1. Agent-Based Modeling of Complex Adaptive Systems Introductions Overview of weeks ABM track classes Complex Adaptive Systems - characteristics Agent-Based Modeling - What, Why, When?

2. 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…

3. 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.

4. 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.

5. 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? Systems 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!

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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