Randomness in the evolution of cooperation

Evolutionary Dynamics of Homophily and Heterophily

Most social interactions do not take place at random. In many situations, individuals choose their interaction partners on the basis of phenotypic cues. When this happens, individuals are often homophilic, that is, they tend to interact with individuals that are similar to them. Here we investigate the joint evolution of phenotypic cues and cue-dependent interaction strategies. By a combination of individual-based simulations and analytical arguments, we show that homophily evolves less easily than earlier studies suggest. The evolutionary interplay of cues and cue-based behaviour is intricate and has many interesting facets. For example, an interaction strategy like heterophily may stably persist in the population even if it is selected against in association with any particular cue. Homophily persisted for extensive periods of time just in those simulations where homophilic interactions provide a lower (rather than a higher) payoff than heterophilic interactions. Our results indicate that even the simplest cue-based social interactions can have rich dynamics and a surprising diversity of evolutionary outcomes.

The early evolution of cooperation in humans. On cheating, group identity and group size

The evolution of cooperation is difficult to understand, because cheaters — individuals who profit without cooperating themselves — have a benefit in interaction with cooperators. Cooperation among humans is even more difficult to understand, because cooperation occurs in large groups, making cheating a bigger threat. Restricting cooperation to members of one’s own group based on some tag-based recognition of non-group members (allorecognition) has been shown to stabilise cooperation. We address how spatial structure and group size affect the opportunities for cheating such tag-based cooperation in a spatially explicit simulation. We show that increased group diversity, under conditions of limited dispersal, reduces the selective opportunities for cheaters. A small number can already be sufficient to keep cheating at a low frequency. We discuss how marginal additional benefits of increased group size, above the benefits of local cooperation, can provide the selective pressure to reduce the number of group identities and discuss possible examples.

The Emergence of Relationship-based Cooperation

This paper investigates the emergence of relationship-based cooperation by coupling two simple mechanisms into the model: tie strength based investment preference and homophily assumption. We construct the model by categorizing game participants into four types: prosocialists (players who prefers to invest in their intimate friends), antisocialists (players who prefer to invest in strangers), egoists (players who never cooperate) and altruists (players who cooperate indifferently with anyone). We show that the relationship-based cooperation (prosocialists) is favored throughout the evolution if we assume players of the same type have stronger ties than different ones. Moreover, we discover that strengthening the internal bonds within the strategic clusters further promotes the competitiveness of prosocialists and therefore facilitates the emergence of relationship-based cooperation in our proposed scenarios. The robustness of the model is also tested under different strategy updating rules and network structures. The results show that this argument is robust against the variations of initial conditions and therefore can be considered as a fundamental theoretical framework to study relationship-based cooperation in reality.

Empirical Studies on the Network of Social Groups: The Case of Tencent QQ

BACKGROUND

Participation in social groups are important but the collective behaviors of human as a group are difficult to analyze due to the difficulties to quantify ordinary social relation, group membership, and to collect a comprehensive dataset. Such difficulties can be circumvented by analyzing online social networks.

METHODOLOGY/PRINCIPAL FINDINGS

In this paper, we analyze a comprehensive dataset released from Tencent QQ, an instant messenger with the highest market share in China. Specifically, we analyze three derivative networks involving groups and their members-the hypergraph of groups, the network of groups and the user network-to reveal social interactions at microscopic and mesoscopic level.

CONCLUSIONS/SIGNIFICANCE

Our results uncover interesting behaviors on the growth of user groups, the interactions between groups, and their relationship with member age and gender. These findings lead to insights which are difficult to obtain in social networks based on personal contacts.