Weak Budget Balance Research Articles

Optimizing cross-regional electricity transaction concerning transmission charges: A new market mechanism design

Given the unbalanced distribution of power resources and demands in geography, cross-regional electricity transactions alleviate the conflict through the long-distance power supply. To ensure sustainable, efficient transactions, the market mechanism addressing the unavoidable transmission charges is essential for balancing the interests of all parties. This research designs a mechanism based on the Generalized Vickrey-Clarke-Groves (G-VCG) and threshold value setting considering generators' withholding behavior and power transmission charges. The theoretical analysis proves that this mechanism maximizes social welfare while satisfying individual rationality, incentive compatibility and weak budget balance. It can encourage all participants to report truthful information and motivate more power generation. Numerical studies of the PJM electricity market also demonstrate the effectiveness of this mechanism in the electricity market. The proposed mechanism contributes to new guidance and practical references for achieving fair and efficient transactions in the crossing-regional electricity market and improving the vigor of market participants.

A market mechanism for multiple air traffic resources

We introduce a model that extends the concept of air traffic flow management slot to the concept of time window, allowing to effectively deal with a network of interacting regulations. The model aims at minimising the total cost of delay of a time window allocation to flights and is based on an integer programming problem. It consists in a market-based mechanism between flights and a central authority to trade time windows, which fulfils the properties of individual rationality (every participating airline has a non-negative profit from the mechanism) and weak budget-balance (the mechanism requires no external subsidisation). Equity is assumed to be respected because the First Planned First Served allocation is an endowment guaranteed to all flights and allocated for free. The proposed market mechanism can be implemented in a distributed manner preventing the disclosure of confidential information by airlines, and is based on the Lagrangian relaxation of the integer optimisation problem, solved through the subgradient algorithm. We present some computational experiments conducted to test the model on some real instances of air traffic data.

A Truthful and Reliable Incentive Mechanism for Federated Learning Based on Reputation Mechanism and Reverse Auction

As a distributed machine learning paradigm, federated learning (FL) enables participating clients to share only model gradients instead of local data and achieves the secure sharing of private data. However, the lack of clients’ willingness to participate in FL and the malicious influence of unreliable clients both seriously degrade the performance of FL. The current research on the incentive mechanism of FL lacks the accurate assessment of clients’ truthfulness and reliability, and the incentive mechanism based on untruthful and unreliable clients is unreliable and inefficient. To solve this problem, we propose an incentive mechanism based on the reputation mechanism and reverse auction to achieve a more truthful, more reliable, and more efficient FL. First, we introduce the reputation mechanism to measure clients’ truthfulness and reliability through multiple reputation evaluations and design a reliable client selection scheme. Then the reverse auction is introduced to select the optimal clients that maximize the social surplus while satisfying individual rationality, incentive compatibility, and weak budget balance. Extensive experimental results demonstrate that this incentive mechanism can motivate more clients with high-quality data and high reputations to participate in FL with less cost, which increases the FL tasks’ economic benefit by 31% and improves the accuracy from 0.9356 to 0.9813, and then promote the efficient and stable development of the FL service trading market.

Differential Privacy-Based Double Auction for Data Market in Blockchain-Enhanced Internet of Things

With the rapid development of the Internet of Things (IoT), large amounts of data are collected, which constitute a valuable business resource. Hence, a suitable IoT data market needs to be established, and the provision of safe and effective trading services for multiple buyers and sellers is required. This paper introduces an IoT data market framework supported by blockchain. It focuses on a transaction realization scheme for multiple buyers and sellers. In the scheme, the mechanisms are designed to determine the corresponding data providers and recipients for the buyers and sellers, respectively, and the transaction prices of both parties. When the data market runs, an inference attack will raise bid information leakage issues. We study a transaction scheme that enables differential privacy protection of bids based on an exponential mechanism. This paper theoretically proves the individual rationality, weak budget balance, and truthfulness of the normal transaction scheme and differential privacy-based transaction scheme. This paper also theoretically proves the effectiveness of the differential privacy protection for bids of transaction participants. Furthermore, this paper verifies the performances of the two schemes through digital simulation experiments. From the experiments, we can also prove that these schemes occupy reasonable social welfare and computational overhead.

