;;;;;;;;;;;;;;; ;;;; SIMPE ;;;; ;;;;;;;;;;;;;;; extensions[csv nw table rnd palette profiler] ; stats globals [ information d data-output-interval data-output-interval1 filename filename1 rewiring-probability edges ; record edge list attribs ; records turtle attribs params params1 onliners n-agents ] undirected-link-breed [offline_friendships offline_friendship] undirected-link-breed [online_friendships online_friendship] links-own[weight] patches-own [pvalue] turtles-own [ ;;attributes my-information ;; represents the information received by the agent discussing national identity nat_id ;; indicates the agent's national identity attitude and consequently group identity grievances ;; indicates the agent's perceptions of the secessionist conflict uncertainty ;; indicates the strength of an agent's convictions about national id attitude engagement ;; indicates the engagement levels or socialisation patterns of a given agent protest_state ;; indicates the current state of the agent (Silent - 0, Protesting - 1) costs ;; indicate the costs associated with participating in protest for an agent ;; data collection changed? ;; indicates whether the agent has changed their attitude already homophily? ;; indicates whether an agent is in a homophilous group or mixed beliefs group in-group ;; Proportion of in-group ties, attitude-supportive neighbors per agent net_ties ;; Proportion of inter-group ties, attitude-discrepant neighbors per agent pro_clust? ;; Proportion of agents in protester-only social network groups sil_clust? ;; Proportion of agents in silent-only social network groups ] to setup ca set d 0.05 set data-output-interval [100 200 300 400] ;; when to save agent data set data-output-interval1 [0 100 200 300 400] ;; when to save model outputs setup-information ;; Importing different nat.id distributions (normal // CEO 2011 Dataset) ifelse Distribution? = "normdis" [ read-agents-norm ] [ read-agents-ceo ] set n-agents count turtles set-characteristics_update ifelse Networks = "random" [ ;; importing random networks (small-world and BA scale-free) import-random-offline import-random-online ] [ ;; rendering the homophilous one since nat.id may change upon setup (Distribution?) set onliners n-of round (n-agents * 0.8) turtles make-initial-links-homophily setup-online_turtles-homophily setup-links-homophily ] ;; Data collection purposes if Data-save? [ let Net ifelse-value Networks = "homophilous" [1][0] let SM ifelse-value Social-Media = "filter-bubble_ON" [1][0] let CEO ifelse-value Distribution? = "CEO2011" [1][0] set filename (word "Net-" Net "_SM-" SM "_" probs-rewiring "_" protesting-opportunity "_" initial-protest-size "_" CEO ".csv") set params (word behaviorspace-run-number "," Net "," SM "," probs-rewiring "," protesting-opportunity "," initial-protest-size) ;; creating different data files for each simulation combination - makes data outputs more manageable if not file-exists? word "attribs-" filename [ file-open word "attribs-" filename file-print "run,Networks,Social-Media,probs.rewiring,protesting.opp,init.protest.size,t,agent,nat_id,protest_state" file-close ] ;; Saving the initial nat_id distribution file-open word "attribs-" filename ask turtles [file-print (word params "," 0 "," who "," nat_id "," protest_state)] file-close ;; creating model output file if not file-exists? word "output-" filename [ file-open word "output-" filename file-print "run,Networks,Social-Media,probs.rewiring,protesting.opp,init.protest.size,t,Nat_ID_variance,Nat_ID_weighted,Nat_ID_weighted_min,Nat_ID_weighted_max,Nat_ID_weighted_med,Nat_ID_weighted_med1,Protester,Homophilous,network_ties,protest_clustering,silent_clustering" file-close ] ;;; when running sensitivity analyses -- comment out previous section and uncomment this section ; set params1 (word behaviorspace-run-number "," Net "," SM "," s "," h) ; set filename1 (word "Net-" Net "_SM-" SM "_" s "_" h "_CEO-" CEO ".csv") ; ;;;creating agent attribs file ; if not file-exists? word "attribs-" filename1 [ ; file-open word "attribs-" filename1 ; file-print "run,Networks,Social-Media,s,h,t,agent,nat_id,protest_state" ; file-close ; ] ; ;; Saving the initial nat_id distribution ; file-open word "attribs-" filename1 ; ask turtles [file-print (word params1 "," 0 "," who "," nat_id "," protest_state)] ; file-close ; ;;; creating model output file ; if not file-exists? word "output-" filename1 [ ; file-open word "output-" filename1 ; file-print "run,Networks,Social-Media,s,h,t,Nat_ID_variance,Nat_ID_weighted,Nat_ID_weighted_min,Nat_ID_weighted_max,Nat_ID_weighted_med,Nat_ID_weighted_med1,Protester,Homophilous,network_ties,protest_clustering,silent_clustering" ; file-close ; ] ] reset-ticks end to go ;;ensuring that the data collectors are clear at the start of the simulation ask turtles [ set in-group 0 set changed? false set homophily? false set pro_clust? false set sil_clust? false set net_ties 0 ] ;; Processes information-receival if random-float 1 < probs-rewiring [rewire] homophily_check share update-attitude protest detect-clusters if Data-save? [ ;; data collection - Every 100 steps, save data if member? ticks data-output-interval [ file-open word "attribs-" filename ask turtles [file-print (word params "," ticks "," who "," nat_id "," protest_state)] file-close ] if