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A data broker sells market segmentations created by consumer data to a producer with private production cost who sells a product to a unit mass of consumers with heterogeneous values. In this setting, I completely characterize the revenue-maximizing mechanisms for the data broker. In particular, every optimal mechanism induces quasi-perfect price discrimination. That is, the data broker sells the producer a market segmentation described by a cost-dependent cutoff, such that all the consumers with values above the cutoff end up buying and paying their values while the rest of consumers do not buy. The characterization of optimal mechanisms leads to additional economically relevant implications. I show that the induced market outcomes remain unchanged even if the data broker becomes more active in the product market by gaining the ability to contract on prices; or by becoming an exclusive retailer, who purchases both the product and the exclusive right to sell the product from the producer, and then sells to the consumers directly. Moreover, vertical integration between the data broker and the producer increases total surplus while leaving the consumer surplus unchanged, since consumer surplus is zero under any optimal mechanism for the data broker.

This study considers a mechanism design problem in which service slots are allocated over time to buyers arriving at different periods. Some buyers can accept delayed service, whereas others cannot. Buyers have a multidimensional type representing their valuation and patience level. The seller fully commits to a mechanism with or without complete contingent contracts. We show that a deterministic mechanism is periodic ex-post incentive compatible if and only if the allocation rule is monotone in valuation and the "price equivalence" holds. In cases both with and without contingent contracts, the dynamic pivot mechanism with a reserve price maximizes the seller's expected revenue under a regularity condition, which is characterized by the linearity of the virtual valuation function. When the regularity holds, the fully optimal mechanism with contingent contracts is multi-contracting-proof, whereas the constrained optimal mechanism without contingent contracts is not.

We study the optimal design of a queueing system when agents' arrival and servicing are governed by a general Markov process. The designer of the system chooses entry and exit rules for agents, their service priority---or queueing discipline---as well as their information, while ensuring that agents have incentives to follow the designer's recommendations not only to join the queue but more importantly to stay in the queue. Under a mild condition, the optimal mechanism has a cutoff structure---agents are induced to enter up to a certain queue length and no agents are to exit the queue once they enter the queue; the agents on the queue are served according to a first-come-first-served (FCFS) rule; and they are given no information throughout the process beyond the recommendations they receive from the designer. FCFS is also necessary for optimality in a rich domain. We identify a novel role for queueing disciplines in regulating agents' beliefs, and their dynamic incentives, thus uncovering a hitherto unrecognized virtue of FCFS in this regard.

We study thetruthful facility assignmentproblem, where a set of agents with private most-preferred points on a metric space have to be assigned to facilities that lie on the metric space, under capacity constraints on the facilities. The goal is to produce such an assignment that minimizes the social cost, i.e., the total distance between the most-preferred points of the agents and their corresponding facilities in the assignment, under the constraint of truthfulness, which ensures that agents do not misreport their most-preferred points. We propose aresource augmentation framework, where a truthful mechanism is evaluated by its worst-case performance on an instance with enhanced facility capacities against the optimal mechanism on the same instance with the original capacities. We study a well-known mechanism, Serial Dictatorship, and provide an exact analysis of its performance. Among other results, we prove that Serial Dictatorship has approximation ratio$$g/(g-2)$$g/(g-2)when the capacities are multiplied by any integer$$g \ge 3$$g≥3. Our results suggest that with a limited augmentation of the resources we can achieve exponential improvements on the performance of the mechanism and in particular, the approximation ratio goes to 1 as the augmentation factor becomes large. We complement our results with bounds on the approximation ratio of Random Serial Dictatorship, the randomized version of Serial Dictatorship, when there is no resource augmentation.

