Strategy-proof Research Articles

Partial Truthfulness in Minimal Peer Prediction Mechanisms With Limited Knowledge

We study minimal single-task peer prediction mechanisms that have limited knowledge about agents' beliefs. Without knowing what agents' beliefs are or eliciting additional information, it is not possible to design a truthful mechanism in a Bayesian-Nash sense. We go beyond truthfulness and explore equilibrium strategy profiles that are only partially truthful. Using the results from the multi-armed bandit literature, we give a characterization of how inefficient these equilibria are comparing to truthful reporting. We measure the inefficiency of such strategies by counting the number of dishonest reports that any minimal knowledge-bounded mechanism must have. We show that the order of this number is θ(log n), where n is the number of agents, and we provide a peer prediction mechanism that achieves this bound in expectation.

MUDA: A Truthful Multi-Unit Double-Auction Mechanism

In a seminal paper, McAfee (1992) presented a truthful mechanism for double auctions, attaining asymptotically-optimal gain-from-trade without any prior information on the valuations of the traders. McAfee's mechanism handles single-parametric agents, allowing each seller to sell a single unit and each buyer to buy a single unit. This paper presents a double-auction mechanism that handles multi-parametric agents and allows multiple units per trader, as long as the valuation functions of all traders have decreasing marginal returns. The mechanism is prior-free, ex-post individually-rational, dominant-strategy truthful and strongly-budget-balanced. Its gain-from-trade approaches the optimum when the market size is sufficiently large.

Average-Case Approximation Ratio of Scheduling Without Payments

Apart from the principles and methodologies inherited from Economics and Game Theory, the studies in Algorithmic Mechanism Design typically employ the worst-case analysis and approximation schemes of Theoretical Computer Science. For instance, the approximation ratio, which is the canonical measure of evaluating how well an incentive-compatible mechanism approximately optimizes the objective, is defined in the worst-case sense. It compares the performance of the optimal mechanism against the performance of a truthful mechanism, for all possible inputs. In this paper, we take the average-case analysis approach, and tackle one of the primary motivating problems in Algorithmic Mechanism Design -- the scheduling problem [Nisan and Ronen 1999]. One version of this problem which includes a verification component is studied by [Koutsoupias 2014]. It was shown that the problem has a tight approximation ratio bound of (n+1)/2 for the single-task setting, where n is the number of machines. We show, however, when the costs of the machines to executing the task follow any independent and identical distribution, the average-case approximation ratio of the mechanism given in [Koutsoupias 2014] is upper bounded by a constant. This positive result asymptotically separates the average-case ratio from the worst-case ratio, and indicates that the optimal mechanism for the problem actually works well on average, although in the worst-case the expected cost of the mechanism is Theta(n) times that of the optimal cost.

Auction-Based Cloud Service Pricing and Penalty with Availability on Demand

Availability is one of the main concerns of cloud users, and cloud providers always try to provide higher availability to improve user satisfaction. However, higher availability results in higher provider costs and lower social welfare. In this paper, taking into account both the users’ valuation and desired availability, we design resource allocation, pricing and penalty mechanisms with availability on demand. Considering two scenarios: public availability in which the desired availabilities of all users are public information, and private availability in which the desired availabilities are private information of users, and, analyzing the possible behaviours of users, we design a truthful deterministic mechanism with 2-approximation in public availability scenario and a universal truthful mechanism with 1 1 + γ approximation in private availability scenario, where γ is the backup ratio of resources with the highest availability. The experiment results show that our mechanisms significantly improve the social welfare compared to the mechanism without considering availability demand of users.

