By Regis Sbudd J. Bates
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The level of information detail will be reduced to aggregated information about former behaviour to keep it as simple as possible. The reputation will be represented by a single value. Strategic behaviour based on the former actions is not taken into account. The input data for computing the reputation value weigh current ratings higher than old ones. Jurca and Faltings preferred an even simpler mechanism by deriving the reputation value as follows: fracnumber of good ratingsnumber of total ratings.
3. The broker decides, on which machines the cost will be waived. The recommendation of the broker in Step 1 is denoted as r(x), ∀x ∈ K. (m) deﬁnes the number of Gridagents receiving a recommendation to choose machine m. The probability of receiving a waiver (f (mx )) depends on (m). Step 3 depends on the selection of the machines by the Gridagents. g. Gridagents always prefer one machine, they would like to use. Let h(m) be the number of Gridagents selecting the machine m. For simplicity we deﬁne the chance for a free machine is same for all agents and thus f (m) = f (mx ), ∀x ∈ A.
The oﬄine mechanism is especially interesting for public resources. We further proposed an online mechanism considering a reputation-based pricing approach. This pricing scheme has the advantage of setting incentives for providing resources and consuming them prudently. But, the eﬀort for determining the right price at the right time is avoided, since scientists prefer rather to concentrate on their own research work than make economic decisions. In fact, the dynamic of prices are based on the reputation of the users, which enforce them to behave cooperatively and elude the price speciﬁcation.