Fast Core Pricing for Rich Advertising Auctions

Standard ad auction formats do not immediately extend to settings where multiple size configurations and layouts are available to advertisers. In these settings, the sale of web advertising space increasingly resembles a combinatorial auction with complementarities, where truthful auctions such as the Vickrey–Clarke–Groves (VCG) auction can yield unacceptably low revenue. In “Fast Core Pricing for Rich Advertising Auctions,” Niazadeh, Hartline, Immorlica, Khani, and Lucier study and suggest core-selecting auctions, which boost revenue by setting payments so that no group of agents, including the auctioneer, can jointly improve their utilities by switching to a different outcome. Their main result is a combinatorial algorithm that finds an approximate bidder-optimal core point with an almost linear number of calls to the welfare-maximization oracle. This algorithm is faster than previously proposed heuristics in the literature and has theoretical guarantees. By accompanying the theoretical study with an experimental study based on Microsoft Bing Ad Auction data, the authors conclude that core pricing is implementable even for very time-sensitive practical use cases such as real-time online advertising and can yield more revenue than the VCG or generalized second price auction.

Modeling the Impacts of Driver Income Distributions on Online Ride-Hailing Services

The online ride-hailing taxi brings new vitality into the traditional taxi market, as well as new issues and challenges. The pricing and profit distribution of online ride-hailing services is one of the major concerns. This study focuses on the pricing and income distribution in the online ride-hailing system. Queuing system model and birth and death process theory are introduced to describe the driver’s flow process in the network. The social welfare maximization model and the platform profit maximization model are constructed based on the dynamic pricing mechanism, from the government’s and platform’s standpoint, respectively. Through numerical experiments, this paper analyzes the income distribution of drivers under different settings and the influence of different factors (average travel time, psychologically expected price of drivers and passengers, and probability of driver leaving the system) on the proportion of income distribution. The results show that the drivers’ income distribution proportion is higher in the pursuit of social welfare maximization than that in the pursuit of platform profit maximization, and in different benefit pursuit models, various factors have a certain influence on the driver’s income distribution proportion. The proposed method and conclusion in this study can be considered as references for online ride-hailing market supervision and policy-making.

Calculation Method for Electricity Price and Rebate Level in Demand Response Programs

Demand response programs (DRs) can be implemented with less investment costs than those in power plants or facilities and enable us to control power demand. Therefore, they are highly expected as an efficient option for power supply–demand-balancing operations. On the other hand, DRs bring new difficulties on how to evaluate the cooperation of consumers and to decide electricity prices or rebate levels with reflecting its results. This paper presents a theoretical approach that calculates electricity prices and rebate levels in DRs based on the framework of social welfare maximization. In the authors’ proposal, the DR-originated changes in the utility functions of power suppliers and consumers are used to set a guide for DR requests. Moreover, optimal electricity prices and rebate levels are defined from the standpoint of minimal burden in DRs. Through numerical simulations and discussion on their results, the validity of the authors’ proposal is verified.

Strategyproof Mechanisms for Additively Separable and Fractional Hedonic Games

Additively separable hedonic games and fractional hedonic games have received considerable attention in the literature. They are coalition formation games among selfish agents based on their mutual preferences. Most of the work in the literature characterizes the existence and structure of stable outcomes (i.e., partitions into coalitions) assuming that preferences are given. However, there is little discussion of this assumption. In fact, agents receive different utilities if they belong to different coalitions, and thus it is natural for them to declare their preferences strategically in order to maximize their benefit. In this paper we consider strategyproof mechanisms for additively separable hedonic games and fractional hedonic games, that is, partitioning methods without payments such that utility maximizing agents have no incentive to lie about their true preferences. We focus on social welfare maximization and provide several lower and upper bounds on the performance achievable by strategyproof mechanisms for general and specific additive functions. In most of the cases we provide tight or asymptotically tight results. All our mechanisms are simple and can be run in polynomial time. Moreover, all the lower bounds are unconditional, that is, they do not rely on any computational complexity assumptions.