We propose various models for lobbying in a probabilistic environment, in which an actor (called "The Lobby") seeks to influence the voters' preferences of voting for or against multiple issues when the voters' preferences are represented in terms of probabilities. In particular, we provide two evaluation criteria and three bribery methods to formally describe these models, and we consider the resulting forms of lobbying with and without issue weighting. We provide a formal analysis for these problems of lobbying in a stochastic environment, and determine their classical and parameterized complexity depending on the given bribery/evaluation criteria. Specifically, we show that some of these problems can be solved in polynomial time, some are NP-complete but fixed-parameter tractable, and some are W-complete. Finally, we provide (in)approximability results.
|Title of host publication||Algorithmic Decision Theory - First International Conference, ADT 2009, Proceedings|
|Number of pages||12|
|State||Published - 2009|
|Event||1st International Conference on Algorithmic Decision Theory, ADT 2009 - Venice, Italy|
Duration: Oct 20 2009 → Oct 23 2009
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||1st International Conference on Algorithmic Decision Theory, ADT 2009|
|Period||10/20/09 → 10/23/09|
Bibliographical noteFunding Information:
The second and sixth authors were supported in part by DFG grants RO 1202/11-1 , RO 1202/12-1 (within the European Science Foundation’s EUROCORES program LogICCC), and RO 1202/15-1 , and the Alexander von Humboldt Foundation’s TransCoop program. The fourth and fifth authors were supported in part by NSF grants CCF-1049360 and ITR-0325063 . The second author was supported in part by National Research Foundation (Singapore) under grant NRF-RF 2009-08 and DFG grant ER 738/1-1 .
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science (all)