Agent-based models are useful tools for understanding complex social systems by simulating the interactions of individuals over time. The document discusses 5 ways that agent-based modeling and other techniques from complexity science can improve campaigns and fundraising: 1. Agent-based models can provide insight into how ideas spread socially through networks of individuals. 2. Social networks are important to capture who is interacting and influencing whom. 3. Understanding the nature of interactions between individuals is key to modeling influence. 4. Behavioral insights into human decision-making can inform the rules governing agents. 5. Data science techniques like social network analysis and sentiment analysis can provide real-world data to inform and validate agent-based models

1. Dr Anthony Woolcock
The Top 5 Ways to Improve How
We Influence, Fundraise and
Campaign
(as suggested to us by Complexity Scientists).
Complexity & Emergence!
Networks & Movement!
Social Influence &
Behavioural Insights!

2. Talk Overview
❖ What is Complexity Science?!
❖ What are Agent Based Models?
Why use them?!
❖ Where do Agent Based Models fit
into a world view of data
science?!
❖ Instructive examples of ABMs.!
❖ What else can we find out with
some data?!
❖ What five ideas can we take
away?

3. Agent
Based
Modelling
Data
Mining
Predictive
Analytics
Focus !
of insight
Amount !
of data
Future
Present
Past
Little Some Lots and Lots

4. Agent
Based
Modelling
Data
Mining
Predictive
Analytics
Focus !
of insight
Amount !
of data
Future
Present
Past
Little Some Lots and Lots

5. Motivating … Agent Based Models
Eastern philosophy !
❖ Connectedness not individuals … ’No man is an island’!
Complexity !
❖ Many individuals interacting giving unexpected collective
behaviour!
Complexity Science!
❖ Non-linear response: Sand piles, stock market crash!
❖ Emergent patterns: Ant colonies, Birds flocking!
❖ Changes to the whole system: Water Boiling, Magnetism!
Agent Based Models !
❖ Recreate complex phenomena!
❖ Used in Sociology, Computer Science, Game Theory,
Ecology, Biology, Physics!
❖ Computer experiment of how the system behaves with
some conditions (model elements - next slide)!
❖ Help us understand a Complex world, trends, social
influence, crowd behaviour

6. Agent Based Models
What is an Agent Based Model? Model elements!
❖ Agent (individual, node, actor)!
❖ Interaction (conversation, influence)!
❖ Connectivity (social network, vertices, ties)!
❖ Update rules specify the interaction!
❖ Some specific randomness in update rules!
❖ Multiple simulations!
❖ Observe and analyse the model behaviour!
Why are they useful?!
❖ We can ask how quickly diseases spread (simple
contagion - one friend required) or ideas spread
(complex contagion - multiple friends required) !
❖ Is there an epidemic of a particular disease? Does a video
go viral? How many people adopt a new behaviour?!
❖ Forecast how many people will click, purchase, sign up,
act?

10. Agent Based Models … examples
Interaction rules!
❖ Social influence!
‘copy neighbour(s) opinion’!
(Festinger 1950, Asch 1956, Latane 1981) !
❖ Homophily !
‘birds of a feather flock together’!
ethnicity, gender, age, religion, education, behaviour,
attitudes, abilities, occupation, aspirations !
(McPherson 1987, 2001)!
ABM examples!
❖ Voter model - consensus!
❖ Axelrod model (1997) - local convergence with
global polarization!
❖ Epstein model (2002)!
❖ Voter with confidence model

14. ABMs and … individuals movement
Thomas Schelling (1971) !
❖ Model of segregation!
❖ Local parameter, number,
threshold of preferences of
neighbours of the same colour!
❖ Agents of two colours and spaces
on a lattice arrangement!
❖ Agents can move to new spaces!
❖ Minor preferences lead to
complete segregation (global
effect)

15. ABMs and … Social Networks
Duncan Watts (2002)!
❖ Solomon Asch (1950s) - conformity
experiments - threshold of number of
others required to make individual
conform!
❖ Watts threshold model - threshold of
peer pressure required to change
opinion (and remain changed)!
❖ Scale-free network - cascades of
opinion changes - include the
population size (and of all sizes)!
❖ Random networks - cascades with
size less than the whole population

16. ABMs and … Social Networks
Nikolas Christakis and James Fowler (2007)!
❖ Obesity study, 32 years, social network, 12,000
people!
❖ Demonstrated a spread in obesity throughout he
social network!
❖ Spread via social influence/ common traits (e.g.
genetic predisposition)/ common external influence
(environment effect)!
Damon Centola (2010)!
❖ 1,500 participants, website with medical advice,
control over network i.e. who could see who!
❖ Random structure (I have 4 friends who don’t know
each other) vs. Denser neighbourhoods (I have 4
friends who do know each other). Each individuals
size of neighbourhood the same!
❖ Behaviour spreads faster through the more locally
dense network

