Forms and Norms of Indecision in Argumentation Theory
- URL: http://arxiv.org/abs/2203.02207v1
- Date: Fri, 4 Mar 2022 09:33:49 GMT
- Title: Forms and Norms of Indecision in Argumentation Theory
- Authors: Daniela Schuster
- Abstract summary: Indecision is often not considered explicitly, but rather taken to be a collection of all unclear or troubling cases.
Current philosophy makes a strong point for taking indecision itself to be a proper object of consideration.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: One main goal of argumentation theory is to evaluate arguments and to
determine whether they should be accepted or rejected. When there is no clear
answer, a third option, being undecided, has to be taken into account.
Indecision is often not considered explicitly, but rather taken to be a
collection of all unclear or troubling cases. However, current philosophy makes
a strong point for taking indecision itself to be a proper object of
consideration. This paper aims at revealing parallels between the findings
concerning indecision in philosophy and the treatment of indecision in
argumentation theory. By investigating what philosophical forms and norms of
indecision are involved in argumentation theory, we can improve our
understanding of the different uncertain evidential situations in argumentation
theory.
Related papers
- Conceptual and Unbiased Reasoning in Language Models [98.90677711523645]
We propose a novel conceptualization framework that forces models to perform conceptual reasoning on abstract questions.
We show that existing large language models fall short on conceptual reasoning, dropping 9% to 28% on various benchmarks.
We then discuss how models can improve since high-level abstract reasoning is key to unbiased and generalizable decision-making.
arXiv Detail & Related papers (2024-03-30T00:53:53Z) - A Unifying Framework for Learning Argumentation Semantics [50.69905074548764]
We present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way.
Our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.
arXiv Detail & Related papers (2023-10-18T20:18:05Z) - Many-valued Argumentation, Conditionals and a Probabilistic Semantics
for Gradual Argumentation [3.9571744700171743]
We propose a general approach to define a many-valued preferential interpretation of gradual argumentation semantics.
As a proof of concept, in the finitely-valued case, an Answer set Programming approach is proposed for conditional reasoning.
The paper also develops and discusses a probabilistic semantics for gradual argumentation, which builds on the many-valued conditional semantics.
arXiv Detail & Related papers (2022-12-14T22:10:46Z) - MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure [129.8481568648651]
We propose a benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios.
Based on the multi-hop chain of reasoning, the explanation form includes three main components.
We evaluate the current best models' performance on this new explanation form.
arXiv Detail & Related papers (2022-10-22T16:01:13Z) - Mining Legal Arguments in Court Decisions [43.09204050756282]
We develop a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights.
Second, we compile and annotate a large corpus of 373 court decisions.
Third, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain.
arXiv Detail & Related papers (2022-08-12T08:59:55Z) - Non-Determinism and the Lawlessness of Machine Learning Code [43.662736664344095]
We show that the effects of non-determinism, and consequently its implications for the law, become clearer from the perspective of reasoning about ML outputs as distributions over possible outcomes.
We conclude with a brief discussion of what work ML can do to constrain the potentially harm-inducing effects of non-determinism.
arXiv Detail & Related papers (2022-06-23T17:05:34Z) - Contrastive Explanations for Argumentation-Based Conclusions [5.1398743023989555]
We discuss contrastive explanations for formal argumentation.
We show under which conditions contrastive explanations are meaningful, and how argumentation allows us to make implicit foils explicit.
arXiv Detail & Related papers (2021-07-07T15:00:47Z) - A General Counterexample to Any Decision Theory and Some Responses [1.713291434132985]
I present an argument and a general schema which can be used to construct a problem case for any decision theory.
I also present and discuss a number of possible responses to this argument.
arXiv Detail & Related papers (2021-01-01T17:47:11Z) - A Note on Bell's Theorem Logical Consistency [0.0]
Counterfactual definiteness is supposed to underlie the Bell theorem.
We show that counterfactual definiteness is an unnecessary and inconsistent assumption.
arXiv Detail & Related papers (2020-12-16T22:14:06Z) - Logical Neural Networks [51.46602187496816]
We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning)
Every neuron has a meaning as a component of a formula in a weighted real-valued logic, yielding a highly intepretable disentangled representation.
Inference is omni rather than focused on predefined target variables, and corresponds to logical reasoning.
arXiv Detail & Related papers (2020-06-23T16:55:45Z) - Cognitive Argumentation and the Suppression Task [1.027974860479791]
This paper addresses the challenge of modeling human reasoning, within a new framework called Cognitive Argumentation.
The framework relies on cognitive principles, based on empirical and theoretical work in Cognitive Science, to adapt a general and abstract framework of computational argumentation from AI.
We argue that Cognitive Argumentation provides a coherent and cognitively adequate model for human conditional reasoning.
arXiv Detail & Related papers (2020-02-24T10:30:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.