top 5 ways to improve how we influence, fundraise and campaign - as suggested by complexity...
TRANSCRIPT
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!
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?
Agent Based
Modelling
Data Mining
Predictive Analytics
Focus !of insight
Amount !of data
Future
Present
Past
Little Some Lots and Lots
Agent Based
Modelling
Data Mining
Predictive Analytics
Focus !of insight
Amount !of data
Future
Present
Past
Little Some Lots and Lots
Motivating … Agent Based ModelsEastern 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
Agent Based ModelsWhat 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?
Agent Based Models … examplesInteraction 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
ABMs and … individuals movementThomas 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)
ABMs and … Social NetworksDuncan 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
ABMs and … Social NetworksNikolas 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
ABMs and … Social NetworksPaul 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
ABMs and … Dynamic NetworksDynamic 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) !
ABMS and … Social InfluenceAlan 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
Interactions … Behavioural insightMeasuring 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
Data: Opinions … Social PhysicsSocial 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
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).
Agent Based
Modelling
Data Mining
Predictive Analytics
Focus !of insight
Amount !of data
Future
Present
Past
Little Some Lots and Lots
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
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