Consensus and Disagreement of Heterogeneous Belief Systems in Influence Networks
Recently, an opinion dynamics model has been proposed to describe a network of individuals discussing a set of logically interdependent topics. For each individual, the set of topics and the logical interdependencies between the topics (captured by a logic matrix) form a belief system. We investigat...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Language: | English |
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IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
2020
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| Online Access: | http://purl.org/au-research/grants/arc/DP160104500 http://hdl.handle.net/20.500.11937/84357 |
| _version_ | 1848764641119830016 |
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| author | Ye, Mengbin Liu, J. Wang, L. Anderson, B.D.O. Cao, M. |
| author_facet | Ye, Mengbin Liu, J. Wang, L. Anderson, B.D.O. Cao, M. |
| author_sort | Ye, Mengbin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Recently, an opinion dynamics model has been proposed to describe a network of individuals discussing a set of logically interdependent topics. For each individual, the set of topics and the logical interdependencies between the topics (captured by a logic matrix) form a belief system. We investigate the role the logic matrix and its structure plays in determining the final opinions, including existence of the limiting opinions, of a strongly connected network of individuals. We provide a set of results that, given a set of individuals' belief systems, allow a systematic determination of which topics will reach a consensus, and of which topics will disagreement arise. For irreducible logic matrices, each topic reaches a consensus. For reducible logic matrices, which indicates a cascade interdependence relationship, conditions are given on whether a topic will reach a consensus or not. It turns out that heterogeneity among the individuals' logic matrices, and a cascade interdependence relationship, are necessary conditions for disagreement. Thus, this article attributes for the first time, a strong diversity of limiting opinions to heterogeneity of belief systems in influence networks, in addition to the more typical explanation that strong diversity arises from individual stubbornness. |
| first_indexed | 2025-11-14T11:22:35Z |
| format | Journal Article |
| id | curtin-20.500.11937-84357 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:22:35Z |
| publishDate | 2020 |
| publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-843572022-01-05T01:28:13Z Consensus and Disagreement of Heterogeneous Belief Systems in Influence Networks Ye, Mengbin Liu, J. Wang, L. Anderson, B.D.O. Cao, M. Science & Technology Technology Automation & Control Systems Engineering, Electrical & Electronic Engineering Cultural differences Limiting Systematics Indexes Social networking (online) Australia Stability analysis Agent-based models influence networks multiagent systems opinion dynamics social networks OPINION DYNAMICS TUTORIAL MODELS Recently, an opinion dynamics model has been proposed to describe a network of individuals discussing a set of logically interdependent topics. For each individual, the set of topics and the logical interdependencies between the topics (captured by a logic matrix) form a belief system. We investigate the role the logic matrix and its structure plays in determining the final opinions, including existence of the limiting opinions, of a strongly connected network of individuals. We provide a set of results that, given a set of individuals' belief systems, allow a systematic determination of which topics will reach a consensus, and of which topics will disagreement arise. For irreducible logic matrices, each topic reaches a consensus. For reducible logic matrices, which indicates a cascade interdependence relationship, conditions are given on whether a topic will reach a consensus or not. It turns out that heterogeneity among the individuals' logic matrices, and a cascade interdependence relationship, are necessary conditions for disagreement. Thus, this article attributes for the first time, a strong diversity of limiting opinions to heterogeneity of belief systems in influence networks, in addition to the more typical explanation that strong diversity arises from individual stubbornness. 2020 Journal Article http://hdl.handle.net/20.500.11937/84357 10.1109/TAC.2019.2961998 English http://purl.org/au-research/grants/arc/DP160104500 http://purl.org/au-research/grants/arc/DP190100887 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC fulltext |
| spellingShingle | Science & Technology Technology Automation & Control Systems Engineering, Electrical & Electronic Engineering Cultural differences Limiting Systematics Indexes Social networking (online) Australia Stability analysis Agent-based models influence networks multiagent systems opinion dynamics social networks OPINION DYNAMICS TUTORIAL MODELS Ye, Mengbin Liu, J. Wang, L. Anderson, B.D.O. Cao, M. Consensus and Disagreement of Heterogeneous Belief Systems in Influence Networks |
| title | Consensus and Disagreement of Heterogeneous Belief Systems in Influence Networks |
| title_full | Consensus and Disagreement of Heterogeneous Belief Systems in Influence Networks |
| title_fullStr | Consensus and Disagreement of Heterogeneous Belief Systems in Influence Networks |
| title_full_unstemmed | Consensus and Disagreement of Heterogeneous Belief Systems in Influence Networks |
| title_short | Consensus and Disagreement of Heterogeneous Belief Systems in Influence Networks |
| title_sort | consensus and disagreement of heterogeneous belief systems in influence networks |
| topic | Science & Technology Technology Automation & Control Systems Engineering, Electrical & Electronic Engineering Cultural differences Limiting Systematics Indexes Social networking (online) Australia Stability analysis Agent-based models influence networks multiagent systems opinion dynamics social networks OPINION DYNAMICS TUTORIAL MODELS |
| url | http://purl.org/au-research/grants/arc/DP160104500 http://purl.org/au-research/grants/arc/DP160104500 http://hdl.handle.net/20.500.11937/84357 |