added latest probabilistic sims and new figures

B_AnalysisCode/AdaptiveAdolescentrProject.Rmd

@@ -34,7 +34,7 @@ Specificly i re sample the reward if it is over 120. Now, as you may see below,
 I am not really happy with that because now, the normative solution is to seek out variance and the story about risk taking is that there is no such normative solution isnt it? 
 
 ```{r fig.width=12,fig.height=6}
-
+library("viridis")
 envirionmentMeanKids=seq(-50,50,length.out=5)#define the range of kid envirionemt
 envirionmentVarianceKids=seq(1,10,length.out=5)
 EnvirionemntKids=expand.grid(Mean=envirionmentMeanKids,Variance=envirionmentVarianceKids)# we sample from this later
@@ -47,28 +47,23 @@ EnvirionemntAdol%>%ungroup()
 
 EnvirionemntAdol%>%dplyr::rowwise()%>%mutate(value=rnorm(1,mean = Mean,sd=Variance))%>%
   ggplot(aes(x=Mean,y=Variance,fill=value))+geom_tile()+
-  scale_fill_distiller(name="Outcome",palette = "Spectral",trans = "reverse")+
+  scale_fill_viridis(name="Outcome",option="plasma",direction = -1)+
   geom_hline(aes(yintercept=40),size=2)+
   geom_vline(aes(xintercept=0),size=2)+
   annotate("text",x=50,y=38,label=c("Low Risk / Reward"))+
   annotate("text",x=-50,y=38,label=c("Low Risk / Loss"))+
   annotate("text",x=50,y=42,label=c("High Risk / Reward"))+
   annotate("text",x=-50,y=42,label=c("High Risk / Loss"))+
-  ggtitle("Adolescent Environment")+theme_cowplot()->Adolescent
+  ggtitle("Adolescent Environment")+theme_minimal()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())->Adolescent
 
 
 EnvirionemntAdol%>%dplyr::rowwise()%>%mutate(value=rnorm(1,mean = Mean,sd=Variance))%>%
   ggplot(aes(x=Mean,y=Variance,fill=value,alpha=Mean>-46&Variance<40&Mean<46))+geom_tile()+
-  scale_fill_distiller(name="Outcome",palette = "Spectral",trans = "reverse")+
-  scale_alpha_discrete(range = c(0.2, 1))+
+  scale_fill_viridis(name="Outcome",option="plasma",direction = -1)+
+  scale_alpha_discrete(range = c(1, 0.6))+
   geom_hline(aes(yintercept=40),size=2)+
   geom_vline(aes(xintercept=0),size=2)+
-  annotate("text",x=50,y=38,label=c("Low Risk / Reward"))+
-  annotate("text",x=-50,y=38,label=c("Low Risk / Loss"))+
-  annotate("text",x=50,y=42,label=c("High Risk / Reward"))+
-  annotate("text",x=-50,y=42,label=c("High Risk / Loss"))+
-  guides(alpha=F)+
-  ggtitle("Kids Environment")+theme_cowplot()->Kid
+  guides(alpha=F)+theme_minimal()+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())->Kid
 
 legend=cowplot::get_legend(Kid)
 cowplot::plot_grid(Kid+theme(legend.position="none"),
@@ -76,10 +71,123 @@ cowplot::plot_grid(Kid+theme(legend.position="none"),
                                     axis.title.y = element_text(colour="white")),
                    legend,ncol =3,rel_widths = c(1,1,0.2))
 
+ggsave(plot=Kid,filename = "/X_Figures/Environment_new2.png")
+
 ggsave(filename = "../X_Figures/Environments.png")
 ```
 
