Experimental Assessment of Aggregation Principles in Argumentation-Enabled Collective Intelligence

On the Web, there is always a need to aggregate opinions from the crowd (as in posts, social networks, forums, etc.). Different mechanisms have been implemented to capture these opinions such as Like in Facebook, Favorite in Twitter, thumbs-up/-down, flagging, and so on. However, in more contested domains (e.g., Wikipedia, political discussion, and climate change discussion), these mechanisms are not sufficient, since they only deal with each issue independently without considering the relationships between different claims. We can view a set of conflicting arguments as a graph in which the nodes represent arguments and the arcs between these nodes represent the defeat relation. A group of people can then collectively evaluate such graphs. To do this, the group must use a rule to aggregate their individual opinions about the entire argument graph. Here we present the first experimental evaluation of different principles commonly employed by aggregation rules presented in the literature. We use randomized controlled experiments to investigate which principles people consider better at aggregating opinions under different conditions. Our analysis reveals a number of factors, not captured by traditional formal models, that play an important role in determining the efficacy of aggregation. These results help bring formal models of argumentation closer to real-world application

Experimental assessment of aggregation principles in argumentation-enabled collective intelligence

On the Web, there is always a need to aggregate opinions from the crowd (as in posts, social networks, forums, etc.). Different mechanisms have been implemented to capture these opinions such as Like in Facebook, Favorite in Twitter, thumbsup/ down, flagging, and so on. However, in more contested domains (e.g. Wikipedia, political discussion, and climate change discussion) these mechanisms are not sufficient since they only deal with each issue independently without considering the relationships between different claims.We can view a set of conflicting arguments as a graph in which the nodes represent arguments and the arcs between these nodes represent the defeat relation. A group of people can then collectively evaluate such graphs. To do this, the group must use a rule to aggregate their individual opinions about the entire argument graph. Here, we present the first experimental evaluation of different principles commonly employed by aggregation rules presented in the literature. We use randomized controlled experiments to investigate which principles people consider better at aggregating opinions under different conditions. Our analysis reveals a number of factors, not captured by traditional formal models, that play an important role in determining the efficacy of aggregation. These results help bring formal models of argumentation closer to real-world application

A dialectical approach for argument-based judgment aggregation

The current paper provides a dialectical interpretation of the argumentation-based judgment aggregation operators of Caminada and Pigozzi. In particular, we define discussion-based proof procedures for the foundational concepts of down-admissible and up-complete. We then show how these proof procedures can be used as the basis of dialectical proof procedures for the sceptical, credulous and super credulous judgment aggregation operators

A model to support collective reasoning: Formalization, analysis and computational assessment

Inspired by e-participation systems, in this paper we propose a new model to represent human debates and methods to obtain collective conclusions from them. This model overcomes drawbacks of existing approaches by allowing users to introduce new pieces of information into the discussion, to relate them to existing pieces, and also to express their opinion on the pieces proposed by other users. In addition, our model does not assume that users' opinions are rational in order to extract information from it, an assumption that significantly limits current approaches. Instead, we define a weaker notion of rationality that characterises coherent opinions, and we consider different scenarios based on the coherence of individual opinions and the level of consensus that users have on the debate structure. Considering these two factors, we analyse the outcomes of different opinion aggregation functions that compute a collective decision based on the individual opinions and the debate structure. In particular, we demonstrate that aggregated opinions can be coherent even if there is a lack of consensus and individual opinions are not coherent. We conclude our analysis with a computational evaluation demonstrating that collective opinions can be computed efficiently for real-sized debates

Articulation of Plural Values in Deliberative Monetary Valuation: Beyond Preference Economisation and Moralisation

The use of deliberative methods to assess environmental values in monetary terms has been motivated by the potential for small group discussion to help with preference formation and the inclusion of non-economic values. In this review, two broad approaches are identified: preference economisation and preference moralisation. The former is analytical, concentrates upon issues of poor respondent cognition and produces a narrow conception of value linked to utilitarianism. The latter emphasises political legitimacy, appeals to community values and tends to privilege arguments made in the public interest. Both approaches are shown to embrace forms of value convergence which undermine the prospects for value pluralism. As a result exclusion and predefinition of values dominates current practice. In order to maintain democratic credentials, the importance attributed to monetary value needs to be left as an open question to be addressed as part of a process determining an ‘agreement to pay’. To this end we identify a discourse-based approach as a third way consistent with the democratic and value plural potential of deliberative monetary valuation.environmental valuation; deliberation; stated preferences; democracy; willingness to pay; value pluralism