Efficient multiunit auctions for normal goods

I study multiunit auction design when bidders have private values, multiunit demands, and non‐quasilinear preferences. Without quasilinearity, the Vickrey auction loses its desired incentive and efficiency properties. I give conditions under which we can design a mechanism that retains the Vickrey auction's desirable incentive and efficiency properties: (1) individual rationality, (2) dominant strategy incentive compatibility, and (3) Pareto efficiency. I show that there is a mechanism that retains the desired properties of the Vickrey auction if there are two bidders who have single‐dimensional types. I also present an impossibility theorem that shows that there is no mechanism that satisfies Vickrey's desired properties and weak budget balance when bidders have multidimensional types.

The Segmentation-Thickness Tradeoff in Online Marketplaces

A core tension in the operations of online marketplaces is between segmentation (wherein platforms can increase revenue by segmenting the market into ever smaller sub-markets) and thickness (wherein the size of the sub-market affects the utility experienced by an agent). An important example of this is in dynamic online marketplaces, where buyers and sellers, in addition to preferences for different matches, also have finite patience (or deadlines) for being matched.We formalize this trade-off via a novel optimization problem that we term as 'Two-sided Facility Location': we consider a market wherein agents arrive at nodes embedded in an underlying metric space, where the distance between a buyer and seller captures the quality of the corresponding match. The platform posts prices and wages at the nodes, and opens a set of virtual clearinghouses where agents are routed for matching. To ensure high match-quality, the platform imposes a distance constraint between an agent and its clearinghouse; to ensure thickness, the platform requires the flow to any clearinghouse be at least a pre-specified lower bound. Subject to these constraints, the goal of the platform is to maximize the social surplus subject to weak budget balance, i.e., profit being non-negative. Our work characterizes the complexity of this problem by providing both hardness results as well as algorithms for this setting; in particular, we present an algorithm that for any constant ε > 0 yields a (1 + ε) approximation for the gains from trade, while relaxing the match quality (i.e., maximum distance of any match) by a constant factor.

Towards Differential Privacy-Based Online Double Auction for Smart Grid

In this paper, to address the issue of demand response in the smart grid with island MicroGrids (MGs), we introduce an effective and secure auction market that allows electric vehicles (EVs) having surplus energy to act as sellers, and the EVs having insufficient energy in the island MGs to act as buyers. There are two primary challenges in designing an effective auction market in the smart grid. First, the auction market scheme shall be online, allowing buyers and sellers to enter the market at any time, and satisfy several critical economic properties (individual rationality, incentive compatibility, and so on.). Second, the sensitive information of participants shall be protected in the auction process. To address these challenges, we present a novel privacy-preserving online double auction scheme based on differential privacy. In our auction market, the MicroGrid Center Controller (MGCC) acts as the auctioneer, aiming at solving the social welfare maximization problem to match buyers and sellers. The principle of differential privacy is leveraged to protect the privacy of EVs’ sensitive bidding information. Via theoretical analysis, we demonstrate that our designed auction scheme satisfies both economic and privacy-preserving properties, including individual rationality, incentive compatibility, weak budget balance, and $\varepsilon $ -differential privacy. We conduct an extensive performance evaluation to measure the effectiveness of our proposed scheme. Our experimental results show that the proposed auction scheme can not only ensure the privacy of participants but also effectively facilitates demand response in the smart grid, with respect to social welfare, satisfaction ratio, social efficiency, and computational overhead.

Randomized Wagering Mechanisms

Wagering mechanisms are one-shot betting mechanisms that elicit agents’ predictions of an event. For deterministic wagering mechanisms, an existing impossibility result has shown incompatibility of some desirable theoretical properties. In particular, Pareto optimality (no profitable side bet before allocation) can not be achieved together with weak incentive compatibility, weak budget balance and individual rationality. In this paper, we expand the design space of wagering mechanisms to allow randomization and ask whether there are randomized wagering mechanisms that can achieve all previously considered desirable properties, including Pareto optimality. We answer this question positively with two classes of randomized wagering mechanisms: i) one simple randomized lottery-type implementation of existing deterministic wagering mechanisms, and ii) another family of randomized wagering mechanisms, named surrogate wagering mechanisms, which are robust to noisy ground truth. Surrogate wagering mechanisms are inspired by an idea of learning with noisy labels (Natarajan et al. 2013) as well as a recent extension of this idea to the information elicitation without verification setting (Liu and Chen 2018). We show that a broad set of randomized wagering mechanisms satisfy all desirable theoretical properties.

