Abstract
A computational model for the development of social relationships is described. The model implements agent strategies for social interaction based on Dunbar's Social Brain Hypothesis (SBH). A trust related process controls the formation and decay of relationships as a function of interaction frequency, the history of interaction, and the agents' strategies. A good fit the SBH predictions was found across a range of model parameter settings, which varied the waning rate of trust, defect/cooperation rates for agents, and linear/log functions for trust increase and decay. Social interaction strategies which favour interacting with existing strong ties or a time variant strategy produced more SBH conformant results than strategies favour more weaker relationships. The prospects for modeling the emergence of social relationships are discussed.
Highlights
1.1 Numerous computational models of social behaviour have been developed that focus on cooperation and the evolution of altruism N( owak & Sigmund 2005, Janssen 2006); and on swarm intelligence and coordination in social behaviour (Bonabeau et al 1999)
In this paper we extend the genre of social network models by investigating how a causal mechanism of trust can lead to the formation and maintenance of social relationships
The distributions were skewed towards a few stronger relationships, with a mid-range showing high variation in trust values, a long tail of low trust relationships with less variation
Summary
1.1 Numerous computational models of social behaviour have been developed that focus on cooperation and the evolution of altruism N( owak & Sigmund 2005, Janssen 2006); and on swarm intelligence and coordination in social behaviour (Bonabeau et al 1999). Trust in relationships wanes over time at a slow rate independent of any interactions This models the empirical observation that relationship strength declines unless it is maintained by social interaction (Roberts & Dunbar in press (b)), Wellman et al 2001, 2006). To model the empirical observation that trust reaches an asymptote in human relationship when it switches from calculative to emotion mode (Ostrom 2002), a law of diminishing returns is implemented so that as trust in a relationship increases, a log ratio algorithm is applied; the rate of increase progressively decreases as the value of trust rises. Since the log algorithm applies to negative as well as positive interaction, high-trust relationships are relatively immune to defections This models the intuition that people forgive alters' indiscretions in high-trust relationships. To ensure that all trust relationships exhibit some degree of waning, the waning equation incorporates a minimal decrease even for strong trust relationships: 3.11 The log trust waning algorithm has two parameters, min_waning (MinWaning) and waning_rate (WaningR), which define the of Min_waning_cost as follows:
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