Sampling and Representation Complexity of Revenue Maximization

We consider (approximate) revenue maximization in auctions where the distribution on input valuations is given via "black box" access to samples from the distribution. We observe that the number of samples required -- the sample complexity -- is tightly related to the representation complexity of an approximately revenue-maximizing auction. Our main results are upper bounds and an exponential lower bound on these complexities

On Profit-Maximizing Pricing for the Highway and Tollbooth Problems

In the \emph{tollbooth problem}, we are given a tree \bT=(V,E) with $n$ edges, and a set of $m$ customers, each of whom is interested in purchasing a path on the tree. Each customer has a fixed budget, and the objective is to price the edges of \bT such that the total revenue made by selling the paths to the customers that can afford them is maximized. An important special case of this problem, known as the \emph{highway problem}, is when \bT is restricted to be a line. For the tollbooth problem, we present a randomized $O(\log n)$-approximation, improving on the current best $O(\log m)$-approximation. We also study a special case of the tollbooth problem, when all the paths that customers are interested in purchasing go towards a fixed root of \bT. In this case, we present an algorithm that returns a $(1-\epsilon)$-approximation, for any $\epsilon > 0$, and runs in quasi-polynomial time. On the other hand, we rule out the existence of an FPTAS by showing that even for the line case, the problem is strongly NP-hard. Finally, we show that in the \emph{coupon model}, when we allow some items to be priced below zero to improve the overall profit, the problem becomes even APX-hard

Social media for openness and accountability in the public sector: cases in the Greek context

This paper explores the use of government social media for opennessand accountability. The extant literature has highlighted the benefits of social media use in this context to enhance citizen participation and engagement in decision-making and policy development, facilitate openness and transparency efforts, and reduce corruption. Yet, there are limited studies that discuss those properties of social media that can afford openness and accountability, and their implications for policy and practise. To address these gaps, a study is conducted in the Greek context using interviews with top managers, policy makers, and relevant stakeholders across five initiatives. We discuss distinct affordances for openness and accountability, and propose their inclusion as building blocks of the national ICT policy for openness and accountability. Finally, we provide the implications of the affordances lens for policy and practise, the limitations of the study and future research avenues

Black-Box Reductions in Mechanism Design

A central question in algorithmic mechanism design is to understand the additional difficulty introduced by truthfulness requirements in the design of approximation algorithms for social welfare maximization. In this paper, by studying the problem of single-parameter combinatorial auctions, we obtain the first black-box reduction that converts any approximation algorithm to a truthful mechanism with essentially the same approximation factor in a prior-free setting. In fact, our reduction works for the more general class of symmetric single-parameter problems. Here, a problem is symmetric if its allocation space is closed under permutations. As extensions, we also take an initial step towards exploring the power of black-box reductions for general single-parameter and multi-parameter problems by showing several positive and negative results. We believe that the algorithmic and game theoretic insights gained from our approach will help better understand the tradeoff between approximability and the incentive compatibility

The stackelberg minimum spanning tree game

We consider a one-round two-player network pricing game, the Stackelberg Minimum Spanning Tree game or STACKMST. The game is played on a graph (representing a network), whose edges are colored either red or blue, and where the red edges have a given fixed cost (representing the competitor's prices). The first player chooses an assignment of prices to the blue edges, and the second player then buys the cheapest possible minimum spanning tree, using any combination of red and blue edges. The goal of the first player is to maximize the total price of purchased blue edges. This game is the minimum spanning tree analog of the well-studied Stackelberg shortest-path game. We analyze the complexity and approximability of the first player's best strategy in STACKMST. In particular, we prove that the problem is APX-hard even if there are only two different red costs, and give an approximation algorithm whose approximation ratio is at most min{k, 3 + 2 In 6,1 + In W}, where k is the number of distinct red costs, b is the number of blue edges, and W is the maximum ratio between red costs. We also give a natural integer linear programming formulation of the problem, and show that the integrality gap of the fractional relaxation asymptotically matches the approximation guarantee of our algorithm. © Springer-Verlag Berlin Heidelberg 2007.Frank Dehne, Jörg-Rüdiger Sack, and Norbert Zeh, editors, Algorithms and Data StructuresSpringer Berlin / Heidelberg10th International Workshop on Algorithms and Data Structures, WADS 2007; Halifax; Canada; 15 August 2007 through 17 August 2007.info:eu-repo/semantics/publishe

Interacting slow and fast dynamics in precise spiking-bursting neurons

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