member? ticks data-output-interval1 [ file-open word "output-" filename file-print ( word params "," ticks "," Nat_ID_variance "," Nat_ID_weighted "," Nat_ID_weighted_min "," Nat_ID_weighted_max "," Nat_ID_weighted_med "," Nat_ID_weighted_med1 "," Protester "," Homophilous "," network_ties "," protest_clustering "," silent_clustering) file-close ] ;; sensitivity analyses data saving procedure -- comment out previous and uncomment this section ; if member? ticks data-output-interval [ ; file-open word "attribs-" filename1 ; ask turtles [file-print (word params1 "," ticks "," who "," nat_id "," protest_state)] ; file-close ; ] ; ; if member? ticks data-output-interval1 [ ; file-open word "output-" filename1 ; file-print ( word params1 "," ticks "," Nat_ID_variance "," Nat_ID_weighted "," Nat_ID_weighted_min "," Nat_ID_weighted_max "," Nat_ID_weighted_med "," Nat_ID_weighted_med1 "," Protester "," Homophilous "," network_ties "," protest_clustering "," silent_clustering) ; file-close ; ] ] tick end ;;;;;;;;;;;;;;;;;; ;;;; Networks ;;;; ;;;;;;;;;;;;;;;;;; to import-random-offline foreach csv:from-file "offline_edges.csv" [edge -> ask turtle item 0 edge [create-offline_friendship-with turtle item 1 edge [set weight item 2 edge]] ] end to import-random-online foreach csv:from-file "online_edges.csv" [edge -> ask turtle item 0 edge [create-online_friendship-with turtle item 1 edge [set weight item 2 edge]] ] end to plot-ONfriends set-current-plot "Degree distribution online friendships (log-log)" let linked turtles with [count my-online_friendships > 0] let max-degree max [count my-online_friendships] of linked plot-pen-reset let degree 1 while [degree <= max-degree] [ let matches turtles with [count my-online_friendships = degree] if any? matches [ plotxy log degree 10 log (count matches) 10] set degree degree + 1 ] set-current-plot "Degree distribution online friendships" set max-degree max [count my-online_friendships] of linked plot-pen-reset set-plot-x-range min [count my-online_friendships] of linked (max-degree + 1.1) histogram [count my-online_friendships] of linked end to plot-OFFfriends set-current-plot "Degree distribution offline friendships (log-log)" let max-degree max [count my-offline_friendships] of turtles plot-pen-reset let degree 1 while [degree <= max-degree] [ let matches turtles with [ count my-offline_friendships = degree] if any? matches [ plotxy log degree 10 log (count matches) 10 ] set degree degree + 1 ] set-current-plot "Degree distribution offline friendships" set max-degree max [count my-offline_friendships] of turtles plot-pen-reset set-plot-x-range 1 (max-degree + 1.1) ; histogram [count my-offline_friendships] of turtles end to save-edgelist if not file-exists? "online_edges.csv" [ file-open "online_edges.csv" file-print "t,end1,end2,weight" file-close ] file-open "online_edges.csv" ask online_friendships [file-print (word ticks "," [who] of end1 "," [who] of end2 "," weight)] file-close if not file-exists? "offline_edges.csv" [ file-open "offline_edges.csv" file-print "t,end1,end2,weight" file-close ] file-open "offline_edges.csv" ask offline_friendships [file-print (word ticks "," [who] of end1 "," [who] of end2 "," weight)] file-close end to make-initial-links-homophily repeat 60 [ ;; this is the seed to create hubs ask one-of onliners [ let nid nat_id let online_option onliners with [abs(nat_id - nid) < s] ifelse any? online_option [ create-online_friendship-with one-of other online_option [set color grey set weight random-float 1] ] [ create-online_friendship-with one-of other onliners [set color grey set weight random-float 1] ] ] ] end to setup-online_turtles-homophily let linked onliners with [count my-online_friendships > 0 ] ask onliners [ let option linked with [abs(nat_id - [nat_id] of myself) < 0.5] ;; make a param to set the network homphily setup repeat 23 [ ifelse random 100 >= 10 [ ifelse any? option ;; based on homophily [create-online_friendship-with one-of other option [set color white set weight random-float 1]] [create-online_friendship-with one-of other linked [set color white set weight random-float 1]] ] [create-online_friendship-with one-of other onliners [set color white set weight random-float 1]] set linked (turtle-set linked self) ] ] let leftovers onliners with [count my-online_friendships < 27] while [any? leftovers][ ask leftovers [ let howmany 27 - count my-online_friendships if howmany > 0 [ ;; we need this because some leftovers might have received extra links in the meantime let option linked with [abs(nat_id - [nat_id] of myself) < 0.5] ;ifelse random 100 >= 10 ; [ ifelse any? option ;; based on homophily [create-online_friendships-with up-to-n-of howmany other option [set color white set weight random-float 1]] [create-online_friendships-with up-to-n-of howmany other linked [set color white set weight random-float 1]] ] set leftovers other leftovers ] ] end to setup-links-homophily let average-node-degree 11.5 let num-links (average-node-degree * n-agents) while [count offline_friendships < num-links] [ ask one-of turtles [ create-offline_friendship-with one-of other turtles with [abs(nat_id - [nat_id] of myself) < s] [set color grey set weight random-float 1] ] ] end to save-edgelist-H1 if not file-exists? "onlineH_edges.csv" [ file-open "onlineH_edges.csv" file-print "t,end1,end2,weight" file-close ] file-open "onlineH_edges.csv" ask online_friendships [file-print (word ticks "," [who] of end1 "," [who] of end2 "," weight)] file-close if not file-exists? "offlineH_edges.csv" [ file-open "offlineH_edges.csv" file-print "t,end1,end2,weight" file-close ] file-open "offlineH_edges.csv" ask offline_friendships [file-print (word ticks "," [who] of end1 "," [who] of end2 "," weight)] file-close end to rewire ask n-of (0.1 * n-agents) turtles [ let caller self ;; we remove the link with one low weight friend let re-tie one-of link-neighbors with [abs(nat_id - [nat_id] of myself) > s or [weight] of link-with myself < s] ifelse re-tie != nobody [ask link-with re-tie [die]][ask one-of my-links [die]] ;; we connect to one FOA (F with high weight) let closest-on-friend nobody let closest-off-friend nobody let new-friend nobody if any? my-online_friendships [ask max-one-of my-online_friendships [weight][set closest-on-friend other-end]] ask max-one-of my-offline_friendships [weight][set closest-off-friend other-end] ifelse closest-on-friend != nobody and random 100 < 70 [ ask closest-on-friend [set new-friend one-of online_friendship-neighbors with [self != caller and not online_friendship-neighbor? caller]] if new-friend != nobody [create-online_friendship-with new-friend] ][ ask closest-off-friend [set new-friend one-of offline_friendship-neighbors with [self != caller and not offline_friendship-neighbor? caller]] if new-friend != nobody [create-offline_friendship-with new-friend] ] let val 1 let total-val count (link-neighbors) + 3 set size 0.1 + 5 * sqrt (val / total-val) ] end ;;;;;;;;;;;;;;;;;;;;;;;;; ;;;; Network Measures;;;; ;;;;;;;;;;;;;;;;;;;;;;;;; ;Average Degree Online to-report report-av-degree-online let av-degree sum([count my-online_friendships] of turtles with [count my-online_friendships > 0]) / count turtles with [count my-online_friendships > 0] report av-degree end ;Average Degree Offline to-report report-av-degree-offline let av-degree sum([count my-offline_friendships] of turtles) / n-agents report av-degree end ;Density Online to-report report-network-density-online let density (count online_friendships) / (( n-agents * ( n-agents - 1)) / 2 );100 agents report density end ;Density Offline to-report report-network-density-offline let density (count offline_friendships) / ( n-agents * ( n-agents - 1)) report density end ;Clustering Online https://ccl.northwestern.edu/netlogo/docs/nw.html#nw:clustering-coefficient to-report clustering-coefficient-online let closed-triads sum [ nw:clustering-coefficient * count my-online_friendships * (count my-online_friendships - 1) ] of turtles let triads sum [ count my-online_friendships * (count my-online_friendships - 1) ] of turtles report closed-triads / triads end ;Clustering Offline to-report clustering-coefficient-offline let closed-triads sum [ nw:clustering-coefficient * count my-offline_friendships * (count my-offline_friendships - 1) ] of turtles let triads sum [ count my-offline_friendships * (count my-offline_friendships - 1) ] of turtles report closed-triads / triads end ;;;;;;;;;;;;;; ;;; Layout ;;; ;;;;;;;;;;;;;; to layout ;; no longer being used since there are 2,500 agents ;;different layouts for different ties repeat 3 [ let factor ((sqrt n-agents ) / 3) layout-spring turtles online_friendships (1 / factor) (7 / factor) (1 / factor) ;online_users display ] repeat 3 [ layout-spring turtles offline_friendships 0.33 8 4 display ] ;;; Centering network ;;; let x-offset max [xcor] of turtles + min [xcor] of turtles let y-offset max [ycor] of turtles + min [ycor] of turtles ;; big jumps look funny, so only adjust a little each time set x-offset limit-magnitude x-offset 0.1 set y-offset limit-magnitude y-offset 0.1 ask turtles [ setxy (xcor - x-offset / 2) (ycor - y-offset / 2) ] end to-report limit-magnitude [number limit] if number > limit [ report limit ] if number < (- limit) [ report (- limit) ] report number end to layout-update repeat 3 [ layout-spring turtles links 0.03 8 5 display ] end to setup-patches ;; placing each agent on the grid depending on nat.id value -- not being used since there are 2,500 agents. ask patches [ let normalized-value (pxcor + (pycor * world-width)) / (world-width * world-height) let patch-value normalized-value * 10 + 5 set pcolor palette:scale-gradient palette:scheme-colors "Divergent" "PRGn" 10 patch-value 0 10 ] ask patches [ set pvalue precision ( 2 * ((pycor - min [pycor] of patches) / (max-pycor - min-pycor)) - 1) 2 ] end to patch-move ;; moving agent depending on changes to nat.id ask turtles [ let naty precision nat_id 2 let target-patches patches with-min [abs(pvalue - naty)] move-to min-one-of target-patches with-min [distance myself] [count turtles-here] ] end ;;;;;;;;;;;;;;;;;;;; ;;; Turtle Setup ;;; ;;;;;;;;;;;;;;;;;;;; to read-agents-norm ; reading in agent attributes (from random-normal distribution) let row 0 foreach csv:from-file "data_normdis.csv" [ag -> let i 1 if row > 0 [ crt 1 [ set nat_id item 0 ag set costs item 1 ag set grievances item 2 ag set uncertainty item 3 ag set engagement item 4 ag set-characteristics ] ] set row row + 1 ] end to read-agents-ceo ; reading in agent attributes (from CEO 2011 left-skewed distribution) let row 0 foreach