EconPapers    
Economics at your fingertips  
 

Leveraging surrounding past strategies to maintain cooperation in the perverse prisoner's dilemma

Akihiro Takahara and Tomoko Sakiyama

Applied Mathematics and Computation, 2025, vol. 493, issue C

Abstract: In spatial game theory, developed models preserving cooperators often incorporate memory to enhance realism. This study examined the role of memory in a spatial prisoner's dilemma. In the proposed model, all players use their own and their neighbors’ past memories and current states to update strategies under specific conditions. When a player's score is lower than that of a neighbor using the same strategy, and that neighbor has the highest score among all neighbors, the player revisits past strategies and adopts a less experienced strategy. This rule adjusts behavior under unfavorable conditions. Results showed that the proposed model effectively retains cooperators. Previous studies have often necessitated the use of long-term memory and intricate systems; however, the present model eliminates these requirements. Instead, it sustains cooperation by employing a strategy that relies on minimal historical information when determining the subsequent course of action.

Keywords: Spatial prisoner's dilemma; Memory; Evolution; Cooperation (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300324007331
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:493:y:2025:i:c:s0096300324007331

DOI: 10.1016/j.amc.2024.129272

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

 
Page updated 2025-05-31
Handle: RePEc:eee:apmaco:v:493:y:2025:i:c:s0096300324007331