modeling aggression & bullying

112 Pages·2013·3.76 MB·English

MODELING AGGRESSION & BULLYING: A
COMPLEX SYSTEMS APPROACH
By
George F. Mudrak
B.S Computer Science,
B.A. Psychology,
University of Colorado at Boulder, Boulder, Colorado, 1996

A thesis submitted to the Graduate Faculty of the
University of Colorado at Colorado Springs in partial fulfillment of the
requirements for the degree of Master of Science
Department of Computer Science
2013

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This thesis for the Master of Science degree by
George F. Mudrak
has been approved for the
Department of Computer Science
by

____________________________________
Sudhanshu K. Semwal

____________________________________
Edward Chow

____________________________________
Chuan Yue

____________________________________
Bonnie Snyder

______________
Date
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Mudrak, George F. (M.S., Computer Science)
Modeling Aggression & Bullying: A Complex Systems Approach
Thesis directed by Professor Sudhanshu K. Semwal

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Table of Contents
INTRODUCTION ...............................................................................................6
PREVIOUS WORK ............................................................................................8
MODELLING BULLYING ..............................................................................13
DESIGN ............................................................................................................17
THE VISUAL MODEL................................................................................................................ 17
Actor Vision Parameters ................................................................................................... 18
ACTOR QUALITIES ................................................................................................................... 20
Attributes ........................................................................................................................... 21
Differentiation ................................................................................................................... 23
ACTOR BEHAVIORAL ALGORITHMS ........................................................................................ 25
Perception Algorithm ........................................................................................................ 25
Aggression Algorithm ....................................................................................................... 26
Defensive Algorithm .......................................................................................................... 27
Movement Algorithm ......................................................................................................... 28
DESIGN CONCLUSION .............................................................................................................. 30
IMPLEMENTATION .......................................................................................32
INTRODUCTION ........................................................................................................................ 32
PRE-DEVELOPMENT ................................................................................................................ 33
Platform and Software ...................................................................................................... 33
Development Decisions ..................................................................................................... 33
USER INTERFACE INPUT .......................................................................................................... 34
Initialization Time ............................................................................................................. 35
Run Time ........................................................................................................................... 36
ACTORS ................................................................................................................................... 39
People ............................................................................................................................... 40
Coercers ............................................................................................................................ 44
Food .................................................................................................................................. 45
SIMULATION PROCESS ............................................................................................................. 46
Initialization ...................................................................................................................... 46
USER INTERFACE DASHBOARD ................................................................................................ 61
Graph Based Feedback ..................................................................................................... 62
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Numeric Based Feedback .................................................................................................. 63
UTILITY MODULES .................................................................................................................. 70
Output ............................................................................................................................... 70
Reports .............................................................................................................................. 71
RESULTS ..........................................................................................................73
ANALYSIS DATA ..................................................................................................................... 73
AGGRESSION & BULLYING ANALYSIS ..................................................................................... 73
Simulation 1 ...................................................................................................................... 73
Simulation 2 ...................................................................................................................... 75
Simulation 3 ...................................................................................................................... 77
Simulation 4 ...................................................................................................................... 82
Simulation 5 ...................................................................................................................... 84
STATISTICAL ANALYSIS .......................................................................................................... 86
Population Attribute Analysis ........................................................................................... 89
Population Behavior Analysis ........................................................................................... 94
OBSERVATIONS ..........................................................................................102
CONCLUSIONS .............................................................................................105
FUTURE WORK ............................................................................................106
TABLE OF FIGURES ....................................................................................108
REFERENCES ................................................................................................111

