extensions [ table ] ;; dictionary extension used for the bar plot turtles-own [ opinion-value tolerance banned? age satisfied? ] articles-own [ article-bias ] globals [ the-article sample-ext-low sample-ext-high sample-mainstream sample-mainstream1 ;; sample editors for a plot initial-art-bias sum-N sum-squares running-mean running-std snr] breed [ mainstreams mainstream ] ;; create breeds for two type of editors breed [ extremists extremist ] breed [ articles article ] ;; breed for the articl to setup clear-all set-default-shape turtles "circle" set-default-shape mainstreams "circle" ;; to distinguish breeds by shape set-default-shape extremists "circle 2" set-default-shape articles "square" let n-ext ratio-of-extremists * number-of-editors ;; calculate exact number of extremists ifelse n-ext = 0 [ set-editors number-of-editors ] [set-mainstreams number-of-editors n-ext ;; create moderate editors [0.45-0.55] set-extremists n-ext ] ;; create editors with strong opinion [0-01, 0.9-1] ask turtles [ set banned? false set satisfied? false set age 0 set color green set tolerance sensitivity - inhomogeneity * abs(opinion-value - 0.5) ;; initiate tolerance, tolererance to article is the same setxy random-float 50 opinion-value * 100 ;; visualize turtles from bottom to up according to their opinion ] create-articles 1 [ ;; create an abstraction of article set size 5 set color green set article-bias random-float 1 ;; initiate article bias / opinion value [0-1] if article-bias >= 0.9 or article-bias <= 0.1 [ set shape "square 2"] ;; make the article outlined square if it reaches extreme bias level setxy 65 article-bias * 100 ;; visualize article bias set satisfied? true ] set the-article one-of articles ;; assign article to the variable for easy interaction set initial-art-bias [article-bias] of the-article set sum-N 0 set sum-squares 0 reset-ticks end to go ;; if precision min-opinion 2 = precision max-opinion 2 [ output-write ticks stop ] ;; if consensus is reached, stop the simulation if all? turtles [ satisfied? ] [ output-write ticks stop ] ask turtles with [breed != articles] [ interact ;; either edit the article or talk ifelse opinion-value >= 0.9 or opinion-value <= 0.1 [set breed extremists ] [set breed mainstreams] ;; update breed based on opinion-value ;;ifelse opinion-value <= 1 set ycor opinion-value * 100 ;;[set ycor max-pycor] ;; update visualization, prevent going out of screen set tolerance sensitivity - inhomogeneity * abs(opinion-value - 0.5) ;; update tolerance of editors if age > 3 [ set color green ] set age age + 1 ;;set size age * 0.05 ] ask articles [ ;; update article's position set ycor article-bias * 100 ifelse article-bias >= 0.9 or article-bias <= 0.1 [ set shape "square 2"] [set shape "square"] ;; update article's shape ] update-art-stat ;; call to procedure for article-bias plot ask turtles with [ breed != articles] [ ifelse abs ( opinion-value - [article-bias] of the-article ) < tolerance [set satisfied? True] [set satisfied? False] ] tick end ;; procedure to create moderate editors (opinion between 0.45 and 0.55) to set-mainstreams [n-editors n-ext] create-mainstreams number-of-editors - n-ext [ set opinion-value random-float 1.001 * (0.55 - 0.45) + 0.45 ] set sample-mainstream one-of mainstreams set sample-mainstream1 one-of mainstreams end ;; procedure to create two sets of "extremists" editors (opinion between 0 and 0.1 or 0.9 and 1) to set-extremists [n-ext] create-extremists n-ext [ set opinion-value random-float 1.001 * (0.1 - 0) + 0 set sample-ext-low one-of extremists] ;; change half of extremists to other side of opinion spectrum ask n-of (n-ext / 2) extremists [ set opinion-value random-float 1.001 * (1 - 0.9) + 0.9 ] set sample-ext-high one-of extremists with [opinion-value > 0.9] end ;; procedure to create pool of editors without extremists specified to set-editors [n-editors] create-turtles n-editors [ set opinion-value random-float 1.001 ] end ;; the main interaction procedure, talk or edit happen with equal probability to interact ifelse random-float 1.001 < 0.5 ;; with equal probability [ talk ] ;; let editor talk or edit article [ ifelse not banned? ;; if editor is not