---
_id: '671'
abstract:
- lang: eng
text: Humans routinely use conditionally cooperative strategies when interacting
in repeated social dilemmas. They are more likely to cooperate if others cooperated
before, and are ready to retaliate if others defected. To capture the emergence
of reciprocity, most previous models consider subjects who can only choose from
a restricted set of representative strategies, or who react to the outcome of
the very last round only. As players memorize more rounds, the dimension of the
strategy space increases exponentially. This increasing computational complexity
renders simulations for individuals with higher cognitive abilities infeasible,
especially if multiplayer interactions are taken into account. Here, we take an
axiomatic approach instead. We propose several properties that a robust cooperative
strategy for a repeated multiplayer dilemma should have. These properties naturally
lead to a unique class of cooperative strategies, which contains the classical
Win-Stay Lose-Shift rule as a special case. A comprehensive numerical analysis
for the prisoner's dilemma and for the public goods game suggests that strategies
of this class readily evolve across various memory-n spaces. Our results reveal
that successful strategies depend not only on how cooperative others were in the
past but also on the respective context of cooperation.
article_processing_charge: Yes (in subscription journal)
author:
- first_name: Christian
full_name: Hilbe, Christian
id: 2FDF8F3C-F248-11E8-B48F-1D18A9856A87
last_name: Hilbe
orcid: 0000-0001-5116-955X
- first_name: Vaquero
full_name: Martinez, Vaquero
last_name: Martinez
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Martin
full_name: Nowak, Martin
last_name: Nowak
citation:
ama: Hilbe C, Martinez V, Chatterjee K, Nowak M. Memory-n strategies of direct reciprocity.
PNAS. 2017;114(18):4715-4720. doi:10.1073/pnas.1621239114
apa: Hilbe, C., Martinez, V., Chatterjee, K., & Nowak, M. (2017). Memory-n strategies
of direct reciprocity. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1621239114
chicago: Hilbe, Christian, Vaquero Martinez, Krishnendu Chatterjee, and Martin Nowak.
“Memory-n Strategies of Direct Reciprocity.” PNAS. National Academy of
Sciences, 2017. https://doi.org/10.1073/pnas.1621239114.
ieee: C. Hilbe, V. Martinez, K. Chatterjee, and M. Nowak, “Memory-n strategies of
direct reciprocity,” PNAS, vol. 114, no. 18. National Academy of Sciences,
pp. 4715–4720, 2017.
ista: Hilbe C, Martinez V, Chatterjee K, Nowak M. 2017. Memory-n strategies of direct
reciprocity. PNAS. 114(18), 4715–4720.
mla: Hilbe, Christian, et al. “Memory-n Strategies of Direct Reciprocity.” PNAS,
vol. 114, no. 18, National Academy of Sciences, 2017, pp. 4715–20, doi:10.1073/pnas.1621239114.
short: C. Hilbe, V. Martinez, K. Chatterjee, M. Nowak, PNAS 114 (2017) 4715–4720.
date_created: 2018-12-11T11:47:50Z
date_published: 2017-05-02T00:00:00Z
date_updated: 2021-01-12T08:08:37Z
day: '02'
department:
- _id: KrCh
doi: 10.1073/pnas.1621239114
ec_funded: 1
external_id:
pmid:
- '28420786'
intvolume: ' 114'
issue: '18'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5422766/
month: '05'
oa: 1
oa_version: Published Version
page: 4715 - 4720
pmid: 1
project:
- _id: 2581B60A-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '279307'
name: 'Quantitative Graph Games: Theory and Applications'
- _id: 2584A770-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 23499-N23
name: Modern Graph Algorithmic Techniques in Formal Verification
- _id: 25863FF4-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11407
name: Game Theory
publication: PNAS
publication_identifier:
issn:
- '00278424'
publication_status: published
publisher: National Academy of Sciences
publist_id: '7053'
quality_controlled: '1'
scopus_import: 1
status: public
title: Memory-n strategies of direct reciprocity
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 114
year: '2017'
...