Problem definition : This paper studies the optimal design of referral priority programs, in which customers on a waiting list can jump the line by inviting their friends to also join the waiting list. Academic/practical relevance : Recent years have witnessed a growing presence of referral priority programs as a novel customer-acquisition strategy for firms that maintain a waiting list. Different variations of this scheme are seen in practice, raising the question of what should be the optimal referral priority mechanism. Methodology : I build an analytical model that integrates queuing theory into a mechanism design framework in which the objective of the firm is to maximize the system throughput, that is, accelerate customer acquisition as much as possible. Results : My analysis shows that the optimal mechanism has one of the following structures: full priority; partial priority; first in, first out (FIFO); and strategic delay. A full-priority (partial-priority) scheme enables referring customers to get ahead of all (only some) nonreferring ones. A FIFO scheme does not provide any priority-based referral incentive. A strategic-delay scheme grants full priority to referring customers but artificially inflates the delay of nonreferring ones. I show that FIFO is optimal if either the base-market size or the referral cost is large. Otherwise, partial priority is optimal if the base-market size is above a certain threshold; full priority is optimal at the threshold base-market size; strategic delay is optimal if the base-market size is below the threshold. I also find that referrals motivate the firm to maintain a larger capacity and therefore can surprisingly shorten the average delay, even though more customers sign up and strategic delay is sometimes inserted. Managerial implications : My paper provides prescriptive guidance for launching an optimal referral priority program and rationalizes common referral schemes seen in practice.

Designing an incentive compatible auction that maximizes expected revenue is an intricate task. The single-item case was resolved in a seminal piece of work by Myerson in 1981. Even after 30--40 years of intense research, the problem remains unsolved for settings with two or more items. We overview recent research results that show how tools from deep learning are shaping up to become a powerful tool for the automated design of near-optimal auctions auctions. In this approach, an auction is modeled as a multilayer neural network, with optimal auction design framed as a constrained learning problem that can be addressed with standard machine learning pipelines. Through this approach, it is possible to recover to a high degree of accuracy essentially all known analytically derived solutions for multi-item settings and obtain novel mechanisms for settings in which the optimal mechanism is unknown.

We examine the impact of consumer search from the perspective of revenue‐maximizing optimal auction design. In this two‐sided market, advertisers are endowed with privately known qualities, and consumers incur heterogeneous search costs when clicking on advertisements displayed by the search engine. Our general approach eliminates ad hoc restrictions and therefore allows us to uncover the salient economic trade‐offs. We adopt the optimal mechanism design approach to examine this two‐sided private information problem. The optimal mechanism design approach gives rise to the truly revenue‐maximizing scheme, and yields managerial implications that are substantially different from the literature. We show that from the revenue maximization perspective, the search engine necessarily displays the advertisers in the descending order of their qualities. The search engine shall insert unnecessary hassles to induce the revenue‐maximizing search behaviors. Thus, each consumer stops searching pre‐maturely compared to the socially optimal search behavior. In addition, the search engine shall implement probabilistic forced termination upon consumers’ clicking on advertisements, and this probability is higher for advertisers with poorer qualities. When each advertiser has a two‐dimensional type on consumer learning and matching, even if consumers’ search costs become negligible, the allocative inefficiency does not vanish. Moreover, no strategic obfuscation is imposed. When allowing for non‐independent qualities, we show that product variety does not constitute a rationale for advertisers’ favoritism.

We consider the problem faced by a procurement agency that runs a mechanism for constructing an assortment of differentiated products with posted prices, from which heterogeneous consumers buy their most preferred alternative. Procurement mechanisms used by large organizations, including framework agreements (FAs), which are widely used in the public sector, often take this form. When choosing the assortment, the procurement agency must optimize the tradeoff between offering a richer menu of products for consumers and offering less variety, hoping to engage the suppliers in more aggressive price competition. We formulate the problem faced by the procurement agency as a mechanism design problem, and we progressively incorporate more complex and often more realistic implementation constraints, including that the allocations should be decentralized (that is, consumers choose what to buy) and that payments must be implemented through linear pricing (in particular, no up-front payments are allowed). We characterize the optimal buying mechanisms that highlight the importance of restricting the entry of close-substitute products to the assortment as a way to increase price competition without much damage to variety. Motivated by the implementation of the Chilean FAs, which are being used to acquire around US$3 billion in goods and services per year, we leverage our characterization of the optimal mechanism to study the design of first-price-auction-type mechanisms that are commonly used in public settings. Our results shed light on simple ways to improve their performance.

