64 research outputs found
On Revenue Maximization with Sharp Multi-Unit Demands
We consider markets consisting of a set of indivisible items, and buyers that
have {\em sharp} multi-unit demand. This means that each buyer wants a
specific number of items; a bundle of size less than has no value,
while a bundle of size greater than is worth no more than the most valued
items (valuations being additive). We consider the objective of setting
prices and allocations in order to maximize the total revenue of the market
maker. The pricing problem with sharp multi-unit demand buyers has a number of
properties that the unit-demand model does not possess, and is an important
question in algorithmic pricing. We consider the problem of computing a revenue
maximizing solution for two solution concepts: competitive equilibrium and
envy-free pricing.
For unrestricted valuations, these problems are NP-complete; we focus on a
realistic special case of "correlated values" where each buyer has a
valuation v_i\qual_j for item , where and \qual_j are positive
quantities associated with buyer and item respectively. We present a
polynomial time algorithm to solve the revenue-maximizing competitive
equilibrium problem. For envy-free pricing, if the demand of each buyer is
bounded by a constant, a revenue maximizing solution can be found efficiently;
the general demand case is shown to be NP-hard.Comment: page2
Pricing Ad Slots with Consecutive Multi-unit Demand
We consider the optimal pricing problem for a model of the rich media
advertisement market, as well as other related applications. In this market,
there are multiple buyers (advertisers), and items (slots) that are arranged in
a line such as a banner on a website. Each buyer desires a particular number of
{\em consecutive} slots and has a per-unit-quality value (dependent on
the ad only) while each slot has a quality (dependent on the position
only such as click-through rate in position auctions). Hence, the valuation of
the buyer for item is . We want to decide the allocations and
the prices in order to maximize the total revenue of the market maker.
A key difference from the traditional position auction is the advertiser's
requirement of a fixed number of consecutive slots. Consecutive slots may be
needed for a large size rich media ad. We study three major pricing mechanisms,
the Bayesian pricing model, the maximum revenue market equilibrium model and an
envy-free solution model. Under the Bayesian model, we design a polynomial time
computable truthful mechanism which is optimum in revenue. For the market
equilibrium paradigm, we find a polynomial time algorithm to obtain the maximum
revenue market equilibrium solution. In envy-free settings, an optimal solution
is presented when the buyers have the same demand for the number of consecutive
slots. We conduct a simulation that compares the revenues from the above
schemes and gives convincing results.Comment: 27page
Smoothed and Average-Case Approximation Ratios of Mechanisms: Beyond the Worst-Case Analysis
The approximation ratio has become one of the dominant measures in mechanism design problems. In light of analysis of algorithms, we define the smoothed approximation ratio to compare the performance of the optimal mechanism and a truthful mechanism when the inputs are subject to random perturbations of the worst-case inputs, and define the average-case approximation ratio to compare the performance of these two mechanisms when the inputs follow a distribution. For the one-sided matching problem, Filos-Ratsikas et al. [2014] show that, amongst all truthful mechanisms, random priority achieves the tight approximation ratio bound of Theta(sqrt{n}). We prove that, despite of this worst-case bound, random priority has a constant smoothed approximation ratio. This is, to our limited knowledge, the first work that asymptotically differentiates the smoothed approximation ratio from the worst-case approximation ratio for mechanism design problems. For the average-case, we show that our approximation ratio can be improved to 1+e. These results partially explain why random priority has been successfully used in practice, although in the worst case the optimal social welfare is Theta(sqrt{n}) times of what random priority achieves.
These results also pave the way for further studies of smoothed and average-case analysis for approximate mechanism design problems, beyond the worst-case analysis
Learning Thresholds with Latent Values and Censored Feedback
In this paper, we investigate a problem of actively learning threshold in
latent space, where the unknown reward depends on the proposed
threshold and latent value and it can be achieved if the
threshold is lower than or equal to the unknown latent value. This problem has
broad applications in practical scenarios, e.g., reserve price optimization in
online auctions, online task assignments in crowdsourcing, setting recruiting
bars in hiring, etc. We first characterize the query complexity of learning a
threshold with the expected reward at most smaller than the optimum
and prove that the number of queries needed can be infinitely large even when
is monotone with respect to both and . On the
positive side, we provide a tight query complexity
when is monotone and the CDF of value
distribution is Lipschitz. Moreover, we show a tight
query complexity can be achieved as long as
satisfies one-sided Lipschitzness, which provides a complete characterization
for this problem. Finally, we extend this model to an online learning setting
and demonstrate a tight regret bound using continuous-arm
bandit techniques and the aforementioned query complexity results.Comment: 18 page
MEV Makes Everyone Happy under Greedy Sequencing Rule
Trading through decentralized exchanges (DEXs) has become crucial in today's
blockchain ecosystem, enabling users to swap tokens efficiently and
automatically. However, the capacity of miners to strategically order
transactions has led to exploitative practices (e.g., front-running attacks,
sandwich attacks) and gain substantial Maximal Extractable Value (MEV) for
their own advantage. To mitigate such manipulation, Ferreira and Parkes
recently proposed a greedy sequencing rule such that the execution price of
transactions in a block moves back and forth around the starting price.
