1,437 research outputs found
A Faithful Distributed Implementation of Dual Decomposition and Average Consensus Algorithms
We consider large scale cost allocation problems and consensus seeking
problems for multiple agents, in which agents are suggested to collaborate in a
distributed algorithm to find a solution. If agents are strategic to minimize
their own individual cost rather than the global social cost, they are endowed
with an incentive not to follow the intended algorithm, unless the tax/subsidy
mechanism is carefully designed. Inspired by the classical
Vickrey-Clarke-Groves mechanism and more recent algorithmic mechanism design
theory, we propose a tax mechanism that incentivises agents to faithfully
implement the intended algorithm. In particular, a new notion of asymptotic
incentive compatibility is introduced to characterize a desirable property of
such class of mechanisms. The proposed class of tax mechanisms provides a
sequence of mechanisms that gives agents a diminishing incentive to deviate
from suggested algorithm.Comment: 8 page
Backpressure meets taxes: Faithful data collection in stochastic mobile phone sensing systems
The use of sensor-enabled smart phones is considered to be a promising solution to large-scale urban data collection. In current approaches to mobile phone sensing systems (MPSS), phones directly transmit their sensor readings through cellular radios to the server. However, this simple solution suffers from not only significant costs in terms of energy and mobile data usage, but also produces heavy traffic loads on bandwidth-limited cellular networks. To address this issue, this paper investigates cost-effective data collection solutions for MPSS using hybrid cellular and opportunistic short-range communications. We first develop an adaptive and distribute algorithm OptMPSS to maximize phone user financial rewards accounting for their costs across the MPSS. To incentivize phone users to participate, while not subverting the behavior of OptMPSS, we then propose BMT, the first algorithm that merges stochastic Lyapunov optimization with mechanism design theory. We show that our proven incentive compatible approaches achieve an asymptotically optimal gross profit for all phone users. Experiments with Android phones and trace-driven simulations verify our theoretical analysis and demonstrate that our approach manages to improve the system performance significantly (around 100%) while confirming that our system achieves incentive compatibility, individual rationality, and server profitability
Truthful and Faithful Monetary Policy for a Stablecoin Conducted by a Decentralised, Encrypted Artificial Intelligence
The Holy Grail of a decentralised stablecoin is achieved on rigorous
mathematical frameworks, obtaining multiple advantageous proofs: stability,
convergence, truthfulness, faithfulness, and malicious-security. These
properties could only be attained by the novel and interdisciplinary
combination of previously unrelated fields: model predictive control, deep
learning, alternating direction method of multipliers (consensus-ADMM),
mechanism design, secure multi-party computation, and zero-knowledge proofs.
For the first time, this paper proves:
- the feasibility of decentralising the central bank while securely
preserving its independence in a decentralised computation setting
- the benefits for price stability of combining mechanism design, provable
security, and control theory, unlike the heuristics of previous stablecoins
- the implementation of complex monetary policies on a stablecoin, equivalent
to the ones used by central banks and beyond the current fixed rules of
cryptocurrencies that hinder their price stability
- methods to circumvent the impossibilities of Guaranteed Output Delivery
(G.O.D.) and fairness: standing on truthfulness and faithfulness, we reach
G.O.D. and fairness under the assumption of rational parties
As a corollary, a decentralised artificial intelligence is able to conduct
the monetary policy of a stablecoin, minimising human intervention
Incentivizing Truth-Telling in MPC-based Load Frequency Control
We present a mechanism for socially efficient implementation of model
predictive control (MPC) algorithms for load frequency control (LFC) in the
presence of self-interested power generators. Specifically, we consider a
situation in which the system operator seeks to implement an MPC-based LFC for
aggregated social cost minimization, but necessary information such as
individual generators' cost functions is privately owned. Without appropriate
monetary compensation mechanisms that incentivize truth-telling,
self-interested market participants may be inclined to misreport their private
parameters in an effort to maximize their own profits, which may result in a
loss of social welfare. The main challenge in our framework arises from the
fact that every participant's strategy at any time affects the future state of
other participants; the consequences of such dynamic coupling has not been
fully addressed in the literature on online mechanism design. We propose a
class of real-time monetary compensation schemes that incentivize market
participants to report their private parameters truthfully at every time step,
which enables the system operator to implement MPC-based LFC in a socially
optimal manner
Backpressure Meets Taxes: Faithful Data Collection in Stochastic Mobile Phone Sensing Systems
The use of sensor-enabled smart phones is considered to be a promising solution to large-scale urban data collection. In current approaches to mobile phone sensing systems (MPSS), phones directly transmit their sensor readings through cellular radios to the server. However, this simple solution suffers from not only significant costs in terms of energy and mobile data usage, but also produces heavy traffic loads on bandwidth-limited cellular networks. To address this issue, this paper investigates cost-effective data collection solutions for MPSS using hybrid cellular and opportunistic short-range communications. We first develop an adaptive and distribute algorithm OptMPSS to maximize phone user financial rewards accounting for their costs across the MPSS.
To incentivize phone users to participate, while not subverting the behavior of OptMPSS, we then propose BMT, the first algorithm that merges stochastic Lyapunov optimization with mechanism design theory. We show that our proven incentive compatible approaches achieve an asymptotically optimal gross profit for all phone users. Experiments with Android phones and trace-driven simulations verify our theoretical analysis and demonstrate that our approach manages to improve the system performance significantly while confirming that our system achieves incentive compatibility, individual rationality, and server profitability
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