Modeling Memes: A Memetic View of Affordance Learning

287 Pages·2014·4.19 MB·English

by  Benjamin D. Nye

University of Pennsylvania
ScholarlyCommons
Publicly accessible Penn Dissertations
Spring 5-16-2011
Modeling Memes: A Memetic View of Affordance
Learning
Benjamin D. Nye
University of Pennsylvania, [email protected]
Follow this and additional works at:http://repository.upenn.edu/edissertations
Part of theArtificial Intelligence and Robotics Commons,Cognition and Perception Commons,
Other Ecology and Evolutionary Biology Commons,Other Operations Research, Systems
Engineering and Industrial Engineering Commons,Social Psychology Commons, and theStatistical
Models Commons
Recommended Citation
Nye, Benjamin D., "Modeling Memes: A Memetic View of Affordance Learning" (2011).Publicly accessible Penn Dissertations.Paper
336.
With all thanks to my esteemed committee, Dr. Silverman, Dr. Smith, Dr. Carley, and Dr. Bordogna. Also, great thanks to the University of Pennsylvania
for all the opportunities to perform research at such a revered institution.
This paper is posted at ScholarlyCommons.http://repository.upenn.edu/edissertations/336
For more information, please [email protected].
Modeling Memes: A Memetic View of Affordance Learning
Abstract
This research employed systems social science inquiry to build a synthesis model that would be useful for
modeling meme evolution. First, a formal definition of memes was proposed that balanced both ontological
adequacy and empirical observability. Based on this definition, a systems model for meme evolution was
synthesized from Shannon Information Theory and elements of Bandura's Social Cognitive Learning Theory.
Research in perception, social psychology, learning, and communication were incorporated to explain the
cognitive and environmental processes guiding meme evolution. By extending the PMFServ cognitive
architecture, socio-cognitive agents were created who could simulate social learning of Gibson affordances.
The PMFServ agent based model was used to examine two scenarios: a simulation to test for potential memes
inside the Stanford Prison Experiment and a simulation of pro-US and anti-US meme competition within the
fictional Hamariyah Iraqi village. The Stanford Prison Experiment simulation was designed, calibrated, and
tested using the original Stanford Prison Experiment archival data. This scenario was used to study potential
memes within a real-life context. The Stanford Prison Experiment simulation was complemented by internal
and external validity testing. The Hamariyah Iraqi village was used to analyze meme competition in a fictional
village based upon US Marine Corps human terrain data. This simulation demonstrated how the implemented
system can infer the personality traits and contextual factors that cause certain agents to adopt pro-US or anti-
US memes, using Gaussian mixture clustering analysis and cross-cluster analysis. Finally, this research
identified significant gaps in empirical science with respect to studying memes. These roadblocks and their
potential solutions are explored in the conclusions of this work.
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Graduate Group
Electrical & Systems Engineering
First Advisor
Barry G. Silverman, Ph.D.
Keywords
Memes, Affordances, Agent Based Model, Cognitive Modeling, Social Systems, Artificial Intelligence
Subject Categories
Artificial Intelligence and Robotics | Cognition and Perception | Other Ecology and Evolutionary Biology |
Other Operations Research, Systems Engineering and Industrial Engineering | Social Psychology | Statistical
Models
This dissertation is available at ScholarlyCommons:http://repository.upenn.edu/edissertations/336
Comments
With all thanks to my esteemed committee, Dr. Silverman, Dr. Smith, Dr. Carley, and Dr. Bordogna. Also,
great thanks to the University of Pennsylvania for all the opportunities to perform research at such a revered
institution.
