SlideShare a Scribd company logo
Moses Boudourides
University of Patras, Greece
4th Joint Japan-North America Mathematical Sociology Conference
Redondo Beach, CA, May 29 – June 1, 2008
• Sites are ether vertices of a finite or an
infinite (but locally finite) graph G or cells
in a lattice Zd (or a hypercube in Zd).
• Each site is in one of κ (≥ 2) colors. Let
ξt(x) denote the color of site x at time t.
• Initially, colors are distributed randomly
(uniformly and independently).
• Rule of interaction (color changes):
If ξt(x) = k, then ξt+1(x) = k + 1 mod κ,
provided that there exist at least θ sites in
the color k + 1 mod κ in the neighborhood
of x within range ρ.
Iterations occur according to one of
the following two scenarios:
• Random timing: At each time of a
rate 1 Poisson process, a randomly
chosen site is updated (Interacting
Particle System or IPS).
• Deterministic timing: At each time
(positive integer), all sites are
successively (and independently)
updated (Cellular Automaton or
CA).
When κ = 2 and θ = ρ = 1, we have the
(classic) voter model (introduced
independently by Clifford & Sudbury 1973,
Holley & Liggett 1975).
Asymptotic behavior of the IPS on Zd
(Holley & Liggett 1975):
• If d ! 2, then P(ξt(x) = ξt(y)) ! 1
(clustering or complete concensus).
• If d ! 3 and p is the density of the initial
product measure, then ξt
p converges in
distribution to ξ!
p, a one parameter family
of stationary distributions.
Let G be a graph of n vertices of one of
the following types:
• Erdös-Rényi random graph
• Barabási-Albert preferential attachment
graph
• Bollobás-Chung or Newman-Watts small-
world graph
Asymptotic behavior of the IPS on G
(Durrett 2007):
The consensus time for ξt
p is O(cpn) (i.e.,
the d ! 3 behavior).
IPS: Bramson & Griffeath 1989
CA: Fisch 1990
On Z1, when ρ = 1 and θ = 1,
if κ ! 4, ξt fluctuates,
if κ ≥ 5, ξt fixates,
where ξt fluctuates if, ! x ! Z1,
P(ξt(x) changes color at arbitrarily large t) = 1,
while ξt fixates if, ! x ! Z1,
P(ξt(x) changes color finitely often) = 1.
Let δ be a fixed integer in:
1 ! δ ! κ – 1.
If ξt(x) = k, then ξt+1(x) is the
closest color in the set of all colors
at range δ from k:
{k + 1, k + 2, …, k + δ} (mod κ)
that are present in the
neighborhood of x within range ρ.
On Z1, when 1 ! δ ! κ – 1 and ρ
≥ 1,
•if θ > ρ, ξt fixates,
•if θ ! ρ, depending on κ and δ,
ξt either fluctuates in one color
(1-fluctuation) or fluctuates
alternating in two colors within
range δ (2-fluctuation).
• Square domains LxL,
• with wrap-around boundary
conditions.
• Two types of neighborhoods within
range ρ = 1:
•ρ = 1D, diamonds (von
Newmann nbhd),
•ρ = 1B, boxes (Moore nbhd).
• External Forcing: Color λ is pushed
‘externally’ at a site, in the sense
that an m-tuple of extra neighbors
(‘influentials’) in color λ are
appended to the site.
• Internal Propensity: Color µ is
promoted ‘internally’ at a site, i.e., it
is inserted at rank q within the δ
range at that site so that the more
lower q is, the more easily the site is
‘influenced’ by color µ.
• The effect of external forcing of any
m-tuples of a color is much lower
than that of internal propensity that
places the same color at the first
upper position.
• This verifies Watts’ & Dodds’
disaffirmation of the ‘influentials
hypothesis.’
• The effect of internal propensity
fades away as the placement position
of the preferred color increases in
the δ range.
• Moreover, the preferred color of
internal propensity may become the
initiator of other colors that turn out
to be inadvertently influential.
Circular Systems Emulating Dynamical Influence Processes
Circular Systems Emulating Dynamical Influence Processes
Circular Systems Emulating Dynamical Influence Processes
Circular Systems Emulating Dynamical Influence Processes

More Related Content

Similar to Circular Systems Emulating Dynamical Influence Processes

Laplacian Colormaps: a framework for structure-preserving color transformations
Laplacian Colormaps: a framework for structure-preserving color transformationsLaplacian Colormaps: a framework for structure-preserving color transformations
Laplacian Colormaps: a framework for structure-preserving color transformations
Davide Eynard
 
Model For Estimating Diversity Presentation
Model For Estimating Diversity PresentationModel For Estimating Diversity Presentation
Model For Estimating Diversity Presentation
David Torres
 
A Quest for Subexponential Time Parameterized Algorithms for Planar-k-Path: F...
A Quest for Subexponential Time Parameterized Algorithms for Planar-k-Path: F...A Quest for Subexponential Time Parameterized Algorithms for Planar-k-Path: F...
A Quest for Subexponential Time Parameterized Algorithms for Planar-k-Path: F...
cseiitgn
 
Performance of Evolutionary Algorithms on NK Landscapes with Nearest Neighbor...
Performance of Evolutionary Algorithms on NK Landscapes with Nearest Neighbor...Performance of Evolutionary Algorithms on NK Landscapes with Nearest Neighbor...
Performance of Evolutionary Algorithms on NK Landscapes with Nearest Neighbor...Martin Pelikan
 
Phase-Type Distributions for Finite Interacting Particle Systems
Phase-Type Distributions for Finite Interacting Particle SystemsPhase-Type Distributions for Finite Interacting Particle Systems
Phase-Type Distributions for Finite Interacting Particle Systems
Stefan Eng
 
The End-to-End Distance of RNA as a Randomly Self-Paired Polymer
The End-to-End Distance of RNA as a Randomly Self-Paired PolymerThe End-to-End Distance of RNA as a Randomly Self-Paired Polymer
The End-to-End Distance of RNA as a Randomly Self-Paired Polymer
Li Tai Fang
 
Continuous and Discrete-Time Analysis of SGD
Continuous and Discrete-Time Analysis of SGDContinuous and Discrete-Time Analysis of SGD
Continuous and Discrete-Time Analysis of SGD
Valentin De Bortoli
 
Random 3-manifolds
Random 3-manifoldsRandom 3-manifolds
Random 3-manifolds
Igor Rivin
 
Intro probability 4
Intro probability 4Intro probability 4
Intro probability 4Phong Vo
 
Analisis Korespondensi
Analisis KorespondensiAnalisis Korespondensi
Analisis Korespondensidessybudiyanti
 
ABC with Wasserstein distances
ABC with Wasserstein distancesABC with Wasserstein distances
ABC with Wasserstein distances
Christian Robert
 
The wild McKay correspondence
The wild McKay correspondenceThe wild McKay correspondence
The wild McKay correspondence
Takehiko Yasuda
 

Similar to Circular Systems Emulating Dynamical Influence Processes (15)

Laplacian Colormaps: a framework for structure-preserving color transformations
Laplacian Colormaps: a framework for structure-preserving color transformationsLaplacian Colormaps: a framework for structure-preserving color transformations
Laplacian Colormaps: a framework for structure-preserving color transformations
 
Model For Estimating Diversity Presentation
Model For Estimating Diversity PresentationModel For Estimating Diversity Presentation
Model For Estimating Diversity Presentation
 
A Quest for Subexponential Time Parameterized Algorithms for Planar-k-Path: F...
A Quest for Subexponential Time Parameterized Algorithms for Planar-k-Path: F...A Quest for Subexponential Time Parameterized Algorithms for Planar-k-Path: F...
A Quest for Subexponential Time Parameterized Algorithms for Planar-k-Path: F...
 
Performance of Evolutionary Algorithms on NK Landscapes with Nearest Neighbor...
Performance of Evolutionary Algorithms on NK Landscapes with Nearest Neighbor...Performance of Evolutionary Algorithms on NK Landscapes with Nearest Neighbor...
Performance of Evolutionary Algorithms on NK Landscapes with Nearest Neighbor...
 
Phase-Type Distributions for Finite Interacting Particle Systems
Phase-Type Distributions for Finite Interacting Particle SystemsPhase-Type Distributions for Finite Interacting Particle Systems
Phase-Type Distributions for Finite Interacting Particle Systems
 
The End-to-End Distance of RNA as a Randomly Self-Paired Polymer
The End-to-End Distance of RNA as a Randomly Self-Paired PolymerThe End-to-End Distance of RNA as a Randomly Self-Paired Polymer
The End-to-End Distance of RNA as a Randomly Self-Paired Polymer
 
Continuous and Discrete-Time Analysis of SGD
Continuous and Discrete-Time Analysis of SGDContinuous and Discrete-Time Analysis of SGD
Continuous and Discrete-Time Analysis of SGD
 