csv:from-file "data_CEO2011.csv" [ag -> let i 1 if row > 0 [ crt 1 [ set nat_id item 0 ag set costs item 1 ag set grievances item 2 ag set uncertainty item 3 ag set engagement item 4 ag set-characteristics ] ] set row row + 1 ] end to set-characteristics ;; visual characteristics let val 1 let total-val count (link-neighbors) + 3 set size 0.1 + 5 * sqrt (val / total-val) set color black ;blue ;turquoise set shape "person" setxy random xcor random ycor ;;tracking attributes set homophily? false set changed? false set pro_clust? false set sil_clust? false set net_ties 0 set protest_state 0 ;;variable attributes set my-information 0 end to set-characteristics_update ask n-of (round ( n-agents / 100) * initial-protest-size) turtles [ set protest_state 1 set grievances grievances + (grievances * 0.2) ] end to-report random-normal-in-bounds [mid dev mmin mmax] let result random-normal mid dev if result < mmin or result > mmax [ report random-normal-in-bounds mid dev mmin mmax ] report result end to setup-information ;; creating information pool set information [] let temp range 100 let pos_information n-values 100 [random-float 1] let neg_information n-values 100 [-1 + random-float 1] set information sentence pos_information neg_information end ;;;;;;;;;;;;;;;;;;; ;;;; Processes ;;;; ;;;;;;;;;;;;;;;;;;; to information-receival ask turtles [ set my-information one-of information ] if Social-Media = "filter-bubble_ON" [filtered-information] end to filtered-information ;; when social-networks filter bubble ON ask turtles with [any? my-online_friendships] [ if abs(my-information - nat_id) > s [ let max_info nat_id + random-float s let min_info nat_id - random-float s let information1 ifelse-value (random-float 1 < s) [max_info] [min_info] if information1 > 1 [ set information1 1 ] if information1 < -1 [ set information1 -1] set my-information information1 ] ] end to homophily_check ask turtles with [any? online_friendship-neighbors] ;; online echo chambers [ set homophily? false set in-group 0 let my_nat_id nat_id let needed (count online_friendship-neighbors) * 0.70 let my_group online_friendship-neighbors with [abs(nat_id - my_nat_id) < s] let my_outgroup online_friendship-neighbors with [abs(nat_id - my_nat_id) > s] if any? my_group [set in-group count my_group] ; calculating the number of out-group members set net_ties count my_outgroup / count online_friendship-neighbors if in-group > needed [set homophily? true ] ] end to change-grievances [delta] ifelse grievances < 0 [ let change_gri grievances - delta if change_gri > 1 [ set change_gri 1 ] if change_gri < -1 [ set change_gri -1] set grievances change_gri ] [ let change_gri grievances + delta if change_gri > 1 [ set change_gri 1 ] if change_gri < -1 [ set change_gri -1] set grievances change_gri ] end to change-nat_id [delta] ifelse nat_id < 0 [ let change_nat nat_id - delta if change_nat > 1 [ set change_nat 1 ] if change_nat < -1 [ set change_nat -1] set nat_id change_nat ] [ let change_nat nat_id + delta if change_nat > 1 [ set change_nat 1 ] if change_nat < -1 [ set change_nat -1] set nat_id change_nat ] end to share ask turtles [ let prob-sharing ifelse-value abs(my-information - nat_id) > s and homophily? = true [0.2][1] if random-float 1 < engagement and random-float 1 < prob-sharing [ let off_friends offline_friendship-neighbors with [not changed?] let on_friends online_friendship-neighbors with [not changed?] let caller-info my-information let caller self if any? on_friends [ ask on_friends ;; one-to-all [ let wt [weight] of link-with caller if abs(caller-info - nat_id) - wt < uncertainty [ set changed? true ; ask link-with caller [set weight wt + (wt * d)] ;; social influence, we become closer together let x (abs(caller-info - nat_id) * d) ;; Attitude update ifelse abs(caller-info - nat_id) < s [ change-nat_id x ] [ change-nat_id (0 - x) ] ] ] ] if any? off_friends [ ask one-of off_friends ;; one-to-one [ let wt [weight] of link-with caller if abs(caller-info - nat_id) - wt < uncertainty [ set changed? true ; ask link-with caller [set weight wt + (wt * d)] ;; social influence, we become closer together let x (abs(caller-info - nat_id) * d) ;; Attitude update ifelse abs(caller-info - nat_id) < s [ change-nat_id x ] [ change-nat_id (0 - x) ] ] ] ] ] ] end to update-attitude let options turtles with [not changed?] if any? options [ ask options [ let eval abs(my-information - nat_id) let x (abs(my-information - nat_id) * d) if eval < uncertainty [ set changed? true ifelse eval < s [ change-nat_id x ] [ change-nat_id (0 - x) ] ] ] ] end to protest ;; There are 2 protesting stages in this model and H=10 ;; claculating the group grievances - only counting in-group members ask turtles with [protest_state != 2 and any? link-neighbors] [ let group_grievances 0 let ppl-who-matter link-neighbors with [abs(nat_id - [nat_id] of myself) < s ] if any? ppl-who-matter [set group_grievances mean [grievances] of ppl-who-matter] ;; combinging own and in-group member grievances, greater weight of own compared to in-group set group_grievances (grievances + group_grievances) / 2 if random-float 1 < protesting-opportunity and protest_state = 0 [ let all count link-neighbors let protest_support (count link-neighbors with [protest_state = 1]) / all * h let protest_against (count