This dissertation is available at ScholarlyCommons:http://repository.upenn.edu/edissertations/336
MODELING MEMES:
A MEMETIC VIEW OF
AFFORDANCE LEARNING
Benjamin D. Nye
A DISSERTATION
in
Electrical and Systems Engineering
Presented to the Faculties of the University of Pennsylvania
in
Partial Fulfillment of the Requirements for the
Degree of Doctor of Philosophy
2011
Supervisor of Dissertation: Dr. Barry Silverman, Professor
Graduate Group Chairperson: Dr. Roch Guerin, Alfred Fitler Moore Professor
Dissertation Committee
Chair: Dr. Tony Smith, University of Pennsylvania
Reader: Dr. Joseph Bordogna, University of Pennsylvania
Reader: Dr. Kathleen Carley, Carnegie Mellon University
Modeling Memes: A Memetic View of Affordance Learning
Copywrite (cid:13)c Benjamin Daniel Nye, 2011
This work is licensed under the Creative Commons Attribution-NonCommercial-
NoDerivs 3.0 Unported License. To view a copy of this license, visit:
http://creativecommons.org/licenses/by-nc-nd/3.0/
or send a letter to: Creative Commons, 171 Second Street, Suite 300, San
Francisco, California, 94105, USA.
Acknowledgments
MygreatestthanksandregardstomyadvisorDr. BarrySilverman,themembers
of the ACASA Lab past and present, and my family who has been fully behind
me since the day I was born (and perhaps before). I would also like to thank
my committee: Dr. Tony Smith, Dr. Joseph Bordogna, and Dr. Kathleen
Carley. I am honored to have such approachable and distinguished members
consulting me on this endeavor. Each of your insights has made me examine
different perspectives that have improved not only this research, but will also
guide me on my next steps.
Additionally, I would like to thank all the external scholars who have assisted
meonthisjourney. ForthedatafromtheStanfordPrisonExperiment, Ireceived
anunprecedentedlevelofassistance. Dr. Zimbardoisoneofthemostapproachable
scholarsIhaveevercontacted,extremelyresponsiveandhelpful. Themembersof
the Archives of the History of American Psychology also graciously let me work
on-site for many days as I collected data. I would like to give a special thanks to
Rhonda Rinehart for being my point of contact and also to Dr. Baker for helping
to arrange my access. Without such assistance, I could not have worked with
such landmark data.
I would also like to thank my wife, Yuko, who has been patient with me
through all the long days and nights toward the completion of this effort. I will
alwaysappreciateyourloveandsupportthroughthisperiod. Yourmerepresence
means the world to me.
Finally, I would like to thank God for giving me the resources and stability
in life to pursue these endeavors, which I hope will someday make some small
benefit to the world. I promise that this work is only the beginning of a long
and dedicated effort toward improving our understanding and interaction with
the world.
iii
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Modeling Memes:
A Memetic View of Affordance Learning
Benjamin D. Nye
Barry G. Silverman
Abstract
Thisresearchemployedsystemssocialscienceinquirytobuildasynthesis
model that would be useful for modeling meme evolution. First, a formal
definition of memes was proposed that balances both ontological adequacy
and empirical observability. Based on this definition, a systems model for
meme evolution was synthesized from Shannon Information Theory and
elementsofBandura’sSocialCognitiveLearningTheory. Researchinperception,
socialpsychology,learning,andcommunicationwereincorporatedtoexplain
the cognitive and environmental processes guiding meme evolution. By
extending the PMFServ cognitive architecture, socio-cognitive agents were
created who could simulate social learning of Gibson affordances. The
PMFServagentbasedmodelwasusedtoexaminetwoscenarios: asimulation
to test for potential memes inside the Stanford Prison Experiment and a
simulation of pro-US and anti-US meme competition within the fictional
Hamariyah Iraqi village. The Stanford Prison Experiment simulation was
designed,calibrated,andtestedusingtheoriginalStanfordPrisonExperiment
archivaldata. Thisscenariowasusedtostudypotentialmemeswithinareal-
lifecontext. TheStanfordPrisonExperimentsimulationwascomplemented
by internal and external validity testing. The Hamariyah Iraqi village was
used to analyze meme competition in a fictional village based upon US
Marine Corps human terrain data. This simulation demonstrated how the
implemented system can infer the personality traits and contextual factors
thatcausecertainagentstoadoptpro-USoranti-USmemes,usingGaussian
mixture clustering analysis and cross-cluster analysis. Finally, this research
identifiedsignificantgapsinempiricalsciencewithrespecttostudyingmemes.
