Navigating the intricacies of digital environments demands effective strategies for fostering cooperation and upholding norms. Banning and moderating the content of influential users on social media platforms is often met with shock and awe, yet clearly has profound implications for online behaviour and digital governance. Drawing inspiration from these actions, we model broadcasting retributive measures. Leveraging analytical modeling and extensive agent-based simulations, we investigate how signaling punitive actions can deter anti-social behaviour and promote cooperation in online and multi-agent systems. Our findings underscore the transformative potential of threat signaling in cultivating a culture of compliance and bolstering social welfare, even in challenging scenarios with high costs or complex networks of interaction. This research offers valuable insights into the mechanisms of promoting pro-social behaviour and ensuring behavioural compliance across diverse digital ecosystems.

Before embarking on a new collective venture, it is important to understand partners’ preferences and intentions and how strongly they commit to a common goal. Arranging prior commitments of future actions has been shown to be an evolutionary viable strategy in the context of social dilemmas. Previous works have focused on simple well-mixed population settings, for ease of analysis. Here, starting from a baseline model of a coordination game with asymmetric benefits for technology adoption in the well-mixed setting, we examine the impact of different population structures, including square lattice and scale-free (SF) networks, capturing typical homogeneous and heterogeneous network structures, on the dynamics of decision-making in the context of coordinating technology adoption. We show that, similarly to previous well-mixed analyses, prior commitments enhance coordination and the overall population payoff in structured populations, especially when the cost of commitment is justified against the benefit of coordination, and when the technology market is highly competitive. When commitments are absent, slightly higher levels of coordination and population welfare are obtained in SF than lattice. In the presence of commitments and when the market is very competitive, the overall population welfare is similar in both lattice and heterogeneous networks; though it is slightly lower in SF when the market competition is low, while social welfare suffers in a monopolistic setting. Overall, we observe that commitments can improve coordination and population welfare in structured populations, but in its presence, the outcome of evolutionary dynamics is, interestingly, not sensitive to changes in the network structure.

Regulating the development of advanced technology such as Artificial Intelligence (AI) has become a principal topic, given the potential threat they pose to humanity’s long term future. First deploying such technology promises innumerable benefits, which might lead to the disregard of safety precautions or societal consequences in favour of speedy development, engendering a race narrative among firms and stakeholders due to value erosion. Building upon a previously proposed game-theoretical model describing an idealised technology race, we investigated how various structures of interaction among race participants can alter collective choices and requirements for regulatory actions. Our findings indicate that strong diversity among race participants, both in terms of connections and peer-influence, can reduce the conflicts which arise in purely homogeneous settings, thereby lessening the need for regulation.

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.