Social contagion and physical activity – Friends can induce laziness

1. Social contagion and physical activity – Friends can induce laziness Danica Vukadinović Greetham Centre for the Mathematics of Human Behavior, University of Reading

2. Coauthored with Abhijit Sengupta1, Juliette Richetin2, Marco Perugini2, Eunate Mayor1,3, Robert Hurling1, Iqbal Adjali1 1Unilever Discover, Colworth UK 2Department of Psychology, University of Milan-Bicocca, Italy 3Department of Law, European University Institute, Italy Presented at the 3rdWorld Congress of Social Simulation, Kassel, September, 2010

3. What this talk is about… • Can my friends cure me of my laziness? • Goal Directed Behaviour • Ajzen, 1991 • Perugini & Conner, 2000 • Perugini & Bagozzi, 2001 Physical Activity Recycling Preparing for exam… Goal Formation Behaviour Intention What neighbours are doing…

4. Questions • What are the emergent patterns of behaviour within groups under influence of both internal and external factors? • What is the nature of interaction between internal and external factors? • Does the nature and structure of external influence have an effect on behavioural outcomes? Generate hypotheses using ABM simulations which can be tested experimentally. Extend the framework to incorporate other goal directed behaviours as well.

5. Models • Model 1: Fixed goal with decay • Goal directed motivation (actual vs. ideal state) • Goals constant over time • Observance of positive and non- behaviour in neighbours (dual threshold model) • Model 2: Variable goal with habit • Shifting goals for some (optimizers vs. satisfiers) • Opportunity cost of behaviour • Habit formation from repeated behaviour

6. Model 1: Fixed Goal with Decay • Behaviour is a cont. and weakly increasing func. of ideal – actual difference • Actual state is increasing in behaviour and incorporates independent decay Behaviour (t) Ideal – Actual (t) Actual (t) Behaviour (t -1 )

7. External Influence • Network topologies • Erdos-Renyi Random Network ER(n,m) • Empirical: Newman PNAS, 2006 • Network influence • “Dual” threshold model • Upper threshold • Lower threshold

8. Simulation Setup • Netlogo environment • 379 agents (empirical network driven) • Ideal state ~ U[80, 120], Actual state ~ U[50, 150] • B ~ # of hours per week • Time step ~ 1 week • Theoretical networks • ER (379, 914) • Parameters tested in Model 1

9. Results (Model 1) No Networks: • There exists a critical value of rho around which the average properties of the system change • Increasing rate of physical decay leads to more physical activity on average

10. Results (Model 1) With Networks The presence of networks reduces average behaviour overall compared to the no network case. There exists ρ* > 0 such that, for all 0 < ρ < ρ* , the ideal actual difference is weakly increasing in π1 but is weakly decreasing in π1 for ρ > ρ*. For all else held constant, the average ideal actual difference is increasing in ρ. Empirical network Random network

11. Model 2: Variable goal with habit • Introducing Optimizers and Satisfiers • Optimizers: Readjusts goal • Satisfiers: Constant goal • Behaviour depends on ideal-actual discrepancy and opportunity cost • Opportunity cost of positive behaviour • Habit formation due to repeated behaviour Opportunity Cost Opportunity Cost Repeated Behaviour Behaviour Behaviour

12. Model 2 : Settings External influence: 12 experiments: • Percentage of satisfiers: 0, 0.2,0.5,0.8 • B (0,1): 0.2,0.5,0.8

13. Results (Model 2) • No Network case: • Avg behaviouris strictly decreasing in prop. of satisfiers and strictly increasing in B. • Avg opp. cost is strictly increasing in prop. of satisfiers and strictly decreasing in B. • Avg motivation is weakly decreasing in prop. of satisfiers.

14. Results (Model 2) • With Networks • Average levels of behaviour and motivational energy fall and opportunity cost rises significantly • Average motivational energy strictly increases in proportion of optimizers. • No big difference between two network types

15. Conclusions Thank you!