258 research outputs found
Information Structure Design in Team Decision Problems
We consider a problem of information structure design in team decision
problems and team games. We propose simple, scalable greedy algorithms for
adding a set of extra information links to optimize team performance and
resilience to non-cooperative and adversarial agents. We show via a simple
counterexample that the set function mapping additional information links to
team performance is in general not supermodular. Although this implies that the
greedy algorithm is not accompanied by worst-case performance guarantees, we
illustrate through numerical experiments that it can produce effective and
often optimal or near optimal information structure modifications
Adaptive Submodular Influence Maximization with Myopic Feedback
This paper examines the problem of adaptive influence maximization in social
networks. As adaptive decision making is a time-critical task, a realistic
feedback model has been considered, called myopic. In this direction, we
propose the myopic adaptive greedy policy that is guaranteed to provide a (1 -
1/e)-approximation of the optimal policy under a variant of the independent
cascade diffusion model. This strategy maximizes an alternative utility
function that has been proven to be adaptive monotone and adaptive submodular.
The proposed utility function considers the cumulative number of active nodes
through the time, instead of the total number of the active nodes at the end of
the diffusion. Our empirical analysis on real-world social networks reveals the
benefits of the proposed myopic strategy, validating our theoretical results.Comment: Accepted by IEEE/ACM International Conference Advances in Social
Networks Analysis and Mining (ASONAM), 201
Two-Stage Submodular Optimization of Dynamic Thermal Rating for Risk Mitigation Considering Placement and Operation Schedule
Cascading failure causes a major risk to society currently. To effectively
mitigate the risk, dynamic thermal rating (DTR) technique can be applied as a
cost-effective strategy to exploit potential transmission capability. From the
perspectives of service life and Braess paradox, it is important and
challenging to jointly optimize the DTR placement and operation schedule for
changing system state, which is a two-stage combinatorial problem with only
discrete variables, suffering from no approximation guarantee and dimension
curse only based on traditional models. Thus, the present work proposes a novel
two-stage submodular optimization (TSSO) of DTR for risk mitigation considering
placement and operation schedule. Specifically, it optimizes DTR placement with
proper redundancy in first stage, and then determines the corresponding DTR
operation for each system state in second stage. Under the condition of the
Markov and submodular features in sub-function of risk mitigation, the
submodularity of total objective function of TSSO can be proven for the first
time. Based on this, a state-of-the-art efficient solving algorithm is
developed that can provide a better approximation guarantee than previous
studies by coordinating the separate curvature and error form. The performance
of the proposed algorithms is verified by case results
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