Occam's razor is insufficient to infer the preferences of irrational agents
Authors: Stuart Armstrong, Sören Mindermann
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper shows (1) that a No Free Lunch result implies it is impossible to uniquely decompose a policy into a planning algorithm and reward function, and (2) that even with a reasonable simplicity prior/Occam s razor on the set of decompositions, we cannot distinguish between the true decomposition and others that lead to high regret. To address this, we need simple normative assumptions, which cannot be deduced exclusively from observations. |
| Researcher Affiliation | Academia | S oren Mindermann * Vector Institute University of Toronto soeren.mindermann@gmail.com Stuart Armstrong* Future of Humanity Institute University of Oxford stuart.armstrong@philosophy.ox.ac.uk Further affiliation: Machine Intelligence Research Institute, Berkeley, USA. |
| Pseudocode | No | The paper presents theoretical concepts, definitions, lemmas, propositions, and proofs but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention or provide access to any source code for the described methodology. |
| Open Datasets | No | The paper is a theoretical work and does not use or reference any publicly available datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any experimental setup, hyperparameters, or training configurations. |