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\begin{thebibliography}{10}
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\end{thebibliography}
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\documentclass[default]{sn-jnl}
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\documentclass[lineno,default]{sn-jnl}
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% Use the lineno option to display guide line numbers if required.
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% Note that the use of elements such as single-column equations
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% may affect the guide line number alignment.
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@ -91,7 +91,7 @@ Many complex collaborative systems in nature, society, and engineering can be mo
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The studies of human cooperation in $n$-person games begin with population games, also known as mean-field games~\cite{maynard_n73,hofbauer_98,cressman_03}. In a well-mixed population, cooperation can hardly prevail with imitative update rules when individuals play non-cooperative games such as the Prisoner's Dilemma (PD) game~\cite{szabo_pr07}. If the population exhibits a relatively stable social structure, the consequence may be different~\cite{santos_prl05, ohtsuki_n06, santos_pnas06, santos_n08, tanimoto_pre07, fu_pre09, lee_s_prl11, rand_pnas14, fu2017leveraging, allen2017evolutionary, fotouhi_rsif19} -- a finding rooted in the seminal paper by Nowak and May~\cite{nowak_n92b}, observing clusters of cooperators on a square lattice that protected them from invading defectors. Nevertheless, social networks are seldom static. People disconnect and then reconnect to form connections with new partners from time to time. This reality has revealed new mechanisms for cooperation that may sustain even under extremely adverse conditions, when the temptation to defect is high and where on static network cooperation is perishing~\cite{perc_bs10}. Moreover, an individual usually does not interact with all his friends all the time but likely does so only occasionally.
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To account for the above-observed phenomena, some researchers considered dynamic networks. Implications of dynamic interactions on human cooperation are profound. Recent human experiments as well as theoretical analysis both have confirmed this to the fullest~\cite{rand_pnas11, fehl_el11, wang_j_pnas12, szolnoki_epl14b, wang_z_njp14, shen_rsos18}. It is argued, for example, that these observations demonstrate the effects of reputation~\cite{melamed_pnas18}. Individuals may connect with unfamiliar individuals after browsing their gaming records but cut some existing connections with unsatisfactory partners. Some may take breaking ties, instead of performing defection, as a way to penalize defectors~\cite{rand_pnas11}. Interestingly, the implication of dynamic reconnection fades out as individuals are taking more specific moves to play games with their partners~\cite{melamed_pnas18}. In light of this, an interesting question is whether dynamic reconnection is relevant to the level of cooperation in a human collaborative system if there is a time limit on the duration of a game. From the perspective of biological markets~\cite{EHB34164}, the dynamic reconnection in such a system is a reallocation of collaboration time in a time-limited collaborative condition. Will too much emphasis put on the structure of our social networks result in neglecting the temporal aspects of our interactions? In this article, this critical question will be addressed.
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||||
To account for the above-observed phenomena, some researchers considered dynamic networks. Implications of dynamic interactions on human cooperation are profound. Recent human experiments as well as theoretical analysis both have confirmed this to the fullest~\cite{rand_pnas11, fehl_el11, wang_j_pnas12, szolnoki_epl14b, wang_z_njp14, shen_rsos18, Otten2022}. It is argued, for example, that these observations demonstrate the effects of reputation~\cite{melamed_pnas18}. Individuals may connect with unfamiliar individuals after browsing their gaming records but cut some existing connections with unsatisfactory partners. Some may take breaking ties, instead of performing defection, as a way to penalize defectors~\cite{rand_pnas11}. Interestingly, the implication of dynamic reconnection fades out as individuals are taking more specific moves to play games with their partners~\cite{melamed_pnas18}. In light of this, an interesting question is whether dynamic reconnection is relevant to the level of cooperation in a human collaborative system if there is a time limit on the duration of a game. From the perspective of biological markets~\cite{EHB34164}, the dynamic reconnection in such a system is a reallocation of collaboration time in a time-limited collaborative condition. Will too much emphasis put on the structure of our social networks result in neglecting the temporal aspects of our interactions? In this article, this critical question will be addressed.
