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We investigate the problem of designing randomized obviously strategy-proof (OSP) mechanisms in several canonical auction settings. Obvious strategy-proofness, introduced by Li [American Economic Review, 2017], strengthens the well-known concept of dominant-strategy incentive compatibility (DSIC). Loosely speaking, it ensures that even agents who struggle with contingent reasoning can identify that their dominant strategy is optimal. Thus, one would hope to design OSP mechanisms with good approximation guarantees. Unfortunately, Ron [SODA,2024] has shown that deterministic OSP mechanisms fail to achieve an approximation better than $\min\{m,n\}$ where $m$ is the number of items and $n$ is the number of bidders, even for the simple settings of additive and unit-demand bidders. We circumvent these impossibilities by showing that randomized mechanisms that are obviously strategy-proof in the universal sense obtain a constant factor approximation for these classes. We show that this phenomenon occurs also for the setting of a multi-unit auction with single-minded bidders. Thus, our results provide a more positive outlook on the design of OSP mechanisms and exhibit a stark separation between the power of randomized and deterministic OSP mechanisms. To complement the picture, we provide impossibilities for randomized OSP mechanisms in each setting. While the deterministic VCG mechanism is well known to output an optimal allocation in dominant strategies, we show that even randomized OSP mechanisms cannot obtain more than $87.5\%$ of the optimal welfare. This further demonstrates that OSP mechanisms are significantly weaker than dominant-strategy mechanisms.
This paper delves into the design of randomized mechanisms that are obviously strategy-proof (OSP) within various auction scenarios. OSP is a stricter form of incentive compatibility than the conventional dominant-strategy incentive compatibility (DSIC), ensuring that agents can easily identify and implement their optimal strategy, even with limited reasoning abilities.
The primary significance lies in circumventing previously established limitations of deterministic OSP mechanisms, which were shown to have poor approximation guarantees. The key innovation is demonstrating that randomization, specifically using universally OSP mechanisms (distributions over deterministic OSP mechanisms), allows for constant-factor approximation algorithms in settings where deterministic mechanisms perform poorly. They show that, although the deterministic VCG mechanism is optimal, even randomized OSP mechanisms cannot obtain more than 87.5% of the optimal welfare.
Main prior ingredients needed:
1. DSIC Mechanisms: The fundamental concept of designing mechanisms where agents’ dominant strategy is truthful reporting.
2. Obviously Strategy-Proofness: The stronger notion of strategic simplicity that allows even cognitively-limited agents to identify truthful strategies.
3. Impossibility Results for Deterministic OSP Mechanisms: This work builds on prior results showing limitations of deterministic OSP mechanisms in achieving good approximation guarantees, motivating the study of randomization.
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