Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
153 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Leveraging Uncertainty in Collective Opinion Dynamics with Heterogeneity (2402.03354v1)

Published 26 Jan 2024 in physics.soc-ph and cs.SI

Abstract: Natural and artificial collectives exhibit heterogeneities across different dimensions, contributing to the complexity of their behavior. We investigate the effect of two such heterogeneities on collective opinion dynamics: heterogeneity of the quality of agents' prior information and of centrality in the network, i.e., the number of immediate neighbors. To study these heterogeneities, we not only consider them in our model, proposing a novel network generator with heterogeneous centrality, but also introduce uncertainty as an additional dimension. By quantifying the uncertainty of each agent, we provide a mechanism for agents to adaptively weigh their individual against social information. As uncertainties develop according to the interactions between agents, they capture information on heterogeneities. Therefore, uncertainty is a relevant additional observable in the study of complex collective opinion dynamics that we use to show the bidirectional relationship of heterogeneous centrality and information. Furthermore, we demonstrate that uncertainty-driven adaptive weighting leads to increased accuracy and speed of consensus, especially under heterogeneity, and provide guidelines for avoiding performance-decreasing errors in uncertainty modeling. These opportunities for improved performance and observability suggest the importance of uncertainty both for the study of natural and the design of artificial heterogeneous systems.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (60)
  1. Inferring the structure and dynamics of interactions in schooling fish. \JournalTitleProceedings of the National Academy of Sciences 108, 18720–18725 (2011).
  2. Leonard, N. E. et al. Collective motion, sensor networks, and ocean sampling. \JournalTitleProceedings of the IEEE 95, 48–74 (2007).
  3. Opinion dynamics: models, extensions and external effects. \JournalTitleParticipatory sensing, opinions and collective awareness 363–401 (2017).
  4. Distributed consensus algorithms in sensor networks with higher-order topology. \JournalTitleEntropy 25, 1200 (2023).
  5. Information acquisition with sensing robots: Algorithms and error bounds. In IEEE International conference on robotics and automation (ICRA), 6447–6454 (2014).
  6. Hamann, H. Swarm robotics: A formal approach, vol. 221 (Springer, 2018).
  7. Bayes bots: collective Bayesian decision-making in decentralized robot swarms. In IEEE international conference on robotics and automation (ICRA), 7186–7192 (2020).
  8. Social influence and the collective dynamics of opinion formation. \JournalTitlePloS one 8, e78433 (2013).
  9. Mass media and heterogeneous bounds of confidence in continuous opinion dynamics. \JournalTitlePhysica A: Statistical Mechanics and its Applications 420, 73–84 (2015).
  10. Hamann, H. Opinion dynamics with mobile agents: Contrarian effects by spatial correlations. \JournalTitleFrontiers in Robotics and AI 5, 63 (2018).
  11. Estimation of continuous environments by robot swarms: Correlated networks and decision-making. In IEEE International Conference on Robotics and Automation (ICRA), 5486–5492 (2023).
  12. Fast and flexible multiagent decision-making. \JournalTitleAnnual Review of Control, Robotics, and Autonomous Systems 7 (2023).
  13. Page, S. E. Diversity and complexity (Princeton University Press, 2010).
  14. Rethinking the baseline in diversity research: Should we be explaining the effects of homogeneity? \JournalTitlePerspectives on Psychological Science 9, 235–244 (2014).
  15. Delusions of homogeneity? Reinterpreting the effects of group diversity. In Looking back, moving forward: A review of group and team-based research, 185–207 (Emerald Group Publishing Limited, 2012).
  16. Individual experience alone can generate lasting division of labor in ants. \JournalTitleCurrent Biology 17, 1308–1312 (2007).
  17. d’Ettorre, P. et al. Individual differences in exploratory activity relate to cognitive judgement bias in carpenter ants. \JournalTitleBehavioural Processes 134, 63–69 (2017).
  18. Individuality in Swarm Robots with the Case Study of Kilobots: Noise, Bug, or Feature? ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference, 35 (2023).
  19. Behavioural individuality in clonal fish arises despite near-identical rearing conditions. \JournalTitleNature communications 8, 15361 (2017).
  20. Brass, D. J. Being in the right place: A structural analysis of individual influence in an organization. \JournalTitleAdministrative science quarterly 518–539 (1984).
  21. Network analysis in the social sciences. \JournalTitleScience 323, 892–895 (2009).
  22. Poel, W. et al. Subcritical escape waves in schooling fish. \JournalTitleScience Advances 8, eabm6385 (2022).
  23. When overconfidence is revealed to others: Testing the status-enhancement theory of overconfidence. \JournalTitleOrganizational Behavior and Human Decision Processes 122, 266–279 (2013).
  24. Information aggregation and collective intelligence beyond the wisdom of crowds. \JournalTitleNature Reviews Psychology 1, 345–357 (2022).
  25. A review on challenges of autonomous mobile robot and sensor fusion methods. \JournalTitleIEEE Access 8, 39830–39846 (2020).
