Quantify the Causes of Causal Emergence: Critical Conditions of Uncertainty and Asymmetry in Causal Structure Article Swipe
Liye Jia
,
Zhou Cong
,
Ka Lok Man
,
Sheng-Uei Guan
,
Jeremy C. Smith
,
Yutao Yue
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.01551
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2212.01551
Beneficial to advanced computing devices, models with massive parameters are increasingly employed to extract more information to enhance the precision in describing and predicting the patterns of objective systems. This phenomenon is particularly pronounced in research domains associated with deep learning. However, investigations of causal relationships based on statistical and informational theories have posed an interesting and valuable challenge to large-scale models in the recent decade. Macroscopic models with fewer parameters can outperform their microscopic counterparts with more parameters in effectively representing the system. This valuable situation is called "Causal Emergence." This paper introduces a quantification framework, according to the Effective Information and Transition Probability Matrix, for assessing numerical conditions of Causal Emergence as theoretical constraints of its occurrence. Specifically, our results quantitatively prove the cause of Causal Emergence. By a particular coarse-graining strategy, optimizing uncertainty and asymmetry within the model's causal structure is significantly more influential than losing maximum information due to variations in model scales. Moreover, by delving into the potential exhibited by Partial Information Decomposition and Deep Learning networks in the study of Causal Emergence, we discuss potential application scenarios where our quantification framework could play a role in future investigations of Causal Emergence.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2212.01551
- https://arxiv.org/pdf/2212.01551
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310822462
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4310822462Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2212.01551Digital Object Identifier
- Title
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Quantify the Causes of Causal Emergence: Critical Conditions of Uncertainty and Asymmetry in Causal StructureWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-03Full publication date if available
- Authors
-
Liye Jia, Zhou Cong, Ka Lok Man, Sheng-Uei Guan, Jeremy C. Smith, Yutao YueList of authors in order
- Landing page
-
https://arxiv.org/abs/2212.01551Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2212.01551Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2212.01551Direct OA link when available
- Concepts
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Granularity, Causality (physics), Redistribution (election), Causal model, Macro, Causal structure, Statistical physics, Representation (politics), Computer science, Econometrics, Mathematics, Physics, Statistics, Political science, Politics, Quantum mechanics, Law, Programming language, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.devices, | 4 |
| abstract_inverted_index.employed | 11 |
| abstract_inverted_index.networks | 171 |
| abstract_inverted_index.patterns | 25 |
| abstract_inverted_index.research | 35 |
| abstract_inverted_index.systems. | 28 |
| abstract_inverted_index.theories | 51 |
| abstract_inverted_index.valuable | 57, 85 |
| abstract_inverted_index.Effective | 100 |
| abstract_inverted_index.Emergence | 112 |
| abstract_inverted_index.Moreover, | 157 |
| abstract_inverted_index.according | 97 |
| abstract_inverted_index.assessing | 107 |
| abstract_inverted_index.asymmetry | 137 |
| abstract_inverted_index.challenge | 58 |
| abstract_inverted_index.computing | 3 |
| abstract_inverted_index.exhibited | 163 |
| abstract_inverted_index.framework | 186 |
| abstract_inverted_index.learning. | 40 |
| abstract_inverted_index.numerical | 108 |
| abstract_inverted_index.objective | 27 |
| abstract_inverted_index.potential | 162, 180 |
| abstract_inverted_index.precision | 19 |
| abstract_inverted_index.scenarios | 182 |
| abstract_inverted_index.situation | 86 |
| abstract_inverted_index.strategy, | 133 |
| abstract_inverted_index.structure | 142 |
| abstract_inverted_index.Beneficial | 0 |
| abstract_inverted_index.Emergence, | 177 |
| abstract_inverted_index.Emergence. | 128, 196 |
| abstract_inverted_index.Transition | 103 |
| abstract_inverted_index.associated | 37 |
| abstract_inverted_index.conditions | 109 |
| abstract_inverted_index.describing | 21 |
| abstract_inverted_index.framework, | 96 |
| abstract_inverted_index.introduces | 93 |
| abstract_inverted_index.optimizing | 134 |
| abstract_inverted_index.outperform | 72 |
| abstract_inverted_index.parameters | 8, 70, 78 |
| abstract_inverted_index.particular | 131 |
| abstract_inverted_index.phenomenon | 30 |
| abstract_inverted_index.predicting | 23 |
| abstract_inverted_index.pronounced | 33 |
| abstract_inverted_index.variations | 153 |
| abstract_inverted_index.Emergence." | 90 |
| abstract_inverted_index.Information | 101, 166 |
| abstract_inverted_index.Macroscopic | 66 |
| abstract_inverted_index.Probability | 104 |
| abstract_inverted_index.application | 181 |
| abstract_inverted_index.constraints | 115 |
| abstract_inverted_index.effectively | 80 |
| abstract_inverted_index.influential | 146 |
| abstract_inverted_index.information | 15, 150 |
| abstract_inverted_index.interesting | 55 |
| abstract_inverted_index.large-scale | 60 |
| abstract_inverted_index.microscopic | 74 |
| abstract_inverted_index.occurrence. | 118 |
| abstract_inverted_index.statistical | 48 |
| abstract_inverted_index.theoretical | 114 |
| abstract_inverted_index.uncertainty | 135 |
| abstract_inverted_index.counterparts | 75 |
| abstract_inverted_index.increasingly | 10 |
| abstract_inverted_index.particularly | 32 |
| abstract_inverted_index.representing | 81 |
| abstract_inverted_index.Decomposition | 167 |
| abstract_inverted_index.Specifically, | 119 |
| abstract_inverted_index.informational | 50 |
| abstract_inverted_index.relationships | 45 |
| abstract_inverted_index.significantly | 144 |
| abstract_inverted_index.investigations | 42, 193 |
| abstract_inverted_index.quantification | 95, 185 |
| abstract_inverted_index.quantitatively | 122 |
| abstract_inverted_index.coarse-graining | 132 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |