In this paper we propose a new model to represent human debates and methods to obtain collective conclusions from them. This model overcomes two drawbacks of existing approaches. First, our model does not assume that participants agree on the structure of the debate. It does this by allowing participants to express their opinion about all aspects of the debate. Second, our model does not assume that participants' opinions are rational, 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. We provide a formal analysis 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 with an empirical evaluation demonstrating that collective opinions can be computed efficiently for real-sized debates.

In the era of social media, people frequently share their own opinions online on various issues and also in the way, get exposed to others' opinions. Be it for selective exposure of news feed recommendation algorithms or our own inclination to listen to opinions that support ours, the result is that we get more and more exposed to opinions closer to ours. Further, any population is inherently heterogeneous i.e. people will hold a varied range of opinions regarding a topic and showcase a varied range of openness to get influenced by others. In this paper, we demonstrate the different behavior put forward by open- and close-minded agents towards an issue, when allowed to freely intermix and communicate. We have shown that the intermixing among people leads to formation of opinion echo chambers i.e. a small closed network of people who hold similar opinions and are not affected by opinions of people outside the network. Echo chambers are evidently harmful for a society because it inhibits free healthy communication among all and thus, prevents exchange of opinions, spreads misinformation and increases extremist beliefs. This calls for reduction in echo chambers, because a total consensus of opinion is neither possible nor is welcome. We show that the number of echo chambers depends on the number of close-minded agents and cannot be lessened by increasing the number of open-minded agents. We identify certain 'moderate'-minded agents, who possess the capability of manipulating and reducing the number of echo chambers. The paper proposes an algorithm for intelligent placement of moderate-minded agents in the opinion-time spectrum by which the opinion echo chambers can be maximally reduced. With various experimental setups, we demonstrate that the proposed algorithm fares well when compared to placement of other agents (open- or close-minded) and random placement of 'moderate'-minded agents.

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.