banned she can edit the article [ edit-article ] [ set banned? false ] ;; overwise just un-ban her for next step ] end ;; talk between editors to talk let interlocutor one-of other turtles ;; choose one of editors to talk let my-opinion opinion-value ;; local variables for comodity let its-opinion [opinion-value] of interlocutor ;; if difference between opinions of editors is not too large to talk.. if abs (its-opinion - my-opinion) < min list tolerance [tolerance] of interlocutor ;; compare with smaller tolerance between two editors [ ;; ... change their respective opinions to average of two set opinion-value ( my-opinion + its-opinion ) / 2 ask interlocutor [ set opinion-value ( my-opinion + its-opinion ) / 2 ] ] ;; otherwise leave opinions as they are end ;; editing of the article to edit-article let my-opinion opinion-value let article-opinion [article-bias] of the-article ;; local variables for comodity ifelse abs( my-opinion - article-opinion ) < tolerance [ set opinion-value my-opinion + (article-opinion - my-opinion ) * convergence ] ;; editor is satisfied and changes her opinion [ ask the-article [set article-bias article-opinion + (my-opinion - article-opinion) * convergence ] ;; editor is not satisfied and edit the article if probability-banning != "no banning" ;; if banning is planned by setup... ;;abs( opinion-value - [article-bias] of article ) > tolerance ;; ...and editor is not satisfied after editing [ ban ] ;; launch ban procedure ] end to ban (ifelse probability-banning = "half cases" [ if random-float 1.001 < 0.5 [set banned? true] ] ;; procedure for 0.5 probability of banning probability-banning = "based on dissatisfaction" ;; procedure for variable probability [ let p ( abs( opinion-value - [article-bias] of the-article ) - tolerance ) ^ 2 ;; calculate probability based on tolerance if random-float 1.001 < p [ set banned? true ] if banned? [ set color red ] ] probability-banning = "based on dissatisfaction + offended" ;; case with banning [ let p ( abs( opinion-value - [article-bias] of the-article ) - tolerance ) ^ 2 ;; calculate probability based on tolerance if random-float 1.001 < p [ set banned? true ] if banned? [ set color red if random-float 1.001 < 0.5 [ hatch 1 [set opinion-value random-float 1 set age 0 set color violet] die ] ;; in half cases add a new editor, set her color to violet and leave the pool ] ] [] ) end ;; procedure to update running statistics about the article to update-art-stat set sum-N sum-N + [article-bias] of the-article set sum-squares sum-squares + [article-bias] of the-article ^ 2 set running-mean sum-N / (ticks + 2) set running-std sqrt ( ( sum-squares - sum-N ^ 2 / (ticks + 2) ) / (ticks + 1) ) set snr running-mean / running-std end ;; custom bar plot to update-editors-plot set-current-plot "Editors by type" clear-plot let editor_types table:make ;; dictionary, add all values table:put editor_types "mainstreams" count mainstreams table:put editor_types "high extremists" count extremists with [ opinion-value >= 0.9 ] table:put editor_types "low extremists" count extremists with [ opinion-value <= 0.1 ] table:put editor_types "banned" count turtles with [ breed != articles and banned? ] table:put editor_types "new" count turtles with [ age < ticks ] let types table:keys editor_types set-plot-x-range 0 5 let step 0.05 let color-list [65 63 67 15 115] (foreach types range 5 [ [ t c ] -> let y table:get editor_types t let col item c color-list create-temporary-plot-pen t set-plot-pen-mode 1 ;; bar set-plot-pen-color col foreach (range 0 y step) [ _y -> plotxy c _y ] set-plot-pen-color black plotxy c y set-plot-pen-color col ]) end ;; reporters to-report max-opinion report max [opinion-value] of turtles with [breed != articles] end to-report min-opinion report min [opinion-value] of turtles with [breed != articles] end to-report init-art-bias report initial-art-bias end to-report rep-age report [age] of turtles with [ breed != articles ] end to-report statistics report ( list running-mean running-std snr ) end @#$#@#$#@ GRAPHICS-WINDOW 489 19 902 533 -1 -1 5.0 1 10 1 1 1 0 0 0 1 0 80 0 100 1 1 1 ticks 30.0 SLIDER 15 49 165 82 number-of-editors number-of-editors 