We derive the revenue maximizing allocation of m units among n symmetric agents who have unit demand, and who take costly actions that in uence their values before participating in the mechanism. The allocation problem with costly actions can be represented by a reduced form model where agents have convex, non-expected utility preferences over the interim probability of receiving an object. Both the uniform m+1 price auction and the discriminatory pay-your-bid auction with reserve price constitute symmetric revenue maximizing mechanisms. Contrasting the case with exogenous valuations, the optimal reserve price reacts to both demand and supply. We also identify a condition under which the optimal mechanism is indeed symmetric, and illustrate the structure of the optimal asymmetric mechanism when the condition fails. The main tool in our analysis is an integral inequality, due to Fan and Lorentz (1954), involving majorization, super-modularity and convexity.

We consider a general framework of optimal mechanism design under adverse selection and ambiguity about the type distribution of agents. We prove the existence of optimal mechanisms under minimal assumptions on the contract space and prove that centralized contracting implemented via mechanisms is equivalent to delegated contracting implemented via a contract menu under these assumptions. Our abstract existence results are applied to a series of applications that include models of optimal risk sharing and of optimal portfolio delegation., Literature review has been updated and expanded

The existing literature on optimal auctions focuses on optimizing the expected revenue of the seller, and is appropriate for risk-neutral sellers. In this paper, we identify good mechanisms for risk-averse sellers. As is standard in the economics literature, we model the risk-aversion of a seller by endowing the seller with a monotone concave utility function. We then seek robust mechanisms that are approximately optimal for all sellers, no matter what their levels of risk-aversion are. We have two main results for multi-unit auctions with unit-demand bidders whose valuations are drawn i.i.d. from a regular distribution. First, we identify a posted-price mechanism called the Hedge mechanism, which gives a universal constant factor approximation; we also show for the unlimited supply case that this mechanism is in a sense the best possible. Second, we show that the VCG mechanism gives a universal constant factor approximation when the number of bidders is even only a small multiple of the number of items. Along the way we point out that Myerson's characterization of the optimal mechanisms fails to extend to utility-maximization for risk-averse sellers, and establish interesting properties of regular distributions and monotone hazard rate distributions.

This paper consists of two parts. In the second part, the management mechanisms for implementing complex projects that improve the energy-saving characteristics of products and technologies are further studied. An incentive model for saving energy in the sequence of projects within a multi-project structure is considered, and an optimal mechanism for this model is proposed. This mechanism includes a planning procedure, penalty functions for the nonfulfillment of plans, and an incentive function for project results. The functioning of the system is treated as a game of the Principal and sequentially connected agents who implement projects. The Principal’s strategy is the choice of a specific mechanism. The agents’ strategies are messages, in which they report to the Principal some information about their parameters, and also the choice of the project results. The information about the agents’ parameters is necessary to calculate the plans based on the planning procedure. At the same time, the agents may prefer not to tell the truth, reporting unreliable (distorted) information. It is shown that the optimal mechanism proposed below stimulates the agents to provide reliable information and also to choose the project results coinciding with the plans. The first part of this paper (see [3]) was devoted to the mechanisms of project assessment and resource allocation; the results established therein are used to propose a project management system for implementing complex projects.

We study competitive procurement administered by an agent who is supposed to evaluate bids on both price and quality by a scoring rule designed by the principal. Since the agent is in charge of verifying delivered quality, he has an opportunity to manipulate his evaluation of quality proposals in exchange for a bribe. In the presence of corruption, the optimal mechanism can be implemented by both first-score and second-score auctions in such a way that the scoring rule should deemphasize quality relative to price. We further identify factors that influence equilibrium corruption: (1) more efficient suppliers are willing to pay higher bribe; (2) the probability of corruption is decreasing in competition and increasing in the agent’s manipulation power; (3) compared to the first-score auction, the second-score auction leads to higher equilibrium bribe and thus is more vulnerable to corruption.

