1. Structural realism and theory development in agent-based models addressing practical problems Volker Grimm

2. Steve Railsback ACKNOWLEDGEMENTS
Department of Ecological Modelling (ÖSA) at UFZ, Leipzig
Teachers and inspirators: Christian Wissel, Janusz Uchmanski, Don DeAngelis
Collaborators and students

3. What do I mean by “theory”?
“A scientific theory is a well-substantiated explanation of some aspect of the natural world that is acquired through the scientific method and repeatedly tested and confirmed through observation and experimentation. …
As used in everyday non-scientific speech, "theory" implies that something is an unsubstantiated and speculative guess, conjecture, or hypothesis; such a usage is the opposite of a scientific theory.” Wikipedia
Agent-based complex systems science

4. THEORY IS GOOD FOR APPLICATION

5. Overview of this lecture
Structural realism
Pattern-oriented modelling
Pattern-oriented theory development
First principles
Individual-based ecology

6. Idea underlying all modelling
Real world/system too complex to understand or to predict behaviour
Create a simplified representation, which only contains essentials with regard to a certain question or problem
Understand and predict behaviour of this simplified representation
Transfer this understanding and these predictions to the real system

7. Problem: verification and validation
the model “mimics the real world well enough for its stated purpose (Giere, 1991)” (Rykiel 1996, p. 230). V2
we can place confidence “in inferences about the real system that are based on model results (Curry et al., 1989)” (Rykiel 1996, p. 230)
Note:
Rykiel combines both aspects under one term, validation
Rykiel 1996

8. Hildenbrandt et al. 2010

9. GENERATIVE MECHANISMS
We want to make sure that our models are “sufficiently good” representations of their real counterparts.
We want to learn about the real world
We want to capture essential elements of a real system’s “internal organization”
We want to capture the “generative mechanisms” that generate the structure and behaviour of real systems

10. Predictive ecology
Only if we capture the “generative mechanisms” sufficiently well will our predictions be good enough for new conditions
Bossel (1992) contrasts descriptive models with “real-structure” models:
“The difference is that between the […] descriptive modelling of the motion of the hands of a clock, and the analysis and real-structure description of the mechanism of the clock; only the latter would be able to predict correctly what would happen if the pendulum were stopped or if the spring were not rewound.” (p. 264).

11. Predictive systems models should be STRUCTURALLY REALISTIC
STRUCTURAL REALISM
Predictive systems models should be STRUCTURALLY REALISTIC
Reproduce observed patterns for the right reasons, i.e., capture the internal organisation (across scales) of the real system
Test: Independent predictions!
Optimize model complexity („sweet spot“, „middle ground“)

12. Models should reproduce patterns, not data
General relationships that preferably hold across different instances of the same system
Robust relationships
Structures or processes that characterize a certain class of systems
Related concept in economics: „stylized facts“

13. Spatial patterns in ecology

14. Spatial patterns in marine ecology
Tremblay et al. (unpublished)

15. More patterns …

16. What scientists do with patterns
Pattern: Beyond random variation
Patterns contain information about internal organization
We develop models that reproduce the pattern
We infer from the mechanism built into the model the real system´s organization
We need to exploit („squeeze“) the pattern

17. Problem with complex systems
A single pattern may not contain enough information
Ecologists tend to focus on (single) patterns observed at one level of observation
Behaviour
Population dynamics
Community composition
Ecosystem function

18. “Monoscopic” view
Most approaches (and modellers) are not making the best use of the information (lemons) available

19. The thing we need is a “multiscope”

20. Multiscope view Take into account multiple patterns
Observed at different scales and/or levels of organisation
Make your model reproduce these patterns simultaneously
Use each pattern as a „filter“ to reject unacceptable submodels or parameterizations
„Pattern-oriented modelling“(Grimm et al. 1996, 2005; Wiegand et al. 2003, 2004; Grimm and Railsback 2005, Railsback and Grimm 2012).

21. Pattern-oriented modelling

22. Example: Oystercatcher mortality (1976-1981)
Pattern-oriented modelling (POM)
Example: Oystercatcher mortality ( )
Definition:
„Multi-criteria design, assessment, and parameterization of models of complex systems“

23. Patterns as filters
Multiple (3 or more) „weak“ patterns may narrow down model structure better than one single „strong“ pattern
Cycles in small mammals („strong“)
Abundance within certain bounds
Recovery after disturbance needs 10 years
Territory size changes with abundance in a certain way

24. Creative scientists (Sherlock Holmes) are using POM all the time
Patterns: Examples
Red shift in spectra of galaxies and stars
Atomic spectra
Iridium layer: mass extinctions
DNA: Chargaff‘s rule, x-ray diffraction patterns, tautomeric properties of building blocks
Periodic system of elements
Creative scientists (Sherlock Holmes) are using POM all the time

25. Pattern-oriented Modelling: Three elements
Design: Provide state variables (entities, processes) so that patterns observed in reality in principle also can emerge in the model
Model selection: Use multiple patterns for contrasting alternative (sub)models of certain adaptive behaviours
Parameterization: Use multiple patterns for determining entire sets of unknown parameters („inverse modelling“)

26. Pattern-oriened theory development
Pattern-oriented Modelling: Three elements
Design: Provide state variables (entities, processes) so that patterns observed in reality in principle also can emerge in the model
Model selection: Use multiple patterns for contrasting alternative (sub)models of certain adaptive behaviours
Parameterization: Use multiple patterns for determining entire sets of unknown parameters („inverse modelling“)
Pattern-oriened theory development

27. Pattern-oriented theory development
Theory in Individual-based Ecology is across-levels
Theory=models of what individuals do that explain system dynamics (Capture enough essence of individual behavior to model the system)‏

28. THEORY DEVELOPMENT CYCLE
Proposed theories: alternative traits for a key agent behavior
ABM
How well does ABM reproduce observed patterns?
Empirical literature, research
Characteristic patterns of emergent behavior

29. EXAMPLE: VULTURES AND CARCASSES
Pattern: # of feeders at a carcass
Jackson et al Biology Letters 4
Cortes-Avizanda A, Jovani R, Donázar JA, Grimm V. Ecology (2014)

30. EXAMPLE: VULTURES AND CARCASSES
Cortes-Avizanda, Jovani, Donázar & Grimm Ecology.

31. EXAMPLE: VULTURES AND CARCASSES
Cortes-Avizanda, Jovani, Donázar & Grimm Ecology.

32. EXAMPLE: VULTURES AND CARCASSES
Cortes-Avizanda, Jovani, Donázar & Grimm Ecology.

33. Better for pattern-oriented theory development:
FIRST PRINCIPLES
Often, we base our theories on ad hoc assumptions.
Better for pattern-oriented theory development:
Start from “first principles”
Physics, chemistry
Fitness seeking

34. First principles: example
Benjamin Martin (PhD student, UFZ)
Daphnia population dynamics in laboratory
Effects of pesticides
My idea: Start from existing model
Ben:
That model is good, but everything is based on empirical (imposed, calibrated) relationships
I want to to do something more generic
I want to try Dynamic Budget Theory (DEB)

35. DEB – Kooijman 2010
where
and

36. DEB
Growth
Food
Maintenance
Reproduction

37. DEB-IBM: Generic NetLogo program
Martin et al. (2012) Methods Ecology and Evolution
Population
Environment
Toxicants
Food
Temperature
Individual
DEB
IBM
Growth
Reproduction
Survival
Density
Size distribution
DEB-IBM

38. Parameterization

39. We could reproduce not only population density at one food level, but density and size distribution for multiple food levels and toxicant expsoure
Low food (0.5mgC d-1)
High food (1.3mgC d-1)
Martin et al Am. Nat.
Data from Preuss et al. 2009

40. ANOTHER INDEPENDENT PREDICTION
Martin, Jager, Nisbet, Preuss & Grimm Am. Nat.

41. Ecology (populations, communities, ecosystems)
Emergent
Specific
environment
Population growth rate, λ
Vital rates, b & d
Wide range of
environmental
conditions
Adaptive behavior (IBM)
Imposed
Empirical
Ecology (populations, communities, ecosystems)

42. Generic models of interaction:
THEORIES OF WHAT
Generic models of interaction:
Zone-of-influence approach
Forest gap models: vertical competition for light (JABOWA)
Size-based trophic, „trait-mediated“
Generic models of behaviours
Foraging
Habitat selection
Home range
Generic models of life history
Bioenergetic models
Ontogenetic Growth Model
Dynamic Energy Budget theory

43. EXAMPLE: TROUT HABITAT SELECTION

44. GENERAL THEORY
Railsback and Harvey Trends Ecol. Evolution.

45. Pattern-oriented modelling Pattern-oriented theory development
So far:
Structural realism
Pattern-oriented modelling
Pattern-oriented theory development
First principles
Individual-based ecology

46. INDIVIDUAL-BASED ECOLOGY
2015. BioScience 65:

47. INDIVIDUAL-BASED ECOLOGY

48. Phase 1: Conceptualization

49. Phase 2: Implementation

50. Phase 3: Diversification

51. Coherence for IBE

52. SUMMARY Role of theory for application
Structural realism: generative mechanisms
Pattern-oriented modelling: multiple patterns as filters
Pattern-oriented theory development: models of behaviours that explain system-leven patterns
First principles: evolution, physics, chemistry
Individual-based ecology: ultimately big science