The Segmentation-Thickness Tradeoff in Online Marketplaces

A core tension in the operations of online marketplaces is between segmentation (wherein platforms can increase revenue by segmenting the market into ever smaller sub-markets) and thickness (wherein the size of the sub-market affects the utility experienced by an agent). An important example of this is in dynamic online marketplaces, where buyers and sellers, in addition to preferences for different matches, also have finite patience (or deadlines) for being matched. We formalize this trade-off via a novel optimization problem that we term as 'Two-sided Facility Location': we consider a market wherein agents arrive at nodes embedded in an underlying metric space, where the distance between a buyer and seller captures the quality of the corresponding match. The platform posts prices and wages at the nodes, and opens a set of virtual clearinghouses where agents are routed for matching. To ensure high match-quality, the platform imposes a distance constraint between an agent and its clearinghouse; to ensure thickness, the platform requires the flow to any clearinghouse be at least a pre-specified lower bound. Subject to these constraints, the goal of the platform is to maximize the social surplus subject to weak budget balance, i.e., profit being non-negative. Our work characterizes the complexity of this problem by providing both hardness results as well as algorithms for this setting; in particular, we present an algorithm that for any constant ε > 0 yields a (1 + ε ) approximation for the gains from trade, while relaxing the match quality (i.e., maximum distance of any match) by a constant factor.

A Sharing Platform for Multi-Tenant PONs

In this paper, we address the sharing incentive issue in multi-tenant passive optical networks (PONs). We propose an economic-robust and efficient sharing platform for new emerging multi-tenant PON networks. This platform is capable of accommodating a diverse range of service providers and enhancing the network utilization. We propose a sharing platform that provides sharing incentives for the incumbent network operators through monetization of inter-operator network sharing. Meanwhile, the platform allows the incumbent operators to operate a virtual instance of the bandwidth scheduling algorithm that enables them to meet their quality of service and latency requirements. Therefore, the proposed sharing platform grants a high degree of control to the operators co-operating the same network while, thanks to the higher resource efficiency, reduces the initial investment. We first model the multi-tenant PON as a market and define the roles of the virtual network operators and the infrastructure provider along with their utility functions. We propose a double auction mechanism to facilitate the trading of excess resources. The proposed double auction satisfies the crucial economic properties of a market while it achieves more efficient resource allocation among the market players. We have theoretically proven the economic robustness of the mechanism including incentive compatibility, individual rationality, and weak budget balance. Through extensive market simulations, we confirmed that the proposed mechanism achieves superior allocative efficiency compared to a reference baseline mechanism.

To Wait or To Pay: A Game Theoretic Mechanism for Low-Cost M2M and Mission-Critical M2M

When it comes to machine-to-machine (M2M) communications in advanced cellular networks, the resource allocation scheme should be re-examined to satisfy both low-cost M2M and mission-critical M2M. Because most M2M applications are uplink-dominated, we propose a mixed waiting-time auction and price-based dedicated uplink resource allocation framework for the low-cost and mission-critical M2M. The prioritized framework guarantees resources for traditional human-to-human (H2H) communications while meeting the needs of low-cost and mission-critical M2M devices on the basis of either time bids or direct price. In addition, the scheme ensures the existence and uniqueness of the Bayesian Nash equilibrium and the interregional and waiting-time-based truth-telling properties. This indirect mechanism holds with Bayesian–Nash incentive compatibility, interim efficiency, interim individual rationality, and weak budget balance. The results show that low-cost M2M devices with lower energy awareness are more willing to participate in the waiting-time auction, while mission-critical M2M with higher energy awareness turn to directly pay for guaranteed access. The delay in connected mode and the optimal price vary according to the M2M/H2H traffic loads and resource pool partitions. This paper contributes insights that with proper mechanism design, low-cost M2M and mission-critical M2M can be served together, while the operator is financially compensated.

