Operators

one-of max-to

any?

min-to

min-to

To select the closest patch from an agentset in NetLogo, you can use the min-one-of function, which returns the agent with the minimum value of a given reporter. In this case, you can use distance as the reporter to find the closest patch.

Here’s how you can modify your move procedure to have turtles move to the closest green patch:

to move
  let close-patches patches in-radius 5
  let green-patches close-patches with [ pcolor = green ]
  if any? green-patches [
    let closest-patch min-one-of green-patches [distance myself]
    move-to closest-patch
  ]
end

Key Points:

• min-one-of: This function is used to find the patch (or any agent) that minimizes the value calculated by the reporter. In this case, it’s [distance myself].
• [distance myself]: This reports the distance from the turtle (the one calling move) to each patch in the agentset, and min-one-of selects the one with the smallest distance.
• Condition if any? green-patches: This ensures that the procedure is only executed if there is at least one green patch in the vicinity, avoiding errors.

This approach will ensure that each turtle moves towards the nearest green patch within its neighborhood defined by the radius.

max list

The max primitive computes the maximum value from one or more inputs. When using it with list, as in max list, you can compare multiple numbers and ensure your value doesn’t drop below a certain threshold.

Example Usage:

let current-value 5
let new-value max list 0 (current-value - 3)

In this example:

• A list is created with two elements: 0 and current-value - 3, which evaluates to 2 (since 5 - 3 = 2).
• max list will return the maximum of these two values, which is 2.
• If current-value - 3 had resulted in a negative number (e.g., -2), max list would ensure new-value remains 0.

This is particularly useful for preventing values from dropping below a certain threshold (like zero), which is a common issue when decrementing counters or similar quantities.

Using max list 0 (expression) is a handy way to ensure that calculations involving subtraction do not result in a negative number when the minimum permissible value is zero.

Yes, in the context of the code you’ve provided, the agent consumes by reducing its “needs” using its “output.” Essentially, the operation simulates the process where the agent’s requirements (needs) are met or alleviated by utilizing its produced or available resources (output).

Here’s a more detailed breakdown of the logic for clarity:

• needs: Represents something that the agent requires or seeks to fulfill. This could metaphorically be hunger, energy, or any deficit the agent needs to address.

• output: Represents what the agent produces, generates, or has available to use. This acts like its resource or commodity that can fulfill the needs.

• Consumption Process:

  • The agent decreases its “needs” by the amount represented by “output.”
  • The subtraction needs - output is meant to reflect this consumption where the “needs” are attempted to be satisfied by the agent’s “output.”
  • The max list 0 (needs - output) ensures that the “needs” cannot be less than zero, preventing any illogical state of having a negative need.

Modeling Example

1. Hunger Model:
   • Assume needs is how hungry the agent is, and output is how much food the agent has.
   • The agent consumes food (output) which reduces its hunger (needs).
2. Energy Model:
   • Consider needs as energy depletion, and output as energy production.
   • The agent’s energy levels (needs) are recharged by the energy produced (output).

This structure is common in agent-based models where agents have to manage resources to maintain their state within logical and acceptable limits. It helps create more realistic simulations where agents react based on their resource availability and requirements.

Max-of

Based on the provided NetLogo code, it seems you’d want some assistance with the search-resources procedure and possibly other parts to ensure the model functions as expected. Let’s break down the key issues and corrections needed:

Clarified Procedures

1. search-resources Procedure:

   to search-resources
     let p-around patches in-radius 1 ;; p-around is the patches around the turtle
     let target max-one-of p-around [resources] ;; find the patch with the max resources in p-around
     move-to target ;; move the turtle to the target patch with max resources
   end

   • Issue: The original logic for finding the max resource was incorrect. You need to use max-one-of to select the patch with the highest resources value within p-around.

2. enrich Procedure:

   to enrich
     set money money + [resources] of patch-here ;; collect resources and convert to money
     ask patch-here [
       set resources 0 ;; deplete the resources once collected
       if resources = 0 [set pcolor brown] ;; change color if resources depleted
     ]
   end

   • Adjusted Explanation: This procedure allows turtles to gain “money” and deplete resources at their current location.

3. Proper Workflow: Ensure that procedures are called in the correct order and that the code is correct, particularly:

   • create-turtles, create-world, and go should be setup properly.
   • Ensure go asks turtles to enrich after search-resources is called.

