Boris Galitsky and Mark Levene (2005)
Simulating the Conflict Between Reputation and Profitability for Online Rating Portals
Journal of Artificial Societies and Social Simulation
vol. 8, no. 2
To cite articles published in the Journal of Artificial Societies and Social Simulation, reference the above information and include paragraph numbers if necessary
Received: 01-Dec-2004 Accepted: 19-Feb-2005 Published: 31-Mar-2005
where #p denotes the number of portals. Indeed, services intend to achieve better rating from portals with higher reputation, so the weighed M(s,p) comes into play (Section 4).
|Figure 1. Two scenarios showing how portals' ratings change|
|Figure 2. The modules of the interaction process|
The simulation that produced the results described in the next section was implemented in Matlab and is available from the first author on request.
|Figure 3. Distributions of average (forced) ratings, reputations and resources. Both partially rational strategies of service subscription offers and portals acceptance are used. Arrows on the top charts show the overall change in average rating/reputation (from zero to 60th simulation step, to match with Figure 4)|
In Figure 3, on the charts for distributions of ratings/reputations shown in the upper half of the figure, diamonds denote the initial ratings and circles denote the final ones. On the charts for resources shown in the lower half of the figure, dots and circles denote the respective values for services (left) and for portals (right).
|Figure 4. The evolution of ratings/reputations and resources of services and portals over time.|
It takes first 10 steps to establish an equilibrium of ratings between the services, and an equilibrium of reputations between the portals (see Figure 4). Once the equilibrium is achieved, an oscillation pattern appears, which is caused by pairs of financial services that have their ratings swapped between position i and position i-1. As a result, the reputations of the portals are interchanged in a similar way, leading to an oscillating pattern between portals as well. The amplitude of oscillations for services is a quarter of unit (one out of four changes to the reputations of portals contributes to this amplitude). On the other hand, for the portals we observe oscillations with amplitudes which are higher than a single unit.
|Figure 5. The evolution of ratings/reputations and resources of services and portals over time, where one portal with a low initial reputation is independent (i.e. it does not accept service subscription)|
|Figure 5a. The evolution of ratings/reputations and resources of services and portals over time, where three portals out of eight with a low, medium and high initial reputation are independent|
A similar situation is depicted at Figure 5a. The portals with initially low reputation gain are the independent ones. This would not be the case if they accepted subscriptions.
|Figure 6. The evolution curves where only the four lowest-rated services subscribe|
At a current step, one service, #1, swaps its rank with another service, #2, being rated by a portal.|
At the next step, #2 selects this portal and swaps its rank with that of #1.
At the next step, #1 selects the same portal and swaps rank with #2 again.
At the next step, #2 ...
As a result, both services #1 and #2 quickly run out of resources without gaining much rating, because they directly compete only with each other. A more rational strategy for a service would be to avoid getting into a cycle by choosing the second best portal with respect to its reputation and obtain the rating of service #1 at this portal. Such increased rationality will be beneficial for #2 but not other services, and will not allow portals to have an "easy ride" with respect to collecting resources and not giving up reputations in the above cycle scenario.
|Figure 7. The evolution charts where services avoid cycles (longer run, 150 steps)|
|Figure 8. Evolution charts where portals accept subscription randomly|
As we presented in the "Economic model" section above, a rational strategy for a service is to maximize the above expression and also subscribe for portals which gives it higher ratings. Moreover, as we mentioned in the previous section, avoiding cycles usually helps to decrease inefficient spending of resources competing with the same service. Hence random strategy prevents services from achieving the above.
|Figure 9. The simulation run with 16 portals and 30 services showing the behaviour similar to the one produced by a smaller number of agents|
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