Trusting: Alone and together

Meylahn, Benedikt V.; Den Boer, Arnoud V.; Mandjes, Michel

doi:10.1080/0022250x.2024.2340135

The Journal of Mathematical Sociology

2024

DOI: 10.1080/0022250x.2024.2340135

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Trusting: Alone and together

Benedikt V. Meylahn,

Arnoud V. Den Boer,

Michel Mandjes

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2024

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Cited by 2 publications

References 26 publications

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Opinion dynamics beyond social influence

Meylahn,

Searle

2024

Net Sci

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We present an opinion dynamics model framework discarding two common assumptions in the literature: (a) that there is direct influence between beliefs of neighboring agents, and (b) that agent belief is static in the absence of social influence. Agents in our framework learn from random experiences which possibly reinforce their belief. Agents determine whether they switch opinions by comparing their belief to a threshold. Subsequently, influence of an alter on an ego is not direct incorporation of the alter’s belief into the ego’s but by adjusting the ego’s decision-making criteria. We provide an instance from the framework in which social influence between agents generalizes majority rules updating. We conduct a sensitivity analysis as well as a pair of experiments concerning heterogeneous population parameters. We conclude that the framework is capable of producing consensus, polarization and fragmentation with only assimilative forces between agents which typically, in other models, lead exclusively to consensus.

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Opinion dynamics beyond social influence

Meylahn,

Searle

2024

Net Sci

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Interpersonal trust: Asymptotic analysis of a stochastic coordination game with multi-agent learning

Meylahn,

den Boer,

Mandjes

2024

Chaos: An Interdisciplinary Journal of Nonlinear Science

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We study the interpersonal trust of a population of agents, asking whether chance may decide if a population ends up with high trust or low trust. We model this by a discrete time, stochastic coordination game with pairwise interactions occurring at random in a finite population. Agents learn about the behavior of the population using a weighted average of what they have observed in past interactions. This learning rule, called an “exponential moving average,” has one parameter that determines the weight of the most recent observation and may, thus, be interpreted as the agent’s memory. We prove analytically that in the long run, the whole population always either trusts or doubts with the probability one. This remains true when the expectation of the dynamics would indicate otherwise. By simulation, we study the impact of the distribution of the payoff matrix and of the memory of the agents. We find that as the agent memory increases (i.e., the most recent observation weighs less), the actual dynamics increasingly resemble the expectation of the process. We conclude that it is possible that a population may converge upon high or low trust between its citizens simply by chance, though the game parameters (context of the society) may be quite telling.

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Trusting: Alone and together

Cited by 2 publications

References 26 publications

Opinion dynamics beyond social influence

Opinion dynamics beyond social influence

Interpersonal trust: Asymptotic analysis of a stochastic coordination game with multi-agent learning

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