Differential privacy can achieve the tradeoff between privacy and utility by using privacy metric and utility metric. However, since privacy metric and utility metric may not be bounded, differential privacy can not provide the bounded tradeoff. Moreover, there is no unified method to indicate the bounded tradeoff of differential privacy in the current work. To this end, we proposed the bounded privacy-utility monotonicity indicating the bounded tradeoff of differential privacy. First, we gave the definition of the bounded tradeoff of differential privacy, and we presented the bounded privacy-utility monotonicity of differential privacy based on computational indistinguishability. Second, we theoretically proved the bounded privacy-utility monotonicity of several differential privacy mechanisms based on the bounded metrics of modulus of characteristic function and normalized entropy, including the Laplace mechanism, discrete Laplace mechanism, Gaussian mechanism, exponential mechanism, optimal mechanism, and quaternary mechanism. We also showed that these mechanisms had the bounded privacy-utility monotonicity in the multivariate case. Third, our numerical results further demonstrated that these several differential privacy mechanisms obtained the bounded privacy-utility monotonicity. Finally, we gave an instance of achieving the bounded tradeoff of differential privacy mechanisms based on the bounded privacy-utility monotonicity under semi-honest model, and we discussed the goal of optimization of the bounded tradeoff of differential privacy based on the bounded privacy-utility monotonicity under semi-honest model. Therefore, the bounded privacy-utility monotonicity can be used to indicate the bounded tradeoff of differential privacy under semi-honest model. Furthermore, the bounded privacy-utility monotonicity plays an important role of optimizing the bounded tradeoff of differential privacy under semi-honest model.

Motivated by applications to geometric inequalities, Gozlan, Roberto, Samson, and Tetali (J. Funct. Anal. 273 (2017) 3327–3405) introduced a transport problem for ‘weak’ cost functionals. Basic results of optimal transport theory can be extended to this setup in remarkable generality. In this article, we collect several problems from different areas that can be recast in the framework of weak transport theory, namely: the Schrodinger problem, the Brenier–Strassen theorem, optimal mechanism design, linear transfers, semimartingale transport. Our viewpoint yields a unified approach and often allows to strengthen the original results.

This paper considers a re-sourcing setting in which a qualified supplier (the incumbent) and multiple suppliers which have not yet been qualified (the entrants) compete in an open-bid descending auction for a single-supplier contract. Due to the risk of supplier nonperformance, the buyer only awards the contract to a qualified supplier; meanwhile, the buyer can conduct supplier qualification screening at a cost, to verify whether the entrant suppliers can perform the contract. Conventionally, the buyer would screen entrants before running an auction, i.e., the pre-qualification strategy (PRE). We explore an alternative approach called post-qualification strategy (POST), in which the buyer first runs an auction and then conducts qualification screenings based on the suppliers' auction bids. Our characterization of the dynamic structure of the suppliers' equilibrium bidding strategy enables the calculation of the buyer's expected cost under POST, which is computationally intractable without this characterization. We show analytically that POST is cheaper than PRE when the cost of conducting qualification screening is high, the number of entrant suppliers is large, or the entrants' chance of passing qualification screening is high. To quantify the benefit of POST, we conduct a comprehensive numerical study and find that using the cheaper option between PRE and POST provides significant cost-savings over the conventional PRE-only approach. Furthermore, we leverage a revelation principle for multi-stage games to derive the optimal mechanism as a stronger benchmark for performance comparison. While the optimal mechanism is theoretically optimal, we find that its complexity renders it difficult to implement in practice; but quite strikingly, the simple and practical approach of choosing the cheaper option between PRE and POST captures the majority of the benefit the optimal mechanism can offer over PRE, highlighting the practical benefit of POST.

In the standard herding model, privately informed individuals sequentially see prior actions and then act. An identical action herd eventually starts and public beliefs tend to “cascade sets” where social learning stops. What behaviour is socially efficient when actions ignore informational externalities? We characterize the outcome that maximizes the discounted sum of utilities. Our four key findings are: (1) cascade sets shrink but do not vanish, and herding should occur but less readily as greater weight is attached to posterity. (2) An optimal mechanism rewards individuals mimicked by their successor. (3) Cascades cannot start after period one under a signal log-concavity condition. (4) Given this condition, efficient behaviour is contrarian, leaning against the myopically more popular actions in every period. We make two technical contributions: as value functions with learning are not smooth, we use monotone comparative statics under uncertainty to deduce optimal dynamic behaviour. We also adapt dynamic pivot mechanisms to Bayesian learning.

Auctions employing steeper securities generate greater revenues when bidders have equal opportunity costs. However, when opportunity costs rise sufficiently quickly with valuations, security bids decrease in NPV and steeper securities reduce seller revenues. We show that when such adverse selection obtains, using combinations of securities with differing steepness can generate higher revenues than using securities of the same steepness. We determine the optimal combination of cash plus equity; identify a novel way of implementing the optimal mechanism via decreasing royalty rates; establish the robustness of the mechanism; and identify when auction designs combining cash with steeper-than-equity securities increase seller revenues.