Theseroadblocksandtheirpotentialsolutionsareexploredintheconclusions
of this work.
Table of Contents
Table of Contents v
List of Tables ix
List of Figures xii
1 Introduction 1
1.1 Studying Memetics Using the Systems Social Science Paradigm . . 2
1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.1 Definition: Formally Defining Memes . . . . . . . . . . . . . 6
1.2.2 Systems Model: Synthesis of Theories to Explains Meme
Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.3 Measurement: IdentifyingandMeasuringMemesEmpirically 7
1.2.4 Implementation: Realizing the Memes Computationally . . 7
1.2.5 Usefulness: ApplyingtheModeltoStudyReal-WorldScenarios 9
1.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 Defining Memes 12
2.1 The Debate on Meme Definition . . . . . . . . . . . . . . . . . . . 13
2.2 Memes as Information . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.3 Memes as Operators . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 Recursive Reproduction of Memes . . . . . . . . . . . . . . . . . . 17
3 The Meme Process: A Systems Model for Memes 19
3.1 Interaction with the Environment . . . . . . . . . . . . . . . . . . . 20
3.2 Synthesis of Models . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3 Information Theory View of Memes . . . . . . . . . . . . . . . . . 24
3.3.1 Behavior as Transmission . . . . . . . . . . . . . . . . . . . 25
3.3.2 The Dynamic Environment . . . . . . . . . . . . . . . . . . 26
3.3.3 Evolutionary Mechanisms . . . . . . . . . . . . . . . . . . . 29
3.4 Memes As Social Learning . . . . . . . . . . . . . . . . . . . . . . . 30
v
TABLE OF CONTENTS vi
3.4.1 Attention . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.4.2 Retention . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.4.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.4.4 Production . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.5 Conceptual Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4 Observability of Memes 57
4.1 Measuring Meme Transmission . . . . . . . . . . . . . . . . . . . . 57
4.1.1 Measuring Diffusion of Behavior . . . . . . . . . . . . . . . 59
4.1.2 Measuring Displacement of Behavior . . . . . . . . . . . . . 60
4.1.3 Measuring Entrenched Behavior . . . . . . . . . . . . . . . 60
4.1.4 Measuring Entrenched Aversions . . . . . . . . . . . . . . . 62
4.2 Socially Learned Affordances . . . . . . . . . . . . . . . . . . . . . 63
5 Model Implementation: Agent Based Approach 67
5.1 Agent Based Simulation . . . . . . . . . . . . . . . . . . . . . . . . 69
5.2 Cognitive Model Architecture . . . . . . . . . . . . . . . . . . . . . 70
5.2.1 From Event Processing To Social Learning . . . . . . . . . 72
5.2.2 Social Influence Module . . . . . . . . . . . . . . . . . . . . 75
5.2.3 Memory Module . . . . . . . . . . . . . . . . . . . . . . . . 80
5.2.4 Learning Model . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.2.5 Attention Module . . . . . . . . . . . . . . . . . . . . . . . 83
5.3 Scenario Architecture . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.4 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6 Experiment Design: Affordance Discovery 93
6.1 Scenario 1: Stanford Prison Experiment Simulation . . . . . . . . . 94
6.1.1 Stanford Prison Experiment Data Sources . . . . . . . . . . 95
6.1.2 Stanford Prison Experiment Scenario Design . . . . . . . . 98
6.1.3 Stanford Prison Experiment Initialization . . . . . . . . . . 109
6.1.4 Stanford Prison Experimental Cases . . . . . . . . . . . . . 113
6.2 Scenario 2: Iraqi Village . . . . . . . . . . . . . . . . . . . . . . . . 114
