SOFTWARE TESTING

Software Testing

Abstract

Measurement of nonlinearity in social service research and evaluation relies primarily on spatial analysis and, to a lesser extent, social network analysis. Recent advances in geographic methods and computing power, however, allow for the greater use of simulation methods. These advances now enable evaluators and researchers to simulate complex adaptive systems (CASs) by applying agent-based modeling (ABM). CASs reflects the interactions of competitive and cooperative tendencies found in agents. ABM simulations create and test generated observable patterns using the fewest number of plausible decision rules and agents. This primer presents essential concepts for understanding ABM as social service applications of complexity theory shift from a metaphorical perspective to a formalized evaluation method. Further developments in ABM methods need to focus on concepts emanating from the study of complexity science, including the concepts of the wisdom of groups, strengths found in diverse perspectives, robustness, interconnectedness, sustainability, and conflict and cooperation. Appropriate software programs for developing and testing agent-based models are provided.

Table of Contents

INTRODUCTION4

NONLINEARITY IN SOCIAL SERVICE EVALUATION8

APPLICATION OF COMPLEXITY THEORY IN SOCIAL SERVICE EVALUATION10

MODELING NONLINEAR BEHAVIOR OF PEOPLE AND ORGANIZATIONS11

CONCEPTS USED IN ABM FOR SOCIAL SERVICE EVALUATION11

Agents13

Agents Are Interconnected14

Systems Display Distinct Patterns of Behavior at the Individual and Group Levels14

Agents' Behaviors Change or Co-evolve with the Context14

Behavior Exists within a Given Range15

Behavior Is Subject to Unpredictable Shifts15

CONCLUSION15

Software Testing

INTRODUCTION

An agent-based model (ABM) is a type of computational model that allows the simulation of actions and interactions of autonomous individuals within an environment, and to determine what effects occur in the whole system combines elements of theory games, complex systems, emergence, computational sociology, multi agent systems and evolutionary programming. The models simulate the simultaneous operations of multiple entities (agents) in an attempt to recreate and predict the actions of complex phenomena. It is a process of emergence from the most basic level (micro) to the higher (macro). Supposedly the individual agents act as they perceive as their own interests, such as reproduction, economic benefit or social status, and their knowledge is limited. MBA agents may experience "learning", adaptation and reproduction.

ABM captures emergent phenomena. Emergent phenomena result from the interactions of one-by-one entities. By delineation, they will not be decreased to the system's parts: the entire is more than the addition of its components because of the interactions between the parts. An emergent occurrence can have properties that are decoupled from the properties of the part. For demonstration, a traffic jam, which outcomes from the demeanor of and interactions between one-by-one vehicle drivers, may be going in the main heading converse that of the vehicles that origin it. This attribute of emergent phenomena makes them tough to realize and predict: emergent phenomena can be counterintuitive. Numerous demonstrations of counterintuitive emergent phenomena will be recounted in the next sections. ABM is, by its very environment, the canonical set about to modeling emergent phenomena: in ABM, one forms and simulates the demeanor of the system's constituent flats (the agents) and their interactions, apprehending emergence from the base up when the replication is ...