18,456 research outputs found
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
An Institutional Frame to Compare Alternative Market Designs in EU Electricity Balancing
The so-called â electricity wholesale marketâ is, in fact, a sequence of several markets. The chain is closed with a provision for â balancing,â in which energy from all wholesale markets is balanced under the authority of the Transmission Grid Manager (TSO in Europe, ISO in the United States). In selecting the market design, engineers in the European Union have traditionally preferred the technical role of balancing mechanisms as â security mechanisms.â They favour using penalties to restrict the use of balancing energy by market actors. While our paper in no way disputes the importance of grid security, nor the competency of engineers to elaborate the technical rules, we wish to attract attention to the real economic consequences of alternative balancing designs. We propose a numerical simulation in the framework of a two-stage equilibrium model. This simulation allows us to compare the economic properties of designs currently existing within the European Union and to measure their fallout. It reveals that balancing designs, which are typically presented as simple variants on technical security, are in actuality alternative institutional frameworks having at least four potential economic consequences: a distortion of the forward price; an asymmetric shift in the participantsâ profits; an increase in the System Operatorâ s revenues; and inefficiencies
Context-Aware Generative Adversarial Privacy
Preserving the utility of published datasets while simultaneously providing
provable privacy guarantees is a well-known challenge. On the one hand,
context-free privacy solutions, such as differential privacy, provide strong
privacy guarantees, but often lead to a significant reduction in utility. On
the other hand, context-aware privacy solutions, such as information theoretic
privacy, achieve an improved privacy-utility tradeoff, but assume that the data
holder has access to dataset statistics. We circumvent these limitations by
introducing a novel context-aware privacy framework called generative
adversarial privacy (GAP). GAP leverages recent advancements in generative
adversarial networks (GANs) to allow the data holder to learn privatization
schemes from the dataset itself. Under GAP, learning the privacy mechanism is
formulated as a constrained minimax game between two players: a privatizer that
sanitizes the dataset in a way that limits the risk of inference attacks on the
individuals' private variables, and an adversary that tries to infer the
private variables from the sanitized dataset. To evaluate GAP's performance, we
investigate two simple (yet canonical) statistical dataset models: (a) the
binary data model, and (b) the binary Gaussian mixture model. For both models,
we derive game-theoretically optimal minimax privacy mechanisms, and show that
the privacy mechanisms learned from data (in a generative adversarial fashion)
match the theoretically optimal ones. This demonstrates that our framework can
be easily applied in practice, even in the absence of dataset statistics.Comment: Improved version of a paper accepted by Entropy Journal, Special
Issue on Information Theory in Machine Learning and Data Scienc
Recursive Inspection Games
We consider a sequential inspection game where an inspector uses a limited
number of inspections over a larger number of time periods to detect a
violation (an illegal act) of an inspectee. Compared with earlier models, we
allow varying rewards to the inspectee for successful violations. As one
possible example, the most valuable reward may be the completion of a sequence
of thefts of nuclear material needed to build a nuclear bomb. The inspectee can
observe the inspector, but the inspector can only determine if a violation
happens during a stage where he inspects, which terminates the game; otherwise
the game continues. Under reasonable assumptions for the payoffs, the
inspector's strategy is independent of the number of successful violations.
This allows to apply a recursive description of the game, even though this
normally assumes fully informed players after each stage. The resulting
recursive equation in three variables for the equilibrium payoff of the game,
which generalizes several other known equations of this kind, is solved
explicitly in terms of sums of binomial coefficients. We also extend this
approach to non-zero-sum games and, similar to Maschler (1966), "inspector
leadership" where the inspector commits to (the same) randomized inspection
schedule, but the inspectee acts legally (rather than mixes as in the
simultaneous game) as long as inspections remain.Comment: final version for Mathematics of Operations Research, new Theorem
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