By S. Muthukrishnan (auth.), Stefano Leonardi (eds.)
This publication constitutes the refereed complaints of the fifth foreign Workshop on web and community Economics, WINE 2009, held in Rome, Italy, in December 2009.
The 34 ordinary and 29 brief revised complete papers provided including three invited talks have been conscientiously reviewed and chosen from 142 submissions. The papers tackle quite a few issues in theoretical machine technology, networking and defense, economics, arithmetic, sociology, and administration sciences dedicated to the research of difficulties coming up within the web and the global internet, comparable to public sale algorithms, computational ads, normal and majority equilibrium, coalitions, collective motion, economics facets of safeguard and privateness in dispensed and community computing, algorithmic layout and online game idea, info economics, community video games, rate dynamics, and social networks.
Read Online or Download Internet and Network Economics: 5th International Workshop, WINE 2009, Rome, Italy, December 14-18, 2009. Proceedings PDF
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Extra resources for Internet and Network Economics: 5th International Workshop, WINE 2009, Rome, Italy, December 14-18, 2009. Proceedings
Sample text
36 P. Lu Therefore the expected approximation ratio R of the mechanism on these instances is t(ka + t − 1) (k + 1 − t)(ka2 + (k − t)a) a+1 + = (t2 −(ak+1)t+a(k 2+k)). ak(k + 1) ak(k + 1) ak(k + 1) For any fixed k and a > 1, this value R is a quadratic polynomial of t. So we have a+1 (ak + 1)2 R≥ (a(k 2 + k) − ). ak(k + 1) 4 For a sufficiently large k, the ratio in the RHS approachs the ratio of the k 2 terms which is a + 1 − a(a+1) . 5625. 5625. We remark that this lower bound only occurs for a sufficiently large number of tasks.
There are k + 1 instances, each containing k + 1 tasks. e. a proba1 bility of k+1 . The i-th (1 ≤ i ≤ k + 1) instance is as following: the running times of the i-th task are ka and ka2 for the first and second machines respectively; and the running times of the other k tasks are 1 and a for the first and second machines respectively. For the i-th instance, the optimal solution is to allocate the i-th task to the first machine and the remaining k tasks to the second machine; the optimal makespan is ka for every instance.
Formally, this alters the individual latency functions i . The specific definitions will be given in the sections below. For our altered games we are interested in stable states, which are pure strategy Nash equilibria of the games. , P is a pure Nash equilibrium if for every player i and every state Q that is obtained from P by replacing i’s path by some other path, it holds i (P ) ≤ i (Q), where i denotes the (altered) latency function of player i. We will not consider mixed Nash equilibria in this paper, and the term Nash equilibrium will refer to the pure version throughout.
