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## Agent-Based Modeling of Complex Adaptive Systems

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**Agent-Based Modeling of Complex Adaptive Systems**• Introductions • Overview of week’s ABM track classes • Complex Adaptive Systems - characteristics • Agent-Based Modeling - What, Why, When?**Introductions**• Rick Riolo, Center for the Study of Complex Systems (CSCS) ABMs of CAS: microbiology, ecology, urban sprawl, common pool (forest) use, logging in central Africa, etc. Evolutionary Computation. • Elizabeth Bruch, CSCS, Sociology, ISR ABMs of racial and economic segregation and marriage markets; discrete choice analysis. TAs • Aaron Bramson, CSCS, Pol. Sci, Philosopy • Sarah Cherng, Public Health (CSCS SysAdmin) • Eric Provins, Pol. Sci. Particpants…**Overview of ABM Week**• Goals • What are ABMs and how used in health sciences to model CAS • Guest Lectures – range of applications, goals, “styles” • “Hands on” experience using and creating ABMs • Run/extend models related to guest lectures • Design and create your own ABM model • Pointers to where to go next for more information • ABM Packages • Papers: Validation, Sensitivity Analysis, etc.**ABM Week Schedule Overview**Monday (afternoon): • Basic overview of ABM • NetLogo basics • Start weeklong project: conceptual design (Pairs) Other Days: • Lecture/Discussion - example ABM in various domains • Lab: use, extend models related to those talks; Introduce additional NetLogo features • Time to work on weeklong project Tuesday: Spatial models Wednesday: Infectious Disease model; Inside-the-Skin (Cell) models Thursday: Behavior and chronic disease models Friday: Project Discussion/Demos; Misc. Topics and discussion.**Complex Adaptive Systems (CAS)**Frequent Q: Is X a complex (adaptive) system? A “real world” system can usefully be considered simple or complex, depending on what we are trying to understand / predict. Ex: “The Human Cannonball” physics; biology; psychology; sociology; economics Why consider a CAS approach to studying some system? • System’s macro-behaviors of interest are “complex” (symptomatic of CAS) • Presumed system components / micro-mechanisms are known to be able to generate complex macro-behaviors • To study “emergence” in itself!**Complex Adaptive Systems – Macro-Behavior**Complex systems exhibit a range of patterns of macro-behaviors • Dynamics in time: mixes of cycles, nonlinearities, tipping points, punctuated equilibria, resurgence, perpetual novelty, chaos • Patterns over distributions of histories: Sensitivity to initial conditions; Path dependence Basins of attraction and multiple equilibria • Patterns in space: clustering, fractals • Patterns over time (events): power laws; self-organized criticality (SOC); highly optimized tolerance (HOT) • Patterns over parameter space: tipping points; regions of robustness vs. instability; regions with different dynamics; … • Same system -> different behaviors at different times / conditions Economic systems: equilibrium, cycles, chaotic**Complex Adaptive Systems – Components and Mechanisms**Entities (“agents”) of various types (microbes, people, organizations) • Diverse – different characteristics, capabilities, interactions, goals (across and within types of agents) • Limited memory and cognitive capabilities (“bounded rationality”) • Rich set of decision rules (continuous or nonlinear) • Adaptive (capabilities, goals, relations): learning; evolution • Embedded in an environment (non-agent, non-uniform, dynamic) • Local, non-random interactions: spatial and/or social network biases • Mix of competitive / cooperative / neutral interactions • Positive and negative feedbacks emerge Note: • Simple agents/rules -> Complex macro-behavior • Complex agents/rules -> Simple macro-behavior**What is an Agent-Based Model?**In short (more later today…): Representation of a system and its dynamics in terms of • the individual entities considered important, and • their actions and interactions with each other and their environment Bottom-up generation of model dynamics and outcomes • Define micro-level mechanisms: agent, environment • Setup parameters and initial conditions, • Run it… • Measure/observe macro-level patterns: aggregate variables in space / time. • Analyze snapshots and dynamics – compare to “the real world” Example: Grass/Sheep/Wolves; El Farol Bar Patrons**Example ABMs**• Wolf / sheep / grass – predator / prey • Sheep: choose to eat grass or move randomly (step size trait) • Wolves: eat sheep or move randomly (step size trait) • Grass grows back (infinite or finite rate) • Bottom-up generation of classic Lotka-Volterra dynamics • Can extend in many ways… ElFarol Bar Patron model • Patrons choose to go or not (alone - no friends!) Predict attendance based on past attendance: go if predict < threshold • At most 60 people can be happy / step – competition, coordination • Agents learn: try different rules, trying to improve predictions Diverse “ecology” of rules (strategies) -- perpetual novelty • Emergence: Aggregate attendance close to threshold**Why use ABM Approach?**• Flexible: can explore wide space of possible models (beyond EBM) • “Natural” representation of CAS components / mechanisms (diverse, adaptive agents; local interactions--space/networks) • Easy to embed in dynamic, complicated non-agent environment • Can use data at many levels (agent characteristics and behavioral propensities, spatial data, aggregate variables and patterns) • Address questions difficult to address with other approaches. • Explanatory Models: generative theories • Exploratory Models: build intuitions, discover novel insights, test hypotheses, explore policy alternatives • Formal, Computational model • Unambiguous, quantitative (like PDE, Game Theory, etc) • Inductive: run computational experiments, analyze results**ABM as Complementary Approach**• Empirical data collection and statistical analysis • Selecting factors, mechanisms to include in ABM • Set model parameter values, Initial conditions • Data for model evaluation (micro- and macro-level outcomes) • Systems Dynamics/Equation Based (EBM), Game Theory: • Compare model results when possible, e.g., set ABM parameters to be equivalent to EBM assumptions • Increase confidence in results • Gain deeper understanding of processes • Network Theory / Methods • Guide ABM design and/or supply initial data • Tools for analysis of ABM results