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||||
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||||
Due to the complexity of temporal systems, using evolutionary game theory to model collaboration behavior is quite challenging. First, the evolution mechanism of a temporal system itself is complicated and hard to describe by a simple mathematical model. Secondly, in a temporal game, the individual strategy involves not only the moves but also the allocation of time in each round of the game. Furthermore, this openness allows individual strategy and network topology to co-evolve in a more flexible way than the existing dynamical gaming networks~\cite{zhang2018gaming, miritello2013time}, which brings up the difficulty in modeling coupled systems.
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|
||||
@ -458,7 +458,7 @@ As a theoretical framework closer to realistic scenarios, the proposed temporal
|
||||
|
||||
%Please check some classical experimental paper in either static or dynamic networks. If there is some possible future extension for temporal game framework we can mention.
|
||||
|
||||
It should be noted that the limitation of time is ubiquitous in human collaboration systems, which is essentially different from the incentives, such as global reputation~\cite{fu_pre08b,gallo_pnas15} and anonymity~\cite{wang2017onymity}, associated with human psychology. In a sense, the behavior observed in the performed experiments is more deterministic than random. Introducing some other mechanisms like rewarding~\cite{sefton_ei07} and costly punishment~\cite{fehr_n02,PCB14e1006347} to the temporal systems will be a natural extension of study in this direction. Apart from the mechanisms, the impact from different types of games, for instance, the snow-drift game~\cite{hauert_n04} and the public goods game~\cite{santos_n08}, is also of interest and significance.
|
||||
It should be noted that the limitation of time is ubiquitous in human collaboration systems, which is essentially different from the incentives, such as global reputation~\cite{fu_pre08b,gallo_pnas15} and anonymity~\cite{wang2017onymity}, associated with human psychology. In a sense, the behavior observed in the performed experiments is more deterministic than random. Introducing some other mechanisms like rewarding~\cite{sefton_ei07} and costly punishment~\cite{fehr_n02,PCB14e1006347} to the temporal systems will be a natural extension of study in this direction. Apart from the mechanisms, the impact from different types of games, for instance, the snow-drift game~\cite{hauert_n04} and the public goods game~\cite{santos_n08}, and the interaction models~\cite{Donahue2020, Park2022}, are also of interest and significance.
|
||||
%
|
||||
%On the other hand, the random allocation of groups in our experiment is a way to constrain group cheat, limiting the social knowledge of subjects. Therefore, a systematic experimental study on the role of global social knowledge~\cite{gallo_pnas15} in the formation of cooperative communities is another promising area of future studies.
|
||||
|
||||
|
||||
60
Main/egt.bib
60
Main/egt.bib
@ -34383,4 +34383,64 @@ DOI = {10.3390/g3020078}
|
||||
pages = {440-442},
|
||||
}
|
||||
|
||||
@Article{Otten2022,
|
||||
author={Otten, Kasper
|
||||
and Frey, Ulrich J.
|
||||
and Buskens, Vincent
|
||||
and Przepiorka, Wojtek
|
||||
and Ellemers, Naomi},
|
||||
title={Human cooperation in changing groups in a large-scale public goods game},
|
||||
journal={Nature Communications},
|
||||
year={2022},
|
||||
month={Oct},
|
||||
day={27},
|
||||
volume={13},
|
||||
number={1},
|
||||
pages={6399},
|
||||
abstract={How people cooperate to provide public goods is an important scientific question and relates to many societal problems. Previous research studied how people cooperate in stable groups in repeated or one-time-only encounters. However, most real-world public good problems occur in groups with a gradually changing composition due to old members leaving and new members arriving. How group changes are related to cooperation in public good provision is not well understood. To address this issue, we analyze a dataset from an online public goods game comprising approximately 1.5 million contribution decisions made by about 135 thousand players in about 11.3 thousand groups with about 234 thousand changes in group composition. We find that changes in group composition negatively relate to cooperation. Our results suggest that this is related to individuals contributing less in the role of newcomers than in the role of incumbents. During the process of moving from newcomer status to incumbent status, individuals cooperate more and more in line with incumbents.},
|
||||
issn={2041-1723},
|
||||
doi={10.1038/s41467-022-34160-5},
|
||||
url={https://doi.org/10.1038/s41467-022-34160-5}
|
||||
}
|
||||
|
||||
|
||||
@Article{Donahue2020,
|
||||
author={Donahue, Kate
|
||||
and Hauser, Oliver P.
