Shared parameters

Utility function - “avg-all” (previously tested - “avg”,min",“max”)

Agent's count - 100

Generation length (in ticks) - 100

Skill choice algorithm - “choice” (select skill in which Agent's most experienced)

Task count (const) - 100

Plan no 7

Heterophily 33%, Homophily 33%, Preferential 33%

##   ExperienceDecay
## 1            TRUE
## 2           FALSE

Cover of subscenarios and sd

## [1] 200

## [1] 0

Number of results per subscenario

## [1] 400

Winning strategies

Ploting time of simulation closure

Box plots of time execution

library(ggplot2)
# Boxplots of task count by scenario number
# observations (points) are overlayed and jittered
qplot(OptionGroup, Tick_Count, data=batch_merged, geom=c("boxplot", "jitter"),
   fill=OptionGroup, main="Tick count per different scenario",
   xlab="", ylab="Number of ticks") + stat_boxplot(geom ='errorbar')

plot of chunk unnamed-chunk-1

Ploting winning strategies

Table with winning strategies is produced here:

library(xtable)
print(xtable(winning_p7), type = "html", include.rownames = T)
OptionGroup homophily heterophily preferential hom_het hom_pre het_pre hom_het_pre
1 ExpDec-0 200 0 0 0 0 0 0
2 ExpDec-1 200 0 0 0 0 0 0

Winning strategy is a strategy which all agents took consequently during 10 generations. In case of more than one strategy, we have a “mix” of winning strategies which stays constant during 10 generations.

Each row represent a single subscenario, with a count of winning strategies per strategy type (hence this value means “how many times in different runs this scenario dominated”, it's not agent's count or any other value).

Plan no 6

Heterophily 10%, Preferential 90%

##   ExperienceDecay
## 1            TRUE
## 2           FALSE

Cover of subscenarios and sd

## [1] 200

## [1] 0

Number of results per subscenario

## [1] 400

Winning strategies

Ploting time of simulation closure

Box plots of time execution

library(ggplot2)
# Boxplots of task count by scenario number
# observations (points) are overlayed and jittered
qplot(OptionGroup, Tick_Count, data=batch_merged, geom=c("boxplot", "jitter"),
   fill=OptionGroup, main="Tick count per different scenario",
   xlab="", ylab="Number of ticks") + stat_boxplot(geom ='errorbar')

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Ploting winning strategies

Table with winning strategies is produced here:

library(xtable)
print(xtable(winning_p6), type = "html", include.rownames = T)
OptionGroup homophily heterophily preferential hom_het hom_pre het_pre hom_het_pre
1 ExpDec-0 0 182 17 0 0 1 0
2 ExpDec-1 0 41 156 0 0 3 0

Winning strategy is a strategy which all agents took consequently during 10 generations. In case of more than one strategy, we have a “mix” of winning strategies which stays constant during 10 generations.

Each row represent a single subscenario, with a count of winning strategies per strategy type (hence this value means “how many times in different runs this scenario dominated”, it's not agent's count or any other value).

Plan no 5

Homophily 10%, Preferential 90%

##   ExperienceDecay
## 1            TRUE
## 2           FALSE

Cover of subscenarios and sd

## [1] 200

## [1] 0

Number of results per subscenario

## [1] 400

Winning strategies

Ploting time of simulation closure

Box plots of time execution

library(ggplot2)
# Boxplots of task count by scenario number
# observations (points) are overlayed and jittered
qplot(OptionGroup, Tick_Count, data=batch_merged, geom=c("boxplot", "jitter"),
   fill=OptionGroup, main="Tick count per different scenario",
   xlab="", ylab="Number of ticks") + stat_boxplot(geom ='errorbar')

plot of chunk unnamed-chunk-5

Ploting winning strategies

Table with winning strategies is produced here:

library(xtable)
print(xtable(winning_p5), type = "html", include.rownames = T)
OptionGroup homophily heterophily preferential hom_het hom_pre het_pre hom_het_pre
1 ExpDec-0 200 0 0 0 0 0 0
2 ExpDec-1 200 0 0 0 0 0 0

Winning strategy is a strategy which all agents took consequently during 10 generations. In case of more than one strategy, we have a “mix” of winning strategies which stays constant during 10 generations.

