FrankRuns · August 17, 2025 11:56
diff --git a/gistfile1.txt b/gistfile1.txt
 ###############################################################
 # TUTORIAL: The “complacency model” for safety incidents
 #
 # Audience: Curious operators and analysts. No math background required.
 #
 # Big idea in plain English:
 # - Instead of assuming risk is constant, let’s assume it RISES
 #   the longer we go without an incident or intervention.
 # - Think of it like tension in a spring. The longer it goes untouched,
 #   the more tightly wound it gets. Eventually something snaps.
 # - This better reflects human systems, where people become complacent
 #   after long clean streaks.
 #
 # This code shows:
 # - How to model risk as "baseline + drift per week without incident"
 # - How that risk resets after a safety drill, training, or actual incident
 # - How it looks on a chart (the sawtooth pattern)
 ###############################################################

 # ---------------------------
 # 1) Define the complacency model
 # ---------------------------

 # Baseline risk (probability of incident the first week after training)
 baseline <- 0.002    # ~0.2% chance in week 1

 # Drift per week (how much risk creeps up each week with no incident)
 drift <- 0.0005      # adds 0.05% risk per week

 # Number of weeks to simulate
 T <- 30

 # Vector for risk each week
 weeks <- 1:T

 # Age since last reset (like "days since last training").
 # For simplicity, assume no incident actually occurs, so age just increases.
 age <- weeks - 1   # starts at 0 in week 1

 # Risk function: probability this week = 1 - exp(-(baseline + drift*age))
 # (This is just a smooth way to map "baseline + drift" into a probability).
 lambda <- baseline + drift * age
 risk_this_week <- 1 - exp(-lambda)

 # ---------------------------
 # 2) Simulate a reset
 # ---------------------------

 # Imagine we do a safety training on week 15 that resets risk back to baseline.
 risk_reset <- risk_this_week
 risk_reset[weeks >= 15] <- 1 - exp(-(baseline + drift * (weeks[weeks >= 15] - 15)))

 # ---------------------------
 # 3) Bundle into a table
 # ---------------------------

 out <- data.frame(
  week = weeks,
  complacency_risk = round(risk_reset, 4)
 )

 print(out, row.names = FALSE)

 # ---------------------------
 # 4) Visualize the sawtooth
 # ---------------------------

 # install.packages("ggplot2")  # if not already installed
 library(ggplot2)

 ggplot(out, aes(week, complacency_risk)) +
  geom_line(color = "red", size = 1.2) +
  geom_vline(xintercept = 15, linetype = "dashed") +
  labs(
    x = "Weeks since start",
    y = "Probability of an incident this week",
    title = "Complacency Model: Risk creeps upward, then resets after incident / training"
  ) +
  annotate("text", x = 15, y = max(out$complacency_risk) - 0.001, vjust = -1,
           label = "Training / reset at week 15")
	###############################################################
	# TUTORIAL: The “complacency model” for safety incidents
	#
	# Audience: Curious operators and analysts. No math background required.
	#
	# Big idea in plain English:
	# - Instead of assuming risk is constant, let’s assume it RISES
	# the longer we go without an incident or intervention.
	# - Think of it like tension in a spring. The longer it goes untouched,
	# the more tightly wound it gets. Eventually something snaps.
	# - This better reflects human systems, where people become complacent
	# after long clean streaks.
	#
	# This code shows:
	# - How to model risk as "baseline + drift per week without incident"
	# - How that risk resets after a safety drill, training, or actual incident
	# - How it looks on a chart (the sawtooth pattern)
	###############################################################

	# ---------------------------
	# 1) Define the complacency model
	# ---------------------------

	# Baseline risk (probability of incident the first week after training)
	baseline <- 0.002 # ~0.2% chance in week 1

	# Drift per week (how much risk creeps up each week with no incident)
	drift <- 0.0005 # adds 0.05% risk per week

	# Number of weeks to simulate
	T <- 30

	# Vector for risk each week
	weeks <- 1:T

	# Age since last reset (like "days since last training").
	# For simplicity, assume no incident actually occurs, so age just increases.
	age <- weeks - 1 # starts at 0 in week 1

	# Risk function: probability this week = 1 - exp(-(baseline + drift*age))
	# (This is just a smooth way to map "baseline + drift" into a probability).
	lambda <- baseline + drift * age
	risk_this_week <- 1 - exp(-lambda)

	# ---------------------------
	# 2) Simulate a reset
	# ---------------------------

	# Imagine we do a safety training on week 15 that resets risk back to baseline.
	risk_reset <- risk_this_week
	risk_reset[weeks >= 15] <- 1 - exp(-(baseline + drift * (weeks[weeks >= 15] - 15)))

	# ---------------------------
	# 3) Bundle into a table
	# ---------------------------

	out <- data.frame(
	week = weeks,
	complacency_risk = round(risk_reset, 4)
	)

	print(out, row.names = FALSE)

	# ---------------------------
	# 4) Visualize the sawtooth
	# ---------------------------

	# install.packages("ggplot2") # if not already installed
	library(ggplot2)

	ggplot(out, aes(week, complacency_risk)) +
	geom_line(color = "red", size = 1.2) +
	geom_vline(xintercept = 15, linetype = "dashed") +
	labs(
	x = "Weeks since start",
	y = "Probability of an incident this week",
	title = "Complacency Model: Risk creeps upward, then resets after incident / training"
	) +
	annotate("text", x = 15, y = max(out$complacency_risk) - 0.001, vjust = -1,
	label = "Training / reset at week 15")
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