+```{r, fig.width=5,fig.height=5}
+x=-300:300
+
+  tibble(y=dnorm(x,mean=100,sd=80),
+                 x=x)%>%
+  ggplot(aes(y=y,x=x,color=x>0))+geom_line(size=3)+
+    scale_y_continuous(name="probability")+
+    scale_x_continuous(name="Outcome value")+
+    scale_color_discrete(name="Outcome",breaks=c("TRUE","FALSE"),labels=c("Loss","Win"))+
+    theme_cowplot()+theme(axis.text.y = element_blank(),
+                          panel.border = element_rect(colour = "black", fill=NA, size=2),
+                          legend.position = c(0.75,0.8))
+  ggsave("../X_Figures/HighRiskHighRew.png",dpi=300,height = 5,width = 5)
+  
+    tibble(y=dnorm(x,mean=100,sd=10),
+                 x=x)%>%
+ggplot(aes(y=y,x=x,color=x>0))+geom_line(size=3)+
+    scale_y_continuous(name="probability")+
+    scale_x_continuous(name="Outcome value")+
+    scale_color_discrete(name="Outcome",breaks=c("TRUE","FALSE"),labels=c("Loss","Win"))+
+    theme_cowplot()+theme(axis.text.y = element_blank(),
+                          panel.border = element_rect(colour = "black", fill=NA, size=2),
+                          legend.position = c(0.75,0.8))
+  ggsave("../X_Figures/LowRiskHighRew.png",dpi=300,height = 5,width = 5)
+
+      tibble(y=dnorm(x,mean=-100,sd=80),
+                 x=x)%>%
+  ggplot(aes(y=y,x=x,color=x>0))+geom_line(size=3)+
+    scale_y_continuous(name="probability")+
+    scale_x_continuous(name="Outcome value")+
+    scale_color_discrete(name="Outcome",breaks=c("TRUE","FALSE"),labels=c("Loss","Win"))+
+    theme_cowplot()+theme(axis.text.y = element_blank(),
+                          panel.border = element_rect(colour = "black", fill=NA, size=2),
+                          legend.position = c(0.75,0.8))
+  ggsave("../X_Figures/HighRiskLowRew.png",dpi=300,height = 5,width = 5)
+
+    tibble(y=dnorm(x,mean=-100,sd=10),
+                 x=x)%>%
+ggplot(aes(y=y,x=x,color=x>0))+geom_line(size=3)+
+    scale_y_continuous(name="probability")+
+    scale_x_continuous(name="Outcome value")+
+    scale_color_discrete(name="Outcome",breaks=c("TRUE","FALSE"),labels=c("Loss","Win"))+
+    theme_cowplot()+theme(axis.text.y = element_blank(),
+                          panel.border = element_rect(colour = "black", fill=NA, size=2),
+                          legend.position = c(0.75,0.8))
+  ggsave("../X_Figures/LowRiskLowRew.png",dpi=300,height = 5,width = 5)
+
+```
+
+
+# Bayes update
+```{r}
+library(viridis)
+x=-20:50
+NormalSamples=tibble(values=rnorm(20,mean=25,sd=6))
+
+rbind(
+tibble(y=dnorm(x,mean=20,sd=10),x=x)%>%mutate(type="1"),
+tibble(y=dnorm(x,mean=25,sd=6),x=x)%>%mutate(type="2")
+)%>%
+ggplot()+
+  geom_line(aes(y=y,x=x,color=type),size=3)+
+  scale_color_viridis_d(name="",option="plasma")+
+  #scale_alpha_discrete(range=c(0.4,1))+
+  geom_dotplot(data = NormalSamples,aes(x=values,color="3"),fill="#F1F592",binwidth = 1,dotsize = 1.5)+
+  #scale_fill_viridis()+
+  coord_cartesian(ylim=c(0,0.15))+
+  theme_cowplot(20)+
+  theme(axis.line.y=element_blank(),
+        axis.title.y =element_blank(),
+        axis.text.y =element_blank(),
+        axis.ticks.y=element_blank()
+        )->Posterior
+
+rbind(
+tibble(y=dnorm(x,mean=20,sd=10),x=x)%>%mutate(type="1")
+#tibble(y=dnorm(x,mean=25,sd=3),x=x)%>%mutate(type="Posterior")
+)%>%
+ggplot()+
+  geom_line(aes(y=y,x=x,color=type),size=3)+
+  scale_color_viridis_d(name="",option="plasma")+
+  #geom_dotplot(data = NormalSamples,aes(x=values,color="Observations (Likelihood)"),fill="grey",binwidth = 1,dotsize = 1.5)+
+  #scale_fill_viridis()+
+  theme_cowplot(20)+
+  coord_cartesian(ylim=c(0,0.15))+
+  theme(axis.line.y=element_blank(),
+        axis.title.y =element_blank(),
+        axis.text.y =element_blank(),
+        axis.ticks.y=element_blank()
+        )->Prior
+
+rbind(
+tibble(y=dnorm(x,mean=20,sd=10),x=x)%>%mutate(type="1")
+#tibble(y=dnorm(x,mean=25,sd=3),x=x)%>%mutate(type="Posterior")
+)%>%
+ggplot()+
+  geom_line(aes(y=y,x=x,color=type),size=3)+
+  scale_color_viridis_d(name="",option="plasma")+
+  geom_dotplot(data = NormalSamples,aes(x=values,color="2"),fill="#F1F592",binwidth = 1,dotsize = 1.5)+
+  #scale_fill_viridis()+
+  coord_cartesian(ylim=c(0,0.15))+theme_cowplot(20)+
+  theme(axis.line.y=element_blank(),
+        axis.title.y =element_blank(),
+        axis.text.y =element_blank(),
+        axis.ticks.y=element_blank()
+        )->Outcomes
+
+ggsave(plot=Posterior,filename="../X_Figures/Posterior.png",dpi=330)
+ggsave(plot=Outcomes,filename="../X_Figures/Outcomes.png",dpi=330)
+ggsave(plot=Prior,filename="../X_Figures/Prior.png",dpi=330)
 