Utilizing this sequencing rule makes it impossible for miners to conduct
sandwich attacks, consequently mitigating the MEV problem.
However, no sequencing rule can prevent miners from obtaining risk-free
profits. This paper systemically studies the computation of a miner's optimal
strategy for maximizing MEV under the greedy sequencing rule, where the utility
of miners is measured by the overall value of their token holdings. Our results
unveil a dichotomy between the no trading fee scenario, which can be optimally
strategized in polynomial time, and the scenario with a constant fraction of
trading fee, where finding the optimal strategy is proven NP-hard. The latter
represents a significant challenge for miners seeking optimal MEV.
Following the computation results, we further show a remarkable phenomenon:
Miner's optimal MEV also benefits users. Precisely, in the scenarios without
trading fees, when miners adopt the optimal strategy given by our algorithm,
all users' transactions will be executed, and each user will receive equivalent
or surpass profits compared to their expectations. This outcome provides
further support for the study and design of sequencing rules in decentralized
exchanges.Comment: 14 Pages, ACM CCS Workshop on Decentralized Finance and Security
(DeFi'23
Revenue and User Traffic Maximization in Mobile Short-Video Advertising
A new mobile attention economy has emerged with the explosive growth of short-video apps such as TikTok. In this internet market, three types of agents interact with each other: the platform, influencers, and advertisers. A short-video platform encourages its influencers to attract users by creating appealing content through short-form videos and allows advertisers to display their ads in short-form videos. There are two options for the advertisers: one is to bid for platform advert slots in a similar way to search engine auctions; the other is to pay an influencer to make engaging short videos and promote them through the influencer's channel. The second option will generate a higher conversion ratio if advertisers choose the right influencers whose followers match their target market. Although displaying influencer ads will generate less revenue, it is more engaging than platform ads, which is better for maintaining user traffic. Therefore, it is crucial for a platform to balance these factors by establishing a sustainable business agreement with its influencers and advertisers. In this paper, we develop a two-stage solution for a platform to maximize short-term revenue and long-term user traffic maintenance. In the first stage, we estimate the impact of user traffic generated by displaying influencer ads and characterize the user traffic the platform should allocate to influencers for overall revenue maximization. In the second stage, we devise an optimal (1 - 1/e)-competitive algorithm for ad slot allocation. To complement this analysis, we examine the ratio of the revenue generated by our online algorithm to the optimal offline revenue. Our simulation results show that this ratio is 0.94 on average, which is much higher than (1 - 1/e) and outperforms four baseline algorithms
Decentralized Funding of Public Goods in Blockchain System:Leveraging Expert Advice
Public goods projects, such as open-source technology, are essential for the blockchain ecosystem's growth. However, funding these projects effectively remains a critical issue within the ecosystem. Currently, the funding protocols for blockchain public goods lack professionalism and fail to learn from past experiences. To address this challenge, our research introduces a human oracle protocol involving public goods projects, experts, and funders. In our approach, funders contribute investments to a funding pool, while experts offer investment advice based on their expertise in public goods projects. The oracle's decisions on funding support are influenced by the reputations of the experts. Experts earn or lose reputation based on how well their project implementations align with their advice, with successful investments leading to higher reputations. Our oracle is designed to adapt to changing circumstances, such as experts exiting or entering the decision-making process. We also introduce a regret bound to gauge the oracle's effectiveness. Theoretically, we establish an upper regret bound for both static and dynamic models and demonstrate its closeness to an asymptotically equal lower bound. Empirically, we implement our protocol on a test chain and show that our oracle's investment decisions closely mirror optimal investments in hindsight
Decentralized Funding of Public Goods in Blockchain System:Leveraging Expert Advice
Public goods projects, such as open-source technology, are essential for the blockchain ecosystem's growth. However, funding these projects effectively remains a critical issue within the ecosystem. Currently, the funding protocols for blockchain public goods lack professionalism and fail to learn from past experiences. To address this challenge, our research introduces a human oracle protocol involving public goods projects, experts, and funders. In our approach, funders contribute investments to a funding pool, while experts offer investment advice based on their expertise in public goods projects. The oracle's decisions on funding support are influenced by the reputations of the experts. Experts earn or lose reputation based on how well their project implementations align with their advice, with successful investments leading to higher reputations. Our oracle is designed to adapt to changing circumstances, such as experts exiting or entering the decision-making process. We also introduce a regret bound to gauge the oracle's effectiveness. Theoretically, we establish an upper regret bound for both static and dynamic models and demonstrate its closeness to an asymptotically equal lower bound. Empirically, we implement our protocol on a test chain and show that our oracle's investment decisions closely mirror optimal investments in hindsight
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