The document discusses agent-based modeling (ABM) and its application to understanding complex social behaviors, illustrated through a model of the 2011 London riots. It outlines the process of building ABMs using Python and various libraries, emphasizing the importance of data in modeling and validation. The goal is to leverage these models for strategic decision-making in social scenarios, particularly in policing and behavior change.

1. Agent-Based Modelling, the London riots, and Python
Thomas French (@trfrench) and Fred Farrell
Sandtable (@_sandtable)

2. Overview 20 June 2015
! Agent-based modelling
! London riots model
! Data Science Platform

3. Context 20 June 2015
! Live in increasingly complex, dynamic world.
! Increasing computing power.
! More and more data collected.
! Understand world: make better decisions.
! “Data only as valuable as the decisions it enables” Ion Stoica (Prof
UC Berkeley)

4. Strategic over tactical problems 20 June 2015
! How will changing attitudes in health impact soft drinks sales?
! Why are people not choosing my store anymore?
! How do people respond to advertising?
! How and why do people quit smoking?
! How do STDs spread through social networks?
! How to get people to wash their hands?
! Goal: behaviour change

5. What is Agent-Based Modelling? 20 June 2015
! Modelling technique
ª Build representations of real systems
! Roots in complexity science
ª Complex adaptive systems
ª Emergence
! Behavioural modelling
ª Hypotheses of behaviour
ª Bottom-up
! Accessible metaphor

6. Agent-Based Models 20 June 2015
AGENT
ENVIRONMENT
SENSORS
MESSAGES
ACTIONS
PERCEPTS
OBJECT
ACTUATORS
Based&on&Bordini&et&al&&(2007)

7. Schelling’s Model of Segregation 20 June 2015
Stay
Happy/
Unhappy
Move
Happy
Unhappy

8. Schelling’s Model of Segregation 20 June 2015
http://www.sandtable.com/a-coin-and-paper-model-of-segregation/

9. How do we derive rules of behaviour? 20 June 2015
! Domain experts
! Survey data
! Behavioural data
! Machine learning

10. Building ABMs 20 June 2015
! NetLogo: IDE; JVM
! Repast: Java & C++
! Hybrid Repast
! Simpy: lightweight ABM library in Python
ª Quick prototyping
ª Agile
ª Ecosystem: pandas; numpy; pytables; cython; scikit-learn
! Sandman: cluster framework for large-scale simulation

11. Typical ABM algorithm 20 June 2015
! Inputs:
ª environment: time series
ª agents: attributes; attitudes; social network
! Simulation loop: 1 to T
ª Agent loop: 1 to A
• Take inputs from environment
• Process messages from social network
• Make decisions
• Send messages over social network

12. How do we build confidence in models? 20 June 2015
! Visual validation
ª Types of behaviour
ª Trends
! Data
ª Macro and micro levels
ª Multiple datasets
ª Validation against reference data
ª Training/ testing data

13. 2011 London riots model 20 June 2015
! Ongoing proof of concept model
! Good example to demonstrate capability of ABM to model behaviour
and explore policing strategies.
! Interactions of two kinds of agents – rioters and police – whose
behaviour is dependent on other agents.

14. 5 days in August 20 June 2015
! Riots began on 6 August 2011
following protests over the
shooting by police of Mark
Duggan.
! Riots spread across cities in
England, lasting 5 days; around
15000 people involved, more
than 5000 crimes committed.
! Around £300 million damage in
London alone.
Images from wikipedia

15. Mathematical model of the riots 20 June 2015

16. Davies et al - mathematical model of riots 20 June 2015
! Individual-based mathematical
model of rioters and police
! Rioters are attracted to areas
where rioters outnumber police
! Individuals from more deprived
areas more likely to riot
! Able to broadly reproduce
geographical distribution of
rioting in London
Davies et al.

17. Our agent-based model 20 June 2015
! We built an ABM inspired by this
work.
! Environment: map of London
containing agents (potential
rioters), police, and retail centres
(potential riot sites).
! Our focus is on police response.

18. Basic model 20 June 2015
! Agents: 12000 citizens; 1000 police
! Time horizon: 48 hours
! Each timestep (1hr):
ª Police move to sites with most rioters
ª Police make arrests
ª Potential rioters decide whether to riot
• Choose site: number of rioters/ police, and distance to site
• Want close sites with many rioters and few police

19. Additional effects 20 June 2015
! Lag in police response: police take a certain time to respond to
changing circumstances
! Media: agents do not have perfect information on riot activity but must
find out about it via media (social/ broadcast)

20. 20 June 2015
Demo

21. Data Science Platform 20 June 2015
! Pandas, numpy, scipy, cython, pytables, scikit-learn
! Jupyter notebooks
! Luigi pipelines
! Cloud-based platform for collaborative, agile data science
! www.sandtable.com/the-sandtable-model-foundry

22. Summary 20 June 2015
! Build ABMs for understanding behaviour.
! Applicable to strategic problems.
! Simulation for exploring scenarios.
! Package as data products.
! Python data stack crucial to our work.
! Developing a collaborative Platform around them.

23. 20 June 2015
Project Title
Thanks!
@trfrench
thomas@sandtable.com

24. References 20 June 2015
! Macal and North ‘Agent-Based Modelling and Simulation’ in
Proceedings of the 2009 Winter Simulation Conference, pg 86-98,
IEEE.
! ‘Model Thinking’ Scott Page on Coursera
! Bordini et al. ‘Programming Multi-Agent Systems in AgentSpeak using
Jason’, 2007, Wiley