extensions [nw] directed-link-breed [opinions opinion] opinions-own [ evaluation intensity ;; propagation intensity ] globals [ turtles_list ] turtles-own [ N_Neighbors self-regard q_list degree ] to setup clear-all reset-ticks set turtles_list [] ;__clear-all-and-reset-ticks nw:set-context turtles links M_presets network_presets create-turtles population [ 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 color random-float 140 set self-regard 0 ] if Display_Mode = "Matrix" [ ask turtles [set hidden? true] ] ask turtles [setup-neighbors set turtles_list fput self turtles_list ] end to M_presets if Model_Presets = "Equality" [ set network_type "None" set condition "Global" set population 40 set vanity 0.3 set rho 0.01 set noise_intensity 0.2 set sigma 0.35 set k_neighbors 5] if Model_Presets = "Elite" [ set network_type "None" set condition "Global" set population 60 set vanity 0.3 set rho 0.1 set noise_intensity 0.2 set sigma 0.3 set k_neighbors 5] if Model_Presets = "Hierarchy" [ set network_type "None" set condition "Global" set population 40 set vanity 0.2 set rho 0.5 set noise_intensity 0.2 set sigma 0.3 set k_neighbors 10] if Model_Presets = "Dominance" [ set network_type "None" set condition "Global" set population 40 set vanity 0.4 set rho 0.8 set noise_intensity 0.2 set sigma 0.3 set k_neighbors 2] if Model_Presets = "Crisis" [ set network_type "None" set condition "Global" set population 40 set vanity 0.4 set rho 0.35 set noise_intensity 0.2 set sigma 0.5 set k_neighbors 2] if Display_Mode = "Matrix" [ resize-world 0 population 0 population set-patch-size 8 ] end to network_presets if network_type = "preferential attachment" [nw:generate-preferential-attachment turtles opinions population ask turtles [ let town out-opinion-neighbors set N_Neighbors town]] if network_type = "random" [nw:generate-random turtles opinions population rewiring-probability ask turtles [ let town out-opinion-neighbors set N_Neighbors town]] if network_type = "small world" [nw:generate-small-world turtles opinions 10 10 2.0 false ask turtles [ let town out-opinion-neighbors set N_Neighbors town]] if network_type = "lattice" [nw:generate-lattice-2d turtles opinions 10 10 false ask turtles [ let town out-opinion-neighbors set N_Neighbors town]] if network_type = "ring" [nw:generate-ring turtles opinions population ask turtles [ let town out-opinion-neighbors set N_Neighbors town]] if network_type = "star" [nw:generate-star turtles opinions population ask turtles [ let town out-opinion-neighbors set N_Neighbors town]] if network_type = "wheel" [nw:generate-wheel turtles opinions population ask turtles [ let town out-opinion-neighbors set N_Neighbors town]] ask turtles [ while [any? other turtles-here] [let empty_patch one-of patches with [any? turtles-here = false] move-to empty_patch ]] layout-spring turtles links 10 10 10 end to START! ask opinions [if evaluation < -1 [set evaluation -1] if evaluation > 1 [set evaluation 1]] ask turtles [ opinion_propagation ; partner_selection ; interaction ; forgetting ] graphics tick end to graphics ask opinions [ if evaluation < 0 [ set color blue] if evaluation > 0 [set color red]] if Display_Mode = "Matrix" [ foreach sort opinions [ask ? [let i [who] of end1 let j [who] of end2 if evaluation < 0 [ask patch i j [set pcolor blue]] if evaluation > 0 [ask patch i j [set pcolor red]]] ] ] end to opinion_propagation if continuous-rewiring? = true [rewiring] let partner find-partner if partner = nobody [set partner one-of other turtles] let i [who] of self let j [who] of partner if opinion i j = nobody [ create-opinion-to partner [set evaluation 0]] if opinion j i = nobody [ask partner [ create-opinion-to turtle i [set evaluation 0]]] if Display_Mode = "Matrix" [ ask links [set hidden? true] ] ;[ask opinion i j [set evaluation 1]] ;ask partner [create-opinion-to myself [set evaluation 0]] let