1. System dynamics, Complex Adaptive Systems, and Agent Based Models: an introduction
Jacopo A. Baggio
Center for the Study of Institutional Diversity
School of Human Evolution and Social Change
Arizona State University

2. Initial Thoughts If I can not grow it, I can not understand it.
[Gell-Man]

3. Outline Systems System dynamics Complex Adaptive Systems
Analysing Complex Adaptive Systems
Agent Based Models

4. 1) What is a system? A system is ...?
‘an interconnected set of elements that is coherently organised in a way that achieves something’ Meadows 2009
A system is ‘greater than the sum of its parts’
Behaviour of a system depends on its structure – i.e. the interaction of its parts
Interaction involves information
A system persists over time
Has a function / purpose (?)
Real world systems are very complex
system participants often don’t understand how the system operates / as an outsider you won’t either

5. 2) System Dynamics Stocks (historical accumulation)
Flows (interconnections & processes)
Rate of in- / out- flows affect stock level
Time  dynamic not static
Function / purpose
Feedback  control response & lags
System dynamics is an approach to understanding the behaviour of complex systems over time. It deals with internal feedback loops and time delays that affect the behaviour of the entire system.[1] What makes using system dynamics different from other approaches to studying complex systems is the use of feedback loops and stocks and flows. These elements help describe how even seemingly simple systems display baffling nonlinearity.
System dynamics is a methodology and mathematical modeling technique for framing, understanding, and discussing complex issues and problems. Originally developed in the 1950s to help corporate managers improve their understanding of industrial processes, system dynamics is currently being used throughout the public and private sector for policy analysis and design.[2]

6. System dynamics models in NR governance
See CIFOR’s participatory modelling project:

7. stocks (the square representing “population of animals”
flows (the taps going in and out the stock, in this example animals being born and dying each year)
converters (the circles representing birth and mortality rate)
time: run the model
In real world this is a sub-system

8. The basics are simple!
BUT, when modelling all elements related to conservation and development outcomes of a landscape some complexity cannot be avoided

9. Malinau modelling exercise
The severe tensions between conservation and development are illustrated by events in Malinau District (Kalimantan, Indonesia). Conservationists decry proposed plans for logging and conversion of pristine tropical forest to oil palm
Although the local government is willing to declare the district a “conservation district,” at the same time, it shows interest in oil palm conversion.
The model explores the impact of the potential conversion of ha of forest to oil palm on forest cover, immigration, and the local economy in Malinau.
The simulation model was developed using STELLA® software, and relies on a combination of empirical data, data from the literature, and stakeholder perceptions.
Sandker et al Will Forests Remain in the Face of Oil Palm Expansion? Simulating Change in Malinau, Indonesia in Ecology & Society 12(2): 37

11. For Malinau, we examine the scenario of clearing ha of forest for oil palm and its consequences for local livelihood income, district revenue, and land-cover change.
Given the employment created by such development, we also examine potential migration into Malinau.
The aim of the paper is to simulate landscape dynamics in order to understand conservation and development trade-offs from the perspectives of different stakeholders.

15. Conclusions
If a company were to clear the forest for timber without planting oil palm (as commonly happens), poverty levels are likely to rise rather than decline over the long term.
If large-scale oil palm plantations were to be established, they could yield significant benefits to local authorities.
However, such development would induce massive employment-driven migration, with wide-ranging consequences for the current inhabitants of the region.
By visualizing and quantifying these trade-offs between conservation and development, the model stimulates debate and information exchange among conservationists, development actors, and district authorities so that well-informed choices can be made.

16. Limitations of model? ? Deterministic
Assumes homogeneity: aggregating heterogeneous agents & landscapes
Adaptive behaviour?
Complicated rather than complex?
Dealing with complexity – sum is greater (/qualitatively different) than parts – what does it really mean & how to represent/model it

17. 3) Dealing with complexity: Complex Systems
Simple
few components, linear and predictable interactions, repeatable, decomposable, knowable
Complicated
many components, cause and effect separated over time & space but repeatable, decomposable, analyzable
Complex
nonlinear interactions, sensitivity to initial conditions, dynamic, adaptable to environment, produce emergent structures & behaviors, can become chaotic
non decomposable, non predictable, non tractable analytically

18. Complexity complicated complex More than 3 × 106 parts
Parts have to work in unison to accomplish a function, one key defect brings the entire system to a halt
Has a limited range of responses to environmental changes
A few hundred elements, assembled as a single “flying object”
No director, no single bird has sense of overall pattern
Patterns are due to local interactions among decentralized components: system is self-organized

19. Properties of Complex Adaptive Systems I
Non-determinism, since it is impossible to precisely determine the behaviour of CAS; the only predictions that can be made are probabilistic;
i.e. initial conditions  “Butterfly effect”
Presence of feedbacks, whether positive or negative, loops are present in such systems and the relationships that forms between the components become more important than the component itself;
Distributed nature, hence it becomes very difficult to precisely locate functions and properties;

20. Properties of Complex Adaptive Systems II
Qualitative difference between larger and slower functions (or cycles) and smaller and faster ones (C.S. Holling, 2001, 2004; Levin, 2002; Waldrop, 1992) (i.e. time and space scales)
Limited decomposability, as the structure of such systems is studied as a whole. Again, the interactions between the components are a fundamental variable, thus it is very difficult if not impossible, to analyze CAS by decomposing it;