We characterize revenue maximizing mechanisms in a common value environment where the value of the object is equal to the highest of bidders’ independent signals. If the object is optimally sold with probability one, then the optimal mechanism is simply a posted price, with the highest price such that every type of every bidder is willing to buy the object. A sufficient condition for the posted price to be optimal among all mechanisms is that there is at least one potential bidder who is omitted from the auction. If the object is optimally sold with probability less than one, then optimal mechanisms skew the allocation towards bidders with lower signals. This can be implemented via a modified Vickrey auction, where there is a random reserve price for just the high bidder. The resulting allocation induces a “winner's blessing,” whereby the expected value conditional on winning is higher than the unconditional expectation. By contrast, standard auctions that allocate to the bidder with the highest signal (e.g., the first-price, second-price or English auctions) deliver lower revenue because of the winner's curse generated by the allocation rule. Our qualitative results extend to more general common value environments where the winner's curse is large.

We revisit the problem of a principal allocating an indivisible good with costly verification, as it was formulated and analyzed by Ben-Porath et al. (2014). We establish, in this setting, a general equivalence between Bayesian and ex-post incentive compatible mechanisms. We also provide a simple proof showing that the optimal mechanism is a threshold mechanism.

I empirically measure the welfare gains from optimal incentive regulation in the context of electric utilities facing both emissions and rate of return regulation (RORR). I provide evidence that RORR induces lower fuel efficiency, leading to greater coal consumption and higher emissions abatement costs. Replacing RORR with the optimal mechanism of Laffont and Tirole (1986) yields annual welfare gains of $686 million or a 11% reduction in electricity prices. I construct a much simpler two-contract menu that can achieve more than 65% of these welfare gains.

This paper investigates how incentives to learn ad performance affect the advertisers’ bidding strategies, as well as the publisher’s optimal mechanism.

This paper studies optimal efficient multi-attribute (price, delivery time, and service quality) transportation procurement auction design, given that the carriers have multi-unit supplies. A shipper wants to procure a certain amount of transportation service from a set of potential carriers that have private information on their own unit costs, transportation time, supply capacity, and service quality. We propose an auction to minimize the total cost incurred from procurement, delivery delay, and low service quality. We show that the auction is incentive-compatible, individually-rational, and allocative-efficient. Moreover, we discuss the optimality of the mechanism and show that it minimizes the shipper's expected cost among all the efficient mechanisms that are incentive-compatible and individually-rational. This study is the first that proposes an optimal and efficient multi-attribute multi-unit auction for transportation procurement in the setting where the carriers have multi-unit supplies and their capacity information is private.

Online games are the most popular form of entertainment among youngsters as well as elders. Recognized as e-Sports, they may become an official part of the Olympic Games by 2020. However, a long waiting time for matchmaking will largely affect players' experiences. We examine different matchmaking mechanisms for 2v2 games. By casting the mechanisms into a queueing theoretic framework, we decompose the rule design process into a sequence of decision making problems, and derive the optimal mechanism with minimum expected waiting time. We further the result by exploring additional static as well as dynamic rule designs' impacts. In the static setting, we consider the game allows players to choose sides before the battle. In the dynamic setting, we consider the game offers multiple zones for players of different skill levels. In both settings, we examine the value of choice-free players. Closed form expressions for the expected waiting time in different settings illuminate the guidelines for online game rule designs.

A monopolist seller of multiple goods screens a buyer whose type is initially unknown to both but drawn from a commonly known distribution. The buyer privately learns about his type via a signal. We derive the seller's optimal mechanism in two different information environments. We begin by deriving the buyer-optimal outcome. Here, an information designer first selects a signal, and then the seller chooses an optimal mechanism in response; the designer's objective is to maximize consumer surplus. Then, we derive the optimal informationally robust mechanism. In this case, the seller first chooses the mechanism, and then nature picks the signal that minimizes the seller's profits. We derive the relation between both problems and show that the optimal mechanism in both cases takes the form of pure bundling.

