs, Stephan Hartmann 0001
CogSci2
2024 Explaining the Conjunction Fallacy
Borut Trpin, Stephan Hartmann 0001
CogSci2
2023 Confirmation, Coherence and the Strength of Arguments
Stephan Hartmann 0001, Borut Trpin
CogSci1
2023 Coherence of Information: What It Is and Why It Matters
Stephan Hartmann 0001, Borut Trpin
CogSci1
2023 Causal Structure and Argumentative Value
Ulrike Hahn, Borut Trpin, Stephan Hartmann 0001, Marko Tesic, Anita Keshmirian, Corina Strößner
CogSci3
2023 Perceived Causal Strength in Chains vs. Common Causes
Anita Keshmirian, Babak Hemmatian, Ulrike Hahn, Stephan Hartmann 0001
CogSci4
2022 The Myside Bias in Argument Evaluation: A Bayesian Model
Edoardo Baccini, Stephan Hartmann 0001
CogSci2
2021 How to Revise Beliefs from Conditionals: A New Proposal
Stephan Hartmann 0001, Ulrike Hahn
CogSci1
2020 A New Approach to Testimonial Conditionals
Stephan Hartmann 0001, Ulrike Hahn
CogSci1
2019 A New Probabilistic Explanation of the Modus Ponens-Modus Tollens Asymmetry
Benjamin Eva, Stephan Hartmann 0001, Henrik Singmann
CogSci2
2017 Rank Aggregation and Belief Revision Dynamics
Igor Volzhanin, Ulrike Hahn, Dell Zhang, Stephan Hartmann 0001
CogSci4
2010 Reliable Methods of Judgement Aggregation
abstract
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.
Stephan Hartmann 0001, Gabriella Pigozzi, Jan Sprenger
J. Log. Comput.1
2007 Judgment aggregation and the problem of truth-tracking
abstract
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.
Gabriella Pigozzi, Stephan Hartmann 0001
TARK2