Predicting the Behavior of Techno-Social Systems

Alessandro VespignaniScience, Vol. 325 24 July 2009

(Prepared by Hasan T Karaoglu)

What is the Question? Possible Answers Reality Mining (and Proxy Networks) Network Thinking Highlights Some Applications and Caveats Additional Slides

◦ Couple of Examples◦ Progress Report

Outline

Techno-social systems◦ Large-scale physical infrastructure (Power Grids,

Transportation Systems, Internet ...)◦ Embedded in Dense Web of Communication◦ Led by Human

While we can predict weather conditions successfully, why we couldn’t achieve same success in predicting social systems’ behavior?

What is the Question?

Quantitative Prediction of Spatio-Temporal Patterns of Pandemics?

Effects of connecting billions of people from China and India using Internet?

Internet Stability and Growth?

What is the Question?

To predict:◦ Real world patterns discovered in data◦ Forming models based on patterns

Not Enough Data ◦ Centuries of Weather Condition Records◦ Not enough data for social systems till lately

Mobility, Adaption of Certain Behavior, Risk Perception

Fundamentals of System Model◦ Physical Laws governing fluids and gases◦ We need better understanding of social

interaction

Possible Answers

Level of Information Flow◦ Not only due to Computational Power◦ Involvement of machines into our life

Machine Sensed Data Related to Our Lives Human Mobility

◦ http://en.eurobilltracker.com and www.wheresgeorge.com

◦ Cell Phones, PDAs, Bluetooth, WiFi, GPS, Sensors◦ Mobile Phone Track of 100K people over 6

months.

Reality Mining

Reality Mining

Reality Mining

Airline Traffic

Reality Mining

Commuting Traffic

What it brings?◦ Dynamics of Epidemics◦ Evolution of Languages and Dialects◦ Bio-invasion◦ Foraging for Information

Reality Mining

Real World Networks are mostly “self-organized”◦ Heavy Tailed and Skewed◦ Heterogenity◦ Similar Behavior in Different Granularities

Complexity of Techno-social Systems◦ “Network Mindset”

Ex:◦ 14th Century Plague Epidemic : Spatial Diffusion◦ SARS : Commercial Air Travel

Network Thinking

Network Thinking

Large Scale Systems:◦ Don’t exhibit engineered or planned behavior◦ Ex: Commuting Networks

Final System behavior is result of:◦ Dynamics of all scales ◦ Events take place at different timelines

Bottom-up Approach◦ Behaviors of Individuals shape the Large-Scale

System Behavior◦ Flocks of Birds, Internet Topology

Highlights

Flocking◦ Separation (don’t crowd your neighbors)◦ Alignment (position yourself in the middle)◦ Cohesion (follow your neighbors)

Internet Topology◦ Wealth Based Topology Generator◦ Establish links as you have money◦ Go bankrupt when you broke◦ Randomly choose whom to connect

Maxwell-Boltzman Distribution

Highlights (Bottom-up Approach)

Social Atoms to Social Aggregate The shift from the study of a small number

of elements to the study of the behavior of large-scale aggregates is equivalent to the shift from atomic and molecular physics to the physics of matter.

Highlights

System Modeling◦ TRANSIM, EPISIM

Counter-Intuitive Ideas◦ Avoiding Cascading Failure◦ Preventing Further Damage, Ex: Wild Fire,

Immunization Limits

◦ Steady State Behavior (Catastrophe ?)◦ Social Adaptive Behavior (Self fulfilling Prophecy)◦ Ethical Issues?

Some Applications and Caveats

Total Disappointment: Twitter Reddit, Diggit Spinn3r? Emotion Extraction?

Progress Report