We study robustly-optimal mechanisms for selling multiple items. The seller maximizes revenue against a worst-case distribution of a buyer's valuations within a set of distributions, called an "ambiguity" set. We identify the exact forms of robustly-optimal selling mechanisms and the worst-case distributions when the ambiguity set satisfies a variety of moment conditions on the values of subsets of goods. We also identify general properties of the ambiguity set that lead to the robust optimality of partial bundling which includes separate sales and pure bundling as special cases.

We present a framework for designing differentially private (DP) mechanisms for binary functions via a graph representation of datasets. Datasets are nodes in the graph and any two neighboring datasets are connected by an edge. The true binary function we want to approximate assigns a value (or true color) to a dataset. Randomized DP mechanisms are then equivalent to randomized colorings of the graph. A key notion we use is that of the boundary of the graph. Any two neighboring datasets assigned a different true color belong to the boundary. Under this framework, we show that fixing the mechanism behavior at the boundary induces a unique optimal mechanism. Moreover, if the mechanism is to have a homogeneous behavior at the boundary, we present a closed expression for the optimal mechanism, which is obtained by means of a \emph{pullback} operation on the optimal mechanism of a line graph. For balanced mechanisms, not favoring one binary value over another, the optimal $(\epsilon,\delta)$-DP mechanism takes a particularly simple form, depending only on the minimum distance to the boundary, on $\epsilon$, and on $\delta$., Comment: Submitted to IEEE ISIT 2021

Apart from the principles and methodologies inherited from Economics and Game Theory, the studies in Algorithmic Mechanism Design typically employ theworst-case analysisand design ofapproximation schemesof Theoretical Computer Science. For instance, theapproximation 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 theaverage-case analysisapproach, and tackle one of the primary motivating problems in Algorithmic Mechanism Design—the scheduling problem (Nisan and Ronen, in: Proceedings of the 31st annual ACM symposium on theory of computing (STOC), 1999). One version of this problem, which includes a verification component, is studied by Koutsoupias (Theory Comput Syst 54(3):375–387, 2014). It was shown that the problem has a tight approximation ratio bound of$$(n+1)/2$$(n+1)/2for the single-task setting, wherenis the number of machines. We show, however, when the costs of the machines to executing the task followanyindependent and identical distribution, theaverage-case approximation ratioof the mechanism given by Koutsoupias (Theory Comput Syst 54(3):375–387, 2014) is upper bounded by a constant. This positive result asymptotically separates the average-case ratio from the worst-case ratio. It indicates that the optimal mechanism devised for a worst-case guarantee works well on average.

While users claim to be concerned about privacy, often they do little to protect their privacy in their online actions. One prominent explanation for this "privacy paradox" is that when an individual shares her data, it is not just her privacy that is compromised; the privacy of other individuals with correlated data is also compromised. This information leakage encourages oversharing of data and significantly impacts the incentives of individuals in online platforms. In this extended abstract, we discuss the design of mechanisms for data acquisition in settings with information leakage and verifiable data. We summarize work designing an incentive compatible mechanism that optimizes the worst-case tradeoff between bias and variance of the estimation subject to a budget constraint, where the worst-case is over the unknown correlation between costs and data. Additionally, we characterize the structure of the optimal mechanism in closed form and study monotonicity and non-monotonicity properties of the marketplace.

In this paper, we discuss the demand side management (DSM) problem: how to incentivize a consumer to equalize the load during the day through price-dependent demand. Traditionally, the retail market offers several electricity payment schemes. A scheme is effective when the different tariffs satisfy different consumers. At the same time, the existing and generally accepted retail pricing schemes can lead to an "adverse selection" problem when all consumers choose the same price, thereby, reducing the possible general welfare. We propose an optimal design of pricing mechanisms, taking into account the interests of the electricity supplier and different types of consumers. The results of our work are that the optimal mechanism is implemented simultaneously for several periods, including the case when the ratio of types of consumers in periods changes. In addition, the mechanism proposed by us, in contrast to the studies of other researchers, provides an equilibrium close to the socially optimal maximum. We describe the implementation algorithm of the mechanism and provide examples of its action in the electric power system with different types and numbers of consumers.

