362,154 research outputs found
Designing cooperation mechanisms for supply chains
The paper defines generic requirements towards cooperative planning in the nucleus of any supply network that is constituted by a pair of autonomous manufacturer and supplier who possess asymmetric information on demand forecast and costs, respectively. Then a novel way is suggested for investigating this problem by means of the apparatus of mechanism design. The analysis results in some provable generic properties as for efficiency and truthfulness, and shows the impossibility of fair cost and profit sharing. Further on, design principles towards a payment scheme are devised that provide incentive for the partners to cooperate in order to minimize costs. This payment can be considered the price for a flexible supply service. As examples, the generic framework is instantiated with two particular cooperative supply mechanisms
Designing cost-sharing methods for Bayesian games
We study the design of cost-sharing protocols for two fundamental resource allocation problems, the Set Cover and the Steiner Tree Problem, under environments of incomplete information (Bayesian model). Our objective is to design protocols where the worst-case Bayesian Nash equilibria, have low cost, i.e. the Bayesian Price of Anarchy (PoA) is minimized. Although budget balance is a very natural requirement, it puts considerable restrictions on the design space, resulting in high PoA. We propose an alternative, relaxed requirement called budget balance in the equilibrium (BBiE).We show an interesting connection between algorithms for Oblivious Stochastic optimization problems and cost-sharing design with low PoA. We exploit this connection for both problems and we enforce approximate solutions of the stochastic problem, as Bayesian Nash equilibria, with the same guarantees on the PoA. More interestingly, we show how to obtain the same bounds on the PoA, by using anonymous posted prices which are desirable because they are easy to implement and, as we show, induce dominant strategies for the players
Designing Networks with Good Equilibria under Uncertainty
We consider the problem of designing network cost-sharing protocols with good
equilibria under uncertainty. The underlying game is a multicast game in a
rooted undirected graph with nonnegative edge costs. A set of k terminal
vertices or players need to establish connectivity with the root. The social
optimum is the Minimum Steiner Tree. We are interested in situations where the
designer has incomplete information about the input. We propose two different
models, the adversarial and the stochastic. In both models, the designer has
prior knowledge of the underlying metric but the requested subset of the
players is not known and is activated either in an adversarial manner
(adversarial model) or is drawn from a known probability distribution
(stochastic model).
In the adversarial model, the designer's goal is to choose a single,
universal protocol that has low Price of Anarchy (PoA) for all possible
requested subsets of players. The main question we address is: to what extent
can prior knowledge of the underlying metric help in the design? We first
demonstrate that there exist graphs (outerplanar) where knowledge of the
underlying metric can dramatically improve the performance of good network
design. Then, in our main technical result, we show that there exist graph
metrics, for which knowing the underlying metric does not help and any
universal protocol has PoA of , which is tight. We attack this
problem by developing new techniques that employ powerful tools from extremal
combinatorics, and more specifically Ramsey Theory in high dimensional
hypercubes.
Then we switch to the stochastic model, where each player is independently
activated. We show that there exists a randomized ordered protocol that
achieves constant PoA. By using standard derandomization techniques, we produce
a deterministic ordered protocol with constant PoA.Comment: This version has additional results about stochastic inpu
TRIDEnT: Building Decentralized Incentives for Collaborative Security
Sophisticated mass attacks, especially when exploiting zero-day
vulnerabilities, have the potential to cause destructive damage to
organizations and critical infrastructure. To timely detect and contain such
attacks, collaboration among the defenders is critical. By correlating
real-time detection information (alerts) from multiple sources (collaborative
intrusion detection), defenders can detect attacks and take the appropriate
defensive measures in time. However, although the technical tools to facilitate
collaboration exist, real-world adoption of such collaborative security
mechanisms is still underwhelming. This is largely due to a lack of trust and
participation incentives for companies and organizations. This paper proposes
TRIDEnT, a novel collaborative platform that aims to enable and incentivize
parties to exchange network alert data, thus increasing their overall detection
capabilities. TRIDEnT allows parties that may be in a competitive relationship,
to selectively advertise, sell and acquire security alerts in the form of
(near) real-time peer-to-peer streams. To validate the basic principles behind
TRIDEnT, we present an intuitive game-theoretic model of alert sharing, that is
of independent interest, and show that collaboration is bound to take place
infinitely often. Furthermore, to demonstrate the feasibility of our approach,
we instantiate our design in a decentralized manner using Ethereum smart
contracts and provide a fully functional prototype.Comment: 28 page
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