1
|
Lu J, Du H, He X. A hypernetwork-based urn model for explaining collective dynamics. PLoS One 2023; 18:e0291778. [PMID: 37725633 PMCID: PMC10508602 DOI: 10.1371/journal.pone.0291778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 09/05/2023] [Indexed: 09/21/2023] Open
Abstract
The topological characterization of complex systems has significantly contributed to our understanding of the principles of collective dynamics. However, the representation of general complex networks is not enough for explaining certain problems, such as collective actions. Considering the effectiveness of hypernetworks on modeling real-world complex networks, in this paper, we proposed a hypernetwork-based Pólya urn model that considers the effect of group identity. The mathematical deduction and simulation experiments show that social influence provides a strong imitation environment for individuals, which can prevent the dynamics from being self-correcting. Additionally, the unpredictability of the social system increases with growing social influence, and the effect of group identity can moderate market inequality caused by individual preference and social influence. The present work provides a modeling basis for a better understanding of the logic of collective dynamics.
Collapse
Affiliation(s)
- Jiali Lu
- School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Haifeng Du
- School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| | - Xiaochen He
- School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
- School of Economics and Finance, Xi’an Jiaotong University, Xi’an, Shaanxi Province, China
| |
Collapse
|
2
|
Theriault JE, Young L, Barrett LF. The sense of should: A biologically-based framework for modeling social pressure. Phys Life Rev 2020; 36:100-136. [PMID: 32008953 DOI: 10.1016/j.plrev.2020.01.004] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 01/21/2020] [Indexed: 11/17/2022]
Abstract
What is social pressure, and how could it be adaptive to conform to others' expectations? Existing accounts highlight the importance of reputation and social sanctions. Yet, conformist behavior is multiply determined: sometimes, a person desires social regard, but at other times she feels obligated to behave a certain way, regardless of any reputational benefit-i.e. she feels a sense of should. We develop a formal model of this sense of should, beginning from a minimal set of biological premises: that the brain is predictive, that prediction error has a metabolic cost, and that metabolic costs are prospectively avoided. It follows that unpredictable environments impose metabolic costs, and in social environments these costs can be reduced by conforming to others' expectations. We elaborate on a sense of should's benefits and subjective experience, its likely developmental trajectory, and its relation to embodied mental inference. From this individualistic metabolic strategy, the emergent dynamics unify social phenomenon ranging from status quo biases, to communication and motivated cognition. We offer new solutions to long-studied problems (e.g. altruistic behavior), and show how compliance with arbitrary social practices is compelled without explicit sanctions. Social pressure may provide a foundation in individuals on which societies can be built.
Collapse
Affiliation(s)
| | - Liane Young
- Department of Psychology, Boston College, Chestnut Hill, MA, USA
| | - Lisa Feldman Barrett
- Department of Psychology, Northeastern University, Boston, MA, USA; Psychiatric Neuroimaging Division, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
3
|
Schleussner CF, Donges JF, Engemann DA, Levermann A. Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure. Sci Rep 2016; 6:30790. [PMID: 27510641 PMCID: PMC4980617 DOI: 10.1038/srep30790] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2016] [Accepted: 07/11/2016] [Indexed: 11/09/2022] Open
Abstract
Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.
Collapse
Affiliation(s)
| | - Jonathan F Donges
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
| | - Denis A Engemann
- Cognitive Neuroimaging Unit, CEA DRF/I2BM, INSERM, Université Paris-Sud, Université Paris-Saclay, NeuroSpin center, 91191 Gif/Yvette, France.,Neuropsychology &Neuroimaging Team, INSERM UMRS 975, ICM, Paris, France
| | - Anders Levermann
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Lamont-Doherty Earth Observatory, Columbia University, New York, USA.,Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany
| |
Collapse
|
4
|
Conflict and Computation on Wikipedia: A Finite-State Machine Analysis of Editor Interactions. FUTURE INTERNET 2016. [DOI: 10.3390/fi8030031] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
|
5
|
Informational and Causal Architecture of Discrete-Time Renewal Processes. ENTROPY 2015. [DOI: 10.3390/e17074891] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
|
6
|
Ciampaglia GL, Shiralkar P, Rocha LM, Bollen J, Menczer F, Flammini A. Computational Fact Checking from Knowledge Networks. PLoS One 2015; 10:e0128193. [PMID: 26083336 PMCID: PMC4471100 DOI: 10.1371/journal.pone.0128193] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Accepted: 04/24/2015] [Indexed: 11/19/2022] Open
Abstract
Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.
