Abstract
The design of mechanisms that encourage pro-social behaviours in populations of self-regarding agents is recognised as a major theoretical challenge within several areas of social, life and engineering sciences. When interference from external parties is considered, several heuristics have been identified as capable of engineering a desired collective behaviour at a minimal cost. However, these studies neglect the diverse nature of contexts and social structures that characterise real-world populations. Here we analyse the impact of diversity by means of scale-free interaction networks with high and low levels of clustering, and test various interference mechanisms using simulations of agents facing a cooperative dilemma. Our results show that interference on scale-free networks is not trivial and that distinct levels of clustering react differently to each interference mechanism. As such, we argue that no tailored response fits all scale-free networks and present which mechanisms are more efficient at fostering cooperation in both types of networks. Finally, we discuss the pitfalls of considering reckless interference mechanisms.
Highlights
The problem of explaining collective behaviours among self-interested individuals in evolving dynamical systems has fascinated researchers from many fields, and is a well studied research topic in evolutionary game theory (Hofbauer and Sigmund, 1998)
In contrast to the study on square lattice networks (Han et al, 2018), as detailed below for each interference mechanism, we found that performing cost-effective interventions on scale-free networks of contacts (SF NOCs) presents multiple concerns
Positive interference in BA models broadly requires very high θ values or a blanketing mechanism that targets all or almost all cooperators, even those which are not necessarily in danger of converting to D
Summary
The problem of explaining collective behaviours among self-interested individuals in evolving dynamical systems has fascinated researchers from many fields, and is a well studied research topic in evolutionary game theory (Hofbauer and Sigmund, 1998) It can be found in a variety of real-world situations, ranging from ecosystems to human organisations and technological innovations and social networks (Santos et al, 2006; Sigmund et al, 2001; Raghunandan and Subramanian, 2012; Han et al, 2019). The decision-maker has to take into account the fact that it will have to repeatedly interfere in the eco-system in order to sustain the level of biodiversity over time That is, it has to find an efficient interference mechanism that leads to its desired goals, while minimising the its total cost
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