Chapter 8 Model Recipe Book

8.1 Introduction: The Cookbook Approach

This chapter provides ready-to-use “recipes” for common organizational research scenarios using the ASA ABM v2. Each recipe addresses a specific research question, providing complete parameter configurations, runnable code, and interpretation guidance.

Think of these recipes as starting points for your own research. You can:

  • Use them directly to replicate common scenarios
  • Modify parameters to explore variations
  • Combine elements from multiple recipes
  • Extract visualization and analysis techniques

Each recipe follows a consistent structure:

  1. Research Question: What organizational phenomenon are we investigating?
  2. Key Parameters: Which model parameters drive this scenario?
  3. Implementation: Complete, runnable R code
  4. Expected Outcomes: What patterns should emerge?
  5. Visualization: How to effectively display results
  6. Insights: What can we learn about real organizations?
# Source simulation engine
source("../simulation/engine.R")

# Common visualization theme
theme_asa <- function() {
  theme_minimal() +
    theme(
      plot.title = element_text(size = 14, face = "bold"),
      plot.subtitle = element_text(size = 12),
      legend.position = "bottom",
      panel.grid.minor = element_blank()
    )
}

# Helper function for parameter tables
show_params <- function(params, highlight = NULL) {
  param_df <- data.frame(
    Parameter = names(params),
    Value = as.character(unlist(params)),
    stringsAsFactors = FALSE
  )
  
  if (!is.null(highlight)) {
    param_df$Key <- ifelse(param_df$Parameter %in% highlight, "★", "")
  }
  
  print(param_df)
}

8.2 Recipe 1: Creating a Homogeneous Organization

Research Question: How do strong selection criteria and social influence create organizational monocultures?

Scenario: An organization with strong cultural fit requirements gradually becomes increasingly homogeneous, potentially limiting innovation and adaptability.

8.2.1 Key Parameters

homogeneous_params <- list(
  # Strong selection for conscientiousness
  selection_criteria = "conscientiousness",
  
  # High homophily preferences
  homophily_preference = list(mean = 0.8, sd = 0.1),
  diversity_preference = list(mean = 0.2, sd = 0.1),
  
  # Frequent interactions reinforce similarity
  n_interactions_per_step = 10,
  interaction_window = 20,
  
  # Low turnover threshold keeps similar people
  turnover_threshold = -15,
  
  # Standard growth
  growth_rate = 0.01,
  hiring_frequency = 12
)

show_params(homogeneous_params, 
           highlight = c("selection_criteria", "homophily_preference", 
                        "n_interactions_per_step"))

8.2.2 Implementation

# Run the homogeneous organization simulation
set.seed(42)
homo_results <- run_asa_simulation(
  n_steps = 260,
  initial_size = 100,
  params = homogeneous_params,
  verbose = FALSE
)

# Extract metrics
homo_metrics <- homo_results$metrics

# Calculate convergence point (when diversity drops below threshold)
diversity_threshold <- 0.5
convergence_step <- homo_metrics[blau_index < diversity_threshold, min(time_step)]
cat("Diversity dropped below", diversity_threshold, "at step:", convergence_step, "\n")

8.2.3 Visualization

# Create multi-panel visualization
library(gridExtra)

# Panel 1: Diversity decline
p1 <- ggplot(homo_metrics, aes(x = time_step)) +
  geom_line(aes(y = blau_index), color = "darkred", size = 1.2) +
  geom_hline(yintercept = diversity_threshold, linetype = "dashed", color = "red") +
  geom_vline(xintercept = convergence_step, linetype = "dotted") +
  annotate("text", x = convergence_step + 10, y = 0.6, 
           label = "Convergence", angle = 90, vjust = 0) +
  labs(title = "Identity Diversity Decline",
       y = "Blau's Index",
       x = "Time Step") +
  theme_asa()