Collapse
Affiliation(s)
- Giovanni Luca Ciampaglia
- Center for Complex Networks and Systems Research, Indiana University, Bloomington, Indiana, United States of America
- * E-mail:
| | - Prashant Shiralkar
- Center for Complex Networks and Systems Research, Indiana University, Bloomington, Indiana, United States of America
| | - Luis M. Rocha
- Center for Complex Networks and Systems Research, Indiana University, Bloomington, Indiana, United States of America
- Instituto Gulbenkian de Ciencia, Oeiras, Portugal
| | - Johan Bollen
- Center for Complex Networks and Systems Research, Indiana University, Bloomington, Indiana, United States of America
| | - Filippo Menczer
- Center for Complex Networks and Systems Research, Indiana University, Bloomington, Indiana, United States of America
| | - Alessandro Flammini
- Center for Complex Networks and Systems Research, Indiana University, Bloomington, Indiana, United States of America
| |
Collapse
|
7
|
Kershenbaum A, Bowles AE, Freeberg TM, Jin DZ, Lameira AR, Bohn K. Animal vocal sequences: not the Markov chains we thought they were. Proc Biol Sci 2014; 281:20141370. [PMID: 25143037 PMCID: PMC4150325 DOI: 10.1098/rspb.2014.1370] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Accepted: 07/24/2014] [Indexed: 11/12/2022] Open
Abstract
Many animals produce vocal sequences that appear complex. Most researchers assume that these sequences are well characterized as Markov chains (i.e. that the probability of a particular vocal element can be calculated from the history of only a finite number of preceding elements). However, this assumption has never been explicitly tested. Furthermore, it is unclear how language could evolve in a single step from a Markovian origin, as is frequently assumed, as no intermediate forms have been found between animal communication and human language. Here, we assess whether animal taxa produce vocal sequences that are better described by Markov chains, or by non-Markovian dynamics such as the 'renewal process' (RP), characterized by a strong tendency to repeat elements. We examined vocal sequences of seven taxa: Bengalese finches Lonchura striata domestica, Carolina chickadees Poecile carolinensis, free-tailed bats Tadarida brasiliensis, rock hyraxes Procavia capensis, pilot whales Globicephala macrorhynchus, killer whales Orcinus orca and orangutans Pongo spp. The vocal systems of most of these species are more consistent with a non-Markovian RP than with the Markovian models traditionally assumed. Our data suggest that non-Markovian vocal sequences may be more common than Markov sequences, which must be taken into account when evaluating alternative hypotheses for the evolution of signalling complexity, and perhaps human language origins.
Collapse
Affiliation(s)
- Arik Kershenbaum
- National Institute for Mathematical and Biological Synthesis, Knoxville, TN, USA
| | - Ann E Bowles
- Hubbs SeaWorld Research Institute, San Diego, CA 92109, USA
| | - Todd M Freeberg
- Department of Psychology, University of Tennessee, Knoxville, TN, USA
| | - Dezhe Z Jin
- Department of Physics and the Center for Neural Engineering, Penn State University, University Park, PA, USA
| | - Adriano R Lameira
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Sciencepark 904, 1098 XH, Amsterdam, The Netherlands Pongo Foundation, Papenhoeflaan 91, 3421 XN, Oudewater, The Netherlands
| | - Kirsten Bohn
- Department of Biological Sciences, Florida International University, Miami, FL, USA
| |
Collapse
|