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

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System dynamics, Complex Adaptive Systems, and Agent Based Models: an introduction


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    1. System dynamics, Complex Adaptive Systems, and Agent Based Models: an introduction Jacopo A. Baggio jbaggio@asu.edu 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

    6. System dynamics models in NR governance See CIFOR’s participatory modelling project: http://www.cifor.cgiar.org/conservation/_ref/research/research.2.htm

    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 500 000 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. 2007 Will Forests Remain in the Face of Oil Palm Expansion? Simulating Change in Malinau, Indonesia in Ecology & Society 12(2): 37

    10. For Malinau, we examine the scenario of clearing 500 000 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. 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.

    12. 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

    13. 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

    14. complicated complex Complexity 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

    15. 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;

    16. 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;

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

    18. Scale matters!

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

    20. 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

    21. CAS Example

    22. 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

    23. 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

    24. no analytic solutions models, simulations Analyzing CAS III

    25. This is not the Solar system and this is not a light ray Models 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

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

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

    28. 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.

    29. 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).

    30. Layout of an Agent Based Model

    31. NetLogo Interface

    32. 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

    33. 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!

    34. 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?

    35. 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.

    36. Advancing transdisciplinary science

    37. 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]