
Computational Social Science and Complex Systems
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This book presents the 9 lectures delivered at the CCIII Summer Course Computational Social Science and Complex Systems, held as part of the International School of Physics Enrico Fermi in Varenna, Italy, from 16-21 July 2018. The course had the aim of presenting some of the recent developments in the interdisciplinary fields of computational social science and econophysics to PhD students and young researchers, with lectures focused on recent problems investigated in computational social science.
Addressing some of the basic questions and many of the subtleties of the emerging field of computational social science, the book will be of interest to students, researchers and advanced research professionals alike.
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Content
- 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. Perfomance 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
- 5. Conclusions
- Introduction to market microstructure and heterogeneity of investors
- 1. Introduction
- 2. A gentle introduction to limit order books
- 3. Market impact and order flow
- 3.1. Order flow
- 3.1.1. Origin of long memory
- 3.1.2. Heterogeneity of investors and long memory
- 3.2. Market impact
- 3.3. Impact of metaorders and square root law
- 3.3.1. Cross-impact
- 3.3.2. Co-impact
- 4. Heterogeneity in time scales
- 5. Conclusions and outlook
- A primer on statistically validated networks
- 1. Introduction
- 2. Disparity filter
- 3. Multiple hypothesis test correction
- 4. Statistically validated networks
- 5. Examples of applications of statistically validated networks
- 6. Community detection in statistically validated networks
- 7. Software for the computation and analysis of statistically validated networks
- 8. Conclusions
- Temporal networks: Characterization, motifs and spreading
- 1. Introduction
- 2. Definition and characterization of temporal networks
- 2.1. Definition and representation
- 2.2. Characterization
- 3. Motifs in temporal networks
- 3.1. Time-evolution of static motifs
- 3.2. Mobility motifs
- 3.3. Temporal motifs
- 4. Spreading on temporal networks
- 5. Outlook
- Temporal networks of face-to-face interactions
- 1. Introduction
- 2. Data, representations of data and structures
- 2.1. Statistics
- 2.2. Aggregated networks
- 2.3. Contact matrices and contact matrices of distributions
- 2.4. Structures
- 3. Models
- 4. Processes on temporal networks
- 5. Using data
- 5.1. Which representation to use
- 5.2. Designing and testing interventions
- 5.3. Incomplete datasets
- 6. Conclusion
- Introduction to modeling disease spread in space
- 1. Spatial spread of infectious disease epidemics
- 2. Spatially structured populations and metapopulation approach
- 2.1. Patches and coupling
- 2.2. Relevant spatial effects
- 3. The stochastic discrete metapopulation scheme
- 3.1. Effective approach
- 3.2. Mechanistic approach
- 4. Local vs. global invasion
- 4.1. Local epidemic threshold
- 4.2. Global invasion threshold
- 5. Going beyond basic assumptions
- 6. Conclusions
- Spatio-temporal infrastructure networks
- 1. Introduction
- 2. Resilience properties of single networks
- 2.1. Traffic
- 2.2. Physiology
- 2.3. Climate
- 2.4. Recovery
- 3. Resilience of interdependent networks
- 3.1. Methods for reducing cascades
- 3.2. Networks of networks
- 3.3. Recovery of interdependent networks
- 3.4. Spatially embedded interdependent networks
- 3.5. Localized attack
- 3.6. Summary and further reading
- List of participants
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