Include references

Author: Daniel Precioso, PhD

modules/agent-based-modeling/assignment.qmd

@@ -2,4 +2,17 @@
 title: "Assignment"
 subtitle: "To-Do"
 format: html
----
+---
+
+Implement a simple agent-based traffic simulation and analyze emergent congestion patterns [@wilensky2015abm; @nagel1992cellular].
+
+## Required
+
+1. Simulate a 1D traffic cellular automaton for at least two densities.
+2. Plot one space-time diagram for each density.
+3. Compare how jam formation changes with random slowdown probability.
+
+## Extra Mile (Optional)
+
+- Add sliders for density and slowdown probability.
+- Compare random vs localized perturbations in the initial condition.

modules/agent-based-modeling/index.qmd

@@ -4,7 +4,7 @@ subtitle: "Individual Rules, Emergent Behavior"
 format: html
 ---
 
-In this session we explore agent-based models: simple local rules that produce global patterns. We focus on simulation, visualization, and interpretation.
+In this session we explore agent-based models: simple local rules that produce global patterns. We focus on simulation, visualization, and interpretation [@wilensky2015abm].
 
 ## Case Studies
 

modules/agent-based-modeling/traffic.qmd

@@ -4,7 +4,7 @@ subtitle: "Nagel-Schreckenberg"
 format: html
 ---
 
-Traffic flow can be modeled with a 1D cellular automaton. Cars occupy cells and move forward each step according to simple rules.
+Traffic flow can be modeled with a 1D cellular automaton. Cars occupy cells and move forward each step according to simple rules [@nagel1992cellular].
 
 ```{python}
 #| label: fig-traffic

modules/cellular-automata/assignment.qmd

@@ -4,7 +4,7 @@ subtitle: "Cellular Automata"
 format: html
 ---
 
-Complete a 1D cellular automaton and a traffic model simulation.
+Complete a 1D cellular automaton and a traffic model simulation [@wolfram1983statistical; @nagel1992cellular].
 
 ## Required
 

modules/cellular-automata/cellular-1d.qmd

@@ -4,7 +4,7 @@ subtitle: "Rules and Space-Time Diagrams"
 format: html
 ---
 
-A 1D cellular automaton updates each cell using its local neighborhood. We will use elementary 3-cell rules, such as Rule 30.
+A 1D cellular automaton updates each cell using its local neighborhood. We will use elementary 3-cell rules, such as Rule 30 [@wolfram1983statistical].
 
 ```{python}
 #| label: fig-ca-rule30

modules/cellular-automata/index.qmd

@@ -4,7 +4,7 @@ subtitle: "1D Rules and Traffic"
 format: html
 ---
 
-In this session we explore **1D cellular automata** and apply them to traffic models.
+In this session we explore **1D cellular automata** and apply them to traffic models [@wolfram1983statistical; @nagel1992cellular].
 
 ## Case Studies
 

modules/collective-motion/vicsek-animation-interactive.qmd

@@ -4,7 +4,7 @@ subtitle: "Interactive Animation"
 format: html
 ---
 
-In this chapter I will guide you to build an **interactive Vicsek animation** in Matplotlib, *one step at a time*.
+In this chapter I will guide you to build an **interactive Vicsek animation** in Matplotlib, *one step at a time* [@vicsek1995novel; @hunter2007matplotlib].
 
 You already have a basic animation of particles moving (see the previous chapter). Now we will add:
 - an order parameter plot,

modules/collective-motion/vicsek-animation.qmd

@@ -4,7 +4,7 @@ subtitle: "Basic Animation (Particles Moving)"
 format: html
 ---
 
-In this chapter I will guide you to build a basic animation of the Vicsek model using Matplotlib. This will be a foundation for more complex visualizations and interactive explorations in later chapters.
+In this chapter I will guide you to build a basic animation of the Vicsek model using Matplotlib [@vicsek1995novel; @hunter2007matplotlib]. This will be a foundation for more complex visualizations and interactive explorations in later chapters.
 
 You already have the Vicsek functions implemented (e.g. `initialize_particles`, `vicsek_equations`, `vicsek_order_parameter`).  
 Your job here is to **assemble** them into a 2D particle animation (with a short *tail*) that shows the recent trajectory of each particle.

modules/collective-motion/vicsek-predator.qmd

@@ -4,7 +4,7 @@ subtitle: "Implementing the Avoidance Rule"
 format: html
 ---
 
-In the Couzin model, particle avoidance takes absolute priority: if an individual senses another particle within a certain radius, it immediately turns directly away, ignoring all other alignment or noise rules. We can implement a similar rule in the Vicsek model to simulate predator avoidance. The predator can be a fixed point, a random walker, or even the mouse cursor. In all cases, if a boid senses the predator within a certain radius, it will turn directly away from it.
+In the Couzin model, particle avoidance takes absolute priority: if an individual senses another particle within a certain radius, it immediately turns directly away, ignoring all other alignment or noise rules [@couzin2002collective]. We can implement a similar rule in the Vicsek model to simulate predator avoidance [@vicsek1995novel]. The predator can be a fixed point, a random walker, or even the mouse cursor. In all cases, if a boid senses the predator within a certain radius, it will turn directly away from it.
 
 Below is a guide on how to implement this in your Vicsek animation. You can choose to implement one or more types of predators (static, random walk, mouse-following) and compare their effects on the collective behavior of the particles.
 

modules/collective-motion/vicsek-validation.qmd

@@ -4,7 +4,7 @@ subtitle: "Static Analysis of Collective Motion"
 format: html
 ---
 
-Before animating the Vicsek model, it is important to validate your implementation and build intuition about how the parameters affect collective motion. Here, you will use static plots to explore how the interaction radius $r$ influences the alignment of particles. This step helps ensure your code is working as expected and provides insight into the model's behavior before moving on to more complex analyses or animations.
+Before animating the Vicsek model, it is important to validate your implementation and build intuition about how the parameters affect collective motion [@vicsek1995novel]. Here, you will use static plots to explore how the interaction radius $r$ influences the alignment of particles. This step helps ensure your code is working as expected and provides insight into the model's behavior before moving on to more complex analyses or animations.
 
 We will run the Vicsek model for a few time steps and visualize the headings of all particles for different values of $r$. This will help you see the transition from random motion to local and global alignment as the interaction radius increases.