The disaster emergency unit scheduling problem to control wildfires

In this paper, we study optimization and mechanism design relative to the disaster emergency unit (DEU) scheduling problem to control wildfires. We consider a single DEU and a set of forestry companies, in a scenario where the resources are constrained and an emergency induces damage to the nearby towns. Each forestry company has information about the forest density, which in addition to the feedrate of wildfires determines its marginal waiting cost. In practice, it generates a waiting cost for each forestry company according to its position in the sequence and the working time for the DEU. The goal is to determine a schedule and the working times of the DEU, so as to minimize the sum of the total damage and the total waiting cost of the forestry companies subject to constraints on the damage and use of the working time of the DEU. We show that the centralized problem can be solved by Karush-Kuhn-Tucker (KKT) conditions and design an easy-to-implement truthful mechanism for the decentralized problem. This design charges the damage to the forestry companies based on the optimal solution properties obtained from the centralized problem, with overcharging bounded by a constant. A numerical example to illustrate the problem and the usefulness of our contributions is described. Finally, we extend our results to similar problems for sequential use of a resource, in which strictly increasing convex isoelastic damage functions are considered.

School Choice: An analysis of the strategic behaviour of students and schools in the student high-school allocation problem

We investigate how the one-to-one deferred acceptance algorithm can be used to solve the many-to-one school choice problem. In the first part of the paper we derive the important properties of the one-to-one Deferred Acceptance algorithm. In the second part of the paper, we consider the Deferred Acceptance algorithm for solving the many-to-one school choice problem. Using insights from the first two parts of the paper we show that the Deferred Acceptance algorithm is strategy proof for students, and manipulable by schools under specific conditions.

Truthfulness in advertising? Approximation mechanisms for knapsack bidders

Quasilinear utility functions are a standard assumption in auction theory allowing for truthful and welfare-maximizing auction mechanisms. However, the literature on advertising markets suggests that the utility model of bidders rather resembles a knapsack problem, where advertisers try to maximize the sum of item values subject to a budget for a marketing campaign. Non-quasilinear environments rarely allow for truthful mechanisms. Nonetheless, some characteristics of the market environment might allow for positive results. In particular, markets are large and bidders typically consider prices as exogenous. We introduce a model of advertising markets, and study whether truthful and prior-free approximation mechanisms with good approximation ratios of the maximal welfare are possible. We analyze the offline mechanism design problem and find a truthful and randomized mechanism with an approximation ratio of only 4. This mechanism draws on a fractional deferred acceptance algorithm and randomized rounding, and it illustrates how the relax-and-round principle can be implemented in this non-quasilinear environment. The article highlights the types of assumptions necessary for truthful mechanisms with good approximation ratios in an important class of non-quasilinear utility functions.

Cross-Monotonic in Cost Sharing Mechanisms Using Ns2

A cost-sharing plan is an arrangement of guidelines characterizing how to distribute the cost among adjusted clients. A cost-sharing strategy is check monotonic in case it satisfies the property that everyone is in a flawless circumstance when the course of action of individuals who get the affiliation creates. CM is a property that passes on a customer's part ought to be more humble when balanced in a more imperative set. CM is an key techniques to achieve the group strategy proofness. By using the multicast plans that objective the enhanced directing, cross-monotonic cost sharing, and stabilize budget. Unsurprising degree cost recuperations is conceivable, and also apply a primal-twofold diagram to in the meantime create an organizing strategy and a cost-sharing course of action, and demonstrate that the subsequent instrument is collect framework proof and ensures off base cost recuperation against a flawless coordinating game plan.

Setting lower bounds on truthfulness

This paper presents inapproximability results for paradigmatic multi-dimensional truthful mechanism design problems.We first show a lower bound of 2−1n for the scheduling problem with n unrelated machines (formulated as a mechanism design problem in the seminal paper of Nisan and Ronen on Algorithmic Mechanism Design). Our lower bound applies to universally-truthful randomized mechanisms, regardless of any computational assumptions on the running time of these mechanisms. Moreover, it holds even for the wider class of truthfulness-in-expectation mechanisms.We then turn to Bayesian settings and show a lower bound of 1.2 for Bayesian Incentive-Compatible (BIC) mechanisms. No lower bounds for truthful mechanisms in multi-dimensional settings which incorporate randomness were previously known.Next, we define the workload-minimization problem in networks. We prove lower bounds for the inter-domain routing setting presented by Feigenbaum, Papadimitriou, Sami, and Shenker.Finally, we prove lower bounds for Max–Min fairness, Min–Max fairness, and envy minimization.