Random 3-manifolds
Random 3-manifoldsRandom 3-manifolds
Random 3-manifolds
 
Igv2008
Igv2008Igv2008
Igv2008
 
Kaifeng_final version1
Kaifeng_final version1Kaifeng_final version1
Kaifeng_final version1
 
Intro probability 4
Intro probability 4Intro probability 4
Intro probability 4
 
Analisis Korespondensi
Analisis KorespondensiAnalisis Korespondensi
Analisis Korespondensi
 
ABC with Wasserstein distances
ABC with Wasserstein distancesABC with Wasserstein distances
ABC with Wasserstein distances
 
Poster_PingPong
Poster_PingPongPoster_PingPong
Poster_PingPong
 
The wild McKay correspondence
The wild McKay correspondenceThe wild McKay correspondence
The wild McKay correspondence
 

Recently uploaded

一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
vcaxypu
 
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
nscud
 
一比一原版(CBU毕业证)卡普顿大学毕业证如何办理
一比一原版(CBU毕业证)卡普顿大学毕业证如何办理一比一原版(CBU毕业证)卡普顿大学毕业证如何办理
一比一原版(CBU毕业证)卡普顿大学毕业证如何办理
ahzuo
 
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
slg6lamcq
 
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
John Andrews
 
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
pchutichetpong
 
Empowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptxEmpowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptx
benishzehra469
 
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
yhkoc
 
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
AbhimanyuSinha9
 
1.Seydhcuxhxyxhccuuxuxyxyxmisolids 2019.pptx
1.Seydhcuxhxyxhccuuxuxyxyxmisolids 2019.pptx1.Seydhcuxhxyxhccuuxuxyxyxmisolids 2019.pptx
1.Seydhcuxhxyxhccuuxuxyxyxmisolids 2019.pptx
Tiktokethiodaily
 
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdfSample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Linda486226
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
nscud
 
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
ewymefz
 
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
ahzuo
 
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdfCriminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP
 
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Subhajit Sahu
 
FP Growth Algorithm and its Applications
FP Growth Algorithm and its ApplicationsFP Growth Algorithm and its Applications
FP Growth Algorithm and its Applications
MaleehaSheikh2
 
Opendatabay - Open Data Marketplace.pptx
Opendatabay - Open Data Marketplace.pptxOpendatabay - Open Data Marketplace.pptx
Opendatabay - Open Data Marketplace.pptx
Opendatabay
 
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
mbawufebxi
 
Machine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptxMachine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptx
balafet
 

Recently uploaded (20)

一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
一比一原版(RUG毕业证)格罗宁根大学毕业证成绩单
 
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
一比一原版(CBU毕业证)卡普顿大学毕业证成绩单
 
一比一原版(CBU毕业证)卡普顿大学毕业证如何办理
一比一原版(CBU毕业证)卡普顿大学毕业证如何办理一比一原版(CBU毕业证)卡普顿大学毕业证如何办理
一比一原版(CBU毕业证)卡普顿大学毕业证如何办理
 
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
 
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
 
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...
 
Empowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptxEmpowering Data Analytics Ecosystem.pptx
Empowering Data Analytics Ecosystem.pptx
 
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
一比一原版(CU毕业证)卡尔顿大学毕业证成绩单
 
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...Best best suvichar in gujarati english meaning of this sentence as Silk road ...
Best best suvichar in gujarati english meaning of this sentence as Silk road ...
 
1.Seydhcuxhxyxhccuuxuxyxyxmisolids 2019.pptx
1.Seydhcuxhxyxhccuuxuxyxyxmisolids 2019.pptx1.Seydhcuxhxyxhccuuxuxyxyxmisolids 2019.pptx
1.Seydhcuxhxyxhccuuxuxyxyxmisolids 2019.pptx
 
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdfSample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
Sample_Global Non-invasive Prenatal Testing (NIPT) Market, 2019-2030.pdf
 
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
一比一原版(CBU毕业证)不列颠海角大学毕业证成绩单
 
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
一比一原版(UofM毕业证)明尼苏达大学毕业证成绩单
 
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
 
Criminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdfCriminal IP - Threat Hunting Webinar.pdf
Criminal IP - Threat Hunting Webinar.pdf
 
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...
 