aii [self-regard] of self let aji [evaluation] of opinion j i let aij [evaluation] of opinion i j let ajj [self-regard] of partner ask opinion i j [ set intensity 1 / (1 + exp (-1 * ((aij - aii) / sigma))) ] let pij [intensity] of opinion i j set self-regard aii + (pij * rho * (aji - aii + noise)) ask opinion i j [set evaluation aij + (pij * rho * (ajj - aij + noise))] ;;STEP 2: REPUTATION ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; let no_gossip_list (list self partner) ;; list of agents that cannot be called upon let t_list [] ask [out-opinion-neighbors] of self [set t_list fput self t_list] ;; creates a list of all neighbors foreach no_gossip_list [ set t_list remove ? t_list ] ;; removes self and partner from the list of neighbors of agent i ; not including 'partner', and also another -1 because we're dealing with repeats ;; thus if you have 2 neighbors, the repeat would be 0? No, to iterate once, it must say 'repeat 1'- repeating 0 causes ;; operation to be skipped. let lnk count out-link-neighbors - 1 let k k_neighbors let rpt min (list lnk k) if length t_list > 0 [ repeat rpt [ ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; let q one-of t_list set t_list remove q t_list let q_i [who] of q let aiq [evaluation] of opinion i q_i if opinion j q_i = nobody [ask partner [create-opinion-to q]] let ajq [evaluation] of opinion j q_i ask partner [ask opinion j q_i [ set evaluation evaluation + (pij * rho * (ajq - aiq + noise)) ] ] ] ] ;;STEP 3: VANITY DYNAMICS ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; let w vanity ask opinion i j [ set evaluation evaluation + (w * (aji - aii + noise))] ;; in words, agent i's new evaluation of j is the old evaluation plus the difference between j's evaluation of i and i's self-evaluation end to-report noise ; not used here let r random 2 ifelse r > 0 [set r 1][set r -1] let p random-float noise_intensity let fuzz p * r ; -1 to +1 report fuzz end to setup-neighbors if Condition = "Global" AND network_type = "None" [set N_Neighbors Other Turtles] if Condition = "Local" AND network_type = "None" [ let town min-n-of Influence_Range other turtles [distance myself] set N_Neighbors town] ; if Condition = "Small Worlds" [ ; let town min-n-of Influence_Range other turtles [distance myself] ; set N_Neighbors town ; small-worlds] end to rewiring ;small-worlds re-wiring formula ifelse N_neighbors = 0 [] [ 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 find-partner ifelse N_Neighbors = 0 [let partner one-of other turtles report partner] [ let partner one-of N_Neighbors report partner ] end @#$#@#$#@ GRAPHICS-WINDOW 196 10 534 369 -1 -1 8.0 1 10 1 1 1 0 1 1 1 0 40 0 40 0 0 1 ticks 30.0 BUTTON 2 10 65 43 NIL setup NIL 1 T OBSERVER NIL NIL NIL NIL 1 BUTTON 65 10 120 43 NIL START! T 1 T OBSERVER NIL NIL NIL NIL 1 SLIDER 1 44 141 77 population population 2 100 40 1 1 NIL HORIZONTAL SLIDER 2 124 150 157 Influence_Range Influence_Range 1 population 4 1 1 NIL HORIZONTAL CHOOSER 0 79 138 124 Condition Condition "Global" "Local" 0 SLIDER 4 391 179 424 rewiring-probability rewiring-probability 0 .5 0.1 .01 1 NIL HORIZONTAL SLIDER -2 157 170 190 sigma sigma 0.1 1 0.35 .05 1 NIL HORIZONTAL SLIDER 0 189 171 222 rho rho 0.01 1 0.01 .01 1 NIL HORIZONTAL SLIDER -1 222 171 255 vanity vanity 0 1 0.3 .1 1 NIL HORIZONTAL SLIDER -1 255 171 288 k_neighbors k_neighbors 0 population - 2 5 1 1 NIL HORIZONTAL MONITOR 703 20 808 65 Average Opinion mean [evaluation] of opinions * 100 5 1 11 MONITOR 704 66 830 111 Variance of Opinions variance [evaluation] of opinions * 100 5 1 11 MONITOR 808 21 865 66 # Links count links 0 1 11 SWITCH 4 359 181 392 continuous-rewiring? continuous-rewiring? 1 1 -1000 SLIDER 0 287 172 320 noise_intensity noise_intensity 0 1 0.2 .01 1 NIL HORIZONTAL TEXTBOX 10 342 219 370 Whether preset networks change. 