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MultiAgent Dynamics Laboratory
Distributed Incentive-Compatible Recommender Systems
People decide what to read/watch/eat based on others' recommendations. The is no absolute "truth", only relative opinions.
Q: How can an agent tell which other one are like him?
We assume selfish (utility-maximizing) agents. The parameters of the problem are:
Fixed cost of consumption.
Reward for consumption: positive or negative.
Fixed cost of communications.
Processing time and memory are free (unlike humans).
Agents do have inherent similarities among themselves.
Developed protocol to enable agents to operate rationally within this environment while also maximizing global utility.
José M. Vidal
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