Peer review (e.g., grading assignments in Massive Open Online Courses (MOOCs), academic paper review) is an effective and scalable method to evaluate the products (e.g., assignments, papers) of a large number of agents when the number of dedicated re
viewing experts (e.g., teaching assistants, editors) is limited. Peer review poses two key challenges: 1) identifying the reviewers intrinsic capabilities (i.e., adverse selection) and 2) incentivizing the reviewers to exert high effort (i.e., moral hazard). Some works in mechanism design address pure adverse selection using one-shot matching rules, and pure moral hazard was addressed in repeated games with exogenously given and fixed matching rules. However, in peer review systems exhibiting both adverse selection and moral hazard, one-shot or exogenous matching rules do not link agents current behavior with future matches and future payoffs, and as we prove, will induce myopic behavior (i.e., exerting the lowest effort) resulting in the lowest review quality. In this paper, we propose for the first time a solution that simultaneously solves adverse selection and moral hazard. Our solution exploits the repeated interactions of agents, utilizes ratings to summarize agents past review quality, and designs matching rules that endogenously depend on agents ratings. Our proposed matching rules are easy to implement and require no knowledge about agents private information (e.g., their benefit and cost functions). Yet, they are effective in guiding the system to an equilibrium where the agents are incentivized to exert high effort and receive ratings that precisely reflect their review quality. Using several illustrative examples, we quantify the significant performance gains obtained by our proposed mechanism as compared to existing one-shot or exogenous matching rules.
We consider a smart grid with an independent system operator (ISO), and distributed aggregators who have energy storage and purchase energy from the ISO to serve its customers. All the entities in the system are foresighted: each aggregator seeks to
minimize its own long-term payments for energy purchase and operational costs of energy storage by deciding how much energy to buy from the ISO, and the ISO seeks to minimize the long-term total cost of the system (e.g. energy generation costs and the aggregators costs) by dispatching the energy production among the generators. The decision making of the entities is complicated for two reasons. First, the information is decentralized: the ISO does not know the aggregators states (i.e. their energy consumption requests from customers and the amount of energy in their storage), and each aggregator does not know the other aggregators states or the ISOs state (i.e. the energy generation costs and the status of the transmission lines). Second, the coupling among the aggregators is unknown to them. Specifically, each aggregators energy purchase affects the price, and hence the payments of the other aggregators. However, none of them knows how its decision influences the price because the price is determined by the ISO based on its state. We propose a design framework in which the ISO provides each aggregator with a conjectured future price, and each aggregator distributively minimizes its own long-term cost based on its conjectured price as well as its local information. The proposed framework can achieve the social optimum despite being decentralized and involving complex coupling among the various entities.
Recent years have seen an explosion in wireless video communication systems. Optimization in such systems is crucial - but most existing methods intended to optimize the performance of multi-user wireless video transmission are inefficient. Some work
s (e.g. Network Utility Maximization (NUM)) are myopic: they choose actions to maximize instantaneous video quality while ignoring the future impact of these actions. Such myopic solutions are known to be inferior to foresighted solutions that optimize the long-term video quality. Alternatively, foresighted solutions such as rate-distortion optimized packet scheduling focus on single-user wireless video transmission, while ignoring the resource allocation among the users. In this paper, we propose an optimal solution for performing joint foresighted resource allocation and packet scheduling among multiple users transmitting video over a shared wireless network. A key challenge in developing foresighted solutions for multiple video users is that the users decisions are coupled. To decouple the users decisions, we adopt a novel dual decomposition approach, which differs from the conventional optimization solutions such as NUM, and determines foresighted policies. Specifically, we propose an informationally-decentralized algorithm in which the network manager updates resource prices (i.e. the dual variables associated with the resource constraints), and the users make individual video packet scheduling decisions based on these prices. Because a priori knowledge of the system dynamics is almost never available at run-time, the proposed solution can learn online, concurrently with performing the foresighted optimization. Simulation results show 7 dB and 3 dB improvements in Peak Signal-to-Noise Ratio (PSNR) over myopic solutions and existing foresighted solutions, respectively.