# Panel 2: Personality convergence
p2 <- ggplot(homo_metrics, aes(x = time_step)) +
  geom_line(aes(y = sd_conscientiousness), color = "darkblue", size = 1.2) +
  geom_line(aes(y = sd_openness), color = "darkgreen", size = 1.2, linetype = "dashed") +
  labs(title = "Personality Homogenization",
       y = "Standard Deviation",
       x = "Time Step") +
  annotate("text", x = 200, y = homo_metrics[time_step == 200, sd_conscientiousness] + 0.02,
           label = "Conscientiousness", color = "darkblue") +
  annotate("text", x = 200, y = homo_metrics[time_step == 200, sd_openness] + 0.02,
           label = "Openness", color = "darkgreen") +
  theme_asa()

# Panel 3: Satisfaction trajectory
p3 <- ggplot(homo_metrics, aes(x = time_step)) +
  geom_line(aes(y = avg_satisfaction), color = "darkgreen", size = 1.2) +
  geom_ribbon(aes(ymin = avg_satisfaction - sd_satisfaction, 
                  ymax = avg_satisfaction + sd_satisfaction),
              alpha = 0.2, fill = "green") +
  labs(title = "Agent Satisfaction",
       y = "Average Satisfaction",
       x = "Time Step") +
  theme_asa()

# Panel 4: Organization size
p4 <- ggplot(homo_metrics, aes(x = time_step)) +
  geom_line(aes(y = organization_size), color = "purple", size = 1.2) +
  labs(title = "Organization Growth",
       y = "Number of Employees",
       x = "Time Step") +
  theme_asa()

# Combine plots
grid.arrange(p1, p2, p3, p4, ncol = 2,
             top = "Recipe 1: Evolution of a Homogeneous Organization")

8.2.4 Expected Outcomes and Insights

  1. Rapid Diversity Loss: Blau’s Index drops below 0.5 within 50-100 steps
  2. Personality Convergence: Standard deviation of selected trait (conscientiousness) approaches zero
  3. High Satisfaction: Homophily leads to high average satisfaction (>5)
  4. Stable Growth: Low turnover enables steady organizational growth

Organizational Insight: While homogeneous cultures can be efficient and harmonious, they risk groupthink and reduced innovation capacity. The high satisfaction masks potential vulnerabilities to environmental changes.

8.3 Recipe 2: The Diversity Paradox

Research Question: Why do diversity initiatives sometimes fail despite good intentions?

Scenario: An organization attempts to increase diversity through hiring but struggles with retention due to unchanged cultural dynamics.

8.3.1 Key Parameters

diversity_paradox_params <- list(
  # No selection on personality traits
  selection_criteria = "random",
  
  # But strong internal homophily
  homophily_preference = list(mean = 0.9, sd = 0.05),
  diversity_preference = list(mean = 0.1, sd = 0.05),
  
  # Lower satisfaction threshold triggers more turnover
  turnover_threshold = -5,
  
  # Aggressive growth to bring in diversity
  growth_rate = 0.05,
  hiring_frequency = 4,
  
  # High interaction frequency amplifies cultural pressure
  n_interactions_per_step = 15,
  interaction_window = 10
)

show_params(diversity_paradox_params,
           highlight = c("selection_criteria", "homophily_preference", 
                        "turnover_threshold"))

8.3.2 Implementation

set.seed(123)
paradox_results <- run_asa_simulation(
  n_steps = 260,
  initial_size = 100,
  params = diversity_paradox_params,
  verbose = FALSE
)

# Analyze hiring vs retention
paradox_org <- paradox_results$final_organization
paradox_metrics <- paradox_results$metrics

# Calculate hire diversity vs organization diversity
hire_diversity <- numeric(nrow(paradox_metrics))
for(i in 1:nrow(paradox_metrics)) {
  new_hires <- paradox_org[hire_date == i & is_active == TRUE]
  if(nrow(new_hires) > 0) {
    hire_diversity[i] <- calculate_blau_index(new_hires$identity_category)
  }
}

paradox_metrics$hire_diversity <- hire_diversity

8.3.3 Visualization

# Panel 1: Hiring diversity vs organizational diversity
p1 <- ggplot(paradox_metrics[hire_diversity > 0], aes(x = time_step)) +
  geom_point(aes(y = hire_diversity), color = "orange", alpha = 0.6) +
  geom_line(aes(y = blau_index), color = "purple", size = 1.2) +
  labs(title = "The Diversity Paradox",
       subtitle = "High diverse hiring (orange points) doesn't translate to organizational diversity (purple line)",
       y = "Diversity (Blau's Index)",
       x = "Time Step") +
  theme_asa()

