Optimal Auctions with Financially Constrained Bidders

We consider an environment where potential buyers of an indi- visible good have liquidity constraints, in that they cannot pay more than their `budget' regardless of their valuation. A buyer's valuation for the good as well as her budget are her private information. We derive constrained-efficient and revenue maximizing auctions for this setting. In general, the optimal auction requires `pooling' both at the top and in the middle despite the maintained assumption of a mono- tone hazard rate. Further, the auctioneer will never¯find it desirable to subsidize bidders with low budgets.the universal type space, the strategic topology; the uniform strategic topology; the uniform-weak topology; interim correlated rationalizable actions

Structural Advantages for Integrated Builders in MEV-Boost

Currently, over 90% of Ethereum blocks are built using MEV-Boost, an auction that allows validators to sell their block-building power to builders who compete in an open English auction in each slot. Shortly after the merge, when MEV-Boost was in its infancy, most block builders were neutral, meaning they did not trade themselves but rather aggregated transactions from other traders. Over time, integrated builders, operated by trading firms, began to overtake many of the neutral builders. Outside of the integrated builder teams, little is known about which advantages integration confers beyond latency and how latency advantages distort on-chain trading. This paper explores these poorly understood advantages. We make two contributions. First, we point out that integrated builders are able to bid truthfully in their own bundle merge and then decide how much profit to take later in the final stages of the PBS auction when more information is available, making the auction for them look closer to a second-price auction while independent searchers are stuck in a first-price auction. Second, we find that latency disadvantages convey a winner's curse on slow bidders when underlying values depend on a stochastic price process that change as bids are submitted

The Strange Case of Privacy in Equilibrium Models

We study how privacy technologies affect user and advertiser behavior in a simple economic model of targeted advertising. In our model, a consumer first decides whether or not to buy a good, and then an advertiser chooses an advertisement to show the consumer. The consumer's value for the good is correlated with her type, which determines which ad the advertiser would prefer to show to her---and hence, the advertiser would like to use information about the consumer's purchase decision to target the ad that he shows. In our model, the advertiser is given only a differentially private signal about the consumer's behavior---which can range from no signal at all to a perfect signal, as we vary the differential privacy parameter. This allows us to study equilibrium behavior as a function of the level of privacy provided to the consumer. We show that this behavior can be highly counter-intuitive, and that the effect of adding privacy in equilibrium can be completely different from what we would expect if we ignored equilibrium incentives. Specifically, we show that increasing the level of privacy can actually increase the amount of information about the consumer's type contained in the signal the advertiser receives, lead to decreased utility for the consumer, and increased profit for the advertiser, and that generally these quantities can be non-monotonic and even discontinuous in the privacy level of the signal

The centralizing effects of private order flow on proposer-builder separation

The current Proposer Builder Separation (PBS) equilibrium has several builders with different backgrounds winning blocks consistently. This paper considers how this equilibrium will shift when transactions are sold privately via order flow auctions (OFAs) rather than forwarded directly to the public mempool. We discuss a novel model that highlights the augmented value of private order flow for integrated builder searchers. We show that private order flow is complementary to top-of-block opportunities, and therefore integrated builder-searchers are more likely to participate in OFAs and outbid non integrated builders. They will then parlay access to these private transactions into an advantage in the PBS auction, winning blocks more often and extracting higher profits than non-integrated builders. To validate our main assumptions, we construct a novel dataset pairing post-merge PBS outcomes with realized 12-second volatility on a leading CEX (Binance). Our results show that integrated builder-searchers are more likely to win in the PBS auction when realized volatility is high, suggesting that indeed such builders have an advantage in extracting top-of-block opportunities. Our findings suggest that modifying PBS to disentangle the intertwined dynamics between top-of-block extraction and private order flow would pave the way for a fairer and more decentralized Ethereum

Fairness Incentives for Myopic Agents

We consider settings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-term profits and property safety) to treat arriving clients fairly, in order to prevent overall discrimination against individuals or groups. We model such settings in both classical and contextual bandit models in which the myopic agents maximize rewards according to current empirical averages, but are also amenable to exogenous payments that may cause them to alter their choices. Our notion of fairness asks that more qualified individuals are never (probabilistically) preferred over less qualified ones [Joseph et al]. We investigate whether it is possible to design inexpensive {subsidy} or payment schemes for a principal to motivate myopic agents to play fairly in all or almost all rounds. When the principal has full information about the state of the myopic agents, we show it is possible to induce fair play on every round with a subsidy scheme of total cost $o(T)$ (for the classic setting with $k$ arms, $\tilde{O}(\sqrt{k^3T})$, and for the $d$-dimensional linear contextual setting $\tilde{O}(d\sqrt{k^3 T})$). If the principal has much more limited information (as might often be the case for an external regulator or watchdog), and only observes the number of rounds in which members from each of the $k$ groups were selected, but not the empirical estimates maintained by the myopic agent, the design of such a scheme becomes more complex. We show both positive and negative results in the classic and linear bandit settings by upper and lower bounding the cost of fair subsidy schemes