21,266 research outputs found
Pareto Optimality and Strategy Proofness in Group Argument Evaluation (Extended Version)
An inconsistent knowledge base can be abstracted as a set of arguments and a
defeat relation among them. There can be more than one consistent way to
evaluate such an argumentation graph. Collective argument evaluation is the
problem of aggregating the opinions of multiple agents on how a given set of
arguments should be evaluated. It is crucial not only to ensure that the
outcome is logically consistent, but also satisfies measures of social
optimality and immunity to strategic manipulation. This is because agents have
their individual preferences about what the outcome ought to be. In the current
paper, we analyze three previously introduced argument-based aggregation
operators with respect to Pareto optimality and strategy proofness under
different general classes of agent preferences. We highlight fundamental
trade-offs between strategic manipulability and social optimality on one hand,
and classical logical criteria on the other. Our results motivate further
investigation into the relationship between social choice and argumentation
theory. The results are also relevant for choosing an appropriate aggregation
operator given the criteria that are considered more important, as well as the
nature of agents' preferences
Manipulation in group argument evaluation.
Given an argumentation framework and a group of agents, the individuals may have divergent opinions on the status of the arguments. If the group needs to reach a common po- sition on the argumentation framework, the question is how the individual evaluations can be mapped into a collective one. This problem has been recently investigated in [1]. In this paper, we study under which conditions these operators are Pareto optimal and whether they are manipulable.Collective decision making; Argumentation; Judgment aggregation; Social choice theory;
Preservation of Semantic Properties during the Aggregation of Abstract Argumentation Frameworks
An abstract argumentation framework can be used to model the argumentative
stance of an agent at a high level of abstraction, by indicating for every pair
of arguments that is being considered in a debate whether the first attacks the
second. When modelling a group of agents engaged in a debate, we may wish to
aggregate their individual argumentation frameworks to obtain a single such
framework that reflects the consensus of the group. Even when agents disagree
on many details, there may well be high-level agreement on important semantic
properties, such as the acceptability of a given argument. Using techniques
from social choice theory, we analyse under what circumstances such semantic
properties agreed upon by the individual agents can be preserved under
aggregation.Comment: In Proceedings TARK 2017, arXiv:1707.0825
The problem of evaluating automated large-scale evidence aggregators
In the biomedical context, policy makers face a large amount of potentially discordant evidence from different sources. This prompts the question of how this evidence should be aggregated in the interests of best-informed policy recommendations. The starting point of our discussion is Hunter and Williams’ recent work on an automated aggregation method for medical evidence. Our negative claim is that it is far from clear what the relevant criteria for evaluating an evidence aggregator of this sort are. What is the appropriate balance between explicitly coded algorithms and implicit reasoning involved, for instance, in the packaging of input evidence? In short: What is the optimal degree of ‘automation’? On the positive side: We propose the ability to perform an adequate robustness analysis as the focal criterion, primarily because it directs efforts to what is most important, namely, the structure of the algorithm and the appropriate extent of automation. Moreover, where there are resource constraints on the aggregation process, one must also consider what balance between volume of evidence and accuracy in the treatment of individual evidence best facilitates inference. There is no prerogative to aggregate the total evidence available if this would in fact reduce overall accuracy
Reasons, Coherence, and Group Rationality
Philosophy and Phenomenological Research, EarlyView
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