Reciprocal Altruism-based Cooperation in a Social Network Game

Takano, Masanori; Wada, Kazuya; Fukuda, Ichiro

doi:10.1007/s00354-016-0304-0

Cited by 13 publications

(15 citation statements)

References 26 publications

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“…They are useful for constructing theoretical models of social phenomena. Studies (Arnaboldi et al, 2012;Song et al, 2012;Fujihara and Miwa, 2014;Hossmann et al, 2011;Arnaboldi et al, 2013b;Hu et al, 2012;Takano et al, 2016a;Saramaki et al, 2014;Dunbar, 2016;Takano et al, 2016b) previously cited in this paper have also analysed data from SNSs, mobile phones, social network games, and SMSs.…”

Section: Introductionmentioning

confidence: 99%

Limitations of time resources in human relationships determine social structures

Takano¹,

Fukuda²

2017

Palgrave Commun

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The number of social relationships that a single human being can possibly be involved in is limited because individuals face time constraints (that is, time costs) in constructing and maintaining social relationships; furthermore, the distribution of the strength of such relationships (as measured by frequency of social interaction) looks significantly skewed (a power law distribution), that is, a few strong relationships and many weak relationships. This skewedness suggests that the costs and benefits of bonding with others depend on the strength of the social relationships: if it involves uniform costs and benefits, the distribution would not be skewed. The bonding is known as social grooming; that is, humans strategically construct their social relationships, and thus, complex human societies should also be strategically constructed. Therefore, it is important to know their strategies for understanding human societies. Previous studies provide evidence of social grooming strategies by examining the evolution and the difference between the various social grooming methods. However, quantitative laws that are important for theoretically understanding human societies are still open to investigation. Social big data is a particularly powerful tool for finding such laws. Therefore, we analyse data from six communication systems (Twitter, a social networking site providing two types of interactions, an avatar chat, a mobile phone and a short message service). We find a simple quantitative law by which social relationships are constrained Nm a (a41); here, N is the number of social relationships and m is a mean of the strength of those relationships. The fact that deep social relationships require higher costs per relationship than shallow relationships is suggested by a41 (if the both is equal then a will be 1), because the effect of m on the constraint increases with m. For exploring why a is greater than 1, we conduct an individual-based simulation where social grooming costs are assumed to increase linearly with the strength of social relationships. Our results indicate that this model fits all data sets; that is, it displays an explanation capacity for the phenomenon. In addition, an analysis of this simulation proves our assumption about the social grooming cost increasing with the strength of social relationships as being true. Moreover, it suggests that its gradient increases the width and shallowness of these relationships. The law and its causes suggest that mankind's evolution of social grooming has enabled changing social structures, and the phenomenon is because of the constraints of the social network generation. These findings will contribute towards an explanation of the evolution of the various social grooming methods of humans and their significantly large social group.

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Section: Introductionmentioning

confidence: 99%

Limitations of time resources in human relationships determine social structures

Takano¹,

Fukuda²

2017

Palgrave Commun

Self Cite

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“…Channel co-ordination (Cui et al, 2007), revenue management (Tereyağoğlu et al, 2017), procedural fairness (Rabin, 1993), advantage and disadvantage fairness (Fehr and Schmidt, 1999) and fairness preference model (Bolton and Ockenfels, 2000; Cui and Wu, 2018) Forecast bias Different means of forecast bias (Goodwin et al, 2018), overconfidence (overplacement, overestimation and over-precision) (Gervais et al, 2003;Moore and Healy, 2008) Reciprocity and altruism Positive and negative reciprocity (Fehr and Gächter, 2000;Pereira et al, 2006;Gal and Pfeffer, 2007), corporate social responsibility (Luo and Zheng, 2013), supply chain co-operation (Simatupang and Sridharan, 2005;Liang and Wan, 2007), supply chain co-ordination (Lin and Hou, 2014;Du et al, 2014), optimal decision making (Xia et al, 2018;Shi et al, 2018;Liu, Yan, X., Wei, Xie and Wang, 2018), order allocation (Baltacioglu et al, 2007;Zhang, Li and Gou, 2017) and altruistic behaviour and subtypes (Takano et al, 2016)…”

Section: Fairness Concernmentioning

confidence: 99%

Service supply chain management: a behavioural operations perspective

Liu

Wang

Long

et al. 2019

MSCRA

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Purpose The purpose of this paper is to provide an overview of the evolution of service supply chain management from a behavioural operations perspective, pointing out future research directions for scholars. Design/methodology/approach This study searched five databases for relevant literature published between 2009 and 2018, selecting 64 papers for this review. The selected literature was categorised according to two dimensions: a service supply chain link perspective and a behavioural factor perspective. Comparative analysis was used to identify gaps in the literature, and five future research agendas were proposed. Findings In terms of the perspective of service supply chain link, extant literature primarily focuses on service supply and service co-ordination management, and less on service demand and integration management. In terms of the behavioural factor’s perspective, most focus on classic behaviour factors, with less attention paid to emerging behaviour factors. This paper thus proposes five research agendas: demand-oriented management and integrated supply chain-oriented behavioural research; broadening the understanding of the scope of behavioural operations; integrating the latest backgrounds and trends of service industry into the research; greater attention to behavioural operations in service sub-industries; and multimethod combination is encouraged to be used to dig into the interesting research problems. Originality/value This study constitutes the first systematic review of service supply chain research from a behavioural perspective. By categorising the literature into two dimensions, the state of existing research is evaluated with an eye towards future research avenues.

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“…The game allows real players to cooperate and compete with others in situations where the player's benefit is represented by a quantitative value, such as a payoff in game theory. A previous study [29] demonstrated the existence of reciprocal relationships where cooperators had more advantages than non-cooperators in this SNG.…”

Section: Introductionmentioning

confidence: 61%

Lightweight Interactions for Reciprocal Cooperation in a Social Network Game

Takano

Wada

Fukuda

2016

Lecture Notes in Computer Science

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The construction of reciprocal relationships requires cooperative interactions during the initial meetings. However, cooperative behavior with strangers is risky because the strangers may be exploiters. In this study, we show that people increase the likelihood of cooperativeness of strangers by using lightweight non-risky interactions in risky situations based on the analysis of a social network game (SNG). They can construct reciprocal relationships in this manner. The interactions involve low-cost signaling because they are not generated at any cost to the senders and recipients. Theoretical studies show that low-cost signals are not guaranteed to be reliable because the low-cost signals from senders can lie at any time. However, people used low-cost signals to construct reciprocal relationships in an SNG, which suggests the existence of mechanisms for generating reliable, low-cost signals in human evolution.

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Reciprocal Altruism-based Cooperation in a Social Network Game

Cited by 13 publications

References 26 publications

Limitations of time resources in human relationships determine social structures

Limitations of time resources in human relationships determine social structures

Service supply chain management: a behavioural operations perspective

Lightweight Interactions for Reciprocal Cooperation in a Social Network Game

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