Strategies most often learned by the Beneficiary with parameters <i>S</i> = 0.2, <i>V</i> = 0.2, <i>r</i> = 0.5 (Case 1), see Fig 3.

Strategies most often learned by the Beneficiary with parameters <i>S</i> = 0.2, <i>V</i> = 0.2, <i>r</i> = 0.5 (Case 1), see Fig 3.

<p>Strategies most often learned by the Beneficiary with parameters <i>S</i> = 0.2, <i>V</i> = 0.2, <i>r</i> = 0.5 (Case 1), see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013302#pcbi.1013302.g003" target="_...

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Bibliographic Details
Main Author:	Olivia Macmillan-Scott (18494117) (author)
Other Authors:	Mirco Musolesi (3313848) (author)
Published:	2025
Subjects:	Cell Biology Evolutionary Biology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified study evolutionary dynamics maynard smith revisited low discount rates evolutionary biology perspective agent reinforcement learning resulting behaviour adopted given threshold leads agents &# 8217 exemplify honest signalling reinforcement learning model honest signalling high learning xlink "> underlying mechanisms strategy space sometimes referred proactive prosociality powerful tool particularly advantageous paper demonstrates originally set original derivation game provides artificial agents also central allows us
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