R is for Reconciliation

1. R is for Reconciliation Using R to help Colombia post guerilla-demilitarization

2. Photo credit : D Sharon Pruitt

3. Disclaimer This presentation will not do justice to the topic. Please learn more at: Program of Alliances for Reconciliation (PAR) acdivoca.org.co/decidoser/

4. Overview: • Violent conflict between: • Guerrillas (communist) • Government (capitalist, want security) • Lasted over 50 years • 265 000 dead • 8.5M victims, of which 6M displaced • Peace accord in 2016

5. Perceptions Attitudes Behavior What are the wants? Wants revenge Lost nothing Lost house Lost income Wants breadwinner back Wants family to be remembered

6. Program of Alliances for Reconciliation (PAR) • PAR generates social and economic programs around reconciliation. • DecidoSer! • A Psychosocial framework that responds to PAR’s Theory of Change. • is not intended to be a course, a workshop, an intervention or a training. It aims to be a process that makes it possible: • to meet; to reflect • To build knowledge from the knowledge in the room • a space for decision-making and transformation

7. Data Problem

8. Data Problem y y POST survey Question 1 Question 2 Question 3 …. Question 16 PRE survey Question 1 Question 2 Question 3 …. Question 16 Our/partners’ activities Dif ∆ 1 ∆ 2 ∆ 3 …. ∆ 16 Govt of Colombia activities Public context "Personal info"

9. Questions -> Dimensions 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Perspectiva – perspective Confianza – trust Seguridad – security Prejuicio – prejudice Aceptacion – acceptance Solidaridad – solidarity Empatia – empathy Dimension 1 Dimension 2…

10. Tendency to the mean 5 5 4 4 3 3 2 2 1 1 post pre

11. Tendency to the mean 5 5 4 4 3 3 2 2 1 1 post pre

12. Tendency to the mean 5 5 4 4 3 3 2 2 1 1 post pre

13. Tendency to the mean 5 5 4 4 3 3 2 2 1 1 post pre

15. Meet the independents Participant Projects • Sex • Victim • Reintegrated • Race • Age Group • Access to bank • Access to finance Strengthening plan Actions • Description • -> LDA model: 3 topics • Number of actions • “Decido Ser” • Actions 1-28 • (driver) • Degree used • Used own strategy

16. Show and tell

17. Concept 1 • Dependent: • ∆ as binary • “Improved” or (“no change” / “Worse”) • Technique: • Decision tree

18. Dimension-specific decision trees

19. Concept 2 • Dependent: • ∆ as binary • “Improved” or (“no change” / “Worse”) • Technique: • Logistic regression/2-class model

20. Concept 3 • Dependent: • Post • Technique: • Regression!

21. Coefficient Analysis Coeff 2 Coeff 3 Coeff 1 Coeff 3 Coeff 2 Coeff 1 Model 1 Model 2

22. Coefficient Analysis Model 1 Model 2 Model 1 Model 1 Model 2 Model 2 Coeff 1 Coeff 2 Coeff 3

23. Linear Model “Pre” Score always inv ∝ To “Post” Score UsedOwnStrategy sometimes inv ∝ To “Post” Score

24. R²=0.72 Variable Importance Driver 1, Driver 2, Driver 3, Driver 4

25. Simple EDA (powerful and beginner-level easy!)

26. Next Steps • Predictive model: • Inputs: • Population descriptors • Geosocial characteristics • Outputs: • Recommended DecidoSer Activities • Potential pre/post difference expectations

27. Questions/Comments? --------------------> Thank you