An Incentive-Compatible Distributed Recommendation Model
An Agent's Choice
An agent must also decide wether to ask an agent for a
recommendation or pick a document at random.
We can calculate the expected utility to be gained from a
recommendation: $x_i(j)$, a classic explore vs. exploit
problem.
So, we use probabilities.
Also, when the agent receives a request it only replies
if it expects more utility from the recommendation
($x_i(j)$) than it costs to send the message ($C_m$).