A bounded-risk mechanism for the kidney exchange game

In this paper, we introduce and study the notion of low risk mechanisms. Intuitively, we say a mechanism is a low risk mechanism if the randomization of the mechanism does not affect the utility of agents by a lot. Specifically, we desire to design mechanisms in which the variances of the utility of agents are small. Inspired by this work, later, Procaccia et al. (0000) study the approximation–variance tradeoffs in mechanism design.In particular, here we present a low risk mechanism for the pairwise kidney exchange game. This game naturally appears in situations that some service providers benefit from pairwise allocations on a network, such as the kidney exchanges between hospitals. Ashlagi et al. (2013) present a 2-approximation randomized truthful mechanism for this problem. This is the best-known result in this setting with multiple players. However, we note that the variance of the utility of an agent in this mechanism may be as large as Ω(n2), where n is the number of vertices. Indeed, this is not desirable in a real application. In this paper, we resolve this issue by providing a 2-approximation randomized truthful mechanism in which the variance of the utility of each agent is at most 2+ϵ.As a side result, we apply our technique to design a deterministic mechanism such that, if an agent deviates from the mechanism, she does not gain more than 2⌈log2m⌉, where m is the number of players.

Implicaciones del reconocimiento de las víctimas militares en el marco de la justicia transicional colombiana

The agreement between the State and members of the FARC-EP resulted in the legislative act 01 of July 31, 2012 as a legal area that provides a constitutional scope and sets guidelines for the post-conflict. The objective of this article is to analyze the recognition of military victims in the framework of the Transitional Justice. In order to do so, the legal rules regarding victims and restorative justice will be identified. In addition, the mechanisms of truth, justice and reparation will be introduced. Finally, it will be established whether it is possible to include the military as victims. Through observation and documentary analysis, this work will be clarified based on Judgment C-370 (2006).

Inapproximability of Truthful Mechanisms via Generalizations of the Vapnik--Chervonenkis Dimension

Algorithmic mechanism design (AMD) studies the delicate interplay between computational efficiency, truthfulness, and optimality. We focus on AMD's paradigmatic problem: combinatorial auctions. We present a new generalization of the Vapnik--Chervonenkis (VC) dimension to multivalued collections of functions, which encompasses the classical VC dimension, Natarajan dimension, and Steele dimension. We present a corresponding generalization of the Sauer--Shelah lemma and harness this VC machinery to establish inapproximability results for deterministic truthful mechanisms. Our results essentially unify all inapproximability results for deterministic truthful mechanisms for combinatorial auctions to date and establish new separation gaps between truthful and nontruthful algorithms.

A Reverse Auction Framework for Hybrid Access in Femtocell Network

In the two-tier macro-femto heterogeneous network, hybrid access is regarded as the most ideal access control approach to mitigating macro-femto cross-tier interference and enhancing overall network performance. However, the implementation of hybrid access is hindered by a lack of incentive market mechanism to motivate private femtocell owners to offer access permissions to macro users. In this paper, we propose a reverse auction framework for access permission transaction between a macrocell operator and multiple femtocell owners to promote hybrid access. Our goal is to maximize social welfare while guaranteeing the truthfulness of the auction. Since the coverage of multiple femtocells may overlap, we partition each cell to adjust the granularity of access permission availability. We first propose a Vickery-Clarke-Grove (VCG)-based mechanism, which costs the least among all auction mechanisms that produce maximum social welfare. As the VCG mechanism is too time-consuming, we propose two alternative truthful mechanisms, namely, generalized second-price and suboptimal mechanism. We further extend the auction framework to the scenario where femtocell owners have heterogeneous valuations for access permissions in different locations.