+```
 
 # load the kalman filter and the UCB rule
 Here i define the kalman filter and the choice rule.
@@ -110,7 +218,7 @@ bayesianMeanTracker <- function(x, y, theta, prevPost=NULL,mu0Par,var0Par){
   allopts<-expand.grid(1:12, 1:12)
   chosen <- which(allopts$Var1==x[1] & allopts$Var2==x[2])
   #Kalman gain
-  kGain <- predictions$sig[chosen] / (predictions$sig[chosen] + EnvirionemntAdol$Variance[chosen]^2)# feed the uncertainty in here.
+  kGain <- predictions$sig[chosen] / (predictions$sig[chosen] + 360)#EnvirionemntAdol$Variance[chosen]^2)# feed the uncertainty in here.
   #update mean
   predictions$mu[chosen] <- predictions$mu[chosen] + (kGain * (y-predictions$mu[chosen]))
   #update variance for observed arm
@@ -157,7 +265,7 @@ beta<-0# this scales risk attitude.
 #get tau
 tau<-0.8
 mu0<-100#exploration bonus
-var0<-5
+var0<-80
 #create a parameter vector
 parVec <- c(lambda, lambda, 1, .0001) 
 #
@@ -392,6 +500,8 @@ for( i in 1:length(unique(newExp$trials))){
 
 saveRDS(newExp,"Derivatives_TrialWise.rds")
 # to compute the derivative
+#newExp<-readRDS("Derivatives_TrialWise.rds")
+
 ```
 
 # Plot
@@ -435,6 +545,7 @@ newExp%>%ungroup()%>%
   ggtitle("Exploration -> Solo")+
   guides(linetype=F)+
   theme_cowplot()
+
 ggsave("../X_Figures/TimecourseExplore.png",width=7,height=4)
 ```
 # Area under the curve