# Panel 2: Turnover by identity category
turnover_summary <- paradox_org[is_active == FALSE, .N, by = identity_category]
total_by_category <- paradox_org[, .N, by = identity_category]
turnover_summary <- merge(turnover_summary, total_by_category, by = "identity_category")
turnover_summary[, turnover_rate := N.x / N.y]

p2 <- ggplot(turnover_summary, aes(x = identity_category, y = turnover_rate)) +
  geom_col(fill = "darkred") +
  labs(title = "Differential Turnover by Identity Category",
       y = "Turnover Rate",
       x = "Identity Category") +
  theme_asa()

# Panel 3: Satisfaction distribution
final_state <- paradox_org[is_active == TRUE]
p3 <- ggplot(final_state, aes(x = satisfaction, fill = identity_category)) +
  geom_histogram(bins = 20, alpha = 0.7, position = "identity") +
  facet_wrap(~ identity_category) +
  labs(title = "Satisfaction Distribution by Identity Category",
       x = "Satisfaction Score",
       y = "Count") +
  theme_asa() +
  theme(legend.position = "none")

# Panel 4: Tenure analysis
p4 <- ggplot(final_state, aes(x = identity_category, y = tenure)) +
  geom_boxplot(aes(fill = identity_category)) +
  labs(title = "Tenure Distribution by Identity Category",
       y = "Tenure (weeks)",
       x = "Identity Category") +
  theme_asa() +
  theme(legend.position = "none")

grid.arrange(p1, p2, p3, p4, ncol = 2,
             top = "Recipe 2: The Diversity Paradox - Hiring Without Inclusion")

8.3.4 Expected Outcomes and Insights

  1. Hiring-Retention Gap: 60-80% diversity in new hires, but organizational diversity remains low
  2. Differential Turnover: Minority categories show 2-3x higher turnover rates
  3. Satisfaction Inequality: Clear satisfaction gaps between dominant and minority groups
  4. Short Tenure: Diverse hires leave quickly, often within 20-30 time steps

Organizational Insight: Diversity initiatives focused solely on hiring without addressing cultural dynamics and inclusion often fail. Success requires systemic change in how organizations value and integrate different perspectives.

8.4 Recipe 3: Rapid Growth Startup Dynamics

Research Question: How does rapid scaling affect organizational culture and cohesion?

Scenario: A startup experiencing hockey-stick growth must balance maintaining culture with rapid hiring.

8.4.1 Key Parameters

startup_params <- list(
  # Aggressive growth phases
  growth_rate = 0.10,  # 10% per hiring cycle
  hiring_frequency = 2,  # Hire every 2 weeks
  
  # Initially strong culture
  selection_criteria = "fit",
  
  # But weakening influence as size grows
  n_interactions_per_step = 5,  # Can't maintain connections
  interaction_window = 5,  # Shorter organizational memory
  
  # Higher turnover during growth
  turnover_threshold = -7,
  
  # Start with strong homophily
  homophily_preference = list(mean = 0.7, sd = 0.1),
  diversity_preference = list(mean = 0.3, sd = 0.1)
)

show_params(startup_params,
           highlight = c("growth_rate", "hiring_frequency", "interaction_window"))

8.4.2 Implementation

set.seed(456)
startup_results <- run_asa_simulation(
  n_steps = 156,  # 3 years
  initial_size = 20,  # Small founding team
  params = startup_params,
  verbose = FALSE
)

# Analyze growth phases
startup_metrics <- startup_results$metrics
startup_metrics[, growth_phase := cut(
  time_step,
  breaks = c(0, 26, 78, 130, 156),
  labels = c("Founding", "Early Growth", "Scaling", "Maturity"),
  include.lowest = TRUE
)]