|
||||
and Nowak, Martin A.
|
||||
and Hilbe, Christian},
|
||||
title={Evolving cooperation in multichannel games},
|
||||
journal={Nature Communications},
|
||||
year={2020},
|
||||
month={Aug},
|
||||
day={04},
|
||||
volume={11},
|
||||
number={1},
|
||||
pages={3885},
|
||||
abstract={Humans routinely engage in many distinct interactions in parallel. Team members collaborate on several concurrent projects, and even whole nations interact with each other across a variety of issues, including trade, climate change and security. Yet the existing theory of direct reciprocity studies isolated repeated games. Such models cannot account for strategic attempts to use the vested interests in one game as a leverage to enforce cooperation in another. Here we introduce a general framework of multichannel games. Individuals interact with each other over multiple channels; each channel is a repeated game. Strategic choices in one channel can affect decisions in another. With analytical equilibrium calculations for the donation game and evolutionary simulations for several other games we show that such linkage facilitates cooperation. Our results suggest that previous studies tend to underestimate the human potential for reciprocity. When several interactions occur in parallel, people often learn to coordinate their behavior across games to maximize cooperation in each of them.},
|
||||
issn={2041-1723},
|
||||
doi={10.1038/s41467-020-17730-3},
|
||||
url={https://doi.org/10.1038/s41467-020-17730-3}
|
||||
}
|
||||
|
||||
@Article{Park2022,
|
||||
author={Park, Peter S.
|
||||
and Nowak, Martin A.
|
||||
and Hilbe, Christian},
|
||||
title={Cooperation in alternating interactions with memory constraints},
|
||||
journal={Nature Communications},
|
||||
year={2022},
|
||||
month={Feb},
|
||||
day={08},
|
||||
volume={13},
|
||||
number={1},
|
||||
pages={737},
|
||||
abstract={In repeated social interactions, individuals often employ reciprocal strategies to maintain cooperation. To explore the emergence of reciprocity, many theoretical models assume synchronized decision making. In each round, individuals decide simultaneously whether to cooperate or not. Yet many manifestations of reciprocity in nature are asynchronous. Individuals provide help at one time and receive help at another. Here, we explore such alternating games in which players take turns. We mathematically characterize all Nash equilibria among memory-one strategies. Moreover, we use evolutionary simulations to explore various model extensions, exploring the effect of discounted games, irregular alternation patterns, and higher memory. In all cases, we observe that mutual cooperation still evolves for a wide range of parameter values. However, compared to simultaneous games, alternating games require different strategies to maintain cooperation in noisy environments. Moreover, none of the respective strategies are evolutionarily stable.},
|
||||
issn={2041-1723},
|
||||
doi={10.1038/s41467-022-28336-2},
|
||||
url={https://doi.org/10.1038/s41467-022-28336-2}
|
||||
}
|
||||
|
||||
|
||||
|
||||
@Comment{jabref-meta: databaseType:biblatex;}
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73
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226
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||||
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||||
(generated)
|
||||
"2nd SI.aux"
|
||||
"2nd SI.log"
|
||||
"2nd SI.out"
|
||||
"2nd SI.pdf"
|
||||
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SI/2nd SI.fls
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SI/2nd SI.fls
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SI/2nd SI.log
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SI/2nd SI.out
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SI/2nd SI.out
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SI/2nd SI.pdf
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SI/2nd SI.pdf
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Loading…
Reference in New Issue
Block a user