The classic Vickrey-Clarke-Groves (VCG) mechanism ensures incentive compatibility, i.e., that truth-telling of all agents is a dominant strategy, for a static one-shot game. However, in a dynamic environment that unfolds over time, the agents' intertemporal payoffs depend on the expected future controls and payments, and a direct extension of the VCG mechanism is not sufficient to guarantee incentive compatibility. In fact, it does not appear to be feasible to construct mechanisms that ensure the dominance of dynamic truth-telling for agents comprised of general stochastic dynamic systems. The contribution of this paper is to show that such a dynamic stochastic extension does exist for the special case of Linear-Quadratic-Gaussian (LQG) agents with a careful construction of a sequence of layered payments over time. For a set of LQG agents, we propose a modified layered version of the VCG mechanism for payments that decouples the intertemporal effect of current bids on future payoffs, and prove that truth-telling of dynamic states forms a dominant strategy if system parameters are known and agents are rational. An important example of a problem needing such optimal dynamic coordination of stochastic agents arises in power systems where an Independent System Operator (ISO) has to ensure balance of generation and consumption at all time instants, while ensuring social optimality. The challenge is to determine a bidding scheme between all agents and the ISO that maximizes social welfare, while taking into account the stochastic dynamic models of agents, since renewable energy resources such as solar wind are stochastic and dynamic in nature, as are consumptions by loads which are influenced by factors such as local temperatures and thermal inertias of facilities.
In recent years, several papers have been written with the aim of exploring issues arising in dynamic mechanism design. In order to achieve ex post incentive compatibility, Bergemann and Valimaki @cite_15 propose a generalization of the VCG mechanism based on the marginal contribution of each agent and show that ex post participation constraints are satisfied under some conditions. Athey and Segal @cite_11 consider a similar model and focus on budget balance of the mechanism. @cite_6 derives first-order conditions under which incentive compatibility is guaranteed by generalizing Mirrlees's @cite_12 envelope formula of static mechanisms. @cite_16 considers a dynamic Markovian model and derives a sequence of Groves-like payments which achieves Markov perfect equilibrium. Bapna and Weber @cite_2 solves a sequential allocation problem by formulating it as a multi-armed bandit problem. Parkes and Singh @cite_13 and Friedman and Parkes @cite_1 consider an environment with randomly arriving and departing agents and propose a delayed'' VCG mechanism to guarantee interim incentive compatibility. @cite_5 and @cite_14 characterize the optimal infinite-horizon mechanism for an agent modeled as a Markov process, with Besanko considering a linear AR(1) process over a continuum of states, and Battaglini focusing on a two-state Markov chain.
@cite_14 @cite_1 @cite_6 @cite_2 @cite_5 @cite_15 @cite_16 @cite_13 @cite_12 @cite_11
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We study a simple dynamic Principal-Agent model in which the agent?s types are serially correlated. In these models, the standard approach consists in first solving a relaxed version in which only local incentive compatibility constraints are considered, and then in proving that the local constraints are sufficient for implementability. We show that, with the exception of few notable examples highlighted in the literature, this approach is not generally valid: even assuming standard regularity conditions, both local and global incentive constraints are generally binding when serial correlation is sufficiently high. We uncover a number of interesting features of the optimal contract that cannot be observed in the special environments in which the standard approach works. Finally, we show that even in complex environments, approximately optimal allocations can be easily characterized by focusing on a particular class of contracts in which the allocation is forced to be monotonic. We consider the problem of designing mechanisms for online problems in which agents arrive over time and truthfully announce their arrival. These problems are becoming extremely common in a wide variety of problems involving wireless networking and webserving. We show how the standard results of mechanism design can be modified to apply to this setting, provide conditions under which efficient and incentive compatible mechanisms exist and analyze several important online models including wireless networks and web serving. We examine the design of incentive-compatible screening mechanisms for dynamic environ- ments in which the agents'types follow a (possibly non-Markov) stochastic process, decisions may be made over time and may aect the type process, and payos need not be time-separable. We derive a formula for the derivative of an agent's equilibrium payowith respect to his cur- rent type in an incentive-compatible mechanism, which summarizes all …rst-order conditions for incentive compatibility and generalizes Mirrlees's envelope formula of static mechanism design. We provide conditions on the environment under which this formula must hold in any incentive- compatible mechanism. When specialized to quasi-linear environments, this formula yields a dynamic "revenue-equivalence"result and an expression for dynamic virtual surplus, which is instrumental for the design of optimal mechanisms. We also provide some su¢ cient conditions for incentive compatibility, and for its robustness to an agent's observation of the other agents' past and future types. We apply these results to a number of novel settings, including the de- sign of pro…t-maximizing auctions and durable-good selling mechanisms for buyers whose values In this paper we consider the problem