Item Bidding for Combinatorial Public Projects
Authors: Evangelos Markakis, Orestis Telelis
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present and analyze a mechanism for the Combinatorial Public Project Problem (CPPP). The problem asks to select k out of m available items, so as to maximize the social welfare for autonomous agents with combinatorial preferences (valuation functions) over subsets of items. ... For fairly expressive classes of the agents valuation functions, we establish existence of socially optimal pure Nash and strong equilibria, that are resilient to coordinated deviations of subsets of agents. Subsequently we derive tight worst-case bounds on the approximation of the optimum social welfare achieved in equilibrium. |
| Researcher Affiliation | Academia | Evangelos Markakis and Orestis Telelis Department of Informatics, Athens University of Economics and Business, Greece. {markakis,telelis}@gmail.com |
| Pseudocode | No | The paper describes methods and mechanisms using text and mathematical equations, but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific link, explicit statement of code release) for the source code of the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve training on datasets; therefore, no public dataset information or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental validation on data, so no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental work, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any software dependencies or specific version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |