Cooperation-dominant Situations in SNS-norms Game on Complex and Facebook Networks

Hirahara, Yuki; Toriumi, Fujio; Sugawara, Toshiharu

doi:10.1007/s00354-016-0305-z

Cited by 8 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To explain this phenomenon, we propose the concept of half free riders (or partial lurkers ) and discuss the interaction structure needed to maintain a cooperation-dominant situation in which the SNS continues to prosper. This is one main difference from the previous studies [ 8 , 10 ]. Our experimental results suggest that user behavior like a half free rider sustains cooperative activity in all types of network.…”

Section: Introductioncontrasting

confidence: 72%

“…Their model includes two games, the rewards game (RG) and the meta-rewards game (MRG), and they indicated that meta-rewards facilitate cooperation, resulting in active use of an SNS. Hirahara et al [ 9 , 10 ] subsequently extended this model to the SNS-norms game that includes the characteristics of the interaction patterns in SNSs. They ran this game in a variety of complex networks and found that users at network hubs facilitate posting articles to some degree even if no meta-reward is provided.…”

Section: Related Workmentioning

confidence: 99%

“…They then analyzed the conditions under which a cooperation-dominant situation arises, where cooperation in this game corresponds to posting an article and a comment and a cooperation-dominant situation corresponds to a situation in which most users post them, so SNSs are active. They found that meta-rewards such as comments on article comments [ 8 ] and a simple (so, low-cost) response mechanism for rewards such as “Like” buttons for articles [ 10 ] play an important role in SNSs. However, these studies did not consider social and personal relationships between peers.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effect of direct reciprocity and network structure on continuing prosperity of social networking services

2017

Self Cite

View full text Add to dashboard Cite

BackgroundSocial networking services (SNSs) are widely used as communicative tools for a variety of purposes. SNSs rely on the users’ individual activities associated with some cost and effort, and thus it is not known why users voluntarily continue to participate in SNSs. Because the structures of SNSs are similar to that of the public goods (PG) game, some studies have focused on why voluntary activities emerge as an optimal strategy by modifying the PG game. However, their models do not include direct reciprocity between users, even though reciprocity is a key mechanism that evolves and sustains cooperation in human society.Proposed methodsWe developed an abstract SNS model called the reciprocity rewards and meta-rewards games that include direct reciprocity by extending the existing models. Then, we investigated how direct reciprocity in an SNS facilitates cooperation that corresponds to participation in SNS by posting articles and comments and how the structure of the networks of users exerts an influence on the strategies of users using the reciprocity rewards game.Experimental resultsWe run reciprocity rewards games on various complex networks and an instance network of Facebook and found that two types of stable cooperation emerged. First, reciprocity slightly improves the rate of cooperation in complete graphs but the improvement is insignificant because of the instability of cooperation. However, this instability can be avoided by making two assumptions: high degree of fun, i.e. articles are read with high probability, and different attitudes to reciprocal and non-reciprocal agents. We then propose the concept of half free riders to explain what strategy sustains cooperation-dominant situations. Second, we indicate that a certain WS network structure affects users’ optimal strategy and facilitates stable cooperation without any extra assumptions. We give a detailed analysis of the different characteristics of the two types of cooperation-dominant situations and the effect of the memory of reciprocal agents on cooperation.

show abstract

Section: Introductioncontrasting

confidence: 72%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effect of direct reciprocity and network structure on continuing prosperity of social networking services

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are very many game-theoretical studies on social networks and social media (e.g., [26][27][28]), and some of these studies insisted that online social networks have common features with public goods [29,30]. Therefore, some studies attempt to model SNSs in the framework of the evolutionary game like the public goods game [3,9,31,32]. For example, Osaka et al [31] extended the rewards game and meta-rewards game [3], which are the dual part of Axelrod's norms and meta-norms games, to include reciprocity between users and then clarified that the half-free rider is a suitable strategy for the continuous use of SNSs, where half-free riders are users who comment on the content posted by their close friends but behave as free riders for other content.…”

Section: Related Workmentioning

confidence: 99%

“…Their results show that cooperation on SNSs evolved by giving a meta reward, i.e., a reward as posting a comment on comments. Then, Hirahara et al [8,9] proposed an SNSnorms game by adding the structural interaction features of SNSs to the meta-rewards game. For example, users who respond to comments on posted articles are usually those who posted the original articles.…”

mentioning

confidence: 99%

Modeling and analyzing users’ behavioral strategies with co-evolutionary process

2021

Self Cite

View full text Add to dashboard Cite

Social networking services (SNSs) are constantly used by a large number of people with various motivations and intentions depending on their social relationships and purposes, and thus, resulting in diverse strategies of posting/consuming content on SNSs. Therefore, it is important to understand the differences of the individual strategies depending on their network locations and surroundings. For this purpose, by using a game-theoretical model of users called agents and proposing a co-evolutionary algorithm called multiple-world genetic algorithm to evolve diverse strategy for each user, we investigated the differences in individual strategies and compared the results in artificial networks and those of the Facebook ego network. From our experiments, we found that agents did not select the free rider strategy, which means that just reading the articles and comments posted by other users, in the Facebook network, although this strategy is usually cost-effective and usually appeared in the artificial networks. We also found that the agents who mainly comment on posted articles/comments and rarely post their own articles appear in the Facebook network but do not appear in the connecting nearest-neighbor networks, although we think that this kind of user actually exists in real-world SNSs. Our experimental simulation also revealed that the number of friends was a crucial factor to identify users’ strategies on SNSs through the analysis of the impact of the differences in the reward for a comment on various ego networks.

show abstract

Influence of Retweeting on the Behaviors of Social Networking Service Users

Yi-zhou

Toriumi

Sugawara

2020

Studies in Computational Intelligence

View full text Add to dashboard Cite

Cooperation-dominant Situations in SNS-norms Game on Complex and Facebook Networks

Cited by 8 publications

References 14 publications

Effect of direct reciprocity and network structure on continuing prosperity of social networking services

Effect of direct reciprocity and network structure on continuing prosperity of social networking services

Modeling and analyzing users’ behavioral strategies with co-evolutionary process

Influence of Retweeting on the Behaviors of Social Networking Service Users

Contact Info

Product

Resources

About