# Calculate phase-specific metrics
phase_summary <- startup_metrics[, .(
  avg_size = mean(organization_size),
  growth_rate = (last(organization_size) - first(organization_size)) / first(organization_size),
  avg_diversity = mean(blau_index),
  avg_satisfaction = mean(avg_satisfaction),
  culture_drift = last(avg_conscientiousness) - first(avg_conscientiousness)
), by = growth_phase]

print(phase_summary)

8.4.3 Visualization

# Panel 1: Growth trajectory with phases
p1 <- ggplot(startup_metrics, aes(x = time_step, y = organization_size)) +
  geom_line(size = 1.5, color = "darkgreen") +
  geom_rect(data = data.frame(
    xmin = c(0, 26, 78, 130),
    xmax = c(26, 78, 130, 156),
    phase = c("Founding", "Early Growth", "Scaling", "Maturity")
  ), aes(xmin = xmin, xmax = xmax, ymin = 0, ymax = Inf, fill = phase),
  alpha = 0.2, inherit.aes = FALSE) +
  scale_fill_manual(values = c("Founding" = "blue", "Early Growth" = "green",
                               "Scaling" = "orange", "Maturity" = "purple")) +
  labs(title = "Startup Growth Trajectory",
       y = "Organization Size",
       x = "Time Step",
       fill = "Growth Phase") +
  theme_asa()

# Panel 2: Cultural metrics evolution
p2 <- ggplot(startup_metrics, aes(x = time_step)) +
  geom_line(aes(y = blau_index, color = "Identity Diversity"), size = 1.2) +
  geom_line(aes(y = avg_satisfaction / 10, color = "Avg Satisfaction (scaled)"), size = 1.2) +
  geom_line(aes(y = sd_conscientiousness * 5, color = "Personality Variance (scaled)"), size = 1.2) +
  scale_color_manual(values = c("Identity Diversity" = "purple",
                               "Avg Satisfaction (scaled)" = "green",
                               "Personality Variance (scaled)" = "orange")) +
  labs(title = "Cultural Evolution During Growth",
       y = "Metric Value",
       x = "Time Step",
       color = "Metric") +
  theme_asa()

# Panel 3: New hire integration
startup_org <- startup_results$final_organization
recent_hires <- startup_org[hire_date > 100 & is_active == TRUE]
veteran_employees <- startup_org[hire_date < 50 & is_active == TRUE]

integration_data <- rbind(
  data.frame(group = "Veterans", satisfaction = veteran_employees$satisfaction,
             conscientiousness = veteran_employees$conscientiousness),
  data.frame(group = "Recent Hires", satisfaction = recent_hires$satisfaction,
             conscientiousness = recent_hires$conscientiousness)
)

p3 <- ggplot(integration_data, aes(x = conscientiousness, y = satisfaction, color = group)) +
  geom_point(alpha = 0.6, size = 3) +
  stat_ellipse(level = 0.95) +
  scale_color_manual(values = c("Veterans" = "darkblue", "Recent Hires" = "darkorange")) +
  labs(title = "Cultural Gap: Veterans vs Recent Hires",
       x = "Conscientiousness",
       y = "Satisfaction",
       color = "Employee Group") +
  theme_asa()

# Panel 4: Phase transition analysis
transition_points <- startup_metrics[, .(
  size_change = diff(organization_size),
  diversity_change = diff(blau_index),
  satisfaction_change = diff(avg_satisfaction)
), by = growth_phase]

p4 <- transition_points %>%
  pivot_longer(cols = ends_with("_change"), names_to = "metric", values_to = "change") %>%
  ggplot(aes(x = growth_phase, y = change, fill = metric)) +
  geom_boxplot(alpha = 0.7) +
  scale_fill_brewer(palette = "Set2",
                    labels = c("Diversity Change", "Satisfaction Change", "Size Change")) +
  labs(title = "Volatility by Growth Phase",
       x = "Growth Phase",
       y = "Step-to-Step Change",
       fill = "Metric") +
  theme_asa()

grid.arrange(p1, p2, p3, p4, ncol = 2,
             top = "Recipe 3: Rapid Growth Startup Dynamics")

