3 research outputs found
Liquidity in Credit Networks with Constrained Agents
In order to scale transaction rates for deployment across the global web,
many cryptocurrencies have deployed so-called "Layer-2" networks of private
payment channels. An idealized payment network behaves like a Credit Network, a
model for transactions across a network of bilateral trust relationships.
Credit Networks capture many aspects of traditional currencies as well as new
virtual currencies and payment mechanisms. In the traditional credit network
model, if an agent defaults, every other node that trusted it is vulnerable to
loss. In a cryptocurrency context, trust is manufactured by capital deposits,
and thus there arises a natural tradeoff between network liquidity (i.e. the
fraction of transactions that succeed) and the cost of capital deposits.
In this paper, we introduce constraints that bound the total amount of loss
that the rest of the network can suffer if an agent (or a set of agents) were
to default - equivalently, how the network changes if agents can support
limited solvency guarantees.
We show that these constraints preserve the analytical structure of a credit
network. Furthermore, we show that aggregate borrowing constraints greatly
simplify the network structure and in the payment network context achieve the
optimal tradeoff between liquidity and amount of escrowed capital.Comment: To be published in TheWebConf 202
The Effect of Network Topology on Credit Network Throughput
Credit networks rely on decentralized, pairwise trust relationships
(channels) to exchange money or goods. Credit networks arise naturally in many
financial systems, including the recent construct of payment channel networks
in blockchain systems. An important performance metric for these networks is
their transaction throughput. However, predicting the throughput of a credit
network is nontrivial. Unlike traditional communication channels, credit
channels can become imbalanced; they are unable to support more transactions in
a given direction once the credit limit has been reached. This potential for
imbalance creates a complex dependency between a network's throughput and its
topology, path choices, and the credit balances (state) on every channel. Even
worse, certain combinations of these factors can lead the credit network to
deadlocked states where no transactions can make progress. In this paper, we
study the relationship between the throughput of a credit network and its
topology and credit state. We show that the presence of deadlocks completely
characterizes a network's throughput sensitivity to different credit states.
Although we show that identifying deadlocks in an arbitrary topology is
NP-hard, we propose a peeling algorithm inspired by decoding algorithms for
erasure codes that upper bounds the severity of the deadlock. We use the
peeling algorithm as a tool to compare the performance of different topologies
as well as to aid in the synthesis of topologies robust to deadlocks