Dynamic Mechanism Design Research Articles

Auxiliary Service Dynamic Compensation Mechanism Design for Incentivizing Market Participants to Provide Flexibility in China

Auxiliary Service Dynamic Compensation Mechanism Design for Incentivizing Market Participants to Provide Flexibility in China

Dynamic Adaptive Mechanism Design and Implementation in VSS for Large-Scale Unified Log Data Collection

This paper studies the collection of large-scale log data of information system and puts forward a dynamic adaptive mechanism for large-scale unified log data collection. Furthermore, we design and implement our method for pan-government industry safety operation management platform. The data flow processing architecture based on message queue is adopted to realize the decoupling of log collection, log processing and log reporting. The traffic peak clipping technology of message queue is adopted to ensure the safety and reliability of log transmission. According to the characteristics of log traffic, a design mode supporting dynamic adjustment of consumption group is proposed to meet the high-performance requirements of the system. The whole system can meet the centralized analysis, security threat perception and intelligent analysis of various security data. Meanwhile, we analyzed and compared with the traditional open-source log collection technology, our proposed method and system has clear advantages.

Dynamic Mechanism Design for Repeated Markov Games with Hidden Actions: Computational Approach

This paper introduces a dynamic mechanism design tailored for uncertain environments where incentive schemes are challenged by the inability to observe players’ actions, known as moral hazard. In these scenarios, the system operates as a Markov game where outcomes depend on both the state of payouts and players’ actions. Moral hazard and adverse selection further complicate decision-making. The proposed mechanism aims to incentivize players to truthfully reveal their states while maximizing their expected payoffs. This is achieved through players’ best-reply strategies, ensuring truthful state revelation despite moral hazard. The revelation principle, a core concept in mechanism design, is applied to models with both moral hazard and adverse selection, facilitating optimal reward structure identification. The research holds significant practical implications, addressing the challenge of designing reward structures for multiplayer Markov games with hidden actions. By utilizing dynamic mechanism design, researchers and practitioners can optimize incentive schemes in complex, uncertain environments affected by moral hazard. To demonstrate the approach, the paper includes a numerical example of solving an oligopoly problem. Oligopolies, with a few dominant market players, exhibit complex dynamics where individual actions impact market outcomes significantly. Using the dynamic mechanism design framework, the paper shows how to construct optimal reward structures that align players’ incentives with desirable market outcomes, mitigating moral hazard and adverse selection effects. This framework is crucial for optimizing incentive schemes in multiplayer Markov games, providing a robust approach to handling the intricacies of moral hazard and adverse selection. By leveraging this design, the research contributes to the literature by offering a method to construct effective reward structures even in complex and uncertain environments. The numerical example of oligopolies illustrates the practical application and effectiveness of this dynamic mechanism design.

Mechanism design with limited commitment: Markov environments

We provide a revelation principle for a class of single-agent dynamic mechanism design settings in which the agent’s private information evolves stochastically over time and the designer can only commit to short-term mechanisms. We restrict attention to Markov environments, in which (i) the agent’s type in period t+1 depends only on her period-t type and the period-t allocation, (ii) the designer’s and the agent’s payoffs are time-separable, and (iii) their period-t payoffs depend only on period-t type and the period-t allocation. We show all equilibrium payoffs can be attained with the designer using flow direct Blackwell mechanisms, which consist of a mapping from the agent’s current type report to posterior beliefs about the current type, and a mapping from these beliefs to allocations. Furthermore, all equilibrium payoffs can be attained with strategies in which the agent participates and truthfully reports her type, and the beliefs that result from the mechanism correspond to the designer’s equilibrium beliefs. This result greatly simplifies the search of optimal dynamic and sequentially rational mechanisms in dynamic mechanism design problems, which include dynamic Mirrlees and social insurance models.

Correlation‐savvy sellers

AbstractA multi‐product monopolist sells sequentially to a buyer who privately learns his valuations. Using big data, the monopolist learns the intertemporal correlation of the buyer's valuations. Perfect price discrimination is generally unattainable—even when the seller learns the correlation perfectly, has full commitment, and in the limit where the consumption good about which the buyer has ex ante private information becomes insignificant. This impossibility is due to informational externalities that require information rents for the buyer's later consumption. These rents induce upward and downward distortions, violating the generalized no distortion at the top principle of dynamic mechanism design.