FP Growth Algorithm and its Applications
FP Growth Algorithm and its ApplicationsFP Growth Algorithm and its Applications
FP Growth Algorithm and its Applications
 
Opendatabay - Open Data Marketplace.pptx
Opendatabay - Open Data Marketplace.pptxOpendatabay - Open Data Marketplace.pptx
Opendatabay - Open Data Marketplace.pptx
 
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
一比一原版(Bradford毕业证书)布拉德福德大学毕业证如何办理
 
Machine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptxMachine learning and optimization techniques for electrical drives.pptx
Machine learning and optimization techniques for electrical drives.pptx
 

Circular Systems Emulating Dynamical Influence Processes

  • 1. Moses Boudourides University of Patras, Greece 4th Joint Japan-North America Mathematical Sociology Conference Redondo Beach, CA, May 29 – June 1, 2008
  • 2. • Sites are ether vertices of a finite or an infinite (but locally finite) graph G or cells in a lattice Zd (or a hypercube in Zd). • Each site is in one of κ (≥ 2) colors. Let ξt(x) denote the color of site x at time t. • Initially, colors are distributed randomly (uniformly and independently). • Rule of interaction (color changes): If ξt(x) = k, then ξt+1(x) = k + 1 mod κ, provided that there exist at least θ sites in the color k + 1 mod κ in the neighborhood of x within range ρ.
  • 3. Iterations occur according to one of the following two scenarios: • Random timing: At each time of a rate 1 Poisson process, a randomly chosen site is updated (Interacting Particle System or IPS). • Deterministic timing: At each time (positive integer), all sites are successively (and independently) updated (Cellular Automaton or CA).
  • 4. When κ = 2 and θ = ρ = 1, we have the (classic) voter model (introduced independently by Clifford & Sudbury 1973, Holley & Liggett 1975). Asymptotic behavior of the IPS on Zd (Holley & Liggett 1975): • If d ! 2, then P(ξt(x) = ξt(y)) ! 1 (clustering or complete concensus). • If d ! 3 and p is the density of the initial product measure, then ξt p converges in distribution to ξ! p, a one parameter family of stationary distributions.
  • 5. Let G be a graph of n vertices of one of the following types: • Erdös-Rényi random graph • Barabási-Albert preferential attachment graph • Bollobás-Chung or Newman-Watts small- world graph Asymptotic behavior of the IPS on G (Durrett 2007): The consensus time for ξt p is O(cpn) (i.e., the d ! 3 behavior).
  • 6. IPS: Bramson & Griffeath 1989 CA: Fisch 1990 On Z1, when ρ = 1 and θ = 1, if κ ! 4, ξt fluctuates, if κ ≥ 5, ξt fixates, where ξt fluctuates if, ! x ! Z1, P(ξt(x) changes color at arbitrarily large t) = 1, while ξt fixates if, ! x ! Z1, P(ξt(x) changes color finitely often) = 1.
  • 7.
  • 8. Let δ be a fixed integer in: 1 ! δ ! κ – 1. If ξt(x) = k, then ξt+1(x) is the closest color in the set of all colors at range δ from k: {k + 1, k + 2, …, k + δ} (mod κ) that are present in the neighborhood of x within range ρ.
  • 9. On Z1, when 1 ! δ ! κ – 1 and ρ ≥ 1, •if θ > ρ, ξt fixates, •if θ ! ρ, depending on κ and δ, ξt either fluctuates in one color (1-fluctuation) or fluctuates alternating in two colors within range δ (2-fluctuation).
  • 10.
  • 11.
  • 12. • Square domains LxL, • with wrap-around boundary conditions. • Two types of neighborhoods within range ρ = 1: •ρ = 1D, diamonds (von Newmann nbhd), •ρ = 1B, boxes (Moore nbhd).
  • 13.
  • 14.
  • 15. • External Forcing: Color λ is pushed ‘externally’ at a site, in the sense that an m-tuple of extra neighbors (‘influentials’) in color λ are appended to the site. • Internal Propensity: Color µ is promoted ‘internally’ at a site, i.e., it is inserted at rank q within the δ range at that site so that the more lower q is, the more easily the site is ‘influenced’ by color µ.
  • 16. • The effect of external forcing of any m-tuples of a color is much lower than that of internal propensity that places the same color at the first upper position. • This verifies Watts’ & Dodds’ disaffirmation of the ‘influentials hypothesis.’
  • 17.
  • 18. • The effect of internal propensity fades away as the placement position of the preferred color increases in the δ range. • Moreover, the preferred color of internal propensity may become the initiator of other colors that turn out to be inadvertently influential.