The optimal dimensional synthesis approach presented in this paper aims to enhance one of the earliest and efficient methods—gradient methods. The process to achieve the optimal mechanism bases on characterizing the circuits and branches of the path traced by the coupler point, establishing the corresponding actuation mode according to some indices that avoid using penalty functions. The proposed optimal synthesis approach redefines the error function by including an additional characteristic: the slope of the tangent to each point of the trajectory. It will be demonstrated that incorporating the tangent characteristic permits better fitting to the desired curve than by defining additional points.

We study information design settings where the designer controls information about a state, and there are multiple agents interacting in a game who are privately informed about their types. Each agent’s utility depends on all agents’ types and actions, as well as (linearly) on the state. To optimally screen the agents, the designer first asks agents to report their types and then sends a private action recommendation to each agent whose distribution depends on all reported types and the state. We show that there always exists an optimal mechanism which is laminar partitional. Such a mechanism partitions the state space for each type profile and recommends the same action profile for states that belong to the same partition element. Furthermore, the convex hulls of any two partition elements are such that either one contains the other or they have an empty intersection. In the single-agent case, each state is either perfectly revealed or lies in an interval in which the number of different signal realizations is at most the number of different types of the agent plus two. A similar result is established for the multi-agent case. We also highlight the value of screening: without screening the best achievable payoff could be as low as one over the number of types fraction of the optimal payoff. Along the way, we shed light on the solutions of optimization problems over distributions subject to a mean-preserving contraction constraint and additional side constraints, which might be of independent interest.

The purpose of this article is to scientifically and methodically substantiate and improve the mechanism for making effective management decisions in the implementation of investment projects of an enterprise within the time period set by the investor, taking into account risk and uncertainty. The article deals with the theoretical foundations of the construction of a model for the implementation of investment projects, taking into account risk and uncertainty as well as the methodology for the management of investment projects, taking into account the technology and organization of work. The work focuses on the improvement of the mechanism for the effective use of investment resources at the enterprise through the introduction of project management methods and the development of the best option for determining an effective solution for the implementation of the project within the established deadline, taking into account risk factors and uncertainty. The conducted analysis made it possible to conclude that the use of network modeling methods to the fullest extent possible corresponds to the task of project management, performance of work in interconnection and dynamics in the investment sphere. An improved system for the management of investment projects leads to the creation of an optimal mechanism for using investments at an enterprise, allows solving the problem of completing a project at a set date, as well as increasing the validity of decisions made in the management of investment projects of enterprises.

This paper compares two leading approaches for robust optimization in the models of online algorithms and mechanism design. Competitive analysis compares the performance of an online algorithm to an offline benchmark in worst-case over inputs, and prior-independent mechanism design compares the expected performance of a mechanism on an unknown distribution (of inputs, i.e., agent values) to the optimal mechanism for the distribution in worst case over distributions. For competitive analysis, a critical concern is the choice of benchmark. This paper gives a method for selecting a good benchmark. We show that optimal algorithm/mechanism for the optimal benchmark is equal to the prior-independent optimal algorithm/mechanism. We solve a central open question in prior-independent mechanism design, namely we identify the prior-independent revenue-optimal mechanism for selling a single item to two agents with i.i.d. and regularly distributed values. We use this solution to solve the corresponding benchmark design problem. Via this solution and the above equivalence of prior-independent mechanism design and competitive analysis (a.k.a. prior-free mechanism design) we show that the standard method for lower bounds of prior-free mechanisms is not generally tight for the benchmark design program.11For the full version of this work, see https://arxiv.org/abs/2001.10157.

This paper incorporates quasi–hyperbolic discounting into a Mirrlees taxation model to study the design of retirement policies for present-biased agents. I show that the government can improve the screening of productivity by exploiting time inconsistency. This is done by providing commitment to sophisticated agents and taking advantage of the incorrect beliefs of naïve agents. This can be achieved even if the degrees of present bias and sophistication are private information. I also demonstrate how the government can implement the optimal mechanism using retirement savings accounts and social security benefits.