of efficiently allocating a given resource or object repeatedly over time. The agents, who may temporarily receive access to the resource, learn more about its value through its use. When the agents' beliefs about their valuations at any given time are public information, this problem reduces to the classic multi-armed bandit problem, the solution to which is obtained by determining a Gittins index for every agent. In the setting we study, agents observe their valuations privately, and the efficient dynamic resource allocation problem under asymmetric information becomes a problem of truthfully eliciting every agent's Gittins index. We introduce two bounding mechanisms, under which agents announce types corresponding to Gittins indices either at least as high or at most as high as their true Gittins indices. Using an announcement-contingent affine combination of the bounding mechanisms it is possible to implement the efficient dynamic allocation policy. We provide necessary and sufficient conditions for global Bayesian incentive compatibility, guaranteeing a truthful efficient allocation of the resource. Using essentially the same method it is possible to approximately implement truthful mechanisms corresponding to a large variety of surplus distribution objectives the principal might have, for instance a dynamic second-price Gittins index auction, which maximizes the principal's revenue subject to implementing an efficient allocation policy. Abstract Multi-period contracts with adverse selection at the time of contracting are studied. With risk-neutrality and independent private information, all inefficiency arises in the first period only. With positive serial correlation, inefficiency is more pervasive but declines over time. We consider truthful implementation of the socially efficient allocation in a dynamic private value environment in which agents receive private information over time. We propose a suitable generalization of the Vickrey-Clarke-Groves mechanism, based on the marginal contribution of each agent. In the marginal contribution mechanism, the ex post incentive and ex post participations constraints are satisfied for all agents after all histories. It is the unique mechanism satisfying ex post incentive, ex post participation and efficient exit conditions. We develop the marginal contribution mechanism in detail for a sequential auction of a single object in which each bidders learn over time her true valuation of the object. We show that a modified second price auction leads to truthtelling. Consider a multi-agent system in a dynamic and uncertain environment. Each agent's local decision problem is modeled as a Markov decision process (MDP) and agents must coordinate on a joint action in each period, which provides a reward to each agent and causes local state transitions. A social planner knows the model of every agent's MDP and wants to implement the optimal joint policy, but agents are self-interested and have private local state. We provide an incentive-compatible mechanism for eliciting state information that achieves the optimal joint plan in a Markov perfect equilibrium of the induced stochastic game. In the special case in which local problems are Markov chains and agents compete to take a single action in each period, we leverage Gittins allocation indices to provide an efficient factored algorithm and distribute computation of the optimal policy among the agents. Distributed, optimal coordinated learning in a multi-agent variant of the multi-armed bandit problem is obtained as a special case. Online mechanism design (MD) considers the problem of providing incentives to implement desired system-wide outcomes in systems with self-interested agents that arrive and depart dynamically. Agents can choose to misrepresent their arrival and departure times, in addition to information about their value for different outcomes. We consider the problem of maximizing the total long-term value of the system despite the self-interest of agents. The online MD problem induces a Markov Decision Process (MDP), which when solved can be used to implement optimal policies in a truth-revealing Bayesian-Nash equilibrium. A bipolar transistor (10) in an IC includes a semiconductor wafer defining a collector area (14) with a first conductivity type, a base area (20) with a second conductivity type formed in the collector area (14), and an emitter formed in the base area. A field oxide is positioned on the surface of the semiconductor wafer surrounding the emitter (30) and substantially covering the base area (20) and an implant of the second conductivity type is positioned in the base area (20) between and spaced from the emitter (30) and the outer periphery of the base area (20). The implant further has a heavier concentration of the second conductivity type than the base area to compensate for loss of the second conductivity type under the field oxide and to separate the transistor current path from the breakdown path, which improves the collector to emitter breakdown voltage (BVCEO) while still maintaining a high beta. This paper constructs an efficient, budget-balanced, Bayesian incentive-compatible mechanism for a general dynamic environment with quasilinear payoffs in which agents observe private information and decisions are made over countably many periods. First, under the assumption of "private values" (other agents' private information does not directly affect an agent's payoffs), we construct an efficient, ex post incentive-compatible mechanism, which is not budget-balanced. Second, under the assumption of "independent types" (the distribution of each agent's private information is not directly affected by other agents' private information), we show how the budget can be balanced without compromising agents' incentives. Finally, we show that the mechanism can be made self-enforcing when agents are sufficiently patient and the induced stochastic process over types is an ergodic finite Markov chain. [PUBLICATION ABSTRACT]