8.4.4 Expected Outcomes and Insights

  1. Exponential Growth: 10x size increase possible in 3 years
  2. Cultural Dilution: Diversity increases 50-70% during scaling
  3. Integration Challenges: Clear clusters between veterans and new hires
  4. Volatility Increases: Higher variance in all metrics during scaling phase

Organizational Insight: Rapid scaling creates inevitable cultural dilution. Successful startups must intentionally reinforce culture through onboarding, rituals, and veteran employee engagement.

8.5 Recipe 4: Parameter Sensitivity Analysis

Research Question: Which parameters most strongly influence organizational outcomes?

Scenario: Systematic exploration of parameter space to understand model dynamics.

8.5.1 Key Parameters

# Define parameter grid for sensitivity analysis
param_grid <- expand.grid(
  growth_rate = c(0.00, 0.01, 0.05),
  turnover_threshold = c(-15, -10, -5),
  n_interactions = c(5, 10, 20),
  homophily_strength = c(0.3, 0.6, 0.9)
)

# Show sample of parameter combinations
cat("Testing", nrow(param_grid), "parameter combinations\n")
print(head(param_grid, 10))

8.5.2 Implementation

# Run sensitivity analysis (subset for demonstration)
set.seed(789)
sensitivity_results <- list()

# Run subset of simulations
sample_indices <- sample(1:nrow(param_grid), 12)  # Run 12 for demo

for (i in sample_indices) {
  params <- list(
    growth_rate = param_grid$growth_rate[i],
    turnover_threshold = param_grid$turnover_threshold[i],
    n_interactions_per_step = param_grid$n_interactions[i],
    homophily_preference = list(mean = param_grid$homophily_strength[i], sd = 0.1)
  )
  
  result <- run_asa_simulation(
    n_steps = 104,  # 2 years
    initial_size = 100,
    params = params,
    verbose = FALSE
  )
  
  # Extract summary metrics
  final_metrics <- tail(result$metrics, 1)
  sensitivity_results[[i]] <- data.frame(
    param_grid[i,],
    final_size = final_metrics$organization_size,
    final_diversity = final_metrics$blau_index,
    final_satisfaction = final_metrics$avg_satisfaction,
    total_turnover = sum(result$final_organization$is_active == FALSE)
  )
}

# Combine results
sensitivity_df <- bind_rows(sensitivity_results)

8.5.3 Visualization

# Panel 1: Growth rate impact
p1 <- ggplot(sensitivity_df, aes(x = factor(growth_rate), y = final_size)) +
  geom_boxplot(aes(fill = factor(turnover_threshold))) +
  scale_fill_brewer(palette = "RdYlBu", name = "Turnover\nThreshold") +
  labs(title = "Organization Size: Growth vs Turnover",
       x = "Growth Rate",
       y = "Final Organization Size") +
  theme_asa()

# Panel 2: Diversity outcomes
p2 <- ggplot(sensitivity_df, aes(x = homophily_strength, y = final_diversity)) +
  geom_point(aes(color = factor(n_interactions), size = final_size), alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE, color = "black", linetype = "dashed") +
  scale_color_brewer(palette = "Set1", name = "Interactions\nper Step") +
  scale_size_continuous(name = "Final Size") +
  labs(title = "Diversity: Homophily vs Interactions",
       x = "Homophily Strength",
       y = "Final Diversity (Blau's Index)") +
  theme_asa()

# Panel 3: Parameter importance (mock random forest importance)
# In practice, would use actual RF or similar
param_importance <- data.frame(
  parameter = c("Homophily Strength", "Turnover Threshold", 
                "Growth Rate", "N Interactions"),
  importance = c(0.35, 0.30, 0.20, 0.15)
)

p3 <- ggplot(param_importance, aes(x = reorder(parameter, importance), y = importance)) +
  geom_col(fill = "darkblue") +
  coord_flip() +
  labs(title = "Parameter Importance for Diversity Outcome",
       x = "",
       y = "Relative Importance") +
  theme_asa()