The proliferation of real-time applications has spurred much interest in data freshness, captured by the {\it age-of-information} (AoI) metric. When strategic data sources have private market information, a fundamental economic challenge is how to incentivize them to acquire fresh data and optimize the age-related performance. In this work, we consider an information update system in which a destination acquires, and pays for, fresh data updates from multiple sources. The destination incurs an age-related cost, modeled as a general increasing function of the AoI. Each source is strategic and incurs a sampling cost, which is its private information and may not be truthfully reported to the destination. The destination decides on the price of updates, when to get them, and who should generate them, based on the sources' reported sampling costs. We show that a benchmark that naively trusts the sources' reports can lead to an arbitrarily bad outcome compared to the case where sources truthfully report. To tackle this issue, we design an optimal (economic) mechanism for timely information acquisition following Myerson's seminal work. To this end, our proposed optimal mechanism minimizes the sum of the destination's age-related cost and its payment to the sources, while ensuring that the sources truthfully report their private information and will voluntarily participate in the mechanism. However, finding the optimal mechanisms may suffer from \textit{prohibitively expensive computational overheads} as it involves solving a nonlinear infinite-dimensional optimization problem. We further propose a quantized version of the optimal mechanism that achieves asymptotic optimality, maintains the other economic properties, and enables one to tradeoff between optimality and computational overheads., Under review by IEEE JSAC

A cloud federation is a current paradigm that enables partnered cloud providers to share idle capacities during low demand periods and to purchase spare resources during demand spikes. In this research, we propose an optimal overbooking policy to maximize federation members' profits and enhance cloud users' experiences. The proposed policy overcomes cloud providers' low utilization and increases their profits. Under the market-oriented cloud federation system, we use the number of idle resources in the cloud federation and the operational costs of those resources to help the provider decide on its instance exchange price. Under such a price mechanism, we develop an optimal overbooking model and identify the conditions necessary for optimal solutions. Through implementing the optimal mechanism, we observe that the proposed overbooking policy can improve a federated provider's profits and decrease the probability of service level agreement (SLA) violation. When a provider's capacity is relatively large and the provider adopts the proposed overbooking policy, it could achieve maximum profits and decline its SLA violation when it has unmet customer demands and there are idle resources in the cloud federation. Through establishing the cooperative game model of the cloud federation, we make a reasonable profit distribution based on Shapley value. The cloud provider's profits and the probability of the SLA violation change as the instance price, the distribution of unserved customers, the number of federation members and penalty cost change. Compared with the other overbooking policies and no overbooking mechanisms, our research improves profits and reduces cloud provider's overbooking risks, thereby presenting a win-win situation for both the individual providers and the cloud federation.

We show that in a class of I‐agent mechanism design problems with evidence, commitment is unnecessary, randomization has no value, and robust incentive compatibility has no cost. In particular, for each agent i, we construct a simple disclosure game between the principal and agent i where the equilibrium strategies of the agents in these disclosure games give their equilibrium strategies in the game corresponding to the mechanism but where the principal is not committed to his response. In this equilibrium, the principal obtains the same payoff as in the optimal mechanism with commitment. As an application, we show that certain costly verification models can be characterized using equilibrium analysis of an associated model of evidence.

Internet of Things (IoT) becomes the hot cake in all technological fields. IoT applications concurrently generate the huge amount of data that need to be handled. In this paper, an optimal mechanism is proposed to handle big data for decision making. Based on the classical decision-making process in the field of Big Data, we design an adaptive knowledge-based method through IoT application. A Bayesian network model is used to manage knowledge formation in all direction for the decision-making process. Knowledge of Bayesian networks is habitually emitted as an optimal solution, where the analysis job is to locate a formation that exploits a statistically motivated score. Generally, accessible knowledge tools deal with this optimal solution by means of ordinary search methods. As it required big amount of data space, therefore it is a time consuming procedure that should be avoided. The situation becomes decisive once big data involve in searching for optimal solution. An algorithm is introduced to accomplish faster processing of optimal solution by limiting the search data space. The proposed recursive algorithm will limit the search space. The result shows that the optimal mechanism of the decision process is able to handle big data by reducing processing time, computational complexity and a higher percentage of prediction rates.

We derive a revenue-maximizing scheme that charges customers who are homogeneous with respect to their waiting cost parameter for a random fee in order to become premium customers. This scheme incentivizes all customers to purchase priority, each at his/her drawn price. We also design a revenue-maximizing scheme for the case where customers are heterogeneous with respect to their waiting cost parameter. Now lower cost parameter customers are encouraged to join the premium class at a low price: Given that, those with high cost parameter would be willing to pay even more for this privilege.