agenta <- data.frame(
'na' = c(80),
'info' = c(50),
'tolerance' = c(100),
'solution' = c(60)
)
agentb <- data.frame(
'na' = c(50),
'info' = c(20),
'tolerance' = c(50),
'solution' = c(100)
)
agentc <- data.frame(
'na' = c(10),
'info' = c(3),
'tolerance' = c(77),
'solution' = c(80)
)
i <- 1
iterations <- 50
actions <- data.frame(
'expressed' = numeric(iterations),
'otherreact1' = numeric(iterations),
'otherreact2' = numeric(iterations)
)
for(period in 1:iterations){
i <- i + 1
# start counter/time
# determine probability that a will express conflict
# with more NA and less tolerance, an agent is more likely to express conflict
aprob <- agenta[i-1, 'na'] / agenta[1, 'tolerance']
if(aprob >= 1){aprob = 1}
agenta_express <- rbinom(1, 1, prob = aprob)
# does agent a express conflict? 1 = yes
if(agenta_express == 1){
actions[i, 'expressed'] <- 'agent a expressed'
# agent a expresses something, which contains both na and info
# na is pulled from a uniform distribution between 1 and 10, same for info
expressed_na <- runif(1, min = 1, max = 10)
expressed_info <- runif(1, min = 1, max = 10)
# agent a will not react to his own expression, so fill in his next rows
# agent a na and info is the same as it was last time
agenta[i, 'na'] <- agenta[i-1, 'na']
agenta[i, 'info'] <- agenta[i-1, 'info']
#
# agent b
#
# is this expression conflict? 2/3rds tendency toward yes
b_is_this_conflict <- sample(c('yes', 'yes', 'no'), 1)
#
# does b perceive to be implicated? 2/3rds tendency toward yes
b_implicated <- sample(c('yes', 'yes', 'no'), 1)
#
# b can make one of three perceptions
# 1: b does not see the expression as conflict
# 2: b sees the expression as conflict but does not believe to be involved
# 3: b sees it as conflict and believes to be involved
# if 1 (b does not see it as conflict), then b's na goes down
# if 2 (b sees it as conflict but not involved), then update na and info but only by a small amount
# if 3 (b sees it as conflict and is involved), then update na and info
# if 1 (b does not see it as conflict), then b's na goes down
if(b_is_this_conflict == 'no'){
# decreases by an amount drawn from a uniform distribution between 1 and 5
agentb[i, 'na'] <- agentb[i - 1, 'na'] - runif(1, min = 1, max = 5)
# info stays the same
agentb[i, 'info'] <- agentb[i-1, 'info']
actions[i, 'otherreact1'] <- 'agent b did not see it as conflict'
}
# if 2 (b sees it as conflict but is not involved), then b's na and info update but by a small amount
if(b_is_this_conflict == 'yes' && b_implicated == 'no'){
# b's na is a function of prior na and expressed na (multiplied by 0.5 to make it smaller)
agentb[i, 'na'] <- expressed_na*0.5 + agentb[i - 1, 'na']
# b's info is a function of prior info and expressed info (multipled by 0.5 to make it smaller)
agentb[i, 'info'] <- expressed_info*0.5 + agentb[i - 1, 'info']
actions[i, 'otherreact1'] <- 'agent b saw it as conflict but not involved'
}
# if 3 (b sees it as conflict and is involved), then b's na and info update
if(b_is_this_conflict == 'yes' && b_implicated == 'yes'){
# b's na is a function of prior na and expressed na
agentb[i, 'na'] <- expressed_na + agentb[i - 1, 'na']
# b's info is a function of prior info and expressed info
agentb[i, 'info'] <- expressed_info + agentb[i - 1, 'info']
actions[i, 'otherreact1'] <- 'agent b saw it as conflict & involved'
}
#
# agent c
c_is_this_conflict <- sample(c('yes', 'yes', 'no'), 1)
c_implicated <- sample(c('yes', 'yes', 'no'), 1)
if(c_is_this_conflict == 'no'){
agentc[i, 'na'] <- agentc[i - 1, 'na'] - runif(1, min = 1, max = 5)
agentc[i, 'info'] <- agentc[i-1, 'info']
actions[i, 'otherreact2'] <- 'agent c did not see it as conflict'
}
if(c_is_this_conflict == 'yes' && c_implicated == 'no'){
agentc[i, 'na'] <- expressed_na*0.5 + agentc[i - 1, 'na']