# Panel 4: Interaction effects heatmap
interaction_summary <- sensitivity_df %>%
  group_by(homophily_strength, turnover_threshold) %>%
  summarise(avg_satisfaction = mean(final_satisfaction), .groups = "drop")

p4 <- ggplot(interaction_summary, aes(x = factor(homophily_strength), 
                                      y = factor(turnover_threshold), 
                                      fill = avg_satisfaction)) +
  geom_tile() +
  scale_fill_gradient2(low = "red", mid = "white", high = "green", 
                       midpoint = 0, name = "Avg\nSatisfaction") +
  labs(title = "Satisfaction: Homophily × Turnover Interaction",
       x = "Homophily Strength",
       y = "Turnover Threshold") +
  theme_asa()

grid.arrange(p1, p2, p3, p4, ncol = 2,
             top = "Recipe 4: Parameter Sensitivity Analysis")

8.5.4 Expected Outcomes and Insights

  1. Growth-Turnover Balance: High growth can’t compensate for very high turnover
  2. Homophily Dominates Diversity: Strongest predictor of final diversity
  3. Interaction Effects: Parameters interact in non-linear ways
  4. Threshold Behaviors: Some parameters show sudden transitions

Organizational Insight: Understanding parameter sensitivity helps identify key leverage points for organizational interventions. Focus on the parameters with highest impact for efficient change.

8.6 Recipe 5: Long-term Organizational Evolution

Research Question: What are the long-term equilibrium states of organizations?

Scenario: Tracking organizations over extended time periods to identify stable states and cycles.

8.6.1 Key Parameters

longterm_params <- list(
  # Moderate, sustainable parameters
  growth_rate = 0.005,  # 0.5% growth
  turnover_threshold = -10,
  
  # Standard interaction patterns
  n_interactions_per_step = 10,
  interaction_window = 20,
  
  # Balanced preferences
  homophily_preference = list(mean = 0.5, sd = 0.2),
  diversity_preference = list(mean = 0.5, sd = 0.2),
  
  # Selection on fit
  selection_criteria = "fit"
)

show_params(longterm_params)

8.6.2 Implementation

set.seed(2024)
longterm_results <- run_asa_simulation(
  n_steps = 520,  # 10 years
  initial_size = 100,
  params = longterm_params,
  verbose = FALSE
)

# Identify equilibrium and cycles
longterm_metrics <- longterm_results$metrics

# Calculate moving averages to identify trends
window_size <- 26  # 6 months
longterm_metrics[, `:=`(
  ma_size = frollmean(organization_size, window_size, align = "right"),
  ma_diversity = frollmean(blau_index, window_size, align = "right"),
  ma_satisfaction = frollmean(avg_satisfaction, window_size, align = "right")
)]

# Detect cycles using autocorrelation
acf_diversity <- acf(longterm_metrics$blau_index[!is.na(longterm_metrics$blau_index)], 
                     lag.max = 100, plot = FALSE)
cycle_length <- which(acf_diversity$acf[-1] == max(acf_diversity$acf[-1]))[1]
cat("Detected cycle length:", cycle_length, "steps\n")

8.6.3 Visualization

# Panel 1: Long-term trajectories
p1 <- ggplot(longterm_metrics[!is.na(ma_size)], aes(x = time_step)) +
  geom_line(aes(y = organization_size), alpha = 0.3, color = "gray") +
  geom_line(aes(y = ma_size), color = "darkblue", size = 1.2) +
  labs(title = "Long-term Organization Size",
       subtitle = "Gray: actual, Blue: 6-month moving average",
       y = "Organization Size",
       x = "Time Step") +
  theme_asa()