17. ABMs and … Social Networks
Paul Adams (2012) (Facebook, Google+)
Influence on the social web!
❖ People in our closest circle of trust hold a
disproportionate amount of influence over
what we think (compared to bloggers or
experts) Trusov (2009), Marsden (1987)!
❖ Network structure (Hubs with many incoming
and outgoing links) more important than
characteristics of individuals (‘influencers’) !
❖ Word of mouth spreads ideas more than
advertising Libai (2001)!
❖ Innovative hubs - open to new ideas - few
connections!
❖ Follower hubs - adopt ideas later - more
connections

18. ABMs and … Dynamic Networks
Dynamic Networks!
❖ Networks change as well as agents !
❖ when diseases spread if an individual is sick then
they do not go to work !
❖ so the network of social connections has also
changed (evolved)!
Vazquez (2007), Centola (2007) !
❖ Co-evolving - Axelrod model (includes social
influence and homophily) !
❖ Vary - how similar you need to be before you
interact !
❖ Three phases of model behaviour: Connected
consensus, Isolated groups (the same number of
physical and cultural groups), Isolated groups
(with greater number of cultural groups) !

19. ABMS and … Social Influence
Alan Fiske - Relational Models (1991)!
1. Communal Sharing (all for one)!
2. Authority Ranking (hierarchy)!
3. Equality Matching (tit for tat)!
4. Market Pricing (exchange)!
Alex Bentley (2001) (Bristol Uni)!
❖ Directed copying (this is simple contagion)!
❖ Undirected copying (this is different to complex
contagion, new ideas appear)!
❖ Few people - few options - rational choice (Economics)!
❖ Few people - many options - random choice!
❖ Many people - few options - directed copying,
conformity!
❖ Many people - many options - undirected copying leads
to turnover of what’s popular through take up of the
idea by the neighbours

20. Interactions … Behavioural insight
Measuring Online Social Bubbles - Nikolov
(2015) !
❖ people access information from a narrower
spectrum of sources (through social media and
email compared to search)!
Hook - Ted Greenwald (2014)!
❖ Reward feedback loop: Trigger, Action, Reward,
Investment, Trigger!
Irrationality and Dishonesty - Dan Ariely!
❖ Behavioural economics - how people make
decisions, what motivates people!
Redirect - Timothy Wilson (2011) !
❖ Social progress through transformation of
individual lives by redirecting the stories we tell
ourselves

21. Data: Opinions … Social Physics
Social Network Analysis!
❖ Measure influence - (Twitter: in-degree,
number mentions, number retweets) !
❖ Clustering - Detect communities in a given
social network !
Mike Thelwall (Uni of Wolverhampton)!
❖ Tweet analysis - sentiment is implicit in the
presence of the tweet not explicit in the
language used in the tweet!
Alex ‘Sandy’ Pentland - Social Physics
(2014)!
❖ Mobile phone location data provides data
to infer social context from which
behaviour and credit rating is estimated

22. The Top 5 Ways to Improve How We Influence, Fundraise and
Campaign (as suggested to us by Complexity Scientists).
How can we Campaign and Fundraise better?!
How do individuals share ideas in a social
context?!
1. Agent Based Models are a useful way to
understand the world (because the social
world is Complex).!
2. Networks are important. Who are people
talking to?!
3. Interactions are important. What are they
saying?!
4. Behavioural insights. What do we know
about human behaviours? !
5. Data is helpful, we can answer some of
these questions (partially).

23. Agent
Based
Modelling
Data
Mining
Predictive
Analytics
Focus !
of insight
Amount !
of data
Future
Present
Past
Little Some Lots and Lots

24. Monte Carlo Sims!
Parameter Sensitivity!
Network Sensitivity!
Expert Systems
Correlation!
Segmentation (PCA)!
Regression!
Decision Trees!
Bayesian Networks!
Linear Programming!
Neural Networks
Predictive
Descriptive!
Predictive / Prescriptive

25. Test future
developments!
Compare parameter choice
consequences!
Find the global effects of different:
individual types, interaction types,
social networks!
Explore scenarios in Complex
environments!
Evaluate strategies
Find related trends!
Find types of individuals!
Select data for predictive models!
Combine data for predictive models!
Find parameters for ABMs
!
Predict future trends!
Understand multiple
communication streams!
Create budget decision tools!
Find network structures!
!
Selective data!
Individual types, Interaction types!
Parameters for ABMs
Network structures
Budget consequences!
Changes in networks!
Changes in individuals
Sample data for analysis!
Verify ABM to real world data