Here’s a corrected version of your model framework:

globals [wealth]
turtles-own [money]
patches-own [resources]

to setup
  clear-all
  create-turtles 10 [
    setxy random-xcor random-ycor
    set money 0
  ]
  create-world
  reset-ticks
end

to create-world
  ask patches [
    set resources random 10
    set pcolor scale-color blue resources 0 10
  ]
end

to go
  world-wealth
  ask turtles [
    search-resources
    enrich
  ]
  tick
end

to search-resources
  let p-around patches in-radius 1
  let target max-one-of p-around [resources] 
  if target != patch-here [
    move-to target
  ]
end

to enrich
  set money money + [resources] of patch-here
  ask patch-here [
    set resources 0
    set pcolor brown
  ]
end

to world-wealth
  set wealth sum [money] of turtles
end

Key Adjustments:

• Ensure max-one-of is used to select patches effectively.
• Properly handle the condition when resources become zero and change pcolor accordingly.
• Make sure all go cycle operations are executed in the intended sequence.

By implementing these corrections and understanding the intended flow of the procedures, your model should function as expected.

with

The with command in NetLogo is used to filter agent sets (like turtles or patches) based on a particular condition or set of conditions. When applied, it creates a new agent set consisting of only those agents that satisfy the specified condition.

Detailed Explanation of with

1. Basic Structure:

   • agent-set with [condition]

   • Here, agent-set can be any set of agents, such as turtles, patches, or a subset of either.

   • [condition] is a logical expression that evaluates to true or false for each agent in the set.

2. Examples:

   • Filter Patches by Color:

     let green-patches patches with [pcolor = green]

     This creates an agent set green-patches consisting of only those patches whose color is green.

   • Filter Turtles by a Property:

     let hungry-turtles turtles with [energy < 5]

     This creates an agent set hungry-turtles consisting of turtles whose energy level is less than 5.

3. Typical Use Cases:

   • Target Selection: Use with to select specific sets of agents that meet criteria, such as moving turtles toward resources (green patches in your case) or away from dangers.

   • Behavior Determination: Use it to decide actions based on agent states, such as moving only those turtles that are hungry towards food patches.

   • Data Collection: Use it to analyze or visualize subsets of agents, like counting or observing only those agents that meet a specific condition.

4. Combining Conditions:

   • You can combine multiple conditions using logical operators like and, or, and not. Here’s how you can do that:

     turtles with [color = red and energy > 5]
     patches with [pcolor = green or pcolor = blue]

5. Performance Considerations:

   • The with command evaluates each agent in the set against the condition, so it’s computationally intensive for large sets. Use it selectively to maintain performance efficiency.

In summary, with is a powerful filtering tool that allows you to work with subsets of agents in NetLogo based on specified conditions, enabling complex behaviors and interactions in simulations.

2. let green-patches close-patches with [pcolor = green]: This filters close-patches to only those that are green.

3. if any? green-patches: This checks if there are any green patches within the radius. If true, the turtle picks one of those green patches.

4. let target one-of green-patches: Selects one green patch at random from the set of green-patches.

5. move-to target: Moves the turtle to the selected green patch.

This should work under the assumption that there are green patches within reach of the turtles. Adjust the number of turtles, number of green patches, and their respective radii as needed.

Let vs own

Here’s a quick explanation of when to use let and when to use turtles-own:

• turtles-own: Use this when you need each turtle (or other agent) to have its own variable that persists and is accessible throughout the simulation. In your case, my-home is a persistent property of each turtle, and that’s why we used turtles-own.

• let: Use let to define a temporary, local variable within a procedure. This is useful when you need to calculate or store something temporarily within a procedure’s scope.

myself

In NetLogo, myself is a special keyword used within certain contexts to refer back to the calling agent from within a nested context, such as nested ask statements.

In the example I provided, of myself is used to resolve the scope of which turtle’s properties are being referred to. Here’s a more detailed explanation:

Usage Context

When you use ask inside another ask, you may need to clarify whose properties you’re referring to. This is where myself becomes useful.

