Consensus and polarization in competing complex contagion processes

Vasconcelos, V. V., Levin, S. A., & Pinheiro, F. L. (2019). Consensus and polarization in competing complex contagion processes. Journal of The Royal Society Interface, 16(155), [20190196]. https://doi.org/10.1098/rsif.2019.0196The rate of adoption of new information depends on reinforcement from multiple sources in a way that often cannot be described by simple contagion processes. In such cases, contagion is said to be complex. Complex contagion happens in the diffusion of human behaviours, innovations and knowledge. Based on that evidence, we propose a model that considers multiple, potentially asymmetric and competing contagion processes and analyse its respective population-wide dynamics, bringing together ideas from complex contagion, opinion dynamics, evolutionary game theory and language competition by shifting the focus from individuals to the properties of the diffusing processes. We show that our model spans a dynamical space in which the population exhibits patterns of consensus, dominance, and, importantly, different types of polarization, a more diverse dynamical environment that contrasts with single simple contagion processes. We show how these patterns emerge and how different population structures modify them through a natural development of spatial correlations: structured interactions increase the range of the dominance regime by reducing that of dynamic polarization, tight modular structures can generate structural polarization, depending on the interplay between fundamental properties of the processes and the modularity of the interaction network.authorsversionpublishersversionpublishe

Rate-Induced Transitions in Networked Complex Adaptive Systems: Exploring Dynamics and Management Implications Across Ecological, Social, and Socioecological Systems

Complex adaptive systems (CASs), from ecosystems to economies, are open systems and inherently dependent on external conditions. While a system can transition from one state to another based on the magnitude of change in external conditions, the rate of change -- irrespective of magnitude -- may also lead to system state changes due to a phenomenon known as a rate-induced transition (RIT). This study presents a novel framework that captures RITs in CASs through a local model and a network extension where each node contributes to the structural adaptability of others. Our findings reveal how RITs occur at a critical environmental change rate, with lower-degree nodes tipping first due to fewer connections and reduced adaptive capacity. High-degree nodes tip later as their adaptability sources (lower-degree nodes) collapse. This pattern persists across various network structures. Our study calls for an extended perspective when managing CASs, emphasizing the need to focus not only on thresholds of external conditions but also the rate at which those conditions change, particularly in the context of the collapse of surrounding systems that contribute to the focal system's resilience. Our analytical method opens a path to designing management policies that mitigate RIT impacts and enhance resilience in ecological, social, and socioecological systems. These policies could include controlling environmental change rates, fostering system adaptability, implementing adaptive management strategies, and building capacity and knowledge exchange. Our study contributes to the understanding of RIT dynamics and informs effective management strategies for complex adaptive systems in the face of rapid environmental change.Comment: 25 pages, 4 figures, 1 box, supplementary informatio

Co-evolutionary dynamics of collective action with signaling for a quorum

Collective signaling for a quorum is found in a wide range of organisms that face collective action problems whose successful solution requires the participation of some quorum of the individuals present. These range from humans, to social insects, to bacteria. The mechanisms involved, the quorum required, and the size of the group may vary. Here we address the general question of the evolution of collective signaling at a high level of abstraction. We investigate the evolutionary dynamics of a population engaging in a signaling N-person game theoretic model. Parameter settings allow for loners and cheaters, and for costly or costless signals. We find a rich dynamics, showing how natural selection, operating on a population of individuals endowed with the simplest strategies, is able to evolve a costly signaling system that allows individuals to respond appropriately to different states of Nature. Signaling robustly promotes cooperative collective action, in particular when coordinated action is most needed and difficult to achieve. Two different signaling systems may emerge depending on Nature's most prevalent states.Funding: This research was supported by FEDER through POFC - COMPETE, FCT-Portugal through grants SFRH/BD/86465/2012, PTDC/MAT/122897/2010, EXPL/EEI-SII/2556/2013, and by multi-annual funding of CMAF-UL, CBMA-UM and INESC-ID (under the projects PEst-OE/BIA/UI4050/2014 and UID/CEC/50021/2013) provided by FCT-Portugal, and by Fundacao Calouste Gulbenkian through the "Stimulus to Research" program for young researchers. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Toxicity in Evolving Twitter Topics

Geller, M., Vasconcelos, V. V., & Pinheiro, F. L. (2023). Toxicity in Evolving Twitter Topics. In J. Mikyška, C. de Mulatier, M. Paszynski, V. V. Krzhizhanovskaya, J. J. Dongarra, & P. M. Sloot (Eds.), Computational Science: Computational Science – ICCS 2023 23rd International Conference, Prague, Czech Republic, July 3–5, 2023, Proceedings, Part IV (pp. 40-54). [Chapter 4] (Lecture Notes in Computer Science; Vol. 10476). Springer. https://doi.org/10.1007/978-3-031-36027-5_4Tracking the evolution of discussions on online social spaces is essential to assess populations’ main tendencies and concerns worldwide. This paper investigates the relationship between topic evolution and speech toxicity on Twitter. We construct a Dynamic Topic Evolution Model (DyTEM) based on a corpus of collected tweets. To build DyTEM, we leverage a combination of traditional static Topic Modelling approaches and sentence embeddings using sBERT, a state-of-the-art sentence transformer. The DyTEM is represented as a directed graph. Then, we propose a hashtag-based method to validate the consistency of the DyTEM and provide guidance for the hyperparameter selection. Our study identifies five evolutionary steps or Topic Transition Types: Topic Stagnation, Topic Merge, Topic Split, Topic Disappearance, and Topic Emergence. We utilize a speech toxicity classification model to analyze toxicity dynamics in topic evolution, comparing the Topic Transition Types in terms of their toxicity. Our results reveal a positive correlation between the popularity of a topic and its toxicity, with no statistically significant difference in the presence of inflammatory speech among the different transition types. These findings, along with the methods introduced in this paper, have broader implications for understanding and monitoring the impact of topic evolution on the online discourse, which can potentially inform interventions and policy-making in addressing toxic behavior in digital communities.authorsversionpublishe

Consensus and Polarization in Competing Complex Contagion Processes

In many situations, the rate of adoption of new information depends on reinforcement from multiple sources in a way that cannot be described by simple contagion processes. In such cases, contagion is said to be complex. This has been found in the diffusion of human behaviors, innovations, and knowledge. Based on that evidence, we propose a new model considering multiple, potentially asymmetric, and competing contagion processes and analyze its respective population-wide complex contagion dynamics. We show that the model spans a dynamical space in which the population exhibits patterns of polarization, consensus, and dominance, a richer dynamical environment that contrasts with single simple contagion processes. We find that these patterns are present for different population structures. We show that structured interactions increase the range of the dominance regime by reducing that of polarization. Finally, we show that external agents designing seeding strategies, to optimize social influence, can dramatically change the coordination threshold for opinion dominance, while being rather ineffective in the remaining dynamical regions

Co-evolutionary dynamics of collective action with signaling for a quorum.

Collective signaling for a quorum is found in a wide range of organisms that face collective action problems whose successful solution requires the participation of some quorum of the individuals present. These range from humans, to social insects, to bacteria. The mechanisms involved, the quorum required, and the size of the group may vary. Here we address the general question of the evolution of collective signaling at a high level of abstraction. We investigate the evolutionary dynamics of a population engaging in a signaling N-person game theoretic model. Parameter settings allow for loners and cheaters, and for costly or costless signals. We find a rich dynamics, showing how natural selection, operating on a population of individuals endowed with the simplest strategies, is able to evolve a costly signaling system that allows individuals to respond appropriately to different states of Nature. Signaling robustly promotes cooperative collective action, in particular when coordinated action is most needed and difficult to achieve. Two different signaling systems may emerge depending on Nature's most prevalent states

Optimization of institutional incentives for cooperation in structured populations

The application of incentives, such as reward and punishment, is a frequently applied way for promoting cooperation among interacting individuals in structured populations. However, how to properly use the incentives is still a challenging problem for incentive-providing institutions. In particular, since the implementation of incentive is costly, to explore the optimal incentive protocol, which ensures the desired collective goal at a minimal cost, is worthy of study. In this work, we consider the positive and negative incentives for a structured population of individuals whose conflicting interactions are characterized by a Prisoner’s Dilemma game. We establish an index function for quantifying the cumulative cost during the process of incentive implementation, and theoretically derive the optimal positive and negative incentive protocols for cooperation on regular networks. We find that both types of optimal incentive protocols are identical and time-invariant. Moreover, we compare the optimal rewarding and punishing schemes concerning implementation cost and provide a rigorous basis for the usage of incentives in the game-theoretical framework. We further perform computer simulations to support our theoretical results and explore their robustness for different types of population structures, including regular, random, small-world and scale-free networks

Species persistence collapses for high rates of environmental change within environmental ranges that are otherwise presumed safe.

(A) Three scenarios are represented for communities without adaptive foraging, perturbed to start with a low initial species abundance (out of equilibrium). (Inset) Stress in communities increases over time as the driver of decline increases at different rates, λ, up to a maximum value, . The black line represents the point of collapse above which a fixed value of the driver of decline leads to the collapse of all communities and below which some communities are sustained. The maximum value of the driver of decline in each simulation is denoted by the fraction θ of the point of collapse . (A) Dotted orange line represents an increase in the driver of decline up to 90% of the critical value, , dot-dashed green line an increase up to 50%, and dashed pink an increase up to 20%. In this panel, species persistence is calculated as the fraction of pollinator species alive relative to the number of species alive at the lowest rate of change measured (λmin = 10−4). (B) The persistence of species decreases as a function of the maximum value of the driver of decline, represented as a fraction θ of the point of collapse, for a fast rate of change (λ = 1). Communities without adaptive foraging see a critical transition in species persistence when the driver of decline increases to a value close to, but lower than, the point of collapse at a fast enough rate. In this panel, species persistence is calculated as the fraction of pollinator species alive relative to the number of species alive at θ = 0 (no external stressor). Initial species abundance Sinit = 0.1 for all simulations. The results are averaged over 100 feasible networks, for which all 15 plant and 35 pollinator species survive under no stress, with the bands representing the first to third quartile ranges. Other parameters in Table A in S1 Text.</p