6.2.1 Iraqi Village Data Sources . . . . . . . . . . . . . . . . . . . 115
6.2.2 Iraqi Village Scenario Design . . . . . . . . . . . . . . . . . 116
6.2.3 Iraqi Village Initialization . . . . . . . . . . . . . . . . . . . 120
6.2.4 Iraqi Village Experimental Cases . . . . . . . . . . . . . . . 121
7 Analysis and Results 122
7.1 Simulation Data Collected . . . . . . . . . . . . . . . . . . . . . . . 122
7.2 Analysis Methodologies and Techniques . . . . . . . . . . . . . . . 125
7.2.1 Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . 125
7.2.2 Multivariate Regression . . . . . . . . . . . . . . . . . . . . 126
TABLE OF CONTENTS vii
7.2.3 Mann-Kendall Trend Tests . . . . . . . . . . . . . . . . . . 128
7.2.4 Meme First Expression Ordering . . . . . . . . . . . . . . . 129
7.2.5 Diffusion Rate Analysis . . . . . . . . . . . . . . . . . . . . 132
7.2.6 Granger Causality Test . . . . . . . . . . . . . . . . . . . . 133
7.2.7 Sub Group Analysis . . . . . . . . . . . . . . . . . . . . . . 134
7.3 Internal Validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.3.1 Verifying Event Salience Component Weights . . . . . . . . 138
7.3.2 Event Salience Component Weights (Simulation) . . . . . . 141
7.3.3 Subsample Regression Analysis . . . . . . . . . . . . . . . . 143
7.3.4 Summary of Correlation Analysis of Simulation . . . . . . . 148
7.3.5 Internal Validity Summary . . . . . . . . . . . . . . . . . . 150
7.4 Stanford Prison Experiment Simulation Analysis . . . . . . . . . . 151
7.4.1 External Validity Measures . . . . . . . . . . . . . . . . . . 151
7.4.2 Exploratory Analysis . . . . . . . . . . . . . . . . . . . . . . 167
7.5 Iraqi Village Simulation Analysis . . . . . . . . . . . . . . . . . . . 175
7.5.1 Diffusion Dynamics. . . . . . . . . . . . . . . . . . . . . . . 175
7.5.2 Meme Transmission Dynamics . . . . . . . . . . . . . . . . 181
7.5.3 Adoption Indicators . . . . . . . . . . . . . . . . . . . . . . 182
7.5.4 Key Indicators for Meme Adoption . . . . . . . . . . . . . . 191
8 Conclusions 193
8.1 Overview of Contributions . . . . . . . . . . . . . . . . . . . . . . . 193
8.2 Studying Available Science . . . . . . . . . . . . . . . . . . . . . . 195
8.2.1 Formal Definition for Memes . . . . . . . . . . . . . . . . . 195
8.2.2 Systems Model for Memes . . . . . . . . . . . . . . . . . . . 196
8.2.3 Observability and Measurement of Memes . . . . . . . . . . 200
8.2.4 Examination of the Stanford Prison Experiment Archival
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
8.3 Component Authoring . . . . . . . . . . . . . . . . . . . . . . . . . 201
8.3.1 Cognitive Effects Operationalized . . . . . . . . . . . . . . . 202
8.3.2 Attentional Salience Integrated Model . . . . . . . . . . . . 203
8.4 Meta-Model Library . . . . . . . . . . . . . . . . . . . . . . . . . . 204
8.4.1 PMFServ Cognitive Component Plug-Ins . . . . . . . . . . 204
8.4.2 Stanford Prison Scenario . . . . . . . . . . . . . . . . . . . 205
8.4.3 Hamariyah Iraqi Village Modifications . . . . . . . . . . . . 205
8.4.4 Modified Inversion Distance Algorithm . . . . . . . . . . . . 205
8.5 Application Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
8.6 Stanford Prison Experiment Simulations . . . . . . . . . . . . . . . 206
8.7 Hamariyah Iraqi Village Simulations . . . . . . . . . . . . . . . . . 207
8.8 Gaps In Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
8.8.1 Existing Gaps Addressed By Research . . . . . . . . . . . . 208
8.8.2 Gaps Exposed By Research . . . . . . . . . . . . . . . . . . 210