Group Size and Gossip Strategies: An ABM Tool for Investigating Reputation-Based Cooperation

Giardini, Francesca; Paolucci, Mario; Adamatti, Diana Francisca; Conte, Rosaria

doi:10.1007/978-3-319-14627-0_8

Cited by 7 publications

(8 citation statements)

References 19 publications

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“…Some field evidence shows that medium‐sized groups are more likely to provide third‐party monitoring and induce higher contributions than smaller groups (Agrawal & Goyal, ). A recent computer simulation also shows that gossip‐based strategies (e.g., choosing group members based on others' reputation) are more effective in promoting cooperation in larger groups (Giardini, Paolucci, Adamatti, & Conte, ).…”

Section: When Does Reputation Promote Cooperation?mentioning

confidence: 99%

Reputation, Gossip, and Human Cooperation

Balliet

Lange

2016

Social & Personality Psych

143

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Why do people cooperate? We address this classic question by analyzing and discussing the role of reputation: people cooperate to maintain a positive reputation in their social environment. Reputation is a key element fueling a system of indirect reciprocity, where cooperators establish a good reputation and are thus more likely to receive future benefits from third parties. The tendencies to monitor, spread, and manage each other's reputation help explain the abundance of human cooperation with unrelated strangers. We review research on the phenomenon of reputation‐based cooperation in the domains of how people manage their reputation in response to varying cues of reputation, when reputation can promote cooperation, and individual differences in reputation management. We also propose three directions for future research: group stability and reputation‐based cooperation, solutions to cope with noise and biased reputation, and the relative efficiency of positive versus negative reputation systems.

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Section: When Does Reputation Promote Cooperation?mentioning

confidence: 99%

Reputation, Gossip, and Human Cooperation

Balliet

Lange

2016

Social & Personality Psych

143

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“…The role of reputation has been explored with more elaborate communication strategies, exploring for example the role of gossip (e.g. Giardini et al, 2014). Reputation can be slow to establish, especially in larger societies, and is potentially computationally costly, making gossip an efficient way of speeding up the process.…”

Section: Agent-based Modellingmentioning

confidence: 99%

“…Reputation can be slow to establish, especially in larger societies, and is potentially computationally costly, making gossip an efficient way of speeding up the process. Giardini et al (2014) analyse gossip strategies at different group sizes, namely 5, 10 and 25. Groups of 10 members perform worst in their model.…”

Section: Agent-based Modellingmentioning

confidence: 99%

Choosing to cooperate: Modelling public goods games with team reasoning

Elsenbroich

Payette

2020

Journal of Choice Modelling

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This paper presents an agent-based model of team reasoning in a social dilemma game. Starting from the conundrum of empirically high levels of cooperation in dilemma games, contradicting traditional utility maximisation assumptions of game theory, Bacharach (1999Bacharach ( , 2006 developed a theory of team reasoning. The idea behind team reasoning is that agents do not try to maximise their own utility but make choices as part of a team. This paper presents a model of preference convergence, mirroring adaptation dynamics of team reasoning. It describes an agent-based model simulating a repeated public goods game between a designated set of agents, a team. In the model agents have a probability to choose cooperation or defection, adjusting this preferences in the face of the revealed preferences of other players. The model is a classic binary choice model mapping an individual's preference for cooperation onto the binary behaviour choice of cooperation and defection. Preferences are updated in reaction to the behaviour choices of the team. Starting from simple stated preferences, the model implements a reframing of utility maximisation as applying to a group rather than an individual, modelling the importance of social interaction for individual preferences and the dependency of choice on social context. Results show that team reasoning, as implemented here, can explain high levels of cooperation found in the real world resulting from a wide range of settings. It also shows that team reasoning, as implemented here, is not a 'sucker' strategy except when adaptation rates are very slow. This paper demonstrates how agent-based models can be used to examine the role of social contexts for individual decision making.

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“…For each experiment, the agents population was formed by 300 agents, 100 of each using a different contribution strategy (i.e., Cooperator, Free-rider, and Utilitarian). Similarly to parameters used by Giardini et al (2014Giardini et al ( , 2015, groups of at most 25 agents were formed in each round, the contribution to the public pool was set to 1 token, and the benefit factor was set to 3. Each agent was endowed with an initial amount of tokens to be used to contribute to the public pool or to sanction others, hereafter called endowment.…”

Section: Methodsmentioning

confidence: 99%

“…Table 4.2 contains an identification of each experiment along with parameter values used. The endowment, enforcement cost, and punishment cost are a linear transformation of the parameters used in a previous work by Giardini et al (2015). Each parameter may be set to either a low or a high value, namely low and high endowment (Lt and Ht), low and high enforcement cost (Le and He), and low and high punishment cost (Lp and Hp).…”

Section: Descriptionmentioning

confidence: 99%

GAVEL: a sanction-based regulation mechanism for normative multiagent systems

Lima¹

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The use of a normative approach to govern multiagent systems (MAS) has been motivated by the increasing interest in balancing between agents' autonomy and global system control. In normative multiagent systems (NMAS), despite the existence of norms specifying rules about how agents ought or ought not to behave, agents have the autonomy to decide whether or not to act in compliance with such norms. A suitable way to govern agents is using sanction-based enforcement mechanisms. These mechanisms allow agents autonomy while maintaining a certain system control level through sanction applications. However, most enforcement mechanisms found in the literature lack support for the association of norm compliance or violation to different sanction categories and strength, thus refraining agents from sanction reasoning and decision capabilities. An exception is the model proposed by Nardin et al. (2016). Based on this latter model, this thesis presents an operational sanctioning enforcement framework, named GAVEL, which endows agents with the capability to decide for the most appropriate sanctions to apply, depending on their context assessed by a set of decision factors. The advantages of applying different sanctions categories and strength are illustrated by a case study from the domain of economics, namely the public goods game (PGG). The experimental results show that allowing agents to decide between material and social sanctions leads to similar cooperation rates but greater wealth levels in comparison to solely using material sanction.

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Group Size and Gossip Strategies: An ABM Tool for Investigating Reputation-Based Cooperation

Cited by 7 publications

References 19 publications

Reputation, Gossip, and Human Cooperation

Reputation, Gossip, and Human Cooperation

Choosing to cooperate: Modelling public goods games with team reasoning

GAVEL: a sanction-based regulation mechanism for normative multiagent systems

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