1,070 research outputs found
Managing Price Uncertainty in Prosumer-Centric Energy Trading: A Prospect-Theoretic Stackelberg Game Approach
In this paper, the problem of energy trading between smart grid prosumers,
who can simultaneously consume and produce energy, and a grid power company is
studied. The problem is formulated as a single-leader, multiple-follower
Stackelberg game between the power company and multiple prosumers. In this
game, the power company acts as a leader who determines the pricing strategy
that maximizes its profits, while the prosumers act as followers who react by
choosing the amount of energy to buy or sell so as to optimize their current
and future profits. The proposed game accounts for each prosumer's subjective
decision when faced with the uncertainty of profits, induced by the random
future price. In particular, the framing effect, from the framework of prospect
theory (PT), is used to account for each prosumer's valuation of its gains and
losses with respect to an individual utility reference point. The reference
point changes between prosumers and stems from their past experience and future
aspirations of profits. The followers' noncooperative game is shown to admit a
unique pure-strategy Nash equilibrium (NE) under classical game theory (CGT)
which is obtained using a fully distributed algorithm. The results are extended
to account for the case of PT using algorithmic solutions that can achieve an
NE under certain conditions. Simulation results show that the total grid load
varies significantly with the prosumers' reference point and their
loss-aversion level. In addition, it is shown that the power company's profits
considerably decrease when it fails to account for the prosumers' subjective
perceptions under PT
When Mobile Blockchain Meets Edge Computing
Blockchain, as the backbone technology of the current popular Bitcoin digital
currency, has become a promising decentralized data management framework.
Although blockchain has been widely adopted in many applications, e.g.,
finance, healthcare, and logistics, its application in mobile services is still
limited. This is due to the fact that blockchain users need to solve preset
proof-of-work puzzles to add new data, i.e., a block, to the blockchain.
Solving the proof-of-work, however, consumes substantial resources in terms of
CPU time and energy, which is not suitable for resource-limited mobile devices.
To facilitate blockchain applications in future mobile Internet of Things
systems, multiple access mobile edge computing appears to be an auspicious
solution to solve the proof-of-work puzzles for mobile users. We first
introduce a novel concept of edge computing for mobile blockchain. Then, we
introduce an economic approach for edge computing resource management.
Moreover, a prototype of mobile edge computing enabled blockchain systems is
presented with experimental results to justify the proposed concept.Comment: Accepted by IEEE Communications Magazin
Traffic Optimization For a Mixture of Self-interested and Compliant Agents
This paper focuses on two commonly used path assignment policies for agents
traversing a congested network: self-interested routing, and system-optimum
routing. In the self-interested routing policy each agent selects a path that
optimizes its own utility, while the system-optimum routing agents are assigned
paths with the goal of maximizing system performance. This paper considers a
scenario where a centralized network manager wishes to optimize utilities over
all agents, i.e., implement a system-optimum routing policy. In many real-life
scenarios, however, the system manager is unable to influence the route
assignment of all agents due to limited influence on route choice decisions.
Motivated by such scenarios, a computationally tractable method is presented
that computes the minimal amount of agents that the system manager needs to
influence (compliant agents) in order to achieve system optimal performance.
Moreover, this methodology can also determine whether a given set of compliant
agents is sufficient to achieve system optimum and compute the optimal route
assignment for the compliant agents to do so. Experimental results are
presented showing that in several large-scale, realistic traffic networks
optimal flow can be achieved with as low as 13% of the agent being compliant
and up to 54%
On the Inducibility of Stackelberg Equilibrium for Security Games
Strong Stackelberg equilibrium (SSE) is the standard solution concept of
Stackelberg security games. As opposed to the weak Stackelberg equilibrium
(WSE), the SSE assumes that the follower breaks ties in favor of the leader and
this is widely acknowledged and justified by the assertion that the defender
can often induce the attacker to choose a preferred action by making an
infinitesimal adjustment to her strategy. Unfortunately, in security games with
resource assignment constraints, the assertion might not be valid; it is
possible that the defender cannot induce the desired outcome. As a result, many
results claimed in the literature may be overly optimistic. To remedy, we first
formally define the utility guarantee of a defender strategy and provide
examples to show that the utility of SSE can be higher than its utility
guarantee. Second, inspired by the analysis of leader's payoff by Von Stengel
and Zamir (2004), we provide the solution concept called the inducible
Stackelberg equilibrium (ISE), which owns the highest utility guarantee and
always exists. Third, we show the conditions when ISE coincides with SSE and
the fact that in general case, SSE can be extremely worse with respect to
utility guarantee. Moreover, introducing the ISE does not invalidate existing
algorithmic results as the problem of computing an ISE polynomially reduces to
that of computing an SSE. We also provide an algorithmic implementation for
computing ISE, with which our experiments unveil the empirical advantage of the
ISE over the SSE.Comment: The Thirty-Third AAAI Conference on Artificial Intelligenc
Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model
Resource allocation is the process of optimizing the rare resources. In the
area of security, how to allocate limited resources to protect a massive number
of targets is especially challenging. This paper addresses this resource
allocation issue by constructing a game theoretic model. A defender and an
attacker are players and the interaction is formulated as a trade-off between
protecting targets and consuming resources. The action cost which is a
necessary role of consuming resource, is considered in the proposed model.
Additionally, a bounded rational behavior model (Quantal Response, QR), which
simulates a human attacker of the adversarial nature, is introduced to improve
the proposed model. To validate the proposed model, we compare the different
utility functions and resource allocation strategies. The comparison results
suggest that the proposed resource allocation strategy performs better than
others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference
GAME-SCORE: Game-based energy-aware cloud scheduler and simulator for computational clouds
Energy-awareness remains one of the main concerns for today's cloud computing (CC) operators.
The optimisation of energy consumption in both cloud computational clusters and computing
servers is usually related to scheduling problems. The definition of an optimal scheduling policy
which does not negatively impact to system performance and task completion time is still
challenging. In this work, we present a new simulation tool for cloud computing, GAME-SCORE,
which implements a scheduling model based on the Stackelberg game. This game presents two
main players: a) the scheduler and b) the energy-efficiency agent. We used the GAME-SCORE
simulator to analyse the efficiency of the proposed game-based scheduling model. The obtained
results show that the Stackelberg cloud scheduler performs better than static energy-optimisation
strategies and can achieve a fair balance between low energy consumption and short makespan in
a very short tim
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