Linked democracy : foundations, tools, and applications

Chapter 1Introduction to Linked DataAbstractThis chapter presents Linked Data, a new form of distributed data on theweb which is especially suitable to be manipulated by machines and to shareknowledge. By adopting the linked data publication paradigm, anybody can publishdata on the web, relate it to data resources published by others and run artificialintelligence algorithms in a smooth manner. Open linked data resources maydemocratize the future access to knowledge by the mass of internet users, eitherdirectly or mediated through algorithms. Governments have enthusiasticallyadopted these ideas, which is in harmony with the broader open data movement

Envisioning Digital Europe 2030: Scenarios for ICT in Future Governance and Policy Modelling

The report Envisioning Digital Europe 2030 is the result of research conducted by the Information Society Unit of IPTS as part of the CROSSROAD Project - A Participative Roadmap on ICT research on Electronic Governance and Policy Modelling (www.crossroad-eu.net ). After outlining the purpose and scope of the report and the methodological approach followed, the report presents the results of a systematic analysis of societal, policy and research trends in the governance and policy modelling domain in Europe. These analyses are considered central for understanding and roadmapping future research on ICT for governance and policy modelling. The study further illustrates the scenario design framework, analysing current and future challenges in ICT for governance and policy modelling, and identifying the key impact dimensions to be considered. It then presents the scenarios developed at the horizon 2030, including the illustrative storyboards representative of each scenario and the prospective opportunities and risks identified for each of them. The scenarios developed are internally consistent views of what the European governance and policy making system could have become by 2030 and of what the resulting implications for citizens, business and public services would be. Finally, the report draws conclusions and presents the proposed shared vision for Digital Europe 2030, offering also a summary of the main elements to be considered as an input for the future development of the research roadmap on ICT for governance and policy modelling.JRC.DDG.J.4-Information Societ

Evaluating the Impact of Defeasible Argumentation as a Modelling Technique for Reasoning under Uncertainty

Limited work exists for the comparison across distinct knowledge-based approaches in Artificial Intelligence (AI) for non-monotonic reasoning, and in particular for the examination of their inferential and explanatory capacity. Non-monotonicity, or defeasibility, allows the retraction of a conclusion in the light of new information. It is a similar pattern to human reasoning, which draws conclusions in the absence of information, but allows them to be corrected once new pieces of evidence arise. Thus, this thesis focuses on a comparison of three approaches in AI for implementation of non-monotonic reasoning models of inference, namely: expert systems, fuzzy reasoning and defeasible argumentation. Three applications from the fields of decision-making in healthcare and knowledge representation and reasoning were selected from real-world contexts for evaluation: human mental workload modelling, computational trust modelling, and mortality occurrence modelling with biomarkers. The link between these applications comes from their presumptively non-monotonic nature. They present incomplete, ambiguous and retractable pieces of evidence. Hence, reasoning applied to them is likely suitable for being modelled by non-monotonic reasoning systems. An experiment was performed by exploiting six deductive knowledge bases produced with the aid of domain experts. These were coded into models built upon the selected reasoning approaches and were subsequently elicited with real-world data. The numerical inferences produced by these models were analysed according to common metrics of evaluation for each field of application. For the examination of explanatory capacity, properties such as understandability, extensibility, and post-hoc interpretability were meticulously described and qualitatively compared. Findings suggest that the variance of the inferences produced by expert systems and fuzzy reasoning models was higher, highlighting poor stability. In contrast, the variance of argument-based models was lower, showing a superior stability of its inferences across different system configurations. In addition, when compared in a context with large amounts of conflicting information, defeasible argumentation exhibited a stronger potential for conflict resolution, while presenting robust inferences. An in-depth discussion of the explanatory capacity showed how defeasible argumentation can lead to the construction of non-monotonic models with appealing properties of explainability, compared to those built with expert systems and fuzzy reasoning. The originality of this research lies in the quantification of the impact of defeasible argumentation. It illustrates the construction of an extensive number of non-monotonic reasoning models through a modular design. In addition, it exemplifies how these models can be exploited for performing non-monotonic reasoning and producing quantitative inferences in real-world applications. It contributes to the field of non-monotonic reasoning by situating defeasible argumentation among similar approaches through a novel empirical comparison

Linked Democracy

This open access book shows the factors linking information flow, social intelligence, rights management and modelling with epistemic democracy, offering licensed linked data along with information about the rights involved. This model of democracy for the web of data brings new challenges for the social organisation of knowledge, collective innovation, and the coordination of actions. Licensed linked data, licensed linguistic linked data, right expression languages, semantic web regulatory models, electronic institutions, artificial socio-cognitive systems are examples of regulatory and institutional design (regulations by design). The web has been massively populated with both data and services, and semantically structured data, the linked data cloud, facilitates and fosters human-machine interaction. Linked data aims to create ecosystems to make it possible to browse, discover, exploit and reuse data sets for applications. Rights Expression Languages semi-automatically regulate the use and reuse of content. ; Links information flow, social intelligence, rights management, and modelling with epistemic democracy Presents examples of regulatory and institutional desig