link-neighbors with [protest_state = 0]) / all * h let are-we-protesting? group_grievances + protest_support > costs + protest_against ; print (word "My griev: " grievances "; group grievance: " group_grievances "; my cost: " costs "; % ppl protesting: " protest_support "; Am I protesting? " are-we-protesting?) ifelse are-we-protesting? [ set protest_state 1 ;; were're assuming that when we protest we change the grievances of our ingroup (similar neighbors) networks ask link-neighbors with [abs(nat_id - [nat_id] of myself) < s ] [ set grievances grievances + (grievances * 0.05) ] ] [ set protest_state 0; ; were're assuming that when we don't protest we change the grievances of our general networks ask link-neighbors [ set grievances grievances - (grievances * 0.05) ] ] ] ] end to detect-clusters ask turtles [ let protesters count link-neighbors with [protest_state = 1] let against count link-neighbors with [protest_state = 0] ifelse protesters >= against [ set pro_clust? true set sil_clust? false ] [ set sil_clust? true set pro_clust? false ] ] end ;;;;;;;;;;;;;;; ;;;; PLOTS ;;;; ;;;;;;;;;;;;;;; to plot-nat_id set-current-plot "National Identity Attitudes" ask turtles [ create-temporary-plot-pen (word "Turtle" (who + 1)) set-plot-pen-color palette:scale-gradient palette:scheme-colors "Divergent" "PRGn" 6 nat_id -1 1 plot nat_id ] end ;to plot-nat_id ;set-current-plot "National Identity Attitudes" ;ask turtles ;[ ; create-temporary-plot-pen (word "Turtle" (who + 1)) ; set-plot-pen-color palette:scale-gradient palette:scheme-colors "Divergent" "PRGn" 6 nat_id -1 1 ; plot nat_id ;] ;end to plot-grievances set-current-plot "Grievance Perceptions" ask turtles [ create-temporary-plot-pen (word "Turtle" (who + 1)) set-plot-pen-color palette:scale-gradient palette:scheme-colors "Divergent" "Spectral" 6 grievances -1 1 ;;RdYlBu - could also work plot grievances ] end to plot-protest_state set-current-plot "Protest state" ask turtles with [protest_state = 0] [ set-current-plot-pen "silent" plot who ] ask turtles with [protest_state = 1] [ set-current-plot-pen "protesting" plot who ] ; ask turtles with [protest_state = 2] ; [ ; set-current-plot-pen "protesting" ; plot who ; ] end ;;;;;;;;;;;;;;;;;; ;;;; REPORTERS;;;; to-report Protester report (count turtles with [protest_state = 1] / n-agents) * 100 end to-report Nat_ID_variance let vars variance [nat_id] of turtles report vars end to-report Nat_ID_weighted let maxi 1 ;max [nat_id] of turtles let mini -1 ;min [nat_id] of turtles let maxi1 maxi - 0.5 let mini1 mini + 0.5 let a (count turtles with [nat_id >= mini and nat_id <= mini1] / n-agents) let b (count turtles with [nat_id >= maxi1 and nat_id <= maxi] / n-agents) let count_mini mini * a let count_maxi maxi * b report abs(count_mini - count_maxi) end to-report Nat_ID_weighted_min let mini -1 ;min [nat_id] of turtles let mini1 mini + 0.5 let a (count turtles with [nat_id >= mini and nat_id < mini1] / n-agents) report a end to-report Nat_ID_weighted_max let maxi 1 ; max [nat_id] of turtles let maxi1 maxi - 0.5 let b (count turtles with [nat_id >= maxi1 and nat_id <= maxi] / n-agents) report b end to-report Nat_ID_weighted_med ; let maxi max [nat_id] of turtles - s let mini -0.5 ;min [nat_id] of turtles + 0.5 let c (count turtles with [nat_id >= mini and nat_id < 0] / n-agents) report c end to-report Nat_ID_weighted_med1 ; let maxi median [nat_id] of turtles let maxi 0.5 ;max [nat_id] of turtles - 0.5 let x (count turtles with [nat_id >= 0 and nat_id < maxi] / n-agents) report x end to-report homophilous let total count turtles with [any? online_friendships] report (count turtles with [homophily?] / total ) * 100 end to-report network_ties ; prop of turtles with half of neighbors from diff. attitudes report (count turtles with [net_ties > 0.5] / n-agents) * 100 end to-report protest_clustering report (count turtles with [pro_clust?]/ n-agents) * 100 end to-report silent_clustering report (count turtles with [sil_clust?] / n-agents) * 100 end ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;;; ADDITIONAL CODE ;;;;;;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; @#$#@#$#@ GRAPHICS-WINDOW 358 76 854 573 -1 -1 14.8 1 10 1 1 1 0 0 0 1 -16 16 -16 16 0 0 1 ticks 30.0 BUTTON 82 499 146 532 Setup setup NIL 1 T OBSERVER NIL NIL NIL NIL 1 BUTTON 165 500 228 533 Go go T 1 T OBSERVER NIL NIL NIL NIL 1 PLOT 493 589 864 836 Grievances Histogram Grievances Agents 0.0 1.0 0.0 100.0 true false "" "" PENS "pen-0" 0.1 1 -7500403 true "" "histogram [grievances] of turtles" SLIDER 11 252 140 285 initial-protest-size initial-protest-size 1 100 15.0 1 1 NIL HORIZONTAL SLIDER 158 253 317 286 protesting-opportunity protesting-opportunity 0 1 0.1 0.1 1 NIL HORIZONTAL PLOT 862 160 1415 414 Protest Status Ticks Protesting state 0.0 10.0 0.0 2.0 true true "" "" PENS "Silent" 1.0 0 -14070903 true "" "plot count turtles with [protest_state = 0]" "Protesting" 1.0 0 -5298144 true "" "plot count turtles with [protest_state = 1]" CHOOSER 13 38 143 83 Networks Networks "homophilous" "random" 1 CHOOSER 9 135 167 180 Social-Media Social-Media "filter-bubble_ON" "filter-bubble_OFF" 0 SLIDER 169 44 284 77 probs-rewiring probs-rewiring 0 1 0.2 0.1 1 NIL HORIZONTAL TEXTBOX 378 21 889 56 Social Identity Model of Protest Emergence (SIMPE) 20 0.0 0 TEXTBOX 17 14 