arXiv subjects

Martino Banchio

Publications and source records attributed to Martino Banchio.

7 recordsLinked to original sources

arXiv2025

Dynamic Threats to Credible Auctions

A seller wants to sell a good to a set of bidders using a credible mechanism. We show that when the seller has private information about her cost, it is impossible for a static mechanism to achieve the optimal revenue. In particular, even the optimal first-price auction is not credible. We show that the English auction can credibly implement the optimal mechanism, unlike the optimal Dutch auction. For symmetric mechanisms in which only winners pay, we also characterize all the static auctions that are credible: They are first-price auctions that depend only on the seller's cost ex post via a secret reserve, and may profitably pool bidders via a bid restriction. Our impossibility result highlights the role of public institutions and helps explain the use of dynamic mechanisms in informal auctions.

econ.TH↗

arXiv2025

Incentives and Outcomes in Bug Bounties

Bug bounty programs have contributed significantly to security in technology firms in the last decade, but little is known about the role of reward incentives in producing useful outcomes. We analyze incentives and outcomes in Google's Vulnerability Rewards Program (VRP), one of the world's largest bug bounty programs. We analyze the responsiveness of the quality and quantity of bugs received to changes in payments, focusing on a change in Google's reward amounts posted in July, 2024, in which reward amounts increased by up to 200% for the highest impact tier. Our empirical results show an increase in the volume of high-value bugs received after the reward increase, for which we also compute elasticities. We further break down the sources of this increase between veteran researchers and new researchers, showing that the reward increase both redirected the attention of veteran researchers and attracted new top security researchers into the program.

cs.SE↗

arXiv2025

Rediscovery

We model search in settings where decision makers know what can be found but not where to find it. A searcher faces a set of choices arranged by an observable attribute. Each period, she either selects a choice and pays a cost to learn about its quality, or she concludes search to take her best discovery to date. She knows that similar choices have similar qualities and uses this to guide her search. We identify robustly optimal search policies with a simple structure. Search is directional, recall is never invoked, there is a threshold stopping rule, and the policy at each history depends only on a simple index.

econ.TH↗

arXiv2025

Ads in Conversations

We study the optimal placement of advertisements for interactive platforms like conversational AI assistants. Importantly, conversations add a feature absent in canonical search markets -- time. The evolution of a conversation is informative about ad qualities, thus a platform could delay ad delivery to improve selection. However, delay endogenously shapes the supply of quality ads, possibly affecting revenue. We characterize the equilibria of first- and second-price auctions where the platform can commit to the auction format but not to its timing. We document sharp differences in the mechanisms' outcomes: first-price auctions are efficient but delay ad delivery, while second-price auctions avoid delay but allocate inefficiently. Revenue may be arbitrarily larger in a second-price auction than in a first-price auction. Optimal reserve prices alleviate these differences but flip the revenue ordering.

econ.TH↗

arXiv2023

Artificial Intelligence and Spontaneous Collusion

We develop a tractable model for studying strategic interactions between learning algorithms. We uncover a mechanism responsible for the emergence of algorithmic collusion. We observe that algorithms periodically coordinate on actions that are more profitable than static Nash equilibria. This novel collusive channel relies on an endogenous statistical linkage in the algorithms' estimates which we call spontaneous coupling. The model's parameters predict whether the statistical linkage will appear, and what market structures facilitate algorithmic collusion. We show that spontaneous coupling can sustain collusion in prices and market shares, complementing experimental findings in the literature. Finally, we apply our results to design algorithmic markets.

econ.TH↗

arXiv2022

Artificial Intelligence and Auction Design

Motivated by online advertising auctions, we study auction design in repeated auctions played by simple Artificial Intelligence algorithms (Q-learning). We find that first-price auctions with no additional feedback lead to tacit-collusive outcomes (bids lower than values), while second-price auctions do not. We show that the difference is driven by the incentive in first-price auctions to outbid opponents by just one bid increment. This facilitates re-coordination on low bids after a phase of experimentation. We also show that providing information about lowest bid to win, as introduced by Google at the time of switch to first-price auctions, increases competitiveness of auctions.

econ.TH↗

arXiv2021

Dynamic Pricing with Limited Commitment

A monopolist wants to sell one item per period to a consumer with evolving and persistent private information. The seller sets a price each period depending on the history so far, but cannot commit to future prices. We show that, regardless of the degree of persistence, any equilibrium under a D1-style refinement gives the seller revenue no higher than what she would get from posting all prices in advance.

econ.TH↗