381 research outputs found
Local non-Bayesian social learning with stubborn agents
We study a social learning model in which agents iteratively update their
beliefs about the true state of the world using private signals and the beliefs
of other agents in a non-Bayesian manner. Some agents are stubborn, meaning
they attempt to convince others of an erroneous true state (modeling fake
news). We show that while agents learn the true state on short timescales, they
"forget" it and believe the erroneous state to be true on longer timescales.
Using these results, we devise strategies for seeding stubborn agents so as to
disrupt learning, which outperform intuitive heuristics and give novel insights
regarding vulnerabilities in social learning
Small-Scale Markets for Bilateral Resource Trading in the Sharing Economy
We consider a general small-scale market for agent-to-agent resource sharing,
in which each agent could either be a server (seller) or a client (buyer) in
each time period. In every time period, a server has a certain amount of
resources that any client could consume, and randomly gets matched with a
client. Our target is to maximize the resource utilization in such an
agent-to-agent market, where the agents are strategic. During each transaction,
the server gets money and the client gets resources. Hence, trade ratio
maximization implies efficiency maximization of our system. We model the
proposed market system through a Mean Field Game approach and prove the
existence of the Mean Field Equilibrium, which can achieve an almost 100% trade
ratio. Finally, we carry out a simulation study motivated by an agent-to-agent
computing market, and a case study on a proposed photovoltaic market, and show
the designed market benefits both individuals and the system as a whole
Many-Sources Large Deviations for Max-Weight Scheduling
In this paper, a many-sources large deviations principle (LDP) for the
transient workload of a multi-queue single-server system is established where
the service rates are chosen from a compact, convex and coordinate-convex rate
region and where the service discipline is the max-weight policy. Under the
assumption that the arrival processes satisfy a many-sources LDP, this is
accomplished by employing Garcia's extended contraction principle that is
applicable to quasi-continuous mappings.
For the simplex rate-region, an LDP for the stationary workload is also
established under the additional requirements that the scheduling policy be
work-conserving and that the arrival processes satisfy certain mixing
conditions.
The LDP results can be used to calculate asymptotic buffer overflow
probabilities accounting for the multiplexing gain, when the arrival process is
an average of \emph{i.i.d.} processes. The rate function for the stationary
workload is expressed in term of the rate functions of the finite-horizon
workloads when the arrival processes have \emph{i.i.d.} increments.Comment: 44 page
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