Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Incentivising Monitoring in Open Normative Systems
Authors: Natasha Alechina, Joseph Halpern, Ian Kash, Brian Logan
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an approach to incentivising monitoring for norm violations in open multi-agent systems such as Wikipedia. In such systems, there is no crisp de๏ฌnition of a norm violation; rather, it is a matter of judgement whether an agent s behaviour conforms to generally accepted standards of behaviour. Agents may legitimately disagree about borderline cases. Using ideas from scrip systems and peer prediction, we show how to design a mechanism that incentivises agents to monitor each other s behaviour for norm violations. The mechanism keeps the probability of undetected violations (submissions that the majority of the community would consider not conforming to standards) low, and is robust against collusion by the monitoring agents. |
| Researcher Affiliation | Collaboration | Natasha Alechina University of Nottingham Nottingham, UK EMAIL Joseph Y. Halpern Cornell University Ithaca, USA EMAIL Ian A. Kash Microsoft Research Cambridge, UK EMAIL Brian Logan University of Nottingham Nottingham, UK EMAIL |
| Pseudocode | No | The paper describes theoretical mechanisms and models, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention providing open-source code for its described methodology. |
| Open Datasets | No | The paper describes a theoretical model and does not involve training on a dataset. No dataset information or access is provided. |
| Dataset Splits | No | The paper presents theoretical work and does not perform empirical validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used for it. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies or version numbers. |
| Experiment Setup | No | The paper focuses on theoretical model design and analysis. It does not include an experimental setup with hyperparameters or training configurations. |