Automated Design of Affine Maximizer Mechanisms in Dynamic Settings

Dynamic mechanism design is a challenging extension to ordinary mechanism design in which the mechanism designer must make a sequence of decisions over time in the face of possibly untruthful reports of participating agents. Optimizing dynamic mechanisms for welfare is relatively well understood. However, there has been less work on optimizing for other goals (e.g., revenue), and without restrictive assumptions on valuations, it is remarkably challenging to characterize good mechanisms. Instead, we turn to automated mechanism design to find mechanisms with good performance in specific problem instances. We extend the class of affine maximizer mechanisms to MDPs where agents may untruthfully report their rewards. This extension results in a challenging bilevel optimization problem in which the upper problem involves choosing optimal mechanism parameters, and the lower problem involves solving the resulting MDP. Our approach can find truthful dynamic mechanisms that achieve strong performance on goals other than welfare, and can be applied to essentially any problem setting---without restrictions on valuations---for which RL can learn optimal policies.

Online installment payments and price guarantees under randomized pricing

Internet technology allows e-tailers to provide installment payment services, allowing consumers to purchase products without immediate payment. In addition, retailers can apply intelligent pricing strategies and provide price-guarantee strategies to encourage consumers to buy in advance. This study uses a dynamic mechanism design to explore the interplay between pricing and financing strategies. Specifically, we combine a consumer credit service with a posterior price guarantee. Online monopolists switch between high and low prices with a certain probability in each period. If the retailer lowers the price during the price-guarantee period, it refunds the difference. Consumers are heterogeneous in their valuations of products and disposable personal income. Our analysis demonstrates that interest-free installments are more profitable when the proportion of consumers with high disposable personal income is low; otherwise, the retailer should provide installment services with a maximal loan interest rate that may increase with consumer valuation heterogeneity. Moreover, adopting price guarantees narrows the use of interest-free installments. Further, the use of installment payments makes the retailer increase the promotion probability and high price and shorten the length of the price-guarantee period when the proportion of consumers with a high disposable personal income is low. We also find that as consumers become more patient, the retailer should reduce the promotion probability and prolong the price-guarantee period.

Technical Note—Dynamic Mechanism Design with Capacity Constraint

When the number of tasks is large, how should a firm design reward and penalty schemes to incentivize its employees? In “Dynamic Mechanism Design with Capacity Constraint,” He studies the role of capacity constraint in a project assignment problem, where a principal needs to assign multiple tasks to an agent. The author fully characterizes the optimal mechanism via a sequence of deadlines. This characterization is used to show that the presence of the capacity constraint reduces the principal’s payoff and delays the completion of projects. It further illustrates that the widely adopted no-capacity constraint framework may provide inaccurate results in various dynamic problems.

Optimal Dynamic Mechanism Design With Stochastic Supply and Flexible Consumers

We consider the problem of designing an expected-revenue maximizing mechanism for allocating multiple non-perishable goods of <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> varieties to flexible consumers over <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> time steps. In our model, a random number of goods of each variety may become available to the seller at each time and a random number of consumers may enter the market at each time. Each consumer is present in the market for one time step and wants to consume one good of one of its desired varieties. Each consumer is associated with a flexibility level that indicates the varieties of the goods it is equally interested in. A consumer&#x2019;s flexibility level and the utility it gets from consuming a good of its desired varieties are its private information. We characterize the allocation rule for a Bayesian incentive compatible, individually rational and expected revenue maximizing mechanism in terms of the solution to a dynamic program. The corresponding payment function is also specified in terms of the optimal allocation function. We leverage the structure of the consumers&#x2019; flexibility model to simplify the dynamic program. Our simplified dynamic program allows us to provide an explicit allocation procedure and a simple payment rule in terms of the solution of the dynamic program.

Optimal dynamic incentive mechanism design for construction and demolition waste recycling with Bayesian learning

A lack of a steady supply of high-quality construction and demolition (C&D) waste hinders the development of the recycling industry, and information asymmetry makes it difficult for a recycling company to determine incentive strategies for a collector. To address the problem, a dynamic moral-hazard model is formulated to obtain the optimal incentive mechanism. Bayesian learning is then introduced to update the estimation of a collector's private information. The results show that the collector is always motivated to voluntarily sustain a high-quality supply of C&D waste in the optimal mechanism. Additionally, the private information is gradually revealed by learning, which is conducive to controlling incentive costs. However, the collector is likely to misreport their information for higher profits, which reduces the learning accuracy and increases the incentive cost. Numerical analyses suggest that in response to misreporting, the recycling company should partially ignore the provided false information and extend the duration of each period of the contract over time, which can improve the learning accuracy. Therefore, this study helps to realize better recycling management from the perspective of interactive decision-making between the recycling company and the collector.

Implications of Unequal Discounting in Dynamic Contracting

This paper studies a canonical dynamic screening problem where the agent has Markovian private information and limited commitment and the principal and the agent have different discount factors. Unequal discounting captures unequal access to capital markets. In comparison to standard models of dynamic mechanism design, the principal no longer finds it optimal to maximally back-load the agent’s information rents: a new force of inter-temporal cost of incentive provision pushes toward front-loading agents’ payoffs. The optimal contract settles into a cycle with infinite memory. The introduction of unequal discounting renders the standard relaxed-problem approach invalid for certain parameters. A simple and approximately optimal contract is then provided. (JEL D21, D61, D82, D86, L14)

Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities

A power system dominated by renewable energy is one of the key measures for achieving carbon neutrality. Demand response (DR) is a promising flexible resource for alleviating the supply-demand matching of high-proportion renewable energy systems. With the application of modern technologies, the potential for residential DR is growing. Electricity price is the key to improving residential DR capacity. However, existing dynamic pricing programs may fail to motivate end-users to adjust demand based on fluctuations in wind and photovoltaic (PV) output. This study proposes a dynamic pricing model that combines the fluctuation characteristics of residential electricity demand and wind and PV output, and utilizes bi-level optimization to coordinately dispatch the flexible loads. A case study of smart residential community consisting of 200 households shows that dynamic pricing incentivizes residential consumers to shift flexible loads from morning and evening to noon or early morning, which effectively improves the degree of matching between wind and PV output and residential electricity demand. Moreover, bi-level optimization effectively alleviates the potential rebound peak caused by large-scale residential participation in DR and achieves a relatively flat net grid demand profile. Furthermore, the dynamic pricing can incentivize residential consumers to participate in DR by reducing electricity bills.

Dynamic Pricing Mechanism Design for Electric Mobility-on-Demand Systems

With the popularization of ride-sharing transportation and increasing penetration of electric vehicles (EVs) in recent years, the electric mobility-on-demand (EMoD) system is emerging as a promising means to provide ride-sharing services in the context of sustainable cities. In this paper, we focus on sequential decision-making for the operator of an EMoD system by considering both the passengers’ utility and system revenue. Specifically, we design a pricing mechanism to incentivize passengers with spatially and temporally different demand to make different mobility choices. After the passengers’ demand is realized, the operator makes operational decisions, including dispatching and repositioning EVs between service regions, and recharging EVs to maintain their energy levels. Therefore, a bi-level and dynamic programming problem is formulated to model these decisions. To solve this problem, we first transform the bi-level problem into a single-level one based on the structural properties of the formulation. Furthermore, we rigorously prove the coordinate-wise concavity of the single-level formulation and efficiently obtain near-optimal solutions based on approximation. Numerical tests show that the proposed dynamic pricing mechanism achieves a significantly better performance than static pricing and other existing model-free approaches (e.g., Q-learning).

On the complexity of dynamic mechanism design

We introduce a simple dynamic mechanism design problem in which the designer offers two items in two consecutive stages to a single buyer. The buyer's joint distribution of valuations for the two items is known, and the buyer knows the valuation for the current item, but not for the one in the future. The designer seeks to maximize expected revenue, and the mechanism must be deterministic, truthful, and ex-post individually rational. We show that finding the optimum deterministic mechanism in this situation — arguably one of the simplest meaningful dynamic mechanism design problems imaginable — is NP-hard. We also prove several positive results, including a polynomial-time linear programming-based algorithm for the revenue optimal randomized mechanism (even for many buyers and many stages). We prove strong separations in revenue between non-adaptive, adaptive, and randomized mechanisms, even when the valuations in the two stages are independent.

A Dynamic Mechanism Design for Controllable and Ergodic Markov Games

A Dynamic Mechanism Design for Controllable and Ergodic Markov Games

Information Design of Dynamic Mechanisms

Two dynamic game forms are said to be behaviorally equivalent if they share the "same" profiles of structurally reduced strategies (Battigalli et al., 2020). In the context of dynamic implementation, behaviorally equivalent game forms are interchangeable under a wide range of solution concepts for the purpose of implementing a social choice function. A gradual mechanism (Chew and Wang, 2022), which designs a procedure of information transmission mediated by a central administrator, enables a formal definition of information flow. We provide a characterization of behavioral equivalence between gradual mechanisms in terms of their informational equivalence -- each agent is designed the "same" information flow. Information flow also helps in defining an intuitive notion of immediacy for gradual mechanisms which is equivalent to their game structures being minimal. Given that the class of gradual mechanisms serves as a revelation principle for dynamic implementation (Li, 2017; Akbarpour and Li, 2020; Mackenzie, 2020; Chew and Wang, 2022), the class of immediate gradual mechanisms provides a refined revelation principle.

