# Diminished F’NF

Model was written in NetLogo 6.3.0
•
Viewed 172 times
•
Downloaded 5 times
•
Run 0 times

Do you have questions or comments about this model? Ask them here! (You'll first need to log in.)

Info tab cannot be displayed because of an encoding error

## Comments and Questions

Please start the discussion about this model!
(You'll first need to log in.)

Click to Run Model

;; _________________________________________________________________________________________________________________ ;; ;; ---------------------- Diminished-F'NF ---------------------------------------------------- Diminished-F'NF ;; Diminished-F'NF ------------------------------------------------ Diminished-F'NF -------------------------- ;; _________________________________________________________________________________________________________________ breed [flibs flib] ;; FLiBs (finite living blobs) are the agents of the model: they are structured as ;; one state finite automata. A binary input signal coming from the previous state of ;; the environment (1 if the bar was crowded, 0 if not) causes it to generates binary ;; signals as a decision to go or not to go. Four strategies are possible. flibs-own [chromosome ;; every flib owns a chromosome, that is a string codifying one of the four schema state ;; the current inner state of the flib: it is always 0 choice ;; choice/prevision expressed by the flib regarding to go or not to go to the bar fitness ;; a measure of flibs' prevision ability to forestall if the bar will be crowded or not ] globals [tot_attend ;; total of attendances during one season to "El Farol" bar (i.e. 100 cycles tournement) sigma_attend ;; an accumulator for the previous variable crowded ;; a switch recording the state of the bar: 1 if crowded, 0 if not lorenz-points ;; list of the Lorenz curve ordinates gini-index-reserve;; counter functional to the calculation of the Gini index sigma-gini ;; an accumulator for the previous variable best ;; the best flibs fitness value worst ;; the worst flibs fitness value ] ;; ---------- SETUP PROCEDURES ---------------------------------------------------------------------------------- ;; ------------------------------------------------------------------------------------------------------------------ to setup ;; initializing the model clear-all ;; create the bar area (yellow) and an elsewhere (blue) ask patches [set pcolor blue - 3] ask patches with [abs pxcor < 10 and abs pycor < 7] [set pcolor yellow] ask patches with [pxcor = 9 and pycor = 7] [set plabel ["El Farol Bar"]] ask patches with [pxcor = 21 and pycor = -21] [set plabel ["Elsewhere"]] ;; create the flibs and give them a random chromosome ask n-of num-flibs patches [sprout-flibs 1 [ set shape "flib" set color white set size 2 set chromosome one-of chrom-couple ] ] reset-ticks end ;; ---------- RUNTIME PROCEDURES -------------------------------------------------------------------------------- ;; ------------------------------------------------------------------------------------------------------------------ to go set tot_attend 0 ask flibs [set fitness 0 set state 0] repeat 100 [el-farol] ;; a 100 cycles tournament could be seen as a season to "El Farol" bar (i.e. 100 evenings) if sum [fitness] of flibs = 0 [ ;; no fitness no progress show "fitness null for every flibs" stop ] analyse ;; some relevant "El Farol" seasonal results are picked and processed ask flibs [move] ;; the world displays a snapshot of the bar after the last evening of the season if best != worst[ ;; after every season, one cloning event can occur: the process is partly selective ask one-of flibs with [fitness = worst] [set chromosome one-of chrom-couple ] ] tick end ;; ONE SEASON TO EL FAROL BAR ;; ------------------------------------------------------------------------------------------------------------------- to el-farol let attendance 0 ;; the variable records the fraction of agents attending the bar during one evening flibs-behaviour ;; bar attendance is the sum of every flibs' choices set attendance sum [choice] of flibs / num-flibs ;; comparing the attendance and the threshold value, the (over)crowded state of the bar is determined if attendance >= threshold [set crowded 1 ;; if the bar is crowded, reward the flibs that are elsewhere ask flibs with [choice = 0] [set fitness fitness + EW_reward] ] if attendance < threshold [set crowded 0 ;; if the bar is not crowded, reward the flibs that are attending ask flibs with [choice = 1] [set fitness fitness + 1] ] set tot_attend tot_attend + attendance ;; the results of every evening of a season is added up end to flibs-behaviour ask flibs [ ;; each flib processes its choice (to go or not to go) set choice read-from-string item (2 * crowded) chromosome ] end to analyse set best max [fitness] of flibs set worst min [fitness] of flibs ask flibs [set color scale-color red fitness 100 0] set sigma_attend sigma_attend + tot_attend / 100 update-lorenz-and-gini end to update-lorenz-and-gini ;; borrowed from Wilensky's model "Wealth Distribution" let sorted-comfort sort [fitness] of flibs let total-comfort sum sorted-comfort let comfort-sum-so-far 0 let index 0 set gini-index-reserve 0 set lorenz-points [] repeat num-flibs [ set comfort-sum-so-far (comfort-sum-so-far + item index sorted-comfort) set lorenz-points lput ((comfort-sum-so-far / total-comfort) * 100) lorenz-points set index (index + 1) set gini-index-reserve gini-index-reserve + (index / num-flibs) - (comfort-sum-so-far / total-comfort) ] set sigma-gini sigma-gini + ((gini-index-reserve / num-flibs) * 2) end to move ifelse choice = 0 [move-to one-of patches with [pcolor = blue - 3]] [move-to one-of patches with [pcolor = yellow]] end ; Copyright 2023 Cosimo Leuci. ; See Info tab for full copyright and license.

There is only one version of this model, created about 1 year ago by Cosimo Leuci.

## Attached files

File | Type | Description | Last updated | |
---|---|---|---|---|

Diminished F’NF.png | preview | preview_image | about 1 year ago, by Cosimo Leuci | Download |

**Parent:** Flibs'NFarol

This model does not have any descendants.