Manipulation-resistant Consensus Formation

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Research Keywords: opinion diffusion and collective decision making, social network analysis, strategic interaction, computational social scienceIn the last decade aggregate polling as a means to predict election outcomes has had its share of failures and critiques, in particular in the UK and in the US, especially given the prevalence of fake news and external machinations. While full of demographic data, this metric is completely sans social topology, that is it has no interest in the role of social ties and connections in influencing individuals.We thus ask how do social networks impact the diffusion of opinions? What happens when communities litter these networks, and are potentially echo chambers? What graph structures and protocols exist in order to prevent various forms of manipulation and that can better inform agents?Stewart et al (2019) showed how the use of bots can help even minority parties/opinions to gain traction, we want to ask what preconditions of the networks make these bots ineffective? How cost-effective and what best way can a mal-aligned party utilise these bots? Can real agents realise their presence and collectively strategise to counter them?Connecting the social network to the geospatial distribution of agents, how does segregation impact opinions? Opinions can cause the emergence of segregation, as in Schelling (1971), but what is now the interplay between opinion dynamics and this segregation? Are there other emergent properties of the opinion dynamics?We wish to validate our models for example using data from the MIT Election Lab on past US elections as well as from the social media platforms the TWS Partners consult on.The context of the research - Recent political elections - primarily in the UK and the US, but increasingly across many other countries - which have been subjected to masses of fake news and un-democratic pressures, especially in social media. Bots and fake agents litter the digital space while gerrymandering can be seen as the manipulation of network edges.Equally opinion formation and diffusion on social media is an extremely prevalent issue that does also impact real elections, understanding the role of online communities and echo chambers also motivates the research. In recent years large tech companies such as Facebook have been under heavy criticism for failing to moderate and censor harmful/offensive/fake information and groups thus having a metric that can identify such polarised social networks would help law-makers and tech companies address such issues.The aims and objectives of the research - To provide additional tools, metrics and methodologies to understand how opinions and the formations thereof are manipulated. To produce graphs that are manipulation-resistant and/or provide a more informative environment for agents.The novelty of the research methodology - Analyse elections from a dynamics and systems perspective - unlike the status quo that uses static and local measures - that considers community structures that can be highly polarised.Unlike current centrality measures - quantifiers of a node/agent's importance - our proposed metrics would explicitly consider opinions/parties when ranking nodes based on their influence.We aim also to produce graph generation models that couples the topology of the graph and the opinions of its nodes, whereas in previous models the graph formation have been entirely divorced from opinions.The potential impact, applications, and benefits - Reducing undemocratic effects of maligned parties/individuals. Identify important agents and structures of agents that most influence opinions.Research;Digital economy, Engineering,Global uncertainties, ICT [Information and Communication Technologies], Mathematical SciencesExternal Partner - TWS Partners