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Computational social science and complex systems /
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam, Netherlands :
IOS Press,
2019.
|
| Colección: | Proceedings of the International School of Physics "Enrico Fermi" ;
Course 203 |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Intro
- Title Page
- Contents
- Preface
- Course group shot
- Virtual social science
- 1. Introduction
- 1.1. What is social science?
- 1.1.1. Social systems are continuously restructuring networks
- 1.2. Social systems are complex systems
- 1.2.1. What is co-evolution?
- 2. A virtual society
- 2.1. The universe: the Pardus game
- 2.1.1. The census of avatars
- 2.1.2. The structure of the universe
- 2.1.3. Trade and economy
- 2.1.4. Communication
- 2.1.5. Friends and enemies
- 2.1.6. Performance measures of players
- ""states
- 2.1.7. Alliances
- 3. How do people interact?
- 3.1. Testing a classic sociological hypothesis of social interaction: weak ties
- 3.1.1. How strong do people interact?
- Kepler's law
- 3.2. Forces between avatars
- Newton's law for social interactions?
- 4. How do people organize?
- 4.1. Dynamics of the ""atoms of society"": triadic closure
- 4.1.1. Testing triadic closure
- the triad-significance profile
- 4.2. Taking triadic closure seriously
- understandingsocial multilayer network structure
- 4.2.1. Characteristic exponents
- 4.3. Degree distributions for negative ties are power laws
- positive are not
- 4.4. Social balance
- 4.4.1. Origin of social balance
- 4.5. Avatars organize in multiples of four
- 4.5.1. Dunbar numbers
- 4.6. The behavioral code
- 4.6.1. Two ways of seeing the same data
- 4.6.2. Behavioral code and predicting behavior
- 4.6.3. Worldlines of players
- 4.6.4. Zipf's law in the human behavioral code
- 4.7. Network-network interactions
- 5. Gender differences
- 5.1. Gender differences in networking
- 5.1.1. Gender differences in network topology
- 5.1.2. Gender differences in temporal behavior
- 6. Mobility
- how avatars move in their universe
- 6.1. Jump- and waiting time distributions
- 6.2. Long-term memory and mobility
- 7. The wealth of virtual nations
- 7.1. More on the Pardus economy
- 7.2. Wealth
- 7.3. Inequality
- 7.4. Behavioral factors for wealth
- 7.4.1. Influence of activity on wealth
- 7.4.2. Influence of achievement factors on wealth
- 7.4.3. Wealth depends on how social you are
- 7.5. Wealth and position in the multilayer network
- 8. Towards a new social science?
- Measuring social and political phenomena on the web
- 1. Background and motivation
- 2. Measuring gender inequality on Wikipedia
- 3. Modeling minorities in social networks
- 4. Measuring voting power and behavior in liquid democracy
- 5. Conclusions
- Science of success: An introduction
- 1. Introduction
- 2. Performance and success
- 2.1. Performance drives success
- 2.2. Performance is bounded
- 3. Success as a collective phenomenon
- 3.1. Success or recognition is unbounded
- 3.2. Success breeds success
- 3.3. Quality times previous success determines future success
- 4. Science of science
- 4.1. Quantifying long-term scientific impact
- 4.2. The Q-model
- 4.3. Credit is based on perception, not performance