Exploring Structural Realism and Theory Development in Agent-Based Models

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

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

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. Agent-based complex systems science 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 11.5.2016

THEORY IS GOOD FOR APPLICATION SEITE 4

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

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 SEITE 6

Problem: verification and validation V1 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 SEITE 7

Hildenbrandt et al. 2010 SEITE 8

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 SEITE 9

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). SEITE 10

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 ) Page 11

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 SEITE 12

Spatial patterns in ecology http://www.gov.nf.ca/nfmuseum/images/empetrumnigrumlivedeadwaveforestmistakenpoint.jpg SEITE 13

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 (unpublished) SEITE 14

More patterns SEITE 15 / 56

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

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

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

The thing we need is a multiscope

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).

Pattern-oriented modelling

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

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 SEITE 23

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 SEITE 24

Pattern-oriented Modelling: Three elements 1. Design: Provide state variables (entities, processes) so that patterns observed in reality in principle also can emerge in the model 2. Model selection: Use multiple patterns for contrasting alternative (sub)models of certain adaptive behaviours 3. Parameterization: Use multiple patterns for determining entire sets of unknown parameters ( inverse modelling ) SEITE 25

Pattern-oriented Modelling: Three elements 1. Design: Provide state variables (entities, processes) so that patterns observed in reality in principle also can emerge in the model 2. Model selection: Use multiple patterns for contrasting alternative (sub)models of certain adaptive behaviours 3. Parameterization: Use multiple patterns for determining entire sets of unknown parameters ( inverse modelling ) Pattern-oriened theory development SEITE 26

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) SEITE 27

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

EXAMPLE: VULTURES AND CARCASSES Pattern: # of feeders at a carcass non-social chains of vultures local enhancement Dfoll/find-sear< Dfoll Searcher Searcher Searcher Dcar< ( Dunocc or Docc ) Dcar< Dunocc Follower Dfind-sear< Dland Finder Finder Finder Dcar< Docc Dcar= 0 Dcar= 0 Feeder Feeder Feeder Feeder Feeder Feeder Jackson et al. 2008. Biology Letters 4 Cortes-Avizanda A, Jovani R, Don zar JA, Grimm V. Ecology (2014) SEITE 29

EXAMPLE: VULTURES AND CARCASSES non-social chains of vultures local enhancement Cortes-Avizanda, Jovani, Don zar & Grimm. 2014. Ecology. SEITE 30

EXAMPLE: VULTURES AND CARCASSES 0.6 1.0 experimental carcasses 'non-social' 0.5 0.8 0.4 0.6 0.3 0.4 0.2 % carcasses 0.2 0.1 0.0 0.0 0.5 'chains of vultures' 0.4 'local enhancement' 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 0 200 400 600 800 1000 0 200 400 600 800 1000 # feeders Cortes-Avizanda, Jovani, Don zar & Grimm. 2014. Ecology. SEITE 31

EXAMPLE: VULTURES AND CARCASSES 80 2500 experimental carcasses 70 Minimum # feeders Maximum # feeders 2000 1,000 simulations 60 50 1500 40 30 1000 20 500 10 0 0 200 300 250 Mean # feeders Median # feeders 150 200 100 150 100 50 50 0 0 'non-social' 'non-social' 'chains of vultures' 'chains of vultures' 'local enhancement' 'local enhancement' Hypothesis Cortes-Avizanda, Jovani, Don zar & Grimm. 2014. Ecology. SEITE 32

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 SEITE 33

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) SEITE 34

DEB Kooijman 2010 where and SEITE 35

DEB Food Maintenance

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

Parameterization Growth Cumulative reproduction 6 140 cumulative offspring per female 120 5 body length (mm) 100 4 80 3 60 2 40 1 20 0 0 0 10 20 time (d) 30 40 0 5 10 time (d) 15 20 25

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) 500 500 Total Total Neonates Neonates 400 400 300 300 200 200 100 100 Abundance Abundance 0 0 500 500 Juveniles Juveniles Adults Adults 400 400 300 300 200 200 100 100 0 0 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 0 10 20 30 40 Day Day Martin et al. 2013. Am. Nat. Data from Preuss et al. 2009

ANOTHER INDEPENDENT PREDICTION Martin, Jager, Nisbet, Preuss & Grimm. 2013. Am. Nat. SEITE 40 / 56

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

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 SEITE 42

EXAMPLE: TROUT HABITAT SELECTION SEITE 43

GENERAL THEORY Railsback and Harvey 2013. Trends Ecol. Evolution. SEITE 44

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

INDIVIDUAL-BASED ECOLOGY 2015. BioScience 65: 140-150 SEITE 46

INDIVIDUAL-BASED ECOLOGY SEITE 47

Phase 1: Conceptualization SEITE 48

Phase 2: Implementation SEITE 49

Phase 3: Diversification SEITE 50