10 500 100.0 10 1 NIL HORIZONTAL BUTTON 13 253 76 286 NIL setup NIL 1 T OBSERVER NIL NIL NIL NIL 1 BUTTON 92 254 155 287 NIL go T 1 T OBSERVER NIL NIL NIL NIL 1 SLIDER 186 49 348 82 ratio-of-extremists ratio-of-extremists 0 1 0.3 0.1 1 NIL HORIZONTAL CHOOSER 14 101 106 146 inhomogeneity inhomogeneity 0.5 0.33 0.2 0.1 0 CHOOSER 126 102 218 147 sensitivity sensitivity 0.25 0.3 0.35 0.4 0 SLIDER 237 102 349 135 convergence convergence 0.1 0.9 0.5 0.1 1 NIL HORIZONTAL CHOOSER 14 170 262 215 probability-banning probability-banning "no banning" "half cases" "based on dissatisfaction" "based on dissatisfaction + offended" 1 MONITOR 14 363 123 408 bias of the article [article-bias] of the-article 4 1 11 MONITOR 138 363 218 408 max opinion max-opinion 2 1 11 MONITOR 233 363 308 408 min opinion min-opinion 2 1 11 OUTPUT 329 363 407 401 11 TEXTBOX 331 410 411 428 Relaxation time 11 0.0 1 PLOT 12 615 434 765 Trajectory of sample editors NIL NIL 0.0 10.0 0.0 100.0 true true "" "" PENS "mainstream" 1.0 0 -14439633 true "" "if is-turtle? sample-mainstream \n[ plot [ycor] of sample-mainstream ]" "high extremist" 1.0 0 -15575016 true "" "if is-turtle? sample-ext-high \n[ plot [ycor] of sample-ext-high ]\n" "low extremist" 1.0 0 -8330359 true "" "if is-turtle? sample-ext-low\n[ plot [ycor] of sample-ext-low ]" "average opinion" 1.0 0 -955883 true "" "plot mean [ ycor ] of turtles with [ breed != articles ] " TEXTBOX 442 233 480 252 Opinion 11 0.0 1 TEXTBOX 469 519 619 537 0 11 0.0 1 TEXTBOX 468 25 618 43 1 11 0.0 1 TEXTBOX 18 11 168 29 Model settings 14 0.0 1 TEXTBOX 17 325 167 343 Performance 14 0.0 1 PLOT 466 617 692 767 Distribution of editors' opinions NIL NIL 0.0 1.0 0.0 10.0 true false "" "" PENS "default" 0.1 1 -13345367 true "" ";;set-histogram-num-bars 10\nhistogram [opinion-value] of turtles" TEXTBOX 503 751 678 779 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 11 0.0 1 PLOT 722 617 1052 767 Running mean and std of the article NIL NIL 0.0 10.0 0.0 1.0 true true "" "" PENS "running mean" 1.0 0 -14070903 true "" "plot first statistics" "running std" 1.0 0 -13791810 true "" "plot item 1 statistics" "signal-to-noise" 1.0 0 -955883 true "" "ifelse ticks > 1 [ plot first statistics / item 1 statistics ] [plot 0]" PLOT 15 443 346 581 Editors by type NIL NIL 0.0 10.0 0.0 10.0 true true "" "update-editors-plot" PENS @#$#@#$#@ ## WHAT IS IT? This model is NetLogo implementation of the model described in "Modelling the Wikipedia to Understand the Dynamics of Long Disputes and Biased Articles" by Csilla Rudas and János Török, it was developed as a study project for the course "Computational Modelling for Complex Systems" at University of Pisa. The model simulates the editing process of a Wikipedia article, it is a specific case of the opinion dynamics model, bound-confidence interactions, described by Deffuant et al. [2000]. It shows how editors communicate and influence each other and the article, changing its tone. The article final tone depends critically on the ratio of editors with extreme opinions, but also on the tolerance of the editors towards each other and on on adopted by community banning strategies. ## HOW IT WORKS The editors, represented by green circles, are "born" with random opinions between 0 and 1. Editors can communicate between each other, if their opinions are not too far from each other (tolerance level), or can edit the article, represented by green square, changing its bias or read the article and change their opinion. Editing process reaches consensus when all editors are satisfied with the tone of the article (its opinion is in their tolerance range). ## HOW TO USE IT Click the SETUP button to set up the editors' pool (green circles) and an article (green square on the right-hand side). The position of editors and the article along the y-axis of the world reflects the value of their opinion/bias. Turtles change their shape to the outlined circle when their opinion is less than 0.1 or more than 0.9. Click the GO button to start simulation The NUMBER-OF-EDITORS slider controls the number of editors in the editor pool of the article (Note: Changes in the NUMBER-OF-EDITORS slider do not take effect