R. W. Eberth Sanderling Research, Inc. 01 May 2007 Validation Methodology for Agent-Based Simulations Workshop Agent-Based Simulations (ABS) Working Definition R. W. Eberth Sanderling Research, Inc. 01 May 2007

Purpose Propose a definition of ABS for this workshop – not for the M&S community as a whole For this workshop, widen the field of view to address other non-physical M&S classes that may require similar V&V approaches Intent is to be Inclusive vice Exclusive

Ground Rules For our purposes: Agent-Based Simulation (ABS) and Agent-Based Modeling or Model (ABM) are synonymous “Agent-Based,” “Individual-Based,” and “Entity-Based” are synonymous

Types of Models/Simulations Physics-Based Outcomes could be calculated, but math is too hard Validity assessable empirically Probability-Based May include physics Account for some/most random effects Range of outcomes could be calculated (see above) Agent-Based Emergent Behaviors Outcomes not predictable beyond small time increments Non-repeatable outcomes Validity assessable empirically?

Common to All Models/Simulations May not account for all (or may misunderstand and thus inaccurately account for): Interactions Cause and effect relationships Account for no unknown unknowns

Building Blocks of Definition Multi-Agent System (MAS): any computational system whose design is fundamentally composed of a collection of interacting parts* Individual-Based Models: simulations based on the global consequences of local interactions of members of a population (a subset of MAS)* Complex Adaptive Systems (CAS): systems having the ability to self-organize and dynamically reorganize their components in ways better suited to survive and excel in their environments** CAS Properties (J. Holland, 1995): Aggregation: allows groups to form Nonlinearity: invalidates simple extrapolation Flows: allow the transfer and transformation of resources and information Diversity: allows agents to behave differently from one another and often leads to the system property of robustness * Derived directly from the work of C. Reynolds ** As cited by D. Samuelson/C. Macal, 2006

Proposed Working Definition of ABS for This Workshop Any computational system whose design is fundamentally composed of: Autonomous decision-making entities (agents) Agents interacting with each other and their environment over time Agents independently sensing and responding to each other and their environment according to their own rule sets Heterogeneous agent population Agent decision-making algorithms that may be extremely simple or highly complex or that may evolve

Other ABS Characteristics To Consider Large trajectory space “Edge of Chaos” outcomes Emergent behaviors Non-linearity Outcomes may be locally predictable (within a very small neighborhood), but are globally unpredictable Whole emerges from the parts Principal distinction between ABS and traditional analytic modeling: Bottom-up versus top-down “A synonym of ABM would be microscopic modeling, and an alternative would be macroscopic modeling.” Not “Can you explain it?” but “Can you grow it?” E. Bonabeau, 2002

For This Workshop Include: Classes of non-physical, non-probabilistic models, including: Decision rule sets Human behavior representation (HBR) Knowledge-based systems (KBS) Cellular automata models Population dynamics Political, Military, Economic, Social Infrastructure and Information (PMESII) models Social models

Related Issues (Only Touched on in This Workshop) Optimization and Heuristics Objective function formulation Selection of one solution from many Data Weighting factors Thresholds Breakpoints Others