B_AnalysisCode/Adaptive_Adolescence_Multi.Rmd

@@ -2,11 +2,10 @@
 title: "Adaptive_Adolescence_multi"
 author: "Simy"
 date: "28/08/2020"
-output: 
-  html_document:
-    code_folding: hide
+output:
+  github_document:
     toc: true
-    toc_float: true
+    toc_depth: 2
 ---
 
 ```{r setup, include=FALSE}
@@ -40,7 +39,7 @@ EnvirionemntAdol%>%dplyr::rowwise()%>%mutate(value=rnorm(1,mean = Mean,sd=Varian
   annotate("text",x=-50,y=38,label=c("Low Risk / Loss"))+
   annotate("text",x=50,y=42,label=c("High Risk / Reward"))+
   annotate("text",x=-50,y=42,label=c("High Risk / Loss"))+
-  ggtitle("Adolescent Environment")+theme_cowplot()->Adolescent
+  ggtitle("Adolescent Environment")+theme_minimal(14)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())->Adolescent
 
 
 EnvirionemntAdol%>%dplyr::rowwise()%>%mutate(value=rnorm(1,mean = Mean,sd=Variance))%>%
@@ -54,7 +53,7 @@ EnvirionemntAdol%>%dplyr::rowwise()%>%mutate(value=rnorm(1,mean = Mean,sd=Varian
   annotate("text",x=50,y=42,label=c("High Risk / Reward"))+
   annotate("text",x=-50,y=42,label=c("High Risk / Loss"))+
   guides(alpha=F)+
-  ggtitle("Kids Environment")+theme_cowplot()->Kid
+  ggtitle("Childhood Environment")+theme_minimal(14)+theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())->Kid
 
 ```
 
@@ -70,7 +69,7 @@ ggsave(filename = "../X_Figures/Environments.png")
 
 # load the kalman filter and the UCB rule
 Here i define the kalman filter and the choice rule.
-The Kalman Agent explores the bandits by representing their mean reward rate and its uncertainty about that mean. Both, beliefs about the mean reward rate and uncertainty about that belief are updated every iteration after observing a new outcome. The Agents behavior can be gouverend by 3 parameters: 
+The Kalman Agent explores the bandits by representing their mean reward rate and its uncertainty about that mean. Both, beliefs about the mean reward rate and uncertainty about that belief are updated every trials after observing a new outcome. The Agents behavior can be gouverend by 3 parameters: 
 
 * mu0, or Optimism: its prior assumption about how rewarding the envirionment will be on average. This Paramter will also gouvern the extend of exploration
 
@@ -97,7 +96,7 @@ bayesianMeanTracker <- function(x, y, theta, prevPost=NULL,mu0Par,var0Par){
   allopts<-expand.grid(1:12, 1:12)
   chosen <- which(allopts$Var1==x[1] & allopts$Var2==x[2])
   #Kalman gain
-  kGain <- predictions$sig[chosen] / (predictions$sig[chosen] + EnvirionemntAdol$Variance[chosen]^2)# feed the uncertainty in here.
+  kGain <- predictions$sig[chosen] / (predictions$sig[chosen] + 3600)# feed the uncertainty in here.
   #update mean
   predictions$mu[chosen] <- predictions$mu[chosen] + (kGain * (y-predictions$mu[chosen]))
   #update variance for observed arm
@@ -144,7 +143,7 @@ cntrl=list(
   #get tau
   tau=0.8,
   mu0=100,#exploration bonus
-  var0=5,
+  var0=40,
   #create a parameter vector
   parVec = c(0.8, 0.8, 1, .0001) ,
   #
@@ -161,8 +160,8 @@ cntrl=list(
 ##
 ##
 ## Here i store the multiple Sims
-nIterations=100
-list_Iter <- vector(mode = "list", length = nIterations)
+ntrialss=100
+list_Iter <- vector(mode = "list", length = ntrialss)
 ```
 
 # Make observations
@@ -185,7 +184,7 @@ if(loadfromdisk==F){
 
