Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms
- DOI
- 10.1016/j.ssaho.2022.100263
- Published
- 2020-10-26T10:25:11Z
- Container
- Social Sciences & Humanities Open 5 (1), 100263, 2022
- Publisher
- arXiv
- Open access
- yes
Credibility signals
uncertain Score 53/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- supportingPublication license: A publication license was supplied: https://info.arxiv.org/help/license/index.html. Presence improves reuse transparency, not research validity.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1016/j.ssaho.2022.100263,
title = {Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms},
author = {Antti J. Tanskanen},
year = {2020},
journal = {Social Sciences \& Humanities Open 5 (1), 100263, 2022},
doi = {10.1016/j.ssaho.2022.100263},
url = {https://doi.org/10.1016/j.ssaho.2022.100263}
}RIS
TY - JOUR TI - Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms AU - Antti J. Tanskanen PY - 2020 JO - Social Sciences & Humanities Open 5 (1), 100263, 2022 DO - 10.1016/j.ssaho.2022.100263 UR - https://doi.org/10.1016/j.ssaho.2022.100263 ER -
APA
Tanskanen, A. J. (2020). Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms. Social Sciences & Humanities Open 5 (1), 100263, 2022. https://doi.org/10.1016/j.ssaho.2022.100263
Source records
- arxiv · retrieved 2026-11-10T12:35:20.523Z