agentc[i, 'info'] <- expressed_info*0.5 + agentc[i - 1, 'info']
actions[i, 'otherreact2'] <- 'agent c saw it as conflict but not involved'
}
if(c_is_this_conflict == 'yes' && c_implicated == 'yes'){
agentc[i, 'na'] <- expressed_na + agentc[i - 1, 'na']
agentc[i, 'info'] <- expressed_info + agentc[i - 1, 'info']
actions[i, 'otherreact2'] <- 'agent c saw it as conflict & involved'
}
}else{
agenta[i, 'na'] <- agenta[i-1, 'na']
agenta[i, 'info'] <- agenta[i-1, 'info']
agentb[i, 'na'] <- agentb[i-1, 'na']
agentb[i, 'info'] <- agentb[i-1, 'info']
agentc[i, 'na'] <- agentc[i-1, 'na']
agentc[i, 'info'] <- agentc[i-1, 'info']
actions[i, 'expressed'] <- 'agent a did not express'
actions[i, 'otherreact1'] <- 0
actions[i, 'otherreact2'] <- 0
}
i <- i + 1
bprob <- agentb[i-1, 'na'] / agentb[1, 'tolerance']
if(bprob >= 1){bprob = 1}
agentb_express <- rbinom(1, 1, prob = bprob)
if(agentb_express == 1){
expressed_na <- runif(1, min = 1, max = 10)
expressed_info <- runif(1, min = 1, max = 10)
agentb[i, 'na'] <- agentb[i-1, 'na']
agentb[i, 'info'] <- agentb[i-1, 'info']
actions[i, 'expressed'] <- 'agent b expressed'
a_is_this_conflict <- sample(c('yes', 'yes', 'no'), 1)
a_implicated <- sample(c('yes', 'yes', 'no'), 1)
if(a_is_this_conflict == 'no'){
agenta[i, 'na'] <- agenta[i - 1, 'na'] - runif(1, min = 1, max = 5)
agenta[i, 'info'] <- agenta[i-1, 'info']
actions[i, 'otherreact1'] <- 'agent a did not see it as conflict'
}
if(a_is_this_conflict == 'yes' && a_implicated == 'no'){
agenta[i, 'na'] <- expressed_na*0.5 + agenta[i - 1, 'na']
agenta[i, 'info'] <- expressed_info*0.5 + agenta[i - 1, 'info']
actions[i, 'otherreact1'] <- 'agent a saw it as conflict but not involved'
}
if(a_is_this_conflict == 'yes' && a_implicated == 'yes'){
agenta[i, 'na'] <- expressed_na + agenta[i - 1, 'na']
agenta[i, 'info'] <- expressed_info + agenta[i - 1, 'info']
actions[i, 'otherreact1'] <- 'agent a saw it as conflict & involved'
}
c_is_this_conflict <- sample(c('yes', 'yes', 'no'), 1)
c_implicated <- sample(c('yes', 'yes', 'no'), 1)
if(c_is_this_conflict == 'no'){
agentc[i, 'na'] <- agentc[i - 1, 'na'] - runif(1, min = 1, max = 5)
agentc[i, 'info'] <- agentc[i-1, 'info']
actions[i, 'otherreact2'] <- 'agent c did not see it as conflict'
}
if(c_is_this_conflict == 'yes' && c_implicated == 'no'){
agentc[i, 'na'] <- expressed_na*0.5 + agentc[i - 1, 'na']
agentc[i, 'info'] <- expressed_info*0.5 + agentc[i - 1, 'info']
actions[i, 'otherreact2'] <- 'agent c saw it as conflict but not involved'
}
if(c_is_this_conflict == 'yes' && c_implicated == 'yes'){
agentc[i, 'na'] <- expressed_na + agentc[i - 1, 'na']
agentc[i, 'info'] <- expressed_info + agentc[i - 1, 'info']
actions[i, 'otherreact2'] <- 'agent c saw it as conflict & involved'
}
}else{
agenta[i, 'na'] <- agenta[i-1, 'na']
agenta[i, 'info'] <- agenta[i-1, 'info']
agentb[i, 'na'] <- agentb[i-1, 'na']
agentb[i, 'info'] <- agentb[i-1, 'info']
agentc[i, 'na'] <- agentc[i-1, 'na']
agentc[i, 'info'] <- agentc[i-1, 'info']
actions[i, 'expressed'] <- 'agent b did not express'
actions[i, 'otherreact1'] <- 0
actions[i, 'otherreact2'] <- 0
}
i <- i + 1
cprob <- agentc[i-1, 'na'] / agentc[1, 'tolerance']
if(cprob >= 1){cprob = 1}
agentc_express <- rbinom(1, 1, prob = cprob)
if(agentc_express == 1){
expressed_na <- runif(1, min = 1, max = 10)
expressed_info <- runif(1, min = 1, max = 10)
agentc[i, 'na'] <- agentc[i-1, 'na']
agentc[i, 'info'] <- agentc[i-1, 'info']
actions[i, 'expressed'] <- 'agent c expressed'
a_is_this_conflict <- sample(c('yes', 'yes', 'no'), 1)
a_implicated <- sample(c('yes', 'yes', 'no'), 1)
if(a_is_this_conflict == 'no'){
agenta[i, 'na'] <- agenta[i - 1, 'na'] - runif(1, min = 1, max = 5)
agenta[i, 'info'] <- agenta[i-1, 'info']
actions[i, 'otherreact1'] <- 'agent a did not see it as conflict'
