Vickrey Auctions with Reserve Pricing

We generalize the Vickrey auction to allow for reserve pricing in a multiple item auction with interdependent values. By withholding quantity in some circumstances, the seller can improve revenues or mitigate collusion. In the Vickrey auction with reserve pricing, the seller determines the quantity to be made available as a function of the bidders' private information, and then efficiently allocates this quantity among the bidders. Truthful bidding is a dominant strategy with private values and an ex post equilibrium with interdependent values. If the auction is followed by resale, then truthful bidding remains an equilibrium in the auction-plus-resale game. In settings where resale exhausts all the gains from trade among the bidders, the Vickrey auction with reserve pricing maximizes seller revenues.Auctions, Vickrey Auctions, Multiple Item Auctions, Resale

A Generalized Vickrey Auction

In auction environments where bidders have pure private values, the Vickrey auction (Vickrey, 1961) provides a simple mechanism for efficiently allocating homogeneous goods. However, in environments where bidders have interdependent values, the Vickrey auction does not generally yield efficiency. This manuscript defines a "generalized Vickrey auction" which yields efficiency when bidders have interdependent values. Each bidder reports her type to the auctioneer. Given the reports, the auctioneer determines the allocation that maximizes surplus. The payment rule is the following extension of Vickrey auction pricing: a bidder is charged for a given unit that she wins according to valuations evaluated at the minimum signal that she could have reported and still won that unit.

Water Supply Planning under Interdependence of Actions: Theory and Application

An ongoing water supply planning problem in the Regional Municipality of Waterloo, Ontario, Canada, is studied to select the best water supply combination, within a multiple-objective framework, when actions are interdependent. The interdependencies in the problem are described and shown to be essential features. The problem is formulated as a multiple-criteria integer program with interdependent actions. Because of the large number of potential actions and the nonconvexity of the decision space, it is quite difficult to find nondominated subsets of actions. Instead, a modified goal programming technique is suggested to identify promising subsets. The appropriateness of this technique is explained, and the lessons learned in applying it to the Waterloo water supply planning problem are described

DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning

The increasing reliance upon cloud services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted in a progressive way due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems, since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to dependency among network nodes and layers. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some dependent network layers exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on Deep Reinforcement Learning (Deep RL). Our simulation results indicate that DeepPR can achieve the near-optimal solutions in certain networks and is more robust to adversarial failures, compared to a baseline heuristic algorithm.Comment: Technical Report, 12 page

How Best to Auction Natural Resources

I study the design of auctions of natural resources, such as oil or mineral rights. A good auction design promotes both an efficient assignment of rights and competitive revenues for the seller. The structure of bidder preferences and the degree of competition are key factors in determining the best design. With weak competition and simple value structures, a simultaneous first-price sealed-bid auction may suffice. With more complex value structures, a dynamic auction with package bids likely is needed to promote efficiency and revenue objectives. Bidding on production shares, rather than bonuses, typically increases government take by reducing oil or mining company risk.Auctions, natural resource auctions, oil auctions

Optimal association of mobile users to multi-access edge computing resources

Multi-access edge computing (MEC) plays a key role in fifth-generation (5G) networks in bringing cloud functionalities at the edge of the radio access network, in close proximity to mobile users. In this paper we focus on mobile-edge computation offloading, a way to transfer heavy demanding, and latency-critical applications from mobile handsets to close-located MEC servers, in order to reduce latency and/or energy consumption. Our goal is to provide an optimal strategy to associate mobile users to access points (AP) and MEC hosts, while contextually optimizing the allocation of radio and computational resources to each user, with the objective of minimizing the overall user transmit power under latency constraints incorporating both communication and computation times. The overall problem is a mixed-binary problem. To overcome its inherent computational complexity, we propose two alternative strategies: i) a method based on successive convex approximation (SCA) techniques, proven to converge to local optimal solutions; ii) an approach hinging on matching theory, based on formulating the assignment problem as a matching game