# Panel 2: Diversity cycles
p2 <- ggplot(longterm_metrics[!is.na(ma_diversity)], aes(x = time_step)) +
  geom_line(aes(y = blau_index), alpha = 0.3, color = "gray") +
  geom_line(aes(y = ma_diversity), color = "purple", size = 1.2) +
  geom_hline(yintercept = mean(longterm_metrics$blau_index, na.rm = TRUE),
             linetype = "dashed", color = "red") +
  labs(title = "Diversity Cycles",
       subtitle = "Red line: long-term average",
       y = "Blau's Index",
       x = "Time Step") +
  theme_asa()

# Panel 3: Phase space plot
phase_data <- longterm_metrics[seq(1, nrow(longterm_metrics), by = 4)]  # Sample every month
p3 <- ggplot(phase_data, aes(x = blau_index, y = avg_satisfaction)) +
  geom_path(alpha = 0.5, color = "darkgreen") +
  geom_point(aes(color = time_step), size = 2) +
  scale_color_gradient(low = "blue", high = "red", name = "Time") +
  labs(title = "Phase Space: Diversity vs Satisfaction",
       x = "Diversity (Blau's Index)",
       y = "Average Satisfaction") +
  theme_asa()

# Panel 4: Equilibrium analysis
# Calculate stability metrics over time windows
stability_windows <- longterm_metrics[, .(
  window = ceiling(time_step / 52),  # Annual windows
  size_stability = 1 / (sd(organization_size) + 1),
  diversity_stability = 1 / (sd(blau_index) + 1),
  satisfaction_stability = 1 / (sd(avg_satisfaction) + 1)
), by = ceiling(time_step / 52)]

stability_long <- stability_windows %>%
  pivot_longer(cols = ends_with("_stability"), 
               names_to = "metric", 
               values_to = "stability")

p4 <- ggplot(stability_long, aes(x = ceiling, y = stability, fill = metric)) +
  geom_col(position = "dodge") +
  scale_fill_brewer(palette = "Set3",
                    labels = c("Diversity", "Satisfaction", "Size")) +
  labs(title = "System Stability Over Time",
       subtitle = "Higher values indicate more stable periods",
       x = "Year",
       y = "Stability Index",
       fill = "Metric") +
  theme_asa()

grid.arrange(p1, p2, p3, p4, ncol = 2,
             top = "Recipe 5: Long-term Organizational Evolution")

8.6.4 Expected Outcomes and Insights

  1. Equilibrium States: Organizations tend toward stable attractor states
  2. Diversity Cycles: Natural oscillations in diversity metrics
  3. Path Dependence: Early conditions influence long-term outcomes
  4. Stability Periods: Alternating periods of stability and change

Organizational Insight: Organizations don’t reach static equilibria but rather dynamic steady states with natural cycles. Understanding these patterns helps anticipate and manage organizational change.

8.7 Best Practices from Recipes

8.7.1 1. Parameter Selection

  • Start with default values and adjust gradually
  • Consider realistic ranges for your context
  • Document parameter choices and rationale
  • Test sensitivity to key parameters

8.7.2 2. Analysis Approach

  • Always visualize time series data
  • Compare multiple scenarios
  • Calculate summary statistics for key periods
  • Look for both trends and cycles
  • Consider phase transitions and threshold effects

8.7.3 3. Validation

  • Run multiple replications with different seeds
  • Check for sensitivity to initial conditions
  • Validate against known organizational patterns
  • Test extreme parameter values
  • Compare with empirical data when available

8.7.4 4. Interpretation

  • Agent-based models show possibilities, not predictions
  • Focus on patterns and dynamics rather than exact values
  • Consider emergent behaviors not explicitly programmed
  • Use results to generate hypotheses for further testing
  • Think about real-world interventions suggested by results

8.8 Code Snippets for Common Tasks

8.8.1 Running Multiple Replications

run_replications <- function(n_reps, base_params, n_steps = 260) {
  results <- list()
  for (i in 1:n_reps) {
    set.seed(i * 1000)  # Reproducible seeds
    results[[i]] <- run_asa_simulation(
      n_steps = n_steps,
      initial_size = 100,
      params = base_params,
      verbose = FALSE
    )
  }
  return(results)
}

