Model Validation as an Integrated Social Process

1. Model Validation asan Integrated Social Process George P. Richardson Rockefeller College of Public Affairs and Policy University at Albany - State University of New York GPR@Albany.edu

2. What do we mean by ‘validation’? • No model has ever been or ever will be thoroughly validated. …‘Useful,’ ‘illuminating,’ or ‘inspiring confidence’ are more apt descriptors applying to models than ‘valid’(Greenberger et al. 1976). • Validation is a process of establishing confidence in the soundness and usefulness of a model. (Forrester 1973, Forrester and Senge 1980).

3. The classic questions • Not ‘Is the model valid,’ but • Is the model suitable for its purposes and the problem it addresses? • Is the model consistent with the slice of reality it tries to capture? (Richardson & Pugh 1981)

4. The system dynamics modeling process Adapted from Saeed 1992

5. Processes focusing on system structure

6. Processes focusing on system behavior

7. Two kinds of validating processes

8. The classic tests Forrester 1973, Forrester & Senge 1980, Richardson and Pugh 1981

9. Validation is present at every step • Conceptualizing: • Do we have the right people? • The right dynamic problem definition? • The right level of aggregation? • Mapping: Developing promising dynamic hypotheses • Formulating: Clarity, logic, and extremes • Simulating: Right behavior for right reasons • Deciding: Implementable conclusions • Implementing: Requires conviction!

10. Do we have the right people?

11. Problem frame stakeholder map High Opposition Low Problem Frame Low Support High Weak Strong Stakeholder Power Bryson, Strategic Planning for Public and Nonprofit Organizations

12. Power versus Interest grid High Interest Low Weak Strong Power Eden & Ackerman 1998

13. Pursuing validity in mapping • Think causally, not correlationally • Think stocks and flows, even if you don’t draw them • Use units to make the causal logic plausible, even if you don’t write them down • Be able to tell a story for every link and loop • Move progressively from less precise to more precise -- from informal map to formal map

14. The standard cautions

15. These arrows mean ‘and then’ • We start with some understandings of the problem and its systemic context, and then we conceptualize (map) the system. • Then we build the beginnings of a model, which we then test to understand it. • Then we reformulate, or reconceptualize, or revise our understandings, or do some of all three, and then continue…

16. Arrows here are flows of material The words here represent stocks. This is not a causal diagram.

17. Only this one is a causal loop No explicit stocks or flows, no clear units, but it tells a compelling story – It’s a good start.

18. Project modeling core structure

19. Identical structure without explicit stocks and flows

20. Pursuing validity writing equations • Recognizable parameters • Robust equation forms • Phase relations • Richardson’s Rule: Every complicated, ugly, excessively mathematical equation and every equation flaw saps confidence in the model.

21. Modeling conflict within & between nations

22. Complexity & flaws destroy confidence • P of int'l conflict = DELAY FIXED ((Lateral pressure/10*Military force effect/Trade and bargaining leverage + International conflict)/Lateral conflict break point, 1 , 0) • Flaws Complexity, discreteness, units confusion and disagreement, disembodied parameter, confusion of the effect of a concept [leverage] with the concept itself, and the wonder what keeps this probability between 0 and 1?

23. Robust equation forms

24. Causal mish-mash

25. Robust equation formulations

26. Robust equation formulations

27. Robust equation formulations

28. Robust equation formulations

29. Pursuing validity in equations: Phasing

30. Phase relations Constant Perceived Value suggests continually rising Resources, but that doesn’t seem correct

31. Phase relations Here, the Perceived Value of Integrated Information sets a planned level of resources

32. Pursuing validity fitting to data • Generally, a weak test of model validity • Whole-model procedures • Optimization • Partial-model procedures • Reporting results • Graphically • Numerically: Theil statistics

33. Example of weakness of fitting to data • Logistic curve • dx/dt = ax - bx2 • Gompertz curve • dx/dt = ax - bx ln(x)

34. Fitting global petroleum with Logistic

35. Fitting global petroleum with Gompertz

36. Presenting model fit visually

37. Presenting model fit numerically • Theil statistics, for example • Based on a breakdown of the mean squared error: • 1 = Bias + Variation + Covariation

38. Presenting model fit numerically

39. Learning from surprise model behavior • Have clear a priori expectations • Follow up all unanticipated behavior to appropriate resolution • Confirm all behavioral hypotheses through appropriate model tests (Mass 1991/1981)

40. Tests to reveal and resolve surprise behavior • Testing the symmetry of policy response (up and down) • Testing large amplitude versus small amplitude response • Testing policies entering at different points • Testing different patterns of behavior • Isolating uniqueness of equilibrium or steady state • Understanding forces producing equilibrium positions (Mass 1991/1981)

41. Summary • Modelers, stakeholders, problem experts, and others in the modeling process pursue validity at every step along the way. • We have rigorous traditions guiding model creation, formulation, exploration, and implications. • We have a powerful, intimidating battery of tests of model structure and behavior. • Model-based conclusions that make it through all this deserve the confidence of everyone in the process.

42. Epilog • Reason is itself a matter of faith. It is an act of faith to assert that our thoughts have any relation to reality. (G.K. Chesterton) • I have no exquisite reason for’t, but I have reason good enough. (Sir Andrew, Twelfth Night)