1. 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

2. 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

3. 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

4. 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?

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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