 ```{r}
 exploreEnv<-function(explore_func,choiceRule,env2,env1,cntrl,iter){
-  #for (rep in 1:nIterations){
+  #for (rep in 1:ntrialss){
   #unpack
   lambda=cntrl$lambda
   #get beta
@@ -296,7 +295,7 @@ exploreEnv<-function(explore_func,choiceRule,env2,env1,cntrl,iter){
 }
 
 if(loadfromdisk==F){
-  Plot_datAll<-foreach(iter=1:nIterations, .combine='rbind') %dopar%{
+  Plot_datAll<-foreach(iter=1:ntrialss, .combine='rbind') %dopar%{
     exploreEnv(explore_func=bayesianMeanTracker,choiceRule=ucb,env2=EnvirionemntAdol,env1=EnvirionemntKids,cntrl=cntrl,iter=iter)
   }
   saveRDS(file="../A_GeneratedFiles/Plot_datAll",object = Plot_datAll)
@@ -318,7 +317,7 @@ While encountered losses decrease and rewards increase across development; the s
 Plot_datAll%>%group_by(x,y,iter)%>%filter(trials<=400)%>%mutate(cumDens=cumsum(sample),
                                                                 Outcome=case_when(
                                                                   (out<0)~"Loss",
-                                                                  (out>=0)~"Win"
+                                                                  (out>=0)~"Gain"
                                                                 )
 )%>%group_by(Outcome,iter)%>%summarise(howMany=n()/144,
                                     howMuch=sum(out))->Kids
@@ -326,7 +325,7 @@ Plot_datAll%>%group_by(x,y,iter)%>%filter(trials<=400)%>%mutate(cumDens=cumsum(s
 Plot_datAll%>%group_by(x,y,iter)%>%filter(trials>=400 & trials<800)%>%mutate(cumDens=cumsum(sample),
                                                                              Outcome=case_when(
                                                                                (out<0)~"Loss",
-                                                                               (out>=0)~"Win"
+                                                                               (out>=0)~"Gain"
                                                                              )
 )%>%group_by(Outcome,iter)%>%summarise(howMany=n()/144,
                                     howMuch=sum(out))->Adolescents
@@ -334,7 +333,7 @@ Plot_datAll%>%group_by(x,y,iter)%>%filter(trials>=400 & trials<800)%>%mutate(cum
 Plot_datAll%>%group_by(x,y,iter)%>%filter(trials>800 & trials<2400)%>%mutate(cumDens=cumsum(sample),
                                                                              Outcome=case_when(
                                                                                (out<0)~"Loss",
-                                                                               (out>=0)~"Win"
+                                                                               (out>=0)~"Gain"
                                                                              )
 )%>%group_by(Outcome,iter)%>%summarise(howMany=n()/144,
                                     howMuch=sum(out)
@@ -348,7 +347,7 @@ Adolescents$Soc="0"
 Kids$Soc="0"
 YoungAdults$Soc="0"
 
-Plot_labels=c("Kids","Adolescents","Adults")
+Plot_labels=c("Children","Adolescents","Adults")
 
 rbind(Kids,Adolescents,YoungAdults)->AllIndi
 
@@ -403,7 +402,7 @@ ggplot(AllIndi[AllIndi$Outcome=="Loss",],aes(x=Stage,y=howMuch,fill=Stage))+
 
