Evolution of trust in structured populations

The trust game, derived from a notable economics experiment, has recently attracted interest in the field of evolutionary dynamics. In a prevalent version of the evolutionary trust game, players adopt one of three strategies: investor, trustworthy trustee, or untrustworthy trustee. Trustworthy trustees enhance and share the investment with the investor, whereas untrustworthy trustees retain the full amount, betraying the investor. Following this setup, we propose a two-player version of the trust game, which is analytically feasible. Based on weak selection and pair approximation, we explore the evolution of trust in structured populations, factoring in four strategy updating rules: pairwise comparison (PC), birth-death (BD), imitation (IM), and death-birth (DB). Comparing structured populations with well-mixed populations, we arrive at two main conclusions. First, in the absence of untrustworthy trustees, there is a saddle point between investors and trustworthy trustees, with collaboration thriving best in well-mixed populations. The collaboration diminishes sequentially from DB to IM to PC/BD updating rules in structured populations. Second, an invasion of untrustworthy trustees makes this saddle point unstable and leads to the extinction of investors. The 3-strategy system stabilizes at an equilibrium line where the trustworthy and untrustworthy trustees coexist. The stability span of trustworthy trustees is maximally extended under the PC and BD updating rules in structured populations, while it decreases in a sequence from IM to DB updating rules, with the well-mixed population being the least favorable. This research adds an analytical lens to understanding the evolution of trust in structured populations.Comment: 15 pages, 5 figure

Leveraging Users’ Trust and Reputation in Social Networks

In on line communities, where there is a huge number of users that interact under anonymous identities, it has been observed that e-word of mouth is a very powerful influence tool. So far, this technology is well known in on-line marketplaces, such as Amazon, eBay or travel based platforms like Tripadvisor or Booking. However, these trust based approach can be leverage in other scenarios from e-democracy to trust based recommendations on e-health context and e-learning systems. The purpose of this contribution is to analyse the main existing trust and reputation mechanisms and to point out new research challenges that needs to be accomplished with the objective of fully exploiting these systems in real world on-line communities.The authors would like to acknowledge the financial support from the EU project H2020-MSCA-IF-2016- DeciTrustNET-746398 and FEDER funds provided in the Spanish project TIN2016-75850-P

A review on trust propagation and opinion dynamics in social networks and group decision making frameworks

On-line platforms foster the communication capabilities of the Internet to develop large- scale influence networks in which the quality of the interactions can be evaluated based on trust and reputation. So far, this technology is well known for building trust and harness- ing cooperation in on-line marketplaces, such as Amazon (www.amazon.com) and eBay (www.ebay.es). However, these mechanisms are poised to have a broader impact on a wide range of scenarios, from large scale decision making procedures, such as the ones implied in e-democracy, to trust based recommendations on e-health context or influence and per- formance assessment in e-marketing and e-learning systems. This contribution surveys the progress in understanding the new possibilities and challenges that trust and reputation systems pose. To do so, it discusses trust, reputation and influence which are important measures in networked based communication mechanisms to support the worthiness of information, products, services opinions and recommendations. The existent mechanisms to estimate and propagate trust and reputation, in distributed networked scenarios, and how these measures can be integrated in decision making to reach consensus among the agents are analysed. Furthermore, it also provides an overview of the relevant work in opinion dynamics and influence assessment, as part of social networks. Finally, it identi- fies challenges and research opportunities on how the so called trust based network can be leveraged as an influence measure to foster decision making processes and recommen- dation mechanisms in complex social networks scenarios with uncertain knowledge, like the mentioned in e-health and e-marketing frameworks.The authors acknowledge the financial support from the EU project H2020-MSCA-IF-2016-DeciTrustNET-746398, FEDER funds provided in the National Spanish project TIN2016-75850-P , and the support of the RUDN University Program 5-100 (Russian Federation)

To trust or not to trust: evolutionary dynamics of an asymmetric N-player trust game

Trusting others and reciprocating the received trust with trustworthy actions are fundaments of economic and social interactions. The trust game (TG) is widely used for studying trust and trustworthiness and entails a sequential interaction between two players, an investor and a trustee. It requires at least two strategies or options for an investor (e.g.to trust versus not to trust a trustee). According to the evolutionary game theory, the antisocial strategies (e.g.not to trust) evolve such that the investor and trustee end up with lower payoffs than those that they would get with the prosocial strategies (e.g.to trust). A generalisation of the TG to a multiplayer (i.e.more than two players) TG was recently proposed. However, its outcomes hinge upon two assumptions that various real situations may substantially deviate from: (i) investors are forced to trust trustees and (ii) investors can turn into trustees by imitation and vice versa. We propose an asymmetric multiplayer TG that allows investors not to trust and prohibits the imitation between players of different roles; instead, investors learn from other investors and the same for trustees. We show that the evolutionary game dynamics of the proposed TG qualitatively depends on the nonlinearity of the payoff function and the amount of incentives collected from and distributed to players through an institution. We also show that incentives given to trustees can be useful and sufficient to cost-effectively promote trust and trustworthiness among self-interested players

Modelling Irrational Agent Beliefs In Online Social Networks

The spread of misinformation through online social network platforms have become a major concern in society. Understanding human behaviour and decision-making in complex systems requires modelling irrational beliefs of actors in social networks. Irrational beliefs can drive people to make decisions that are counter to their own interests or the greater good, producing outcomes that are less than ideal for both the individual and society. This thesis addresses the problem of modelling irrational beliefs in social networks by creating a framework that reflects the impact of such beliefs on agent behaviour. Graph neural networks are increasingly employed to model how beliefs propagate across a network of interconnected agents and to explore how they affect outcomes in a social system. This research presents a comprehensive review of the latest advancements in the use of graph neural networks for the purpose of modelling irrational agent beliefs in social networks. The approach represents agents and their interactions as nodes and edges in a graph. GNNs’ are then used to learn the underlying structure and dynamics of the network, with a focus on understanding how irrational beliefs propagate through the network. The proposed framework incorporates the effects of social influence and biases into a GNN model of agent behaviour and is intended to provide insights into how misinformation and other forms of irrationality can spread within social networks and may have implications for understanding and mitigating the effects of disinformation and other forms of misinformation