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Network Pollution Games.

Eleftherios Anastasiadis1, Xiaotie Deng2, Piotr Krysta1

  • 11University of Liverpool, Liverpool, UK.

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|March 16, 2019
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Summary
This summary is machine-generated.

This study introduces a computational network model for pollution control, developing approximation algorithms for trees and planar graphs. It achieves optimal results for pollution management and social welfare maximization.

Keywords:
Algorithmic mechanism designApproximation algorithmsPlanar graphsPollution control

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Area of Science:

  • Computational environmental economics
  • Network theory
  • Game theory applications

Background:

  • Pollution control is primarily studied in environmental economics using game theory.
  • This research pioneers a computational approach to pollution control modeling.

Purpose of the Study:

  • To introduce a novel network model for pollution control.
  • To develop computationally efficient algorithms for pollution management and social welfare maximization.

Main Methods:

  • Formulated a graph-based network model where nodes are pollution sources and edges represent pollution spread.
  • Proved the NP-hard nature of the optimization problem, even for tree structures.
  • Developed approximation algorithms for tree and planar graph networks, including deterministic and expected truthful mechanisms.

Main Results:

  • Achieved best possible approximation results for pollution control under standard complexity assumptions.
  • Introduced a novel decomposition technique for planar graphs, applicable beyond this study.
  • Designed a two-level dynamic programming approach for an FPTAS on trees, addressing global pollution constraints.

Conclusions:

  • The proposed network model and algorithms offer significant advancements in computational pollution control.
  • The novel techniques developed for planar graphs and trees have broader implications in algorithmic research.
  • This work bridges environmental economics and theoretical computer science for effective pollution management.