 cowplot::plot_grid(One,Two,Three,NULL,Four,ncol=5,rel_widths = c(1,1,1,0.3,1.7))->Solo
 ggsave(plot = Solo,"../X_Figures/EmergentBehavior_summary.png",width = 11,height = 4)
-#print(Solo)
+print(Solo)
 ```
 
 # Number of explorative decisions
@@ -416,10 +415,10 @@ So i now declare an explorative decision as switching a bandit. This is done by
 #create count for each new decision.
 
 # do it in parralel again
-compute_sampling_strategy<-function(Plot_datAll,iteration){
+compute_sampling_strategy<-function(Plot_datAll,subject){
   library(dplyr)
   
-  Plot_datAll%>%filter(iter==iteration)%>%group_by(x,y)%>%mutate(cumDens=cumsum(sample))%>%ungroup()%>% group_by(x,y, trials) %>%
+  Plot_datAll%>%filter(iter==subject)%>%group_by(x,y)%>%mutate(cumDens=cumsum(sample))%>%ungroup()%>% group_by(x,y, trials) %>%
     mutate(newC = ifelse(cumDens == 1 & lag(cumDens)==0, 1, 0))->newExp# if i take the derivative of this this might be exaclty what wouter was intersted in.
   newExp[is.na(newExp$newC),]$newC=1#
   #now i have to "reset" so that each trial there can only be one new decision and because otherwise this will scale up the cumulative sum too much
@@ -427,19 +426,23 @@ compute_sampling_strategy<-function(Plot_datAll,iteration){
   newExp$generalExplore=0
   for( i in 1:length(unique(newExp$trials))){
     newExp[newExp$trials==i,]$newExplore=xor(newExp[newExp$trials==i,]$newC,newExp[newExp$trials==i-1,]$newC)#
+    print(i)
     #  newExp[newExp$trials==i,]$generalExplore=xor(newExp[newExp$trials==i,]$sample,newExp[newExp$trials==i-1,]$sample)
   }
-  newExp$iter=iteration
+  newExp$iter=subject
   return(newExp)
 }
 #saveRDS(newExp,"Derivatives_TrialWise.rds")
 if (loadfromdisk==F){
-  newExp_all<-foreach(iteration=1:nIterations, .combine='rbind') %dopar%{
-    compute_sampling_strategy(Plot_datAll,iteration)
+  #newExp_all<-NULL
+  #for(subject in 1:ntrialss){
+  #  print(subject)
+    newExp_all<-foreach(subject = 1:ntrialss,.combine = "rbind",.verbose=T) %dopar%{
+      compute_sampling_strategy(Plot_datAll,subject)
   }
-  saveRDS(file = "../A_GeneratedFiles/solotraj.rds",object = newExp_all)
+  saveRDS(file = "../A_GeneratedFiles/solotraj2.rds",object = newExp_all)
 }else {
-  newExp_all<-readRDS(file = "../A_GeneratedFiles/solotraj.rds")
+  newExp_all<-readRDS(file = "../A_GeneratedFiles/solotraj2.rds")
 }
 
 # to compute the derivative
@@ -460,39 +463,43 @@ newExp_all%>%group_by(iter)%>%
     explore2=explore-lag(explore,50)
   )%>%mutate(
     Stage=case_when(
-      (trials<401)~"Kids",
+      (trials<401)~"Children",
       (trials>400&trials<800)~"Adolescents",
       (trials>800)~"Adults"
-    )
-  )%>%filter(!is.na(Stage) & trials %in% round(seq(50,1200,length.out = 24)))%>%
+    ),
+    Which="Solo"
+  )%>%#filter(trials>36 & (trials <401 | trials >508))%>%
+  filter(!is.na(Stage) & trials %in% round(seq(50,1200,length.out = 24)))%>%
   ggplot(aes(x=trials,y=explore2))+
-  
-  
   stat_summary(geom="ribbon", fun.data="mean_sdl",alpha=0.2)+
   stat_summary(geom="line",fun.y = "mean",color="black")+
-  #geom_jitter()+
+ # geom_jitter()+
   #geom_rect(aes(xmin=50,xmax=400,ymin=-5,ymax=100,fill="1"),alpha=0.1)+
   #geom_rect(aes(xmin=400,xmax=800,ymin=-5,ymax=100,fill="2"),alpha=0.1)+
   #geom_rect(aes(xmin=801,xmax=1200,ymin=-5,ymax=100,fill="3"),alpha=0.1)+
-  #geom_spline(size=1)+
+  geom_spline(size=1)+
   geom_point(aes(shape=Stage),size=1,alpha=0.1)+
   stat_summary(aes(shape=Stage),geom="point",fun.y = "mean",size=2.5,color="black",fill="white")+
-  scale_shape_manual(name="Developmental Stage",values=c(22,21,24))+
+  scale_shape_manual(name="Developmental\nStage",values=c(22,21,24))+
   geom_vline(xintercept = 420, linetype="dotted",color="red")+
   geom_vline(xintercept = 410, linetype="dotted",color="red")+
   geom_vline(xintercept = 800, linetype="dotted",color="red")+
   geom_vline(xintercept = 790, linetype="dotted",color="red")+
-  annotate("text",x=150,y=60,label=c("Childhood"))+
-  annotate("text",x=600,y=60,label=c("Adolescence"))+
-  annotate("text",x=950,y=60,label=c("Adulthood"))+
+  annotate("text",x=150,y=60,label=c("Children"))+
+  annotate("text",x=600,y=60,label=c("Adolescents"))+
+  annotate("text",x=950,y=60,label=c("Adults"))+
   #scale_fill_brewer(name="Developmental Stage",labels=c("Kids","Adolescents","Adults"),palette = "YlGnBu")+
   #coord_cartesian(ylim=c(0,))+
-  scale_y_continuous(name="Explorative Decisions")+
-  scale_x_continuous(name="iteration")+
-  ggtitle("Exploration -> Solo")+
+  scale_y_continuous(name="Exploration decisions")+
+  scale_x_continuous(name="trials")+
   guides(linetype=F)+
-  theme_cowplot()->ExploreSoloPlot
-ggsave(plot=ExploreSoloPlot,filename="../X_Figures/TimecourseExplore.png",width=7,height=4)
+  theme_minimal(14)
+
+
+#->ExploreSoloPlot
+
+
+#ggsave(plot=ExploreSoloPlot,filename="../X_Figures/TimecourseExplore.png",width=6.3,height=5)
 ```
 