Example

ask turtles [
  ; Turtle context
  ask my-home [
    ; Patch context
    set pcolor [color] of myself
    ; Use 'color' of the turtle (myself) that initiated this ask block
  ]
]

• In this example, when we’re inside the ask my-home block, we’re in the context of the patch. By using [color] of myself, we specify that we want the color of the turtle that initiated the ask to my-home.

General Use

• of myself: References attributes of the agent calling the ask block.
• Use Case: Helpful when you need to access agent-specific properties within nested contexts.

In your code, set pcolor [color] of myself is effectively telling the patch to take the color of the turtle that is making this command. Without of myself, NetLogo would become confused because within the patch context, it would default to looking for patch properties (and patches don’t have a color property of their own—they have pcolor).

This is a common technique in NetLogo to deal with scope resolution issues in nested commands, allowing you to access and use properties from different agent types within your model.

Your code snippet has a couple of issues in the make-home procedure. Let’s address them and correct your code:

1. You’re not defining and using my-home correctly. You should first define it as part of turtles-own.
2. The way you assign and use patches and pcolor needs adjustment.

Here’s a corrected version of your code:

turtles-own [ my-home ]  ;; Define a turtles-own variable to store the home patch

to setup
  clear-all
  create-turtles 10 [
    setxy random-xcor random-ycor
    set my-home patch-here  ;; Store the current patch as the turtle's home
    set color blue  ;; Optional: set a color to differentiate turtles
  ]
  reset-ticks
end

to go
  ask turtles [
    make-home
    ;; Add any other behavior if required
  ]
  tick
end

to make-home
  ;; Optionally, you can color the home patch
  ask my-home [ set pcolor [color] of myself ]  ;; Set the patch color to the turtle's color
end

Changes and Corrections:

• I added turtles-own [ my-home ] to define my-home as a property of each turtle.
• The setup procedure now initializes each turtle’s my-home to the patch where it starts.
• Within make-home, the turtle colors its home patch using set pcolor [color] of myself. This sets the color of the home patch to match the turtle’s color.
• Be sure to call reset-ticks at the end of your setup, as this is standard practice to manage the simulation time in NetLogo.

This code assumes you’re interested in marking the turtle’s home visually by changing the patch color. Adjust the setup and behaviors depending on your goal for the simulation, whether it’s marking homes or other actions tied to visiting the home patch.

Map

In NetLogo, the map function is used to apply a given reporter (or function) to each item in a list and report a new list containing the results. It is a powerful tool for manipulating lists, as it allows you to perform operations on each element without the need for explicit loops.

Syntax

map <reporter> <list1> <list2> ... <listN>

Parameters

• <reporter>: This is the function or operation you want to apply to each item of the list(s). It is typically an anonymous function enclosed in brackets. Inside this anonymous function, you can use ?, ?1, ?2, etc., as placeholders for the elements of the lists.

• <list1>, <list2>, …, <listN>: These are the lists of items you wish to process. The number of lists provided must match the number of arguments the reporter can handle.

Usage Examples

1. Single List:

   let numbers [1 2 3 4 5]
   let squares map [? * ?] numbers
   ; Result: squares will be [1 4 9 16 25]

2. Multiple Lists: In cases where you use more than one list, the function can take multiple placeholder arguments:

   let list1 [1 2 3]
   let list2 [4 5 6]
   let sums map [?1 + ?2] list1 list2
   ; Result: sums will be [5 7 9]

Explanation in Context

In the example provided in response to your initial question:

map [exp ? * beta1] income

• exp ? * beta1 is the operation applied to each element. Here, ? acts as a placeholder for each element in the income list.
• The result is a list where each element from income has been transformed using the expression exp ? * beta1.

The map function is extremely useful for list processing, particularly when you want to perform transformations or computations over each item in a list or in parallel across multiple lists.

Random-float

To generate a random floating-point number between 0.15 and 0.25 in NetLogo, you can use the following code snippet:

let randomValue 0.15 + random-float (0.25 - 0.15)
show randomValue

Here’s what each part does:

• random-float (0.25 - 0.15): Generates a random floating-point number between 0 and 0.10 (since 0.25 - 0.15 equals 0.10).
• 0.15 +: Shifts the range from [0, 0.10] to [0.15, 0.25].

This will give you a random floating-point number between 0.15 and 0.25, and show randomValue will display this value in the NetLogo Command Center.