335 44 Select the network properties & rewiring probabilities 12 0.0 1 TEXTBOX 12 108 257 136 Select the information sorting algorithm 12 0.0 1 TEXTBOX 16 209 328 244 Select the protesting predisposition, protesting chance & initial N of protesters 12 0.0 1 TEXTBOX 151 401 341 433 Protest Social Influence constant 13 0.0 1 PLOT 95 587 465 840 National Identity Histogram National Identity Agent Count -1.0 1.0 0.0 10.0 true false "" "" PENS "default" 0.08 1 -16777216 true "" "histogram [nat_id] of turtles " SLIDER 186 428 289 461 h h 0 1 0.1 0.01 1 NIL HORIZONTAL CHOOSER 11 335 149 380 Distribution? Distribution? "normdis" "CEO2011" 1 TEXTBOX 15 304 207 322 Select which data to import 12 0.0 1 SLIDER 15 429 120 462 s s 0 1 0.5 0.1 1 NIL HORIZONTAL TEXTBOX 16 400 166 418 Similarity threshold 13 0.0 1 SWITCH 198 336 317 369 Data-save? Data-save? 1 1 -1000 @#$#@#$#@ # SOCIAL IDENTITY MODEL OF PROTEST EMERGENCE (SIMPE) The purpose of this model is to simulate protest emergence in a given country where there is an independence movement, fostering the self-categorisation process of national identification. In order to contextualised SIMPE, I have used Catalonia, where an ongoing secessionist movement since 2011 has been present, national identity has shown signs of polarisation, and where numerous mobilisations have taken place over the last decade. Data from the Catalan Centre of Opinion Studies (CEO) has been used to inform some of the model parameters. This model integrates mehcanisms and assumptions from previous models of opinion dynamics, social influence, and collective action (see Deffuant et al., 2002; Hegselmann and Krause 2002; Mäs and Flache, 2010; Epstein, 2003;2013, Edmonds,2021) to understand the role of national identity polarisation in protest emergence. In particular, SIMPE applies social identity theory to Epstein's Agent Zero (2013) and Epstein's civil violence (2003) models to explain protest mobilisation. Agents get exposed to information, from the media and their social networks. This model has a weighted multilayer multiplex nextwork, representing offline and online social networks. The weights, assigned at random, represent social influence. Each layer has a different network topology, small-world and scale-free to represent the characteristics of each. Additionally, there are two initial social network scenarios that determine how ties are formed. Either randomly or based on national identity similarity, following the principle of homophily. Similarly, the initial national identity distributions in the agent population (N=2,500) can be random-normal or informed with the 2011 survey data on political attitudes obtained from Catalan Centre of Opinion Studies (Centre D’Estudie D’Opinion, CEO). There two separate social media information exposure scenarios. One where individuals are exposed to information based on random probability (Filter-bubble-Off). Alternatively, agents are selectively exposed to primarily information that is similar to their national identities (Filter-Bubble-On) or within the bounded confidence threshold. There are three key elements when it comes to protesting: grievances, costs, and social influence. Agents will account not just for their own grievances but those of their in-group members (based on national identity similarity). Changes to grievaces take place through social influence and are linked to national identities since this model is interested in understanding the role of national identity polarisation in protest emergence. Agents also factor in the mobilisation of their social networks (who, if anyone of their social networks is participating) as well as weighting in the costs of participating in protest. The initial number of protesters at the start of the simulation captures the critical mass theory of collective action and can be adjusted. The protesting opportunity abstractly represents the opportunities agents have in the simulated society to join a protest which can also be adjusted. Lastly, the rewiring probability allows to set the likelihood of agents creating new ties and dissolving old ones. This mechanism follows the principle of homophily. There are three plots on the interface showing the distribution of national identities, grievances and the number of protesting agents at each step of the simulation. # HOW IT WORKS At setup, social networks are created. Offline social networks have a small-world topology with an average node degree of 27 whereas the online social networks have a scale-free topology with an average node degree of 45. Both values have been obtained from empirical studies (see Lubbers et al., 2019 and Dunbar et al., 2015). It should be noted that not every agent has online social networks and the proportion of agents that do have such networks is modelled after the Spanish internet use penetration value since this model focuses on the Catalan secessionist movement. There are two social network scenarios, random and homophilous. *Random* each agent makes a tie with another agent based on random probability. *Homophilous*: the probability that an agent gets chosen depends on national identity similarity or homophil). This means that initially, ties are formed with those within the similarity threshold (see Deffuant et al., 2000;2004;2008) and