14,611 research outputs found

    The Trade-Climate Nexus: Assessing the European Union’s Institutionalist Approach. College of Europe EU Diplomacy Paper 04/2019

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    The European Union (EU) is considered a global leader both in trade and climate policies. Nonetheless, trade liberalisation has been widely criticised for its negative effects on the environment and for directly contributing to the rising levels of annual greenhouse gas emissions. This paper addresses the trade-climate nexus by assessing to what extent the EU is effectively integrating its environmental objectives within its trade policies. First, the legal spaces for the EU’s action in this policy nexus are identified. Second, the analysis looks into how effectively the EU is achieving its own set of objectives for trade and climate. The assessment draws on an innovative analytical matrix examining four Trade-Climate Agenda items: (i) international competitiveness, (ii) climate-friendly goods and services, (iii) international aviation and maritime shipping, and (iv) product labelling and standards. The paper then evaluates to what extent the externalisation mechanisms of Manners’ ‘Normative Power Europe’ and Damro’s ‘Market Power Europe’ are deployed in order to achieve the above objectives. The findings show that the EU’s performance in the effective management of the nexus is overall moderate to weak

    Knowledge, Assertion and Intellectual Humility

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    This paper has two central aims. First, we motivate a puzzle. The puzzle features four independently plausible but jointly inconsistent claims. One of the four claims is the sufficiency leg of the knowledge norm of assertion (KNA-S), according to which one is properly epistemically positioned to assert that p if one knows that p. Second, we propose that rejecting (KNA-S) is the best way out of the puzzle. Our argument to this end appeals to the epistemic value of intellectual humility in social-epistemic practice

    Analysis of Dialogical Argumentation via Finite State Machines

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    Dialogical argumentation is an important cognitive activity by which agents exchange arguments and counterarguments as part of some process such as discussion, debate, persuasion and negotiation. Whilst numerous formal systems have been proposed, there is a lack of frameworks for implementing and evaluating these proposals. First-order executable logic has been proposed as a general framework for specifying and analysing dialogical argumentation. In this paper, we investigate how we can implement systems for dialogical argumentation using propositional executable logic. Our approach is to present and evaluate an algorithm that generates a finite state machine that reflects a propositional executable logic specification for a dialogical argumentation together with an initial state. We also consider how the finite state machines can be analysed, with the minimax strategy being used as an illustration of the kinds of empirical analysis that can be undertaken.Comment: 10 page

    Impact of Argument Type and Concerns in Argumentation with a Chatbot

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    Conversational agents, also known as chatbots, are versatile tools that have the potential of being used in dialogical argumentation. They could possibly be deployed in tasks such as persuasion for behaviour change (e.g. persuading people to eat more fruit, to take regular exercise, etc.) However, to achieve this, there is a need to develop methods for acquiring appropriate arguments and counterargument that reflect both sides of the discussion. For instance, to persuade someone to do regular exercise, the chatbot needs to know counterarguments that the user might have for not doing exercise. To address this need, we present methods for acquiring arguments and counterarguments, and importantly, meta-level information that can be useful for deciding when arguments can be used during an argumentation dialogue. We evaluate these methods in studies with participants and show how harnessing these methods in a chatbot can make it more persuasive

    Strategic Argumentation Dialogues for Persuasion: Framework and Experiments Based on Modelling the Beliefs and Concerns of the Persuadee

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    Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is good in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants showing that our automated persuasion system based on this technology is superior to a baseline system that does not take the beliefs and concerns into account in its strategy.Comment: The Data Appendix containing the arguments, argument graphs, assignment of concerns to arguments, preferences over concerns, and assignment of beliefs to arguments, is available at the link http://www0.cs.ucl.ac.uk/staff/a.hunter/papers/unistudydata.zip The code is available at https://github.com/ComputationalPersuasion/MCC

    Strategic argumentation dialogues for persuasion: Framework and experiments based on modelling the beliefs and concerns of the persuadee

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    Persuasion is an important and yet complex aspect of human intelligence. When undertaken through dialogue, the deployment of good arguments, and therefore counterarguments, clearly has a significant effect on the ability to be successful in persuasion. Two key dimensions for determining whether an argument is 'good' in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience. In this paper, we present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues. Our approach is based on the Monte Carlo Tree Search which allows optimization in real-time. We provide empirical results of a study with human participants that compares an automated persuasion system based on this technology with a baseline system that does not take the beliefs and concerns into account in its strategy
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