4 research outputs found
Simple, Credible, and Approximately-Optimal Auctions
We identify the first static credible mechanism for multi-item additive
auctions that achieves a constant factor of the optimal revenue. This is one
instance of a more general framework for designing two-part tariff auctions,
adapting the duality framework of Cai et al [CDW16]. Given a (not necessarily
incentive compatible) auction format satisfying certain technical
conditions, our framework augments the auction with a personalized entry fee
for each bidder, which must be paid before the auction can be accessed. These
entry fees depend only on the prior distribution of bidder types, and in
particular are independent of realized bids. Our framework can be used with
many common auction formats, such as simultaneous first-price, simultaneous
second-price, and simultaneous all-pay auctions. If all-pay auctions are used,
we prove that the resulting mechanism is credible in the sense that the
auctioneer cannot benefit by deviating from the stated mechanism after
observing agent bids. If second-price auctions are used, we obtain a truthful
-approximate mechanism with fixed entry fees that are amenable to tuning
via online learning techniques. Our results for first price and all-pay are the
first revenue guarantees of non-truthful mechanisms in multi-dimensional
environments; an open question in the literature [RST17]
Credible, Strategyproof, Optimal, and Bounded Expected-Round Single-Item Auctions for All Distributions
We consider a revenue-maximizing seller with a single item for sale to multiple buyers with independent and identically distributed valuations. Akbarpour and Li (2020) show that the only optimal, credible, strategyproof auction is the ascending price auction with reserves which has unbounded communication complexity. Recent work of Ferreira and Weinberg (2020) circumvents their impossibility result assuming the existence of cryptographically secure commitment schemes, and designs a two-round credible, strategyproof, optimal auction. However, their auction is only credible when buyers\u27 valuations are MHR or ?-strongly regular: they show their auction might not be credible even when there is a single buyer drawn from a non-MHR distribution. In this work, under the same cryptographic assumptions, we identify a new single-item auction that is credible, strategyproof, revenue optimal, and terminates in constant rounds in expectation for all distributions with finite monopoly price
Dynamic Posted-Price Mechanisms for the Blockchain Transaction Fee Market
In recent years, prominent blockchain systems such as Bitcoin and Ethereum
have experienced explosive growth in transaction volume, leading to frequent
surges in demand for limited block space and causing transaction fees to
fluctuate by orders of magnitude. Existing systems sell space using first-price
auctions; however, users find it difficult to estimate how much they need to
bid in order to get their transactions accepted onto the chain. If they bid too
low, their transactions can have long confirmation times. If they bid too high,
they pay larger fees than necessary.
In light of these issues, new transaction fee mechanisms have been proposed,
most notably EIP-1559, aiming to provide better usability. EIP-1559 is a
history-dependent mechanism that relies on block utilization to adjust a base
fee. We propose an alternative design -- a {\em dynamic posted-price mechanism}
-- which uses not only block utilization but also observable bids from past
blocks to compute a posted price for subsequent blocks. We show its potential
to reduce price volatility by providing examples for which the prices of
EIP-1559 are unstable while the prices of the proposed mechanism are stable.
More generally, whenever the demand for the blockchain stabilizes, we ask if
our mechanism is able to converge to a stable state. Our main result provides
sufficient conditions in a probabilistic setting for which the proposed
mechanism is approximately welfare optimal and the prices are stable. Our main
technical contribution towards establishing stability is an iterative algorithm
that, given oracle access to a Lipschitz continuous and strictly concave
function , converges to a fixed point of
Rationality-Robust Information Design: Bayesian Persuasion under Quantal Response
Classic mechanism/information design imposes the assumption that agents are
fully rational, meaning each of them always selects the action that maximizes
her expected utility. Yet many empirical evidence suggests that human decisions
may deviate from this full rationality assumption. In this work, we attempt to
relax the full rationality assumption with bounded rationality. Specifically,
we formulate the bounded rationality of an agent by adopting the quantal
response model (McKelvey and Palfrey, 1995).
We develop a theory of rationality-robust information design in the canonical
setting of Bayesian persuasion (Kamenica and Gentzkow, 2011) with binary
receiver action. We first identify conditions under which the optimal signaling
scheme structure for a fully rational receiver remains optimal or approximately
optimal for a boundedly rational receiver. In practice, it might be costly for
the designer to estimate the degree of the receiver's bounded rationality
level. Motivated by this practical consideration, we then study the existence
and construction of robust signaling schemes when there is uncertainty about
the receiver's bounded rationality level