6,017 research outputs found
Rational Fair Consensus in the GOSSIP Model
The \emph{rational fair consensus problem} can be informally defined as
follows. Consider a network of (selfish) \emph{rational agents}, each of
them initially supporting a \emph{color} chosen from a finite set .
The goal is to design a protocol that leads the network to a stable
monochromatic configuration (i.e. a consensus) such that the probability that
the winning color is is equal to the fraction of the agents that initially
support , for any . Furthermore, this fairness property must
be guaranteed (with high probability) even in presence of any fixed
\emph{coalition} of rational agents that may deviate from the protocol in order
to increase the winning probability of their supported colors. A protocol
having this property, in presence of coalitions of size at most , is said to
be a \emph{whp\,--strong equilibrium}. We investigate, for the first time,
the rational fair consensus problem in the GOSSIP communication model where, at
every round, every agent can actively contact at most one neighbor via a
\emph{pushpull} operation. We provide a randomized GOSSIP protocol that,
starting from any initial color configuration of the complete graph, achieves
rational fair consensus within rounds using messages of
size, w.h.p. More in details, we prove that our protocol is a
whp\,--strong equilibrium for any and, moreover, it
tolerates worst-case permanent faults provided that the number of non-faulty
agents is . As far as we know, our protocol is the first solution
which avoids any all-to-all communication, thus resulting in message
complexity.Comment: Accepted at IPDPS'1
Individual and global adaptation in networks
The structure of complex biological and socio-economic networks affects the selective pressures or behavioural incentives of components in that network, and reflexively, the evolution/behaviour of individuals in those networks changes the structure of such networks over time. Such âadaptive networksâ underlie how gene-regulation networks evolve, how ecological networks self-organise, and how networks of strategic agents co-create social organisations. Although such domains are different in the details, they can each be characterised as networks of self-interested agents where agents alter network connections in the direction that increases their individual utility. Recent work shows that such dynamics are equivalent to associative learning, well-understood in the context of neural networks. Associative learning in neural substrates is the result of mandated learning rules (e.g. Hebbian learning), but in networks of autonomous agents âassociative inductionâ occurs as a result of local individual incentives to alter connections. Using results from a number of recent studies, here we review the theoretical principles that can be transferred between disciplines as a result of this isomorphism, and the implications for the organisation of genetic, social and ecological networks
Mechanism design for decentralized online machine scheduling
Traditional optimization models assume a central decision maker who optimizes a global system performance measure. However, problem data is often distributed among several agents, and agents take autonomous decisions. This gives incentives for strategic behavior of agents, possibly leading to sub-optimal system performance. Furthermore, in dynamic environments, machines are locally dispersed and administratively independent. Examples are found both in business and engineering applications. We investigate such issues for a parallel machine scheduling model where jobs arrive online over time. Instead of centrally assigning jobs to machines, each machine implements a local sequencing rule and jobs decide for machines themselves. In this context, we introduce the concept of a myopic best response equilibrium, a concept weaker than the classical dominant strategy equilibrium, but appropriate for online problems. Our main result is a polynomial time, online mechanism that |assuming rational behavior of jobs| results in an equilibrium schedule that is 3.281-competitive with respect to the maximal social welfare. This is only lightly worse than state-of-the-art algorithms with central coordination
Wardrop Equilibrium in Discrete-Time Selfish Routing with Time-Varying Bounded Delays
This paper presents a multi-commodity, discrete-
time, distributed and non-cooperative routing algorithm, which is
proved to converge to an equilibrium in the presence of
heterogeneous, unknown, time-varying but bounded delays.
Under mild assumptions on the latency functions which describe
the cost associated to the network paths, two algorithms are
proposed: the former assumes that each commodity relies only on
measurements of the latencies associated to its own paths; the
latter assumes that each commodity has (at least indirectly) access
to the measures of the latencies of all the network paths. Both
algorithms are proven to drive the system state to an invariant set
which approximates and contains the Wardrop equilibrium,
defined as a network state in which no traffic flow over the
network paths can improve its routing unilaterally, with the latter
achieving a better reconstruction of the Wardrop equilibrium.
Numerical simulations show the effectiveness of the proposed
approach
Agent-Based Simulations of Blockchain protocols illustrated via Kadena's Chainweb
While many distributed consensus protocols provide robust liveness and
consistency guarantees under the presence of malicious actors, quantitative
estimates of how economic incentives affect security are few and far between.
In this paper, we describe a system for simulating how adversarial agents, both
economically rational and Byzantine, interact with a blockchain protocol. This
system provides statistical estimates for the economic difficulty of an attack
and how the presence of certain actors influences protocol-level statistics,
such as the expected time to regain liveness. This simulation system is
influenced by the design of algorithmic trading and reinforcement learning
systems that use explicit modeling of an agent's reward mechanism to evaluate
and optimize a fully autonomous agent. We implement and apply this simulation
framework to Kadena's Chainweb, a parallelized Proof-of-Work system, that
contains complexity in how miner incentive compliance affects security and
censorship resistance. We provide the first formal description of Chainweb that
is in the literature and use this formal description to motivate our simulation
design. Our simulation results include a phase transition in block height
growth rate as a function of shard connectivity and empirical evidence that
censorship in Chainweb is too costly for rational miners to engage in. We
conclude with an outlook on how simulation can guide and optimize protocol
development in a variety of contexts, including Proof-of-Stake parameter
optimization and peer-to-peer networking design.Comment: 10 pages, 7 figures, accepted to the IEEE S&B 2019 conferenc
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