 
@@ -519,11 +526,11 @@ ggplot(AUCSoloTbl,aes(x=Stage,y=AUC))+
   #geom_jitter(aes(group=iter,color=Stage),alpha=0.1)+
   stat_summary(aes(x=Stage,y=AUC,fill=Stage),geom="bar",color="black",fun.y = "mean",stat="identity")+
   stat_summary(aes(color=Stage),geom="pointrange",color="black",fun.data = "mean_cl_boot")+
-  scale_x_discrete(name="Developmental Stage",breaks=c(1,2,3),labels=c("Kids","Adolescents","Adults"))+
+  scale_x_discrete(name="Developmental Stage",breaks=c(1,2,3),labels=c("Children","Adolescents","Adults"))+
   ggtitle("Exploration (AUC) - Solo")+
-  scale_fill_brewer(palette = "Set2",breaks=c(1,2,3),labels=c("Kids","Adolescents","Adults"))+
+  scale_fill_brewer(palette = "Set2",breaks=c(1,2,3),labels=c("Children","Adolescents","Adults"))+
   scale_y_continuous(name=c("AUC Explore"),limits=c(0,ylimauc))+
-  theme_cowplot()->AUCSolo
+  theme_minimal(14)->AUCSolo
 ```
 
 
@@ -549,9 +556,9 @@ loadfromdisk=T
 cntrl_social<-list(
   beta=0,# this scales risk attitude.
   #get tau
-  tau=0.8,
+  tau=1,
   mu0=100,#exploration bonus
-  var0=5,
+  var0=40,
   #create a parameter vector
   parVec <- c(0.8, 0.8, 1, .0001) ,
   #loop through trials
@@ -561,8 +568,8 @@ cntrl_social<-list(
   overallCnt=1,
   dat_social=expand.grid(x1=1:12,x2=1:12),
   
-  HowManyOthers=18,
-  diminishingSocial=0.9
+  HowManyOthers=19,
+  diminishingSocial=0.8
   # info about the agents
 )
 
@@ -669,7 +676,7 @@ WhereIsEverybody<-function(HowManyOthers,others,otherLoc,dat_social,X_oth,y_oth,
 ```{r}