then with the remainder of the agents. Since there are online social networks, there are two scenarios that control the information that agents get exposed to through social media platforms. *Filter-Bubble_ON* represents a media environment where agents get predominantly exposed to attitude-consistent information while also getting exposed to attitude-discrepant information in a smaller proportion. *Filter-Bubble_OFF* represents a diverse media environment where agents get equally exposed to attitude-consistent and attitude-discrepant information. Since the key component of this model are national identity attitudes, two alternative distributions for this variable are provided. *normdis* : each agent has a randomly distributed national identity attitude ranging from -1 to 1. *CEO2011*: each agent's national identity value was taken from the CEO October 2011 survey dataset (reference at end of document) resulting in a left-skewed distribution ranging from -1 to 1. This scenario is considered calibrated compared to the *normdis* one. Additionally to national identity a, agents have a randomly distributed grievance value from 0 to 1 which indicates their discontent with the situation and promotes protesting. Costs of participating in protest are also drawn from a random distribution and range from 0 to 1. Similarly, agents have (social) engagement levels, ranging from 0 to 1, representing their socialisation patterns and attitude uncertainty, ranging from 0 to 1, representing the strength of their views and room for persuasion to change their attitudes. At each tick, each agent will receive information whether from another agent or from the media's information hub. Three decisions will be made by agents: sharing the information with their social networks, updating their national identities, and protesting which updates their grievances. Agents have two states: Silent (blue) and Protesting (red). If all agents are protesting, the simulation ends since the goal is to observe the emergence of protests. # HOW TO USE IT The Protesting-opportunity and Initial-protest-size sliders set the protesting opportunities available for agents and the initial number of protesting agents in the population at the start of the simulation. The Social-Media switch enables the filtering algorithm that preferentially sends attitude-supportive information to agents. If off, agents have equal probabilities of receiving attitude-supportive and attitude-discrepant messages. Networks switch enables the creation of homophilous networks at the start of the simulation, following the bounded confidence principle, links will be created with agents that are similar to oneself. Alternatively, links will be created at random regardless of the national identity similarity between agents. Press SETUP to populate the world with agents creating offline and online links. GO will run the simulation continuously. The View panel currently doesn't show the agents in different protest states as well as their ties, grey or white corresponding to offline and online connections because of the large size of the population (N= 2,500) which takes a very long time to render. For that reason we have two separate plots that visualise the changes in national identities, grievance perceptions, and protesting states. The National Identity Histogram shows changes in national identity at the population-level over time. The Grievance Perceptions Histogram shows the distribution of grievance perceptions. Lastly the Protest Status Plot shows the number of agents in each of the two protesting states, silent or protesting. Data-save? allows to save agent attributes and model outputs every 100 steps of the simulation. There are two separate data collection procedures in the Code, one for the general behaviour space and another for sensitivity analyses. You will need to (un)comment accordingly. In any case, it creates a separate file for the model outputs and agent attributes for the parameter combination. This is to allow to read them into R/Python more easily than a single 25GB file. # THINGS TO NOTICE After running the model for a while, national identities converge towards the centre of the distribution or they divide into two or more distinct clusters. This depends on the combination of initial social network configurations and social media filtering algorithms. It is also interesting to see how the effect of protesting opportunities on protest mobilisation plateaus for values larger than 0.5. # THINGS TO TRY How does the presence of filter bubbles affect national identities and protest mobilisation? How does starting from a random network compared to starting from a homophilous network setup affect national identities and protest mobilisation? How does the initial number of protesting agents affect the resulting protest mobilisation dynamics? # CREDITS AND REFERENCES Acknowledgements to Dr. Stefano Picascia for his technical support and guidance. Adam-Troian, J, Mahfud Y, Urbanska K, and Guimond S. (2020) The role of social identity in the explanation of collective action: An intergroup perspective on the Yellow Vests movement. Journal of Applied Social Psychology, 21(0), pp.1–17. Guillaume D., Amblard, F., Weisbuch, A., and Faure, T. (2002) How can extremism prevail? A