Ratings in Distributed Systems: A Bayesian Approach Ratings in Distributed Systems: A Bayesian Approach


  author         = {Lik Mui and Mojdeh Mohtashemi and Cheewee Ang and Peter Szolovits and Ari Halberstadt},
  title          = {Ratings in Distributed Systems: A Bayesian Approach},
  year           = {2001},
  review-dates   = {2004-08-08, 2004-08-09},
  value          = {ba},
  booktitle      = {Workshop on Information Technologies and Systems},
  hardcopy       = {yes},
  key            = {mui-2001a}


Introduces "Bayesian probabilistic framework" for trust to include contextualized and personalized ratings. The context issue though raised isn't really addressed other than as a subscript and the assumption that all the math takes place within the framework of the context. It's essentially relegated to follow-up work.

There are agents and objects and boolean ratings and encounters.

There are attributes (countably infinite) which describe context in environment. Context is defined in terms of applicability of a set of attributes.

Reputation is R: AxAxC -> [0,1]

Update rule is newstate: RxD -> R.

Although authors make an effort to define trust and reputation, they then blur trust and reputation while using the model only for local reputation inference (based on an individual's encounters with another agent, without third party reporting.)

[ ] It's not clear to me at this time how we get from the priors of uniform distribution and then blend into the formulation to known strangers along a continuum, as in one case we are using the priors and another we are using all the encounters along a reputation chain.

Indirect trust (reputation) is modelled by recursively applying rules, but when there are parallel streams of info on an individual agent, the assertions are proportionally weighted along a scheme that uses Chernoff bounds to provide an upper limit of weighting and then pro-rates those not meeting the bound.

Key Factors

How placed in context with other work:

Problem Addressed: Informal nature of existing reputation calculation schemes.

Main Claim and Evidence: This is a better mathematical formulation for calculation of reputation; prior work relies mostly on intuitive appeal. Prior work too ad hoc.


Next steps:

Remaining open questions:


Originality is outstanding.
Contribution/Significance is excellent.
Quality of organization is outstanding.
Quality of writing is excellent.