Optimal Taxation and R&amp;D Policies

We study the optimal design of corporate taxation and R&amp;D policies as a dynamic mechanism design problem with spillovers. Firms have heterogeneous research productivity, and that research productivity is private information. There are non‐internalized technological spillovers across firms, but the asymmetric information prevents the government from correcting them in the first best way. We highlight that key parameters for the optimal policies are (i) the relative complementarities between observable R&amp;D investments, unobservable R&amp;D inputs, and firm research productivity, (ii) the dispersion and persistence of firms' research productivities, and (iii) the magnitude of technological spillovers across firms. We estimate our model using firm‐level data matched to patent data and quantify the optimal policies. In the data, high research productivity firms get disproportionately higher returns to R&amp;D investments than lower productivity firms. Very simple innovation policies, such as linear corporate taxes combined with a nonlinear R&amp;D subsidy—which provides lower marginal subsidies at higher R&amp;D levels—can do almost as well as the unrestricted optimal policies. Our formulas and theoretical and numerical methods are more broadly applicable to the provision of firm incentives in dynamic settings with asymmetric information and spillovers, and to firm taxation more generally.

Nonlinear dynamic analysis of multiple grooved water-lubricated journal bearings using attenuation rate interpolation method

Purpose The stringent requirements for environmental protection have induced the extensive applications of water-lubricated journal bearings in marine propulsion. The nonlinear dynamic analysis of multiple grooved water-lubricated bearings (MGWJBs) has not been fully covered so far in the literature. This study aims to conduct the nonlinear dynamic analysis of the instability for MGWJBs. Design/methodology/approach An attenuation rate interpolation method is proposed for the determination of the critical instability speed. Based on a structured mesh movement algorithm, the transient hydrodynamic force model of MGWJBs is set up. Furthermore, the parameters’ analysis of nonlinear instability for MGWJBs is conducted. The minimum water film thickness, side leakage, friction torque and power loss of friction are fully analyzed. Findings With the increase of speed, the journal orbits come across the steady state equilibrium motion, sub-harmonic motion and limit circle motion successively. At the limit circle motion stage, the orbits are much larger than that of steady state equilibrium and sub-harmonic motion. The critical instability speed increases when the spiral angle decreases or the groove angle increases. The minimum water film thickness peak is at the rotor speed of 4,000 r/min for the MGWJB with Sa = 0°. As rotor speed increases, the side leakage decreases slightly while the friction torque and the power loss of friction increase gradually. Originality/value Present research provides a beneficial reference for the dynamic mechanism analysis and design of MGWJBs.

Dynamic mechanism design on social networks

This paper studies a mechanism design problem with networked agents and stochastically evolving private information. In contrast to the canonical mechanism design theory focusing only on information asymmetry, we also pay attention to the topology of social network among agents. We find that the standard first-order approach for mechanism design is invalid in dynamic environment. As a remedy, a novel ironing technique is proposed, which produces a perfectly sorting allocation. Based on the optimal dynamic mechanism obtained, we define and compare some important nodes and edges in a network for different ranges of synergy parameter. We further discuss the network intervention problem, in which the principal can intervene to change the ex-ante distribution of individual types.

Dynamic incentive mechanism design for regulation‐aware systems

As the gig economy continues to grow, behaviors of workers on gig service platforms have an increasing impact on service satisfaction. For example, fatigue driving behaviors of drivers in ride-hailing platforms may cause serious damages, both for individuals and society. Therefore, regulating behaviors of workers is urgent and challenging. A lot of studies are conducted to detect workers' noncompliance behaviors, such as detecting fatigue driving by computer vision or pattern recognition methods. However, few of them indicate how to efficiently exploit the detection results to regulate workers' behaviors. In this paper, we point out that workers' noncompliance behaviors and their incomes should be correlated, and propose a quantifiable computation framework that includes a price-based incentive mechanism and a method to verify the effectiveness of the mechanism. Historical behaviors of workers are summarized as credits and stored in nonfungible token called CreditToken to ensure that it cannot be tampered with. CreditToken will further affect workers' incomes. We abstract the decision-making behavior of workers as a Markov decision process and demonstrate the effectiveness of the incentive mechanism with model checking and formal methods. The analysis shows that our framework is able to provide a rational price strategy formation for gig service platforms, and can be flexibly integrated into existing pricing schemes to maximize the value of the detection results. Extensive experiments illustrate the advanced nature and practicality of our framework.