EUMAS 09. Information-Based Reputation. Invited talk

Invited talk by Carles Sierra

The overview: beginning from individual opinion, how it is transformed into group opinion to achieve some reputation. Reputation is actually a grupal opinion about someone or something.

The example: Liquid publishing, about how people change their opinion by arguieng and considering reputation (the example is about paper review for a conference). The reputation of author, reviewers, the paper or the conference itself are considered.

First step: forming individual opinions. Agent receives messages that contribute to agent’s knowledge creating a distribution about the quality (or the true value) of the predicate. And this quality decays with time, having a decay limit distribution. This process (updating information and decaying with time) is reactive reasoning (giving formula about all these things). But we can have two types of opinion: verifiable (tomorrow will rain) and unverifiable (Earth will exist in 1 million years).

Second step: To structure the knowledge (as we organize sections in a paper). So the opinions can be given about each one of the identified elements and these opinions can be used to create a reputation value. So opinions are associated to nodes in the structure.

With that, entering in the third step: how group opinion are formed. To do that, (i) a language to share opinions, (ii) distances about opinions and (iii) methods to aggregate opinions are needed. So we are looking for a function \(\gamma\) that summarizes the group opinion. About the language, we can inform opinions (somehow a subjective thing) and experiences (objective facts). About getting information, citations can be good, but opinions can be even better (problem with ‘the rich get richer), and this can be done using distances between distributions: (i) by calculating how similar two functions are, (ii) calculating the distances between them (EMD)-not euclidean for opinions- Explaining different methods to combine opinions. Something interesting: reputation labels; inexorable, predetermination, persuasiveness, compliance, dogmatic and adherence that describe the position of an agent with respect to the opinion ofa group. BUt individuals have social relationships and this information ca be also taken into account.

So, to summarize:

  • reputation is becoming crucial for all sort of web-related applications
  • current model ignore the structure of the knowledge, as social relations
  • integrated models that deals with all these information (social, structural, dependence…) are required
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