26. Pieter Bruegel the Elder, The Peasant Wedding (1568), Vienna, Kunsthistorisches Museum.

30. Scale matters!

31. Properties of CAS III
Self-similarity, implies that a system will have the same structures at different scales

32. Properties of CAS IV
Emergence and self-organization, universal structures might emerge in complex adaptive systems, although it is not possible to foresee these by looking at its components. e.g. Self-organizing birds or Cyclical domination

33. CAS Example

34. Analysing CAS I (Amaral & Ottino, 2004)
Non linear dynamics
deterministic chaos, bifurcation/singularity theory, stability/instability measures, symbolic dynamics
nonlinear time series analysis
Statistical mechanics
phase transitions & critical phenomena
Network theory

35. Analyzing CAS II
Critical values are the boundaries between different regions (phase transitions)
Non-linear relations imply that small perturbations can lead to unpredictable or amplified effects (avalanches)
Percolation and Critical Transition

36. Analyzing CAS III
models, simulations
no analytic solutions

37. Models and this is not a light ray This is not the Solar system
A pattern, plan, representation, or description designed to show the structure or workings of an object, system, or concept
A representation of a system that allows for investigation of the properties of the system and, in some cases, prediction of future outcomes

38. Modelling a system:
Statistical Modelling (e.g. time series analysis, regression analysis)

39. Modelling a system:
Mathematical Modelling (e.g. differential equations, partial differential equations)

40. Modelling a system: Agent Based Modelling
Agent-based models (ABM) (or individual-based models -IBM- as often called in ecology) allow simulation of a system from the bottom-up, that is, through an ensemble of individual entities called agents.
Agents behave according to a predetermined set of rules and are subject to defined initial parameter configurations: agents react to certain environmental conditions interacting between themselves.
Some ABM incorporate learning and adaptive behaviour of agents.

41. ABM building process
First, one needs to conceptualise the system that will be represented, thus defining the purpose, the “research question(s)” and identifying the crucial variables of the system with their interrelations.
Second, it is necessary to find a set of formal specifications that is able to fully characterise the conceptual model.
Third, the model needs to be coded and implemented.
The model is iterative, every agent receives input from the environment, processes it, and acts generating a new environmental input until a pre-determined condition is met (e.g. time limit or all agents find themselves in a given condition).

42. Layout of an Agent Based Model

43. NetLogo Interface

44. ABM Results
ABMs can generate series (time-series in most cases) of state variables at different scales.
The results should be analysed using advanced statistical techniques and tools (e.g. network theoretical tools, or time-series analysis).
A single simulation run is just a particular case in the parameter space.
Hands on NetLogo

45. Careful! K.I.S.S.!!! (i.e. Keep It Simple and Stupid)
Careful understanding and planning of how single agents will behave.
Choice of the rules that will allow them to interact with the environment and between themselves is a central issue.
Need for a systematic procedure and it is necessary to avoid assumptions that are not confirmed by the “general wisdom” (existing literature, experts assessments etc.).
Continuous interaction and feedback between researchers and “experts” is necessary, so that it may be possible to shed light on the appropriate parameter space region to explore, and the interactions that exist between agents, and to assess the appropriateness of the model in its different stages (initiation, running, validation)
Still: Errors and Artifacts!

46. Evaluating ABM I
1) Do the results of a simulation correspond to those of the real world (if data are available)?
2) Does the process by which agents and the environment interact correspond to the one that happens in the real world (if the processes in the real world are known)?
3) Is the model coded correctly so that it is possible to state that the outcomes are a result solely of the model assumptions?

47. Evaluating ABM II
Answering the first two questions allows us to assess the validity of the representation (model):
how well the real system we want to describe is captured and explained by its representation.
Answering the third question guarantees that the model’s behaviour is what the modeller really intended it to.
Still Evaluation needs data from the real world and the involvement of knowledgeable experts that might be able to give insights into the “real” processes and dynamics to evaluate its plausibility as representation of reality.

49. Advancing transdisciplinary science
Experiments
Case Studies
ABM

50. Concluding Remarks
ABMs explain rather than predict, allowing for a qualitative understanding of the fundamental processes underlying the system modelled.
Significant, social (and social-ecological system) science contributions will emerge more quickly if science-based beliefs are based on the joint results of both agent-modelling and subsequent empirical corroboration
[Henrickson & McKelvey]

51. Concluding Remarks II
And in this it is not likely that all are mistaken the conviction is rather to be held as testifying that the power of judging aright and of distinguishing truth from error, which is properly what is called good sense or reason, is by nature equal in all men; and that the diversity of our opinions, consequently, does not arise from some being endowed with a larger share of reason than others, but solely from this, that we conduct our thoughts along different ways, and do not fix our attention on the same objects.
For to be possessed of a vigorous mind is not enough; the prime requisite is rightly to apply it.
René Descartes, Discourse on the Method, 1637
Good sense is, of all things among men, the most equally distributed; for every one thinks himself so abundantly provided with it, that those even who are the most difficult to satisfy in everything else, do not usually desire a larger measure of this quality than they already possess.

52. Thank you for your attention