}
if(a_is_this_conflict == 'yes' && a_implicated == 'no'){
agenta[i, 'na'] <- expressed_na*0.5 + agenta[i - 1, 'na']
agenta[i, 'info'] <- expressed_info*0.5 + agenta[i - 1, 'info']
actions[i, 'otherreact1'] <- 'agent a saw it as conflict but not involved'
}
if(a_is_this_conflict == 'yes' && a_implicated == 'yes'){
agenta[i, 'na'] <- expressed_na + agenta[i - 1, 'na']
agenta[i, 'info'] <- expressed_info + agenta[i - 1, 'info']
actions[i, 'otherreact1'] <- 'agent a saw it as conflict & involved'
}
#
# agent b
#
# is this expression conflict? 2/3rds tendency toward yes
b_is_this_conflict <- sample(c('yes', 'yes', 'no'), 1)
#
# does b perceive to be implicated? 2/3rds tendency toward yes
b_implicated <- sample(c('yes', 'yes', 'no'), 1)
#
# b can make one of three perceptions
# 1: b does not see the expression as conflict
# 2: b sees the expression as conflict but does not believe to be involved
# 3: b sees it as conflict and believes to be involved
# if 1 (b does not see it as conflict), then b's na goes down
# if 2 (b sees it as conflict but not involved), then update na and info but only by a small amount
# if 3 (b sees it as conflict and is involved), then update na and info
# if 1 (b does not see it as conflict), then b's na goes down
if(b_is_this_conflict == 'no'){
# decreases by an amount drawn from a uniform distribution between 1 and 5
agentb[i, 'na'] <- agentb[i - 1, 'na'] - runif(1, min = 1, max = 5)
# info stays the same
agentb[i, 'info'] <- agentb[i-1, 'info']
actions[i, 'otherreact2'] <- 'agent b did not see it as conflict'
}
# if 2 (b sees it as conflict but is not involved), then b's na and info update but by a small amount
if(b_is_this_conflict == 'yes' && b_implicated == 'no'){
# b's na is a function of prior na and expressed na (multiplied by 0.5 to make it smaller)
agentb[i, 'na'] <- expressed_na*0.5 + agentb[i - 1, 'na']
# b's info is a function of prior info and expressed info (multipled by 0.5 to make it smaller)
agentb[i, 'info'] <- expressed_info*0.5 + agentb[i - 1, 'info']
actions[i, 'otherreact2'] <- 'agent b saw it as conflict but not involved'
}
# if 3 (b sees it as conflict and is involved), then b's na and info update
if(b_is_this_conflict == 'yes' && b_implicated == 'yes'){
# b's na is a function of prior na and expressed na
agentb[i, 'na'] <- expressed_na + agentb[i - 1, 'na']
# b's info is a function of prior info and expressed info
agentb[i, 'info'] <- expressed_info + agentb[i - 1, 'info']
actions[i, 'otherreact2'] <- 'agent b saw it as conflict & involved'
}
}else{
agenta[i, 'na'] <- agenta[i-1, 'na']
agenta[i, 'info'] <- agenta[i-1, 'info']
agentb[i, 'na'] <- agentb[i-1, 'na']
agentb[i, 'info'] <- agentb[i-1, 'info']
agentc[i, 'na'] <- agentc[i-1, 'na']
agentc[i, 'info'] <- agentc[i-1, 'info']
actions[i, 'expressed'] <- 'agent c did not express'
actions[i, 'otherreact1'] <- 0
actions[i, 'otherreact2'] <- 0
}
if(agenta[i, 'info'] >= agenta[1, 'solution']){
agenta[i, 'na'] <- agenta[i, 'na'] - 10
}
if(agentb[i, 'info'] >= agentb[1, 'solution']){
agentb[i, 'na'] <- agentb[i, 'na'] - 10
}
if(agentc[i, 'info'] >= agentc[1, 'solution']){
agentc[i, 'na'] <- agentc[i, 'na'] - 10
}
# if anyone's na is below zero, exit
if(agenta[i, 'na'] < 0){
break
}
if(agentb[i, 'na'] < 0){
break
}
if(agentc[i, 'na'] < 0){
break
}
}
library(tidyverse)
agenta <- agenta %>% mutate(agent = 'a',
time = 1:nrow(agenta))
agentb <- agentb %>% mutate(agent = 'b',
time = 1:nrow(agentb))
agentc <- agentc %>% mutate(agent = 'c',
time = 1:nrow(agentc))
df <- bind_rows(agenta, agentb, agentc)
ldf <- df %>% pivot_longer(
cols = c(na, info),
names_to = 'variable',
values_to = 'value'
)
ggplot(ldf, aes(x = time, y = value, color = variable)) +
geom_point() +
geom_line() +
facet_wrap(~agent)