Truthful Mechanisms for Medical Surplus Product Allocation

We analyze a resource allocation problem faced by Medical Surplus Recovery Organizations (MSROs) that recover medical surplus products to fulfill the needs of under-served healthcare facilities in developing countries. Due to the uncertain, uncontrollable supply and limited information about recipient needs, delivering the right product to the right recipient in MSRO supply chains is particularly challenging. The objective of this study is to identify strategies to improve MSROs' value provision capability. In particular, we propose a mechanism design approach, and determine which recipient to serve at each shipping opportunity based on recipients' reported preference rankings of different products. We find that when MSRO inventory information is shared with recipients, the only truthful mechanism is random selection among recipients, which defeats the purpose of eliciting information. Consequently, we propose two operational strategies to improve MSROs' value provision: i) not sharing MSRO inventory information with recipients; and ii) withholding information regarding other recipients. We characterize the set of truthful mechanisms under each setting, and show that eliminating inventory and competitor information provision both improve MSROs' value provision. Further, we investigate the value of cardinal mechanisms where recipients report their valuations. We show that in our setting, eliciting valuations has no value added beyond eliciting rankings under a wide class of implementable mechanisms. Finally, we present a calibrated numerical study based on historical data from a partner MSRO, and show that a strategy consisting of a ranking-based mechanism in conjunction with eliminating inventory and competitor information can significantly improve MSROs' value provision.

Balancing Efficiency and Equality in Vehicle Licenses Allocation

Due to traffic and air quality concerns in urban cities, many big cities have begun to adopt the vehicle licenses quantitative control policies. In these cities, a limited number of vehicle licenses are allocated among a very large number of potential car buyers every one or two months. The current allocation mechanisms differ from city to city. Several mechanisms have been developed and implemented in reality, such as auction, lottery, lottery with reserved price, and the simultaneous auction and lottery. In this work, we target to design the optimal mechanism to balance efficiency and equality in practice. We first propose a unified two-group mechanism framework that either includes or outperforms all the existing mechanisms. Besides, the unified framework also leads to easy implementation in reality due to its truthfulness and simple structure. Under this framework, assuming the players' private values are drawn independently from a common distribution, we prove the optimal mechanism is always sequential auction and lottery. Besides, the optimal allocation rule depends only on the total number of players and the total number of licenses for all commonly used distributions. We then extend the two-group framework to a general multi-group framework. The experimental results show us the optimal two-group mechanism is the best choice in practice. Thus, our work provides an effective tool for social planner to design truthful mechanisms to maximize the social efficiency under any equality level. We also discuss possible applications of our result to resource allocation in other settings.

Incentive compatible mechanisms for scheduling two-parameter job agents on parallel identical machines to minimize the weighted number of late jobs

We consider the problem of designing polynomial time truthful mechanisms for machine scheduling problems with parallel identical machines where some of the jobs’ characteristics are private information of their respective owners and a central decision maker is in charge of computing the schedule. We study a two-parameter setting, where weights and due dates are private information while processing times are publicly known. The global objective is to minimize the sum of the weights of those jobs that are completed after their due dates. We derive a set of properties that is equivalent to the well known condition of cycle monotonicity, which is a general condition for truthful mechanisms in non-convex valuation function domains. Our results utilize knowledge about the underlying scheduling problem, so that the resulting properties are easier to implement and verify than the general condition of cycle monotonicity. We illustrate the use of our results by analyzing an example algorithm that has recently been proposed in the literature for the case of one machine.