study based on the relative agreement interaction model. Journal of Artificial Societies and Social Simulation 5(4)1. Dunbar, R.I.M., Arnaboldi, V., Conti, M., and Passarella, A. (2015) The structure of online social networks mirrors those in the offline world, Social Networks, 43, pp.39-47, https://doi.org/10.1016/j.socnet.2015.04.005 Edmonds, B. (2021) “A data-informed bounded-confidence opinion dynamics model” (Version 0.3.0). CoMSES Computational Model Library. Retrieved from: https://www.comses.net/codebases/537b40cf-e300-4245-bcc6-427b53515bf6/releases/0.3.0/ Epstein, J. (2002) Modeling civil violence: An agent-based computational approach. Proceedings of the National Academy of Sciences, 99, pp.7243-7250. Epstein, J. (2013) Agent zero: Toward neurocognitive foundations for generative social science. Princeton University Press. Flache, F. (2018) Between monoculture and cultural polarization: Agent-based models of the interplay of social influence and cultural diversity.Journal of Archaeological Methods Theory, 25, pp.996–1023 Flache, A., Mas, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S. and Lorenz, J. (2017) Models of social influence: Towards thenext frontiers. Journal of Artificial Societies and Social Simulations, 20, pp.1–31 Flaxman, S., and Goel, S., and Rao, J. M (2016) Filter bubbles, echo chambers, and online news consumption, Public Opinion Quarterly, 80(S1), pp.298-320 Hegselmann, R., and Krause, U. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulations, 5, pp. 1-33. Lubbers, M.; Molina, J.L. and Valenzuela-García, H. (2019) When networks speak volumes: Variation in the size of broader acquaintanceship networks, Social Networks, 56, pp.55-69 Mas and Flache, A. (2013) Differentiation without distancing, explainingbi-polarization of opinions without negative influence, PLoS ONE, 8:pp.e74516 Noelle-Neumann, E. (1974) The Spiral of Silence a Theory of Public Opinion, Journal of communication, 24(2), pp. 43-51 Sazarulo, L. (2006) A Continuous Opinion Dynamics Model Based on the Principle of Meta-Contrast, Journal of Artificial Societies and Social Simulation, 9(1), pp. 1-29 Tajfel, H. (1984) Intergroup relations, social myths and social justice in social psychology (H. Tajfel, Ed.). In H. Tajfel (Ed.), The social dimension. Cambridge: Cambridge University Press. Turner, J.C. (1991) Social Influence. Bristol: Open University Press. # COPYRIGHT AND LICENSE If you mention this model or the NetLogo software in a publication, we ask that you include the citations below. For the model itself: Chueca Del Cerro, C. (2022) Social Identity Model of Protest Emergence (SIMPE),(Version 1.0.0).UK Data Service, accessible at https://reshare.ukdataservice.ac.uk/856155/ 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. @#$#@#$#@ default true 0 Polygon -7500403 true true 150 5 40 250 150 205 260 250 airplane true 0 Polygon -7500403 true true 150 0 135 15 120 60 120 105 15 165 15 195 120 180 135 240 105 270 120 285 150 270 180 285 210 270 165 240 180 180 285 195 285 165 180 105 180 60 165 15 arrow true 0 Polygon -7500403 true true 150 0 0 150 105 150 105 293 195 293 195 150 300 150 box false 0 Polygon -7500403 true true 150 285 285 225 285 75 150 135 Polygon -7500403 true true 150 135 15 75 150 15 285 75 Polygon -7500403 true true 15 75 15 225 150 285 150 135 Line -16777216 false 150 285 150 135 Line -16777216 false 150 135 15 75 Line -16777216 false 150 135 285 75 bug true 0 Circle -7500403 true true 96 182 108 Circle -7500403 true true 110 127 80 Circle -7500403 true true 110 75 80 Line -7500403 true 150 100 80 30 Line -7500403 true 150 100 220 30 butterfly true 0 Polygon -7500403 true true 150 165 209 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84 268 100 267 103 261 77 239 79 231 100 207 98 196 119 201 143 202 160 195 166 210 172 213 173 238 167 251 160 248 154 265 169 264 178 247 186 240 198 260 200 271 217 271 219 262 207 258 195 230 192 198 210 184 227 164 242 144 259 145 284 151 277 141 293 140 299 134 297 127 273 119 270 105 Polygon -7500403 true true -1 195 14 180 36 166 40 153 53 140 82 131 134 133 159 126 188 115 227 108 236 102 238 98 268 86 269 92 281 87 269 103 269 113 x false 0 Polygon -7500403 true true 270 75 225 30 30 225 75 270 Polygon -7500403 true true 30 75 75 30 270 225 225 270 @#$#@#$#@ NetLogo 6.2.0 @#$#@#$#@ @#$#@#$#@ @#$#@#$#@ setup go export-all-plots "/home/cristina/Documents/SIMPE/plots.csv" ;save-network ;save-network1 setup go export-all-plots "C:/Users/cchue/Dropbox/BB/AAPhD/Programming/Netlogo work/Final/SIMPE-model/Simulation runs/plots.csv" ;save-network ;save-network1 protesters0 protesters1 protesters2 grievances_neg grievances_pos nat_id_neg nat_id_pos sharers homophilous setup go export-all-plots "/home/cristina/Documents/SIMPE/plots.csv" ;save-network ;save-network1 setup go ;export-all-plots "/home/cristina/Documents/SIMPE/plots.csv" setup go ;export-all-plots "/home/cristina/Documents/SIMPE/plots.csv" setup go ;export-all-plots "/home/cristina/Documents/SIMPE/plots.csv" setup go ;export-all-plots "/home/cristina/Documents/SIMPE/plots.csv" setup go setup go setup go @#$#@#$#@ @#$#@#$#@ default 0.0 -0.2 0 0.0 1.0 0.0 1 1.0 0.0 0.2 0 0.0 1.0 link direction true 0 Line -7500403 true 150 150 90 180 Line -7500403 true 150 150 210 180 @#$#@#$#@ 0 @#$#@#$#@