Efficient Multi-Unit Auctions for Normal Goods

I study efficient multi-unit auction design when bidders have private values, multi-unit demands, and non-quasilinear preferences. Without quasilinearity, the Vickrey auction loses its desired incentive and efficiency properties. Instead of assuming that bidders have quasilinear preferences, I assume that bidders have positive wealth effects. This nests cases where bidders are risk averse, face financial constraints, or have budgets.With two bidders, I show that there is a mechanism that retains the desirable properties of the Vickrey auction if and only if bidders have single dimensional types. If bidders have multi-dimensional types, there is no mechanism that satisfies (1) individual rationality, (2) dominant strategy incentive compatibility, (3) ex-post Pareto efficiency, and (4) weak budget balance. When there are more than two bidders, I show that there is no mechanism with desirable incentive and efficiency properties, even if bidders have single dimensional types.

Mechanism Design for Federated Sponsored Search Auctions

Recently there is an increase in smaller, domain-specific search engines that scour the deep web finding information that general-purpose engines are unable to discover. These search engines play a crucial role in the new generation of search paradigms where federated search engines (FSEs) integrate search results from heterogeneous sources. In this paper we pose, for the first time, the problem to design a revenue mechanism that ensures profits both to individual search engines and FSEs as a mechanism design problem. To this end, we extend the sponsored search auction models and we discuss possibility and impossibility results on the implementation of an incentive compatible mechanism. Specifically, we develop an execution-contingent VCG (where payments depend on the observed click behavior) that satisfies both individual rationality and weak budget balance in expectation.

Redistribution Mechanisms for Assignment of Heterogeneous Objects

There are p heterogeneous objects to be assigned to n competing agents (n > p) each with unit demand. It is required to design a Groves mechanism for this assignment problem satisfying weak budget balance, individual rationality, and minimizing the budget imbalance. This calls for designing an appropriate rebate function. When the objects are identical, this problem has been solved which we refer as WCO mechanism. We measure the performance of such mechanisms by the redistribution index. We first prove an impossibility theorem which rules out linear rebate functions with non-zero redistribution index in heterogeneous object assignment. Motivated by this theorem, we explore two approaches to get around this impossibility. In the first approach, we show that linear rebate functions with non-zero redistribution index are possible when the valuations for the objects have a certain type of relationship and we design a mechanism with linear rebate function that is worst case optimal. In the second approach, we show that rebate functions with non-zero efficiency are possible if linearity is relaxed. We extend the rebate functions of the WCO mechanism to heterogeneous objects assignment and conjecture them to be worst case optimal.

A cooperative approach to collusion in auctions

The elegant Vickrey Clarke Groves (VCG) mechanism is well-known for the strong properties it offers: dominant truth-revealing strategies, efficiency and weak budget-balance in quite general settings. Despite this, it suffers from several drawbacks, prominently susceptibility to collusion. By jointly setting their bids, colluders may increase their utility by achieving lower prices for their items. The colluders can use monetary transfers to share this utility, but they must reach an agreement regarding their actions. We analyze the agreements that are likely to arise through a cooperative game theoretic approach, transforming the auction setting into a cooperative game. We examine both the setting of a multi-unit auction as well as path procurement auctions.

Designing Fast Converging Cost Sharing Methods for Multicast Transmissions

We study a multicast game in non-cooperative directed networks in which a source sends the same message or service to a set of r receiving users and the cost of the used links is divided among the receivers according to a given cost sharing method. By following the approach recently proposed by Chen et al. (Proceedings of the 19th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 854–863, 2008), we analyze the performances of a family of methods satisfying certain desiderata, namely, weak and strong budget-balance, fairness and separability. We show that any fair method may require an arbitrary number of selfish moves in order to converge to a pure Nash equilibrium, hence we focus on the solutions obtained after one round of selfish moves. We evaluate their quality according to two global social functions: the overall cost of the solution and the maximum shared cost of users. The only method satisfying all the properties is the well-known Shapley value for which we show an approximation ratio of the solutions reached after a one round walk equal to Θ(r 2). We then prove that relaxing the strong budget balance and separability properties (we call feasible any method satisfying weak budget balance and fairness) leads to improved performances since we determine a feasible method achieving an approximation ratio of the solutions reached after a one round walk equal to O(r). This bound is asymptotically optimal since we also show that any method satisfying weak budget balance cannot achieve an approximation ratio of the solutions reached after a one round walk smaller than r. Finally, we prove the NP-hardness of computing the sequence of moves leading to the best possible global performance and extend most of the results to undirected networks.