until the next SETUP.) The RATIO-OF-EXTREMISTS slider controls the proportion of editors with extreme opinions (above 0.9 and below 0.1). The rest of editors are created as mainstreams, with opinions between 0.45 and 0.55. When the value is 0 a pool of NUMBER-OF-EDITORS editors created with opinions randomly distributed between 0 and 1. The INHOMOGENEITY chooser lets you set up the proportion of difference between editors' opinions that are taken to calculate their tolerance. The SENSITIVITY chooser controls the maximum level of tolerance in the world. The CONVERGENCE slider simulates how difficult it is to change the tone of the article (it's inverse of the article's length), lower values denote more difficult change. The PROBABILITY-BANNING chooser controls the banning strategy adopted in the editors' pool. The second and third choices prevent editors from editing the article for one time step with different probabilities. The "BASED ON DISSATISFACTION + OFFENDED" strategy also let editors leave the pool in half cases. After an editor leaves, she is replaced with the new one, the opinion of the new editor is chosen randomly. Behaviour space has a setup for two simulations described in the article. ## THINGS TO NOTICE The model explores how different conditions affect RELAXATION TIME for the article's editing (shown in the output window at the end of simulation). Watch how the opinion of the editors changes during the interaction and editing process. Editors and the article become outlined, when their opinion is in "extreme" range. You can see the clusters of editors move in the world window or follow sample editors on the plot TRAJECTORY OF SAMPLE EDITORS, notice that when the editor leaves the pool his trajectory is interrupted. When editors are BANNED, their color turns red for 2 time steps and newly arrived editors have violet color for the same amount of time. You can monitor the quantity of editors of each type with EDITORS BY TYPE plot, notice how large is the pool of new editors at the end of the editing cycle with the banning strategy "BASED ON DISSATISFACTION + OFFENDED". Follow how ARTICLE-BIAS changes over time with the monitor BIAS OF ARTICLE, notice on the RUNNING MU AND STD OF THE ARTICLE plot that running mean and standard deviation is fairly regular, so signal-to-noise ratio is high. ## THINGS TO TRY Set the INHOMOGENEITY to high value, SENSITIVITY and CONVERGENCE to low values and "no banning" PROBABILITY-BANNING. Observe how the editing process runs for a very long time and how limited the communication is. Change the banning strategy and see what happens. Fix all parameters to average values and only change RATIO-OF-EXTREMISTS value with the slider, watch the behavior of editors and the final bias value of the article. ## EXTENDING THE MODEL Try to build networks inside the editor pool. ## NETLOGO FEATURES The article is designed as a separate breed, so are the extremists and mainstream editors. All interactions are chosen randomly. ## CREDITS AND REFERENCES This model is based on the paper: Rudas, C., & Török, J. (2018). Modeling the Wikipedia to Understand the Dynamics of Long Disputes and Biased Articles. Historical Social Research, 43(1), 72-88. https://doi.org/10.12759/hsr.43.2018.1.72-88 Guillaume Deffuanft at al, 2000. Mixing beliefs among interacting agents. Advances in Complex Systems 3: 87-98 The bar plot code is adapted from the example of Nicolas Payette at https://stackoverflow.com/questions/49122819/how-do-you-generate-a-histogram-in-netlogo ## HOW TO CITE If you mention this model or the NetLogo software in a publication, we ask that you include the citations below. For the model itself: Mikhaylova, D., Milazzo, P. (2021). NetLogo model for Wikipedia editing process. Department of Computer Science, University of Pisa, Italy. Please cite the NetLogo software as: Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. ## COPYRIGHT AND LICENSE Copyright 2021 Daria Mikhaylova, Paolo Milazzo ![CC BY-NC-SA 3.0](http://ccl.northwestern.edu/images/creativecommons/byncsa.png) This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. 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