Each row represent a single subscenario, with a count of winning strategies per strategy type (hence this value means “how many times in different runs this scenario dominated”, it's not agent's count or any other value).

Plan no 4

Heterophily 90%, Preferential 10%

##   ExperienceDecay
## 1            TRUE
## 2           FALSE

Cover of subscenarios and sd

## [1] 200

## [1] 0

Number of results per subscenario

## [1] 400

Winning strategies

Ploting time of simulation closure

Box plots of time execution

library(ggplot2)
# Boxplots of task count by scenario number
# observations (points) are overlayed and jittered
qplot(OptionGroup, Tick_Count, data=batch_merged, geom=c("boxplot", "jitter"),
   fill=OptionGroup, main="Tick count per different scenario",
   xlab="", ylab="Number of ticks") + stat_boxplot(geom ='errorbar')

plot of chunk unnamed-chunk-7

Ploting winning strategies

Table with winning strategies is produced here:

library(xtable)
print(xtable(winning_p4), type = "html", include.rownames = T)
OptionGroup homophily heterophily preferential hom_het hom_pre het_pre hom_het_pre
1 ExpDec-0 0 173 27 0 0 0 0
2 ExpDec-1 0 96 104 0 0 0 0

Winning strategy is a strategy which all agents took consequently during 10 generations. In case of more than one strategy, we have a “mix” of winning strategies which stays constant during 10 generations.

Each row represent a single subscenario, with a count of winning strategies per strategy type (hence this value means “how many times in different runs this scenario dominated”, it's not agent's count or any other value).

Plan no 3

Heterophily 90%, Homophily 10%

##   ExperienceDecay
## 1            TRUE
## 2           FALSE

Cover of subscenarios and sd

## [1] 200

## [1] 0

Number of results per subscenario

## [1] 400

Winning strategies

Ploting time of simulation closure

Box plots of time execution

library(ggplot2)
# Boxplots of task count by scenario number
# observations (points) are overlayed and jittered
qplot(OptionGroup, Tick_Count, data=batch_merged, geom=c("boxplot", "jitter"),
   fill=OptionGroup, main="Tick count per different scenario",
   xlab="", ylab="Number of ticks") + stat_boxplot(geom ='errorbar')

plot of chunk unnamed-chunk-9

Ploting winning strategies

Table with winning strategies is produced here:

library(xtable)
print(xtable(winning_p3), type = "html", include.rownames = T)
OptionGroup homophily heterophily preferential hom_het hom_pre het_pre hom_het_pre
1 ExpDec-0 200 0 0 0 0 0 0
2 ExpDec-1 200 0 0 0 0 0 0

Winning strategy is a strategy which all agents took consequently during 10 generations. In case of more than one strategy, we have a “mix” of winning strategies which stays constant during 10 generations.

Each row represent a single subscenario, with a count of winning strategies per strategy type (hence this value means “how many times in different runs this scenario dominated”, it's not agent's count or any other value).

Plan no 2

Homophily 90%, Preferential 10%

##   ExperienceDecay
## 1            TRUE
## 2           FALSE

Cover of subscenarios and sd

## [1] 200

## [1] 31.81981

Number of results per subscenario

## [1] 355

Winning strategies

Ploting time of simulation closure

Box plots of time execution

library(ggplot2)
# Boxplots of task count by scenario number
# observations (points) are overlayed and jittered
qplot(OptionGroup, Tick_Count, data=batch_merged, geom=c("boxplot", "jitter"),
   fill=OptionGroup, main="Tick count per different scenario",
   xlab="", ylab="Number of ticks") + stat_boxplot(geom ='errorbar')

plot of chunk unnamed-chunk-11

Ploting winning strategies

Table with winning strategies is produced here:

library(xtable)
print(xtable(winning_p2), type = "html", include.rownames = T)
OptionGroup homophily heterophily preferential hom_het hom_pre het_pre hom_het_pre
1 ExpDec-0 200 0 0 0 0 0 0
2 ExpDec-1 155 0 0 0 0 0 0

Winning strategy is a strategy which all agents took consequently during 10 generations. In case of more than one strategy, we have a “mix” of winning strategies which stays constant during 10 generations.

