Presentation is loading. Please wait.

Presentation is loading. Please wait.

System dynamics, Complex Adaptive Systems, and Agent Based Models: an introduction Jacopo A. Baggio Center for the Study of Institutional.

Similar presentations


Presentation on theme: "System dynamics, Complex Adaptive Systems, and Agent Based Models: an introduction Jacopo A. Baggio Center for the Study of Institutional."— Presentation transcript:

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 2 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 3

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 dont understand how the system operates / as an outsider you wont either 4

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 5

6 SYSTEM DYNAMICS MODELS IN NR GOVERNANCE See CIFORs participatory modelling project: 6

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 7

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

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 9

10 10

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. 11

12 12

13 13

14 14

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. 15

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 16

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 17

18 Complexity 18 complicated complex More than 3 × 10 6 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 effectButterfly 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; 19

20 Properties of Complex Adaptive Systems II 20 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;

21

22

23

24

25

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

27

28

29

30 Scale matters! 30

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

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 dominationSelf-organizing birdsCyclical domination 32

33 CAS Example 33

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 34

35 Analyzing CAS II 35 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 Percolation and Critical TransitionCritical Transition

36 Analyzing CAS III 36 models, simulations no analytic solutions

37 Models 37 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 and this is not a light ray

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

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

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. 40

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). 41

42 Layout of an Agent Based Model 42

43 NetLogo Interface 43

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 44

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! 45

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? 46

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 models 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. 47

48 48

49 Advancing transdisciplinary science 49 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] 50

51 Concluding Remarks II 51 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 52


Download ppt "System dynamics, Complex Adaptive Systems, and Agent Based Models: an introduction Jacopo A. Baggio Center for the Study of Institutional."

Similar presentations


Ads by Google