9 0.0 1 CHOOSER 704 124 842 169 Model_Presets Model_Presets "None" "Equality" "Elite" "Hierarchy" "Dominance" "Crisis" 1 CHOOSER 706 172 844 217 Display_Mode Display_Mode "Matrix" "Links" 0 CHOOSER 707 219 887 264 network_type network_type "None" "preferential attachment" "random" "small world" "lattice" "ring" "star" "wheel" 0 @#$#@#$#@ ## WHAT IS IT? Deffuant, Guillaume, Timoteo Carletti, and Sylvie Huet. “The Leviathan Model: Absolute Dominance, Generalised Distrust, Small Worlds and Other Patterns Emerging from Combining Vanity with Opinion Propagation.” Journal of Artificial Societies and Social Simulation 16, no. 1 (2012): 5. Available here: http://jasss.soc.surrey.ac.uk/16/1/5.html Here is a copy of the abstract: "We propose an opinion dynamics model that combines processes of vanity and opinion propagation. The interactions take place between randomly chosen pairs. During an interaction, the agents propagate their opinions about themselves and about other people they know. Moreover, each individual is subject to vanity: if her interlocutor seems to value her highly, then she increases her opinion about this interlocutor. On the contrary she tends to decrease her opinion about those who seem to undervalue her. The combination of these dynamics with the hypothesis that the opinion propagation is more efficient when coming from highly valued individuals, leads to different patterns when varying the parameters. For instance, for some parameters the positive opinion links between individuals generate a small world network. In one of the patterns, absolute dominance of one agent alternates with a state of generalised distrust, where all agents have a very low opinion of all the others (including themselves)." Each agent has a list of opinions about herself and other agents, ranging from -1 to +1. In this version, the parameter "condition" establishes the potential partners of each agent: global = all agents; local = agents within "influence_range"; and "small worlds" (see below). NOTE: The article expresses the influence of j on i using the subscripts ij. Here, I use the subscript ij to express the influence of i on j. ROUTINE: STEP 1: Opinion Propagation. Ask turtles, create an out-link "opinion" from turtle i to turtle j, and vice-versa, Oij and Oji. The "propagation coefficient" is called "intensity" here and is an attribute of opinions. The logistic equation is used. Intensity = 1 / (1 + exp (-1 * ((aij - aii) / sigma))), where aij is the evaluation of opinion i of j, and aii is the "self-regard" of agent i for herself. The basic idea expressed here is that "if i has a high opinion of j, then j is more influential." "Self-regard" is a turtle attribute. Sigma is a parameter which can be set in the interface. The influence of j on i for i's self-regard is expressed as follows: aii = aii + (intensityij * rho * (aji - aii + noise)), where aii is self-regard of agent i, and "rho" is a parameter 'ruling the importance of opinion propagation.' Now that the self-regard of agent i is updated, the "evaluation" of agent j by agent i (aij) is calculated as follows: aij = aij + (intensityij * rho * (ajj - aij + noise)), where aij is the "evaluation" of opinion_ij, and ajj is the self-regard of agent j. STEP 2: REPUTATION A list is created for agent i of all of its out-opinion neighbors excluding itself and its current partner j. The "Evaluation" of opinion_iq is recorded, where q is the randomly selected neighbor. Then, the evaluation of opinion_iq is used to update or influence j's evaluation of q, opinion_jq, using the same equation above. The parameter "k_neighbors" determines the number of other agents about which agent i gossips- i.e. the # of reputations of other turtles i communicates to j. STEP 3: VANITY DYNAMICS. The idea is as follows: "agents tend to reward the agents that value them more positively than they value themselves and to punish the ones that value