# Social Norms (Emperor's Dilemma)

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breed [believers believer] breed [disbelievers disbeliever] breed [recovers recover] globals [infected_per infected_mean infected_list ] turtles-own [ Beliefs ; B = 1 if believes; B = -1 if doesn't believe; Strength ; strength of belief varies from 0 to 1 Compliance ; binary; 1 if complies/enforces norm, and 0 otherwise. Enforcement ; 1 if enforces norm; -1 if enforces deviance. Enforcement_Need ; Wi = 1-(Bi/Ni)SumCj / 2 ;; is just the proportion of i's neighbors whose behavior does not conform with it's Beliefs B Enforcement_Need_2 ;; same # but divided by 2; as used in the article. N_Neighbors Convert ] ;; CODE: ;; BELIEVERS = arrow, DISBELIEVERS = default shape ;; COMPLIANCE = RED, DEVIANCE = BLUE ;; ENFORCEMENT = HEADING TO THE RIGHT (90) ; NO ENFORCEMENT = HEADING TO THE LEFT (270) ;; Basic MODEL of ED: 1. agents observe neighbors compliance and enforcement. 2. Each agent then makes two decisions: ;; (i) whether to comply with the norm, and (ii) whether to enforce the norm. to setup clear-all reset-ticks let IB initial_believers let ID population - IB ;if Condition = "Local Random" OR Condition = "Global" [ create-believers IB [ set size 1 set color red ;setxy random-pxcor random-pycor while [any? other turtles-here] [ let empty_patch one-of patches with [any? turtles-here = false] move-to empty_patch ] ] if Condition = "Local Clustered" [ let a 20 let b 12 let p patch 20 12 let d (list believers) foreach d [ d_i -> ask d_i [move-to p ; set p patch-at-heading-and-distance 45 1] set p patch-at 1 1] ] ] ask believers[ set Beliefs 1 set Strength 1 ;; by default all true believers initially comply! set compliance 1 set convert 0 set shape "arrow" set heading 90 ; NO INITIAL ENFORCEMENT ] create-disbelievers ID [ set size 1 set color blue ; BLUE COLOR BECAUSE NOT COMPLYING WITH ; setxy random-pxcor random-pycor while [any? other turtles-here] [ let empty_patch one-of patches with [any? turtles-here = false] move-to empty_patch ] set Beliefs -1 set Strength random-float 0.38 set convert 0 set compliance -1 set heading 90 ; NO INITIAL ENFORCEMENT ] ask turtles [setup-map] end to START! ED update-plots tick end to setup-map if Condition = "Global" [set N_Neighbors Other Turtles] if Condition = "Local Clustered" OR Condition = "Local Random" [ set N_Neighbors turtle-set turtles-on neighbors if small_worlds? = true [ small-worlds ] ] end to ED ask turtles [ if small_worlds? = true AND Continuous-Rewiring? = true AND Condition != "Global" [ small-worlds ] let Ni_list (list N_Neighbors) let Ni count N_Neighbors if Ni = 0 [set Ni 1] let Bi [Beliefs] of self let NCi count N_Neighbors with [Compliance = Bi] set Enforcement_Need 1 - (NCi / Ni) set Enforcement_Need_2 Enforcement_Need / 2 ; output-print Enforcement_Need_2 COMPLY? ENFORCE? ] end TO COMPLY? ;; disbeliever complies if the proportion of neighbors enforcing compliance is greater than the strength of disbeliever's belief; let S [strength] of self let Bi [Beliefs] of self let Ej count N_Neighbors with [Enforcement = -1 * Bi] ;; neighbors enforcing opposite belief let Ni count N_Neighbors if Ni = 0 [set Ni 1] ifelse (Ej / Ni) > S [set compliance -1 * Bi] [set compliance Bi] if Compliance = 1 [set color red] if Compliance = -1 [set color blue] end to ENFORCE? let S [strength] of self let Bi [Beliefs] of self let Ci [Compliance] of self let Ej count N_Neighbors with [Enforcement = -1 * Bi] ;; neighbors enforcing opposite belief let Ni count N_Neighbors if Ni = 0 [set Ni 1] let Wi Enforcement_Need_2 ifelse (Ej / Ni) > (S + K) AND Bi != Ci [set Enforcement -1 * Bi] ; Enforcement is opposite of belief if: ; a) the proportion of enforcement against belief is greater than the strength of belief plus the cost of enforcement, AND ; b) agent already complies against agent's own belief; violates one's own belief already. ;; THIS MEANS THAT AGENTS CANNOT ENFORCE COMPLIANCE UNLESS THEY HAVE ALREADY COMPLIED. [ ifelse S * Wi > K AND Bi = Ci [set Enforcement Bi] [set Enforcement 0] ] if enforcement = 1 [set heading 270] ;; to better visualize enforcement if conversion? = true [CV] end to CV let a conversion / 10000 ; 1 will equal .0001 - the learning parameter set in the article if Enforcement != Beliefs [ set convert convert - (a * Enforcement * Beliefs) if convert > Strength AND Beliefs != compliance [ hatch-believers 1 [ set color red set Beliefs 1 set compliance 1 set convert 0 set shape "arrow" set heading 90 ];; NOTICE THAT I AM NOT RESETTING THE STRENGTH OF THEIR CONVICTIONS. THESE NEW CONVERTS ARE A LESS CONVINCED GROUP OF BELIEVERS THAN THE ORIGINAL! die ;; THE ORIGINAL DISBELIVER DIES ] ] end to small-worlds let a self let N_list [] let h turtle-set turtles-on neighbors let g turtle-set N_Neighbors ; ask N_Neighbors [set color yellow] ask N_Neighbors [ ;; whether to rewire it or not? ifelse (random-float 1) < rewiring-probability [ let b (turtle-set a h g) ; a = self, original turtle; N_neighbors list here includes this turtle replacing itself with another random turtle let c one-of turtles while [member? c b = true] [set c one-of turtles] ; keeps changing the turtle until it isn't itself or a neighbor ask a [set N_list fput c N_list] ; set N_list replace-item (? - 1) N_list c ;show N_list ask c [set color brown] ] [ask a [set N_list fput myself N_list]] ;;myself or self? ] ;; must be ? - 1 to replace the correct turtle ask a [set N_Neighbors turtle-set N_list] ; must go back and ask original turtle to do this! end to-report prcnt_comply let comply count turtles with [compliance = 1] let Ni count turtles if Ni = 0 [set Ni Ni + 1] report (comply / Ni) * 100 end to-report prcnt_enforce let enforce count turtles with [enforcement = 1] let Ni count turtles if Ni = 0 [set Ni Ni + 1] report (enforce / Ni) * 100 end to-report prcnt_believe let B count believers let Ni count turtles if Ni = 0 [set Ni Ni + 1] report (B / Ni) * 100 end to-report false_comply ;; proportion of disbelievers who falsely comply let D count disbelievers if D = 0 [set D D + 1] let F count disbelievers with [Compliance = 1] report (F / D) * 100 end to-report false_enforce let D count disbelievers if D = 0 [set D D + 1] let F count disbelievers with [Enforcement = 1] report (F / D) * 100 end

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Social Norms (Emperor's Dilemma).png | preview | Preview for 'Social Norms (Emperor's Dilemma)' | about 6 years ago, by John Bradford | Download |

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