|Títol||Aggregation operators to support collective reasoning|
|Publication Type||Book Chapter|
|Year of Publication||2016|
|Authors||Rodríguez-Aguilar JA, Serramia M, López-Sánchez M|
|Book Title||Modeling Decisions for Artificial Intelligence|
|Volume||Lecture Notes in Artificial Intelligence|
|Edition||Torra, V., Narukawa, Y., Navarro-Arribas, G., Yañez, C.|
Moderation poses one of the main Internet challenges. Currently, many Internet platforms and virtual communities deal with it by intensive human labour, some big companies --such as YouTube or Facebook-- hire people to do it, others --such as 4chan or fanscup-- just ask volunteer users to get in charge of it. But in most cases the policies that they use to decide if some contents should be removed or if a user should be banned are not clear enough to users. And, in any case, typically users are not involved in their definition.
Nobel laureate Elinor Ostrom concluded that societies --such as institutions that had to share scarce resources-- that involve individuals in the definition of their rules performed better --resources lasted more or did not deplete-- than those organisations whose norms where imposed externally. Democracy also relies on this same idea of considering peoples' opinions.
In this vein, we argue that participants in a virtual community will be more prone to behave correctly --and thus the community itself will be "healthier"-- if they take part in the decisions about the norms of coexistence that rule the community. With this aim, we investigate a collective decision framework that: (1) structures (relate) arguments issued by different participants; (2) allows agents to express their opinions about arguments; and (3) aggregates opinions to synthesise a collective decision. More precisely, we investigate two aggregation operators that merge discrete and continuous opinions. Finally, we analyse the social choice properties that our discrete aggregator operator satisfies.
- Quant a IIIA