Towards automatic argumentation about voting rules

Voting rules aggregate a group\u27s preferences to make decisions. As multiple reasonable voting rules exist, the axiomatic approach has been proposed to exhibit both their merits and paradoxical behaviors. It is however a difficult task to characterize a voting rule by such axioms, and even when a proof exists, it may be difficult to understand why a specific rule fails to satisfy a given axiom. In this article, we present an automatic method which determines whether a given rule satisfies a set of axioms. It produces evidence which can be used by non-expert users to comprehend why a rule violates some axiom and may serve to argue in favor of rules which satisfy it. Our method is based on the software analysis technique “bounded model checking”, which enables bounded verification of software programs. The method can be applied to arbitrary voting rules; we demonstrate it on the case of the Borda axiomatization and compare the Borda rule to both the Black and the Copeland voting rules

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

Political Economy of Public Deficit: Perspectives for Constitutional Reform

The paper uses a dynamic inconsistency model known from monetary policy to assess three alternative proposals how to reform fiscal constitution in order to limit government’s incentive to use fiscal policy for maximizing political support. The return to ever-balanced-budget rule, state-contingent rules, and the establishment of an independent Fiscal Policy Committee with power to set public deficit with the aim of stabilizing the economy are discussed from the constitutional perspective, analyzing different incentives that these proposals create for government and alternative means to enhance credibility of the arrangement.fiscal policy; dynamic inconsistency; political economy; public deficit

Argumentation for machine learning: a survey

Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future

Parsing Argumentation Structures in Persuasive Essays

In this article, we present a novel approach for parsing argumentation structures. We identify argument components using sequence labeling at the token level and apply a new joint model for detecting argumentation structures. The proposed model globally optimizes argument component types and argumentative relations using integer linear programming. We show that our model considerably improves the performance of base classifiers and significantly outperforms challenging heuristic baselines. Moreover, we introduce a novel corpus of persuasive essays annotated with argumentation structures. We show that our annotation scheme and annotation guidelines successfully guide human annotators to substantial agreement. This corpus and the annotation guidelines are freely available for ensuring reproducibility and to encourage future research in computational argumentation.Comment: Under review in Computational Linguistics. First submission: 26 October 2015. Revised submission: 15 July 201

A Roadmap for Self-Evolving Communities

Self-organisation and self-evolution is evident in physics, chemistry, biology, and human societies. Despite the existing literature on the topic, we believe self-organisation and self-evolution is still missing from the IT tools (whether online or offline) we are building and using. In the last decade, human interactions have been moving more and more towards social media. The time we spend interacting with others in virtual communities and networks is tremendous. Yet, the tools supporting those interactions remain rigid. This position paper argues the need for self-evolving software-enabled communities, and proposes a roadmap for achieving this required self-evolution. The proposal is based on building normative-based communities, where community interactions are regulated by norms and community members are free to discuss and modify their community's norms. The evolution of communities is then dictated by the evolution of its norms.Peer Reviewe