A preference-based, multi-unit auction for pricing and capacity allocation

We study a pricing and allocation problem of a seller of multiple units of a homogeneous item, and present a semi-market mechanism in the form of an iterative ascending-bid auction. The auction elicits buyers’ preferences over a set of options offered by the seller, and processes them with a random-priority assignment scheme to address buyers’ “fairness” expectations. The auction’s termination criterion is derived from a mixed-integer programming formulation of the preference-based capacity allocation problem. We show that the random priority- and preference-based assignment policy is a universally truthful mechanism which can also achieve a Pareto-efficient Nash equilibrium. Computational results demonstrate that the auction mechanism can extract a substantial portion of the centralized system’s profit, indicating its effectiveness for a seller who needs to operate under the “fairness” constraint.

Multi-layer Resources Fair Allocation in Big Data with Heterogeneous Demands

The resource fair allocation is very important for the most systems. This paper focuses on the resource allocation in a big data system. This system has the characteristics of multiple resources owning and heterogeneous resource demanding. Firstly, a basic allocation scheme is proposed. Some allocation properties are introduced including sharing incentive, strategy proofness, envy-freeness, Pareto optimality, resource efficiency, single-layer multi-resource fairness, multi-layer single-resource fairness. Secondly, a single layer dominant and max–min fair allocation (SDMMF) is proposed. The basic idea is that the resources are firstly allocated using dominant resource fairness scheme. If some resources are not fully allocated, using max–min fairness scheme allocate the surplus ones. The SDMMF is theoretically proved satisfying all the hierarchical resource allocation properties. Thirdly, a Multi-layer resource allocation scheme, namely multi-layer dominant and max–min fair allocation (MDMMF) is proposed. The basic idea is that a multi-layer resource demand hierarchy can be divided into a series of single layer resource demand. So a resource demand vector can be built in the hierarchy from down to top. Then, the resource allocation of first layer is calculated using SDMMF allocation. The result of such allocation is further allocating in the third layer. Recursively do the MDMMF allocation until the bottom layer nodes obtain resource allocation shares. Lastly, The MDMMF is also theoretically proved satisfying all the hierarchical resource allocation properties.

A Priority-Aware Truthful Mechanism for Supporting Multi-Class Delay-Sensitive Medical Packet Transmissions in E-Health Networks

In this paper, the design of priority-aware truthful mechanisms for multi-class delay-sensitive medical packet transmissions in electronic health (e-health) networks is studied. Unlike most of existing works, we focus on beyond wireless body area network (beyond-WBAN) communications, and consider the absolutely prioritized transmission scheduling as the realization of medical-grade quality of service, i.e., more critical medical packets have to always be transmitted prior to the ones with less emergency. In our model, medical packets arrive randomly at each WBAN-gateway (which ordinarily stands for one patient), and their beyond-WBAN transmission requests are reported to the network regulator (i.e., the base station) with different packet priorities which reflect their medical importance. The base station then dynamically manages the beyond-WBAN transmission service by formulating a multi-class multi-server priority queueing system. Taking into account the potential strategic behaviors from smart gateways, we design a truthful mechanism which can guarantee that all gateways will honestly report the actual priorities of their medical packets, while at the same time incentivize the base station to provide channel usages for e-health services. Theoretical analyses and simulation results examine the desired properties of our proposed mechanism, and demonstrate its feasibility and superiority compared to counterparts.

TRUDA: a truthful auction mechanism with non‐uniform payment for heterogeneous spectrum access in wireless networks

Auction is a highly effective trading form for distributing resource among buyers in a market at competitive prices, and has been applied to many domains, e.g. spectrum allocation in wireless networks and the virtual machine allocation in cloud computing. Most of existing auction mechanisms based on McAfee double auction calculate the uniform clearing prices for winning buyers no matter what channel they acquired, which does not reflect the differences of buyers' personalised preferences for heterogeneous spectrums. Hence, in this paper, we propose a truthful double auction scheme, named TRUDA, which incorporates the marginal effect of buyer–seller pair into auction mechanism design and considers the case in which buyers are mutually exclusive. We show analytically that this auction mechanism guarantees the economic-robustness of the auction and has polynomial time complexity.