publications

2024

1. Influence Maximisation and Adaptive Control of Opinion Dynamics on Complex Networks

   Guillermo Romero Moreno

   University of Southampton, 2024

   Abs

   Opinion shaping, by which a strategic attempts to influence the opinion of a social group, is a pervasive phenomenon in human behaviour, with clear examples in current societies in information campaigns, political competition, or marketing. However, the effect that influence attempts have in societies is not easy to foresee, as they are complex systems where interactions can cascade and compound in unpredictable ways. The research subfield of opinion dynamics employs a mathematical modelling approach to study these phenomena using tools from the more general field of complex systems. In this approach, the opinions held by an individual are typically modelled as mathematical variables, with changes in opinion dictated by simple rules triggering upon interactions, which typically only happen interact according to a complex network that reflects their social structure. By drawing from techniques from statistical physics, agent–based simulations, and optimisation research, this thesis studies the effects that different external control strategies have on opinion formation processes. We first focus on a case where the external controller is a ‘perfect optimiser’, i.e. they can split their influence targets among the individuals and have information to do so strategically as to achieve the best possible result — a problem commonly known as Influence Maximisation. We place this optimising controller against an opponent in a scenario where individuals have preferences over two possible choices (e.g. products or political parties), propose an optimisation algorithm to find optimal targetting strategies, and analyse the characteristics of these to understand why they are effective. We find that optimal strategies can be characterised by two heuristics, shadowing and shielding, while the structure of the network only plays a secondary role. Shadowing entails targeting the same individuals as the opponent to directly block her influence in the network, unless the opponent’s influencing power is much higher, in which case these are avoided. Shielding entails ring–fencing the individuals targeted by the opponent to indirectly block the spreading of her influence. We then modify the previous scenario to incorporate different levels of bias against either opinion in individuals, and analyse optimal targetting strategies when the population is structured in different network topologies. We find a general pattern in which individuals that are difficult to control (i.e. biased against the controller, highly connected, or targeted by the opponent) are avoided if the influencing power is small and sought if the influencing power is high. Last, we shift the scenario to one in which the controllers are not ‘perfect optimisers’ any more but only have very limited information and perform local moves to improve their situation in the short term. Therefore, their control strategies are ‘adaptive’, reacting to the dynamics of the opinions as they unfold and creating a dynamic interaction between the two. We focus on the specific agenda–setting scenario where political parties seek to increase votes by affecting the importance that different political dimensions have and how their strategies interferes with the process of arriving at consensus or polarisation within the social group. We find in this scenario that party competition often fosters the arrival of a polarised state with most individuals gathering in two opposed camps, although if parties perform frequent shifts in the issues they give importance to, their behaviour inadvertently fosters the arrival at a consensus in opinion in social group.

2. Multimorbidity analysis with low condition counts: a robust Bayesian approach for small but important subgroups

   Guillermo Romero Moreno, Valerio Restocchi, Jacques D. Fleuriot, and 3 more authors

   eBioMedicine, Apr 2024

   Abs DOI

   Background Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when evidence is limited. We have developed a Bayesian inference framework that is robust to sparse data and used it to quantify morbidity associations in the oldest old, a population with limited available data. Methods We conducted a retrospective cross-sectional study of a representative dataset of primary care patients in Scotland as of March 2007. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex (3039 men, 8970 women). We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with our proposed measure, Associations Beyond Chance (ABC). To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC. Findings Our Bayesian framework was appropriately more cautious in attributing association when evidence is lacking, particularly in uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex and affected the aggregated measures of multimorbidity and network representations. Interpretation Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when evidence is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity. Funding National Institute for Health and Care Research.

2023

1. Associations between Morbidities in Small But Important Subgroups: A Novel Bayesian Approach for Robust Multimorbidity Analysis with Small Sample Sizes

   Guillermo Romero Moreno, Valerio Restocchi, Jacques D Fleuriot, and 3 more authors

   Apr 2023

   Abs DOI

   Background: Robustly examining associations between long-term conditions may be important in identifying opportunities for intervention in multimorbidity but is challenging when data is limited. We have developed a Bayesian inference framework that is robust to sparse data and have used it to quantify morbidity associations in the oldest old, a population with limited available data. Methods: We conducted a retrospective cross-sectional cohort study of a representative dataset of primary care patients in Scotland. We included 40 long-term conditions and studied their associations in 12,009 individuals aged 90 and older, stratified by sex, to study the effect of small sample sizes in the estimation of associations between long-term conditions. We analysed associations obtained with Relative Risk (RR), a standard measure in the literature, and compared them with a new measure of associations, Associations Beyond Chance (ABC), that utilises a Bayesian framework. To enable a broad exploration of interactions between long-term conditions, we built networks of association and assessed differences in their analysis when associations are estimated by RR or ABC. Findings: Our Bayesian framework was appropriately more cautious in attributing association when evidence is small, as it dismissed six of the top ten associations reported by RR, most of which relate to uncommon conditions. This caution in reporting association was also present in reporting differences in associations between sex, for which ABC only reported as significant about one-fifth of those reported by RR. Last, the presence of potentially inaccurate associations by RR also affected the aggregated measures of multimorbidity and network representations. Interpretation: Incorporating uncertainty into multimorbidity research is crucial to avoid misleading findings when data is limited, a problem that particularly affects small but important subgroups. Our proposed framework improves the reliability of estimations of associations and, more in general, of research into disease mechanisms and multimorbidity.