Each row represent a single subscenario, with a count of winning strategies per strategy type (hence this value means “how many times in different runs this scenario dominated”, it's not agent's count or any other value).

Plan no 1

Heterophily 10%, Homophily 90%

##   ExperienceDecay
## 1            TRUE
## 2           FALSE

Cover of subscenarios and sd

## [1] 143

## [1] 16.26346

Number of results per subscenario

## [1] 263

Winning strategies

Ploting time of simulation closure

Box plots of time execution

library(ggplot2)
# Boxplots of task count by scenario number
# observations (points) are overlayed and jittered
qplot(OptionGroup, Tick_Count, data=batch_merged, geom=c("boxplot", "jitter"),
   fill=OptionGroup, main="Tick count per different scenario",
   xlab="", ylab="Number of ticks") + stat_boxplot(geom ='errorbar')

plot of chunk unnamed-chunk-13

Ploting winning strategies

Table with winning strategies is produced here:

library(xtable)
print(xtable(winning_p1), type = "html", include.rownames = T)
OptionGroup homophily heterophily preferential hom_het hom_pre het_pre hom_het_pre
1 ExpDec-0 142 0 0 1 0 0 0
2 ExpDec-1 120 0 0 0 0 0 0

Winning strategy is a strategy which all agents took consequently during 10 generations. In case of more than one strategy, we have a “mix” of winning strategies which stays constant during 10 generations.

Each row represent a single subscenario, with a count of winning strategies per strategy type (hence this value means “how many times in different runs this scenario dominated”, it's not agent's count or any other value).

Evolutionary game

Experience decay TRUE

matrix_expd_TRUE

##              homophily heterophily preferential
## homophily           -1           5            5
## heterophily          0          -1            0
## preferential         2           2           -1

Experience decay FALSE

matrix_expd_FALSE

##              homophily heterophily preferential
## homophily           -1           5            5
## heterophily          2          -1            2
## preferential         0           0           -1

Game winners / loosers on heatmaps

matrix_expd_TRUE[matrix_expd_TRUE == -1] <- NA
matrix_expd_FALSE[matrix_expd_FALSE == -1] <- NA

melted_avg_expd1 <- melt(matrix_expd_TRUE)
melted_avg_expd0 <- melt(matrix_expd_FALSE)

colnames(melted_avg_expd1) <- c("winning", "looses", "times")
colnames(melted_avg_expd0) <- c("winning", "looses", "times")

p_avg_expd1 <- ggplot(melted_avg_expd1, aes(looses, winning)) +
   geom_tile(aes(fill = cut(times, breaks=-1:5, labels=0:5)), colour = "grey") +
   scale_fill_manual(drop=FALSE, values=colorRampPalette(c("white","red"))(6), na.value="#EAEAEA", name="Times") +
   xlab("looses against") + ylab("winning over") +
   ggtitle("Avg() function, experience decay ON") +
   scale_y_discrete(limits=c("preferential", "heterophily", "homophily"))
p_avg_expd0 <- ggplot(melted_avg_expd0, aes(looses, winning)) +
   geom_tile(aes(fill = cut(times, breaks=-1:5, labels=0:5)), colour = "grey") +
   scale_fill_manual(drop=FALSE, values=colorRampPalette(c("white","red"))(6), na.value="#EAEAEA", name="Times") +
   xlab("looses against") + ylab("winning over") +
   ggtitle("Avg() function, experience decay OFF") +
   scale_y_discrete(limits=c("preferential", "heterophily", "homophily"))

multiplot(p_avg_expd1, p_avg_expd0, cols=1)

plot of chunk unnamed-chunk-18