# Aggregate results across replications
aggregate_replications <- function(rep_results) {
  metrics_list <- lapply(rep_results, function(x) {
    x$metrics[, rep := which(sapply(rep_results, identical, x))]
  })
  
  combined_metrics <- rbindlist(metrics_list)
  
  summary_metrics <- combined_metrics[, .(
    avg_size = mean(organization_size),
    sd_size = sd(organization_size),
    avg_diversity = mean(blau_index),
    sd_diversity = sd(blau_index),
    avg_satisfaction = mean(avg_satisfaction),
    sd_satisfaction = sd(avg_satisfaction)
  ), by = time_step]
  
  return(summary_metrics)
}

8.8.2 Custom Visualization Functions

# Plot with confidence intervals
plot_with_ci <- function(summary_metrics, metric_name, color = "darkblue") {
  avg_col <- paste0("avg_", metric_name)
  sd_col <- paste0("sd_", metric_name)
  
  ggplot(summary_metrics, aes_string(x = "time_step")) +
    geom_ribbon(aes_string(ymin = paste0(avg_col, " - ", sd_col),
                          ymax = paste0(avg_col, " + ", sd_col)),
                alpha = 0.3, fill = color) +
    geom_line(aes_string(y = avg_col), color = color, size = 1.2) +
    labs(title = paste("Average", metric_name, "with Standard Deviation"),
         y = metric_name,
         x = "Time Step") +
    theme_asa()
}

# Create dashboard of key metrics
create_dashboard <- function(metrics_data, title = "Simulation Dashboard") {
  p1 <- ggplot(metrics_data, aes(x = time_step, y = organization_size)) +
    geom_line(color = "darkgreen") + 
    labs(title = "Organization Size", y = "Size") + 
    theme_asa()
  
  p2 <- ggplot(metrics_data, aes(x = time_step, y = blau_index)) +
    geom_line(color = "purple") + 
    labs(title = "Diversity", y = "Blau's Index") + 
    theme_asa()
  
  p3 <- ggplot(metrics_data, aes(x = time_step, y = avg_satisfaction)) +
    geom_line(color = "darkblue") + 
    labs(title = "Satisfaction", y = "Average") + 
    theme_asa()
  
  p4 <- ggplot(metrics_data, aes(x = time_step, y = avg_conscientiousness)) +
    geom_line(color = "darkorange") + 
    labs(title = "Conscientiousness", y = "Average") + 
    theme_asa()
  
  grid.arrange(p1, p2, p3, p4, ncol = 2, top = title)
}

8.8.3 Advanced Analysis Functions

# Detect organizational phases
detect_phases <- function(metrics_data, window = 26) {
  # Calculate rolling statistics
  metrics_data[, `:=`(
    growth_rate = c(NA, diff(organization_size)) / organization_size,
    diversity_change = c(NA, diff(blau_index)),
    satisfaction_change = c(NA, diff(avg_satisfaction))
  )]
  
  # Identify phase transitions
  metrics_data[, phase := "stable"]
  metrics_data[abs(growth_rate) > 0.05, phase := "growth"]
  metrics_data[growth_rate < -0.02, phase := "contraction"] 
  metrics_data[abs(diversity_change) > 0.05, phase := "transition"]
  
  return(metrics_data)
}

# Calculate organizational health index
calculate_health_index <- function(metrics_data) {
  metrics_data[, health_index := 
    (organization_size / max(organization_size)) * 0.25 +
    blau_index * 0.25 +
    (avg_satisfaction / 10) * 0.25 +
    (1 - sd_satisfaction / 5) * 0.25
  ]
  
  return(metrics_data)
}

8.9 Conclusion

These recipes provide starting points for exploring organizational dynamics with the ASA ABM v2. By modifying parameters and combining elements from different recipes, researchers can investigate a wide range of organizational phenomena. Remember that the value of agent-based modeling lies not in precise prediction but in understanding the mechanisms that drive organizational behavior and identifying leverage points for intervention.