2022

1. Sensing Enhancement on Social Networks: The Role of Network Topology

   Markus Brede, and Guillermo Romero-Moreno

   Entropy, May 2022

   ISBN: 9783030934125

   Abs DOI

   Sensing and processing information from dynamically changing environments is essential for the survival of animal collectives and the functioning of human society. In this context, previous work has shown that communication between networked agents with some preference towards adopting the majority opinion can enhance the quality of error-prone individual sensing from dynamic environments. In this paper, we compare the potential of different types of complex networks for such sensing enhancement. Numerical simulations on complex networks are complemented by a mean-field approach for limited connectivity that captures essential trends in dependencies. Our results show that, whilst bestowing advantages on a small group of agents, degree heterogeneity tends to impede overall sensing enhancement. In contrast, clustering and spatial structure play a more nuanced role depending on overall connectivity. We find that ring graphs exhibit superior enhancement for large connectivity and that random graphs outperform for small connectivity. Further exploring the role of clustering and path lengths in small-world models, we find that sensing enhancement tends to be boosted in the small-world regime.

2021

1. Shadowing and shielding: Effective heuristics for continuous influence maximisation in the voting dynamics

   Guillermo Romero Moreno, Sukankana Chakraborty, and Markus Brede

   PLOS ONE, Jun 2021

   Abs DOI

   Influence maximisation, or how to affect the intrinsic opinion dynamics of a social group, is relevant for many applications, such as information campaigns, political competition, or marketing. Previous literature on influence maximisation has mostly explored discrete allocations of influence, i.e. optimally choosing a finite fixed number of nodes to target. Here, we study the generalised problem of continuous influence maximisation where nodes can be targeted with flexible intensity. We focus on optimal influence allocations against a passive opponent and compare the structure of the solutions in the continuous and discrete regimes. We find that, whereas hub allocations play a central role in explaining optimal allocations in the discrete regime, their explanatory power is strongly reduced in the continuous regime. Instead, we find that optimal continuous strategies are very well described by two other patterns: (i) targeting the same nodes as the opponent (shadowing) and (ii) targeting direct neighbours of the opponent (shielding). Finally, we investigate the game-theoretic scenario of two active opponents and show that the unique pure Nash equilibrium is to target all nodes equally. These results expose fundamental differences in the solutions to discrete and continuous regimes and provide novel effective heuristics for continuous influence maximisation.

2. A normative set of criteria to increase political competence through voting advice applications

   Javier Padilla, Javier Ramos, Guillermo Romero, and 1 more author

   International Journal of Electronic Governance, Jul 2021

   Abs DOI

   This paper argues that current voting advice applications (VAAs) do not sufficiently fulfil their stated aim of increasing voters’ political competence. First, we define four criteria to evaluate whether their methods are likely to increase political competence: informativeness, respect for users’ way of comparing and aggregating policy issues, reliability, and transparency. Second, we argue that current VAAs compare and aggregate users’ and parties’ policy preferences following a weak method that fails in two of them. To prove it, we analyse the methodology of most currents VAAs and use the outcomes from the EU-Vox 2014 in several countries. Third, we discuss two possibilities by which VAAs could improve: (1) by using ex-ante survey data to fill their gaps, or (2) by creating a learning algorithm to adapt the VAA to users’ preferences. We found that some changes need to be made if VAAs aim to have an impact on users’ political competence.

3. The effects of party competition on consensus formation

   Guillermo Romero Moreno, Javier Padilla, and Markus Brede

   Jul 2021

   Abs

   The fight over setting the political agenda is one of the basic mechanisms of party competition of every democracy. How- ever, this political game may have side effects in other as- pects of the public debate. One aspect of general interest is how it may alter consensus formation processes among citi- zens, which may result in states of consensus, polarisation, or opinion fragmentation in the population. In this paper, we study the interrelated dynamics of two processes affecting opinion dynamics when multiple issues are debated. First, we model party competition via campaigning and its effect on the saliency or importance with which citizens perceive different political issues. Second, we consider a bounded–confidence model to describe the dynamics of citizens’ opinion and con- sensus formation. We find that the effects of party competi- tion on consensus formation are rich and non-trivially depen- dent on the configuration of party positions in the political space. We illustrate that —as one would intuitively expect— there are party configurations that foster a paradigmatic state of polarisation for a wide range of model parameters. How- ever, we also show that other party configurations have the opposite effect, and can facilitate reaching a consensus state that could otherwise not have been achieved. Our results il- lustrate the richness of possible outcomes of interrelations be- tween party competition and consensus formation. Introduction

2020

1. Continuous Influence Maximisation for the Voter Dynamics : Is Targeting High-Degree Nodes a Good Strategy ? Extended Abstract

   Guillermo Romero Moreno, and Markus Brede

   Jul 2020
2. Zealotry and Influence Maximization in the Voter Model: When to Target Partial Zealots?

   Guillermo Romero Moreno, Edoardo Manino, Long Tran-Thanh, and 1 more author

   In Springer Proceedings in Complexity, Jul 2020

   ISSN: 22138692

   Abs DOI

   In this paper, we study influence maximization in the voter model in the presence of biased voters (partial zealots) on complex networks. Under what conditions should an external controller with finite budget who aims at maximizing its influence over the system target partial zealots? Our analysis, based on both analytical and numerical results, shows a rich diagram of preferences and degree-dependencies of allocations to partial zealots and normal agents varying with the budget. We find that when we have a large budget or for low levels of zealotry, optimal strategies should give larger allocations to partial zealots and allocations are positively correlated with node degree. In contrast, for low budgets or highly-biased zealots, optimal strategies give higher allocations to normal agents, with some residual allocations to partial zealots, and allocations to both types of agents decrease with node degree. Our results emphasize that heterogeneity in agent properties strongly affects strategies for influence maximization on heterogeneous networks.