  26. Eppner, C. et al. Lessons from the amazon picking challenge: Four aspects of building robotic systems. In Robotics: Science and Systems, 4831–4835 (2016).
  27. Coupled recursive estimation for online interactive perception of articulated objects. \JournalTitleThe International Journal of Robotics Research 41, 741–777 (2022).
  28. Kalman, R. E. A New Approach to Linear Filtering and Prediction Problems. \JournalTitleJournal of Basic Engineering 82, 35–45 (1960).
  29. A non-divergent estimation algorithm in the presence of unknown correlations. In Proceedings of the American Control Conference, vol. 4, 2369–2373 (1997).
  30. Probabilistic robotics (MIT Press, 2005).
  31. Combining motion and appearance for robust probabilistic object segmentation in real time. In IEEE International Conference on Robotics and Automation (ICRA), 683–689 (2023).
  32. DeGroot, M. H. Reaching a consensus. \JournalTitleJournal of the American Statistical Association 69, 118–121 (1974).
  33. Learning a linear influence model from transient opinion dynamics. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 401–410 (2014).
  34. Recent advances in the modelling and analysis of opinion dynamics on influence networks. \JournalTitleInternational Journal of Automation and Computing 16, 129–149 (2019).
  35. Consensus reaching in social network DeGroot model: The roles of the self-confidence and node degree. \JournalTitleInformation Sciences 486, 62–72 (2019).
  36. Opinion dynamics and bounded confidence models, analysis and simulation. \JournalTitleJournal of Artificial Societies and Social Simulation 5 (2002).
  37. Opinion dynamics in heterogeneous networks: Convergence conjectures and theorems. \JournalTitleSIAM Journal on Control and Optimization 50, 2763 (2012).
  38. Opinion dynamics in networks with heterogeneous confidence and influence. \JournalTitlePhysica A: Statistical Mechanics and its Applications 392, 2248–2256 (2013).
  39. Opinion dynamics with confirmation bias. \JournalTitlePLOS ONE 9, 1–14 (2014).
  40. Theoretical development of a probabilistic fuzzy model for opinion formation in social networks. \JournalTitleFuzzy Sets and Systems 454, 125–148 (2023).
  41. Learning and forecasting opinion dynamics in social networks. \JournalTitleAdvances in neural information processing systems 29 (2016).
  42. Extended message passing algorithm for inference in loopy gaussian graphical models. \JournalTitleAd Hoc Networks 2, 153–169 (2004).
  43. Models for the diffusion of beliefs in social networks: An overview. \JournalTitleIEEE Signal Processing Magazine 30, 16–29 (2013).
  44. Bayesian decision making in groups is hard. \JournalTitleOperations Research 69, 632–654 (2021).
  45. Large-sample learning of Bayesian networks is NP-hard. \JournalTitleJournal of Machine Learning Research 5, 1287–1330 (2004).
  46. Davidson, J. D. et al. Collective detection based on visual information in animal groups. \JournalTitleJournal of the Royal Society Interface 18, 20210142 (2021).
  47. Interindividual variability in social insects–proximate causes and ultimate consequences. \JournalTitleBiological Reviews 89, 671–687 (2014).
  48. Hogg, M. A. A social identity theory of leadership. \JournalTitlePersonality and social psychology review 5, 184–200 (2001).
  49. Swarm intelligence: From natural to artificial systems (Oxford university press, 1999).
  50. Collective decision-making in honey bees: How colonies choose among nectar sources. \JournalTitleBehavioral Ecology and Sociobiology 28, 277–290 (1991).
  51. Network dynamics of social influence in the wisdom of crowds. \JournalTitleProceedings of the national academy of sciences 114, E5070–E5076 (2017).
  52. Rethinking centrality: Methods and examples. \JournalTitleSocial networks 11, 1–37 (1989).
  53. On random graphs I. \JournalTitlePublications Mathematicae 6, 290–297 (1959).
  54. Statistical mechanics of complex networks. \JournalTitleReviews of modern physics 74, 47 (2002).
  55. Collective dynamics of ‘small-world’networks. \JournalTitleNature 393, 440–442 (1998).
  56. Probing perceptual mechanism of shape-contingent color after-images via interconnected recursive filters. \JournalTitleJournal of Vision 23, 4885–4885 (2023).
  57. Snijders, T. A. The degree variance: An index of graph heterogeneity. \JournalTitleSocial networks 3, 163–174 (1981).
  58. Niehsen, W. Information fusion based on fast covariance intersection filtering. In Proceedings of the Fifth International Conference on Information Fusion, vol. 2, 901–904 (2002).
  59. Speed-vs-accuracy tradeoff in collective estimation: An adaptive exploration-exploitation case. In 2021 International Symposium on Multi-Robot and Multi-Agent Systems (MRS), 47–55 (2021).
  60. Measure for degree heterogeneity in complex networks and its application to recurrence network analysis. \JournalTitleRoyal Society open science 4, 160757 (2017).
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now