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Chapter I
INTRODUCTION
Almost daily, news articles from around the world highlight the most recent cases of
bullying among adults and children. We read about the devastating and lasting
consequences of bullying in the same news, and feel a greater impact when we hear of
another child taking their life or the lives of others as a consequence. What makes
bullying behaviors so insidious is they cut across people, age, cultures and nations. The
only requirement is being a human. A dramatic range of consequences have manifested
over the years from hurt feelings on the light end, to loss of life on the extreme end.
Bullying is defined as a specific form of unwanted, intentional, and repeated aggression,
that involves a disparity of power between the victim and perpetrator(s) [1]. For our
purposes, we shall consider it directed aggression between people, as the above is
included. Bullying is a difficult area of study due to the complex nature of the social
systems. As in most social sciences, conducting experiments is impossible or undesirable
and achieving isolation generally impossible. Further, treating one system while not
treating the control is often ethically undesirable [2]. Further, as a recognized complex
social system, bullying and aggression become very difficult to model and control in the
real world, ethical issues aside.
Computer Science provides the necessary tools to address this complex social system
through the use of Complex Systems Modeling (CSM). CSM provides a means to study
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the core of these behaviors within environments created and controlled by the
experimenter [3]. A CSM facilitates research of core theories by supporting the creation
of a virtual environment in which we can normalize the participants and environmental
factors. This yields higher repeatability, emergent behavioral effects, and the ability to
apply various experiential criteria values.
My personal experiences with bullying and my strong interest in Social Complex
Systems lead me to this thesis topic.

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Chapter II
PREVIOUS WORK
Complex systems can be found in computer science, sociology, psychology, economics,
game theory, physics, biology, and many other fields. These various fields have created
their own names, definitions, and characteristics for complex systems within their
domains. For our purposes, when we speak of Complex Systems, we are referring to the
general views espoused by Melanie Mitchell in her book "Complexity A Guided Tour"
and Nigel Gilbert in his book "Agent-Based Models".
Melanie Mitchell identifies three commonly exhibited properties of complex systems [4]:
 Complex Collective Behavior: Systems in which large networks of individual
components following relatively simple rules give rise to complex, hard-to-predict
and changing patterns of behavior.
 Signaling & Information Processing: Systems produce and consume
information from their internal and external environments.
 Adaptation: Systems change their behavior to improve their chances of survival
or success.
Mitchell further provides a succinct complex system definition as "a system that exhibits
nontrivial emergent and self-organizing behaviors" [4].
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Melanie Mitchell's properties of a complex system provides the following insight into
these basic elements comprising a complex system:
 Environment: The "space" in which the actors operate, inclusive of its own set of
properties.
 Actors: Atomic individuals that inhabit the environment. They posses their own
set of properties.
 Behaviors: Actor behaviors to interact with actors and/or the environment.
 Population: A collection of homogeneous agents.
Nigel Gilbert further expands on these elements as feature of an Agent-Based Model
(ABM) and brings us closer to mapping from Melanie Mitchell's complex system
properties to modeling requirements [5]:
 Ontological Correspondence: A direct correspondence between the
computational agents in the model and real-world actors.
 Heterogeneous Agents: A mix of dissimilar agents operating according to their
defined preferences and rules.
 Representation of the Environment: The "environment" in which the agents
operate.
 Agent Interaction: The ability for agents to affect one another.
 Bounded Rationality: Limiting the cognitive abilities of the agent and thus the
degree to which they can optimize their utility.
 Learning: Agents learn from their experiences through evolutionary and social
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learning.
Taken as is, Melanie Mitchell's and Nigel Gilbert's statements of the various attributes of
complex systems provide a framework for what we should expect. However, these
attributes alone do not provide us with a context around what we are really after, and the
advantage complex systems modeling brings to non-traditional computing of social
problems.
Joshua Epstein and Robert Axtell take these attributes further in their work "Growing
Artificial Societies - Social Science from the Bottom Up" [6] by providing additional
context. Epstein and Axtell describe the traditional emphasis of the social sciences,
which assumes a highly rational and resource unlimited actor (agent) with infinite
computational ability. These social scientists work to keep their agents homogeneous
through the active suppression of heterogeneous agents. As a consequence of the
homogeneous emphasis, the richness and unpredictability of a population are stripped
out. This process reduces to a set of behaviors in question, with little emphasis left on
variance. While this top-down reductionist modeling reaffirms macro-level behaviors, it
does not emphasize micro-level behaviors. In addition, this emphasis does not lend itself
to one of the hallmarks of complex systems - emergent behavior.
Epstein and Axtell place an emphasis on emergent behaviors arising from the local
interactions of agents following simple local. Similarly, we are interested in "growing
the behavior" versus the traditional mathematical approach of applying differential
equations, etc.
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