24 research outputs found
Towards a Theory of Maximal Extractable Value II: Uncertainty
Maximal Extractable Value (MEV) is value extractable by temporary monopoly
power commonly found in decentralized systems. This extraction stems from a
lack of user privacy upon transaction submission and the ability of a
monopolist validator to reorder, add, and/or censor transactions. There are two
main directions to reduce MEV: reduce the flexibility of the miner to reorder
transactions by enforcing ordering rules and/or introduce a competitive market
for the right to reorder, add, and/or censor transactions. In this work, we
unify these approaches via \emph{uncertainty principles}, akin to those found
in harmonic analysis and physics. This provides a quantitative trade-off
between the freedom to reorder transactions and the complexity of an economic
payoff to a user in a decentralized network. This trade off is analogous to the
Nyquist-Shannon sampling theorem and demonstrates that sequencing rules in
blockchains need to be application specific. Our results suggest that neither
so-called fair ordering techniques nor economic mechanisms can individually
mitigate MEV for arbitrary payoff functions
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
Attacks on Dynamic DeFi Interest Rate Curves
As decentralized money market protocols continue to grow in value locked,
there have been a number of optimizations proposed for improving capital
efficiency. One set of proposals from Euler Finance and Mars Protocol is to
have an interest rate curve that is a proportional-integral-derivative (PID)
controller. In this paper, we demonstrate attacks on proportional and
proportional-integral controlled interest rate curves. The attack allows one to
manipulate the interest rate curve to take a higher proportion of the earned
yield than their pro-rata share of the lending pool. We conclude with an
argument that PID interest rate curves can actually \emph{reduce} capital
efficiency (due to attack mitigations) unless supply and demand elasticity to
rate changes are sufficiently high
A primer on perpetuals
We consider a continuous-time financial market with no arbitrage and no
transactions costs. In this setting, we introduce two types of perpetual
contracts, one in which the payoff to the long side is a fixed function of the
underlyers and the long side pays a funding rate to the short side, the other
in which the payoff to the long side is a fixed function of the underlyers
times a discount factor that changes over time but no funding payments are
required. Assuming asset prices are continuous and strictly positive, we derive
model-free expressions for the funding rate and discount rate of these
perpetual contracts as well as replication strategies for the short side. When
asset prices can jump, we derive expressions for the funding and discount
rates, which are semi-robust in the sense that they do not depend on the
dynamics of the volatility process of the underlying risky assets, but do
depend on the intensity of jumps under the market's pricing measure. When asset
prices can jump and the volatility process is independent of the underlying
risky assets, we derive an explicit replication strategy for the short side of
a perpetual contract. Throughout the paper, we illustrate through examples how
specific perpetual contracts relate to traditional financial instruments such
as variance swaps and leveraged exchange traded funds
The Specter (and Spectra) of Miner Extractable Value
Miner extractable value (MEV) refers to any excess value that a transaction
validator can realize by manipulating the ordering of transactions. In this
work, we introduce a simple theoretical definition of the 'cost of MEV', prove
some basic properties, and show that the definition is useful via a number of
examples. In a variety of settings, this definition is related to the
'smoothness' of a function over the symmetric group. From this definition and
some basic observations, we recover a number of results from the literature
The Geometry of Constant Function Market Makers
Constant function market makers (CFMMs) are the most popular type of
decentralized trading venue for cryptocurrency tokens. In this paper, we give a
very general geometric framework (or 'axioms') which encompass and generalize
many of the known results for CFMMs in the literature, without requiring strong
conditions such as differentiability or homogeneity. One particular consequence
of this framework is that every CFMM has a (unique) canonical trading function
that is nondecreasing, concave, and homogeneous, showing that many results
known only for homogeneous trading functions are actually fully general. We
also show that CFMMs satisfy a number of intuitive and geometric composition
rules, and give a new proof, via conic duality, of the equivalence of the
portfolio value function and the trading function. Many results are extended to
the general setting where the CFMM is not assumed to be path-independent, but
only one trade is allowed. Finally, we show that all 'path-independent' CFMMs
have a simple geometric description that does not depend on any notion of a
'trading history'
Differential Privacy in Constant Function Market Makers
Constant function market makers (CFMMs) are the most popular mechanism for facilitating decentralized trading. While these mechanisms have facilitated hundreds of billions of dollars of trades, they provide users with little to no privacy. Recent work illustrates that privacy cannot be achieved in CFMMs without forcing worse pricing and/or latency on end users. This paper more precisely quantifies the trade-off between pricing and privacy in CFMMs. We analyze a simple privacy-enhancing mechanism called Uniform Random Execution and prove that it provides -differential privacy. The privacy parameter depends on the curvature of the CFMM trading function and the number of trades executed. This mechanism can be implemented in any blockchain system that allows smart contracts to access a verifiable random function. We also investigate the worst case complexity over all private CFMM mechanisms using recent results from private PAC learning. These results suggest that one cannot do much better than Uniform Random Execution in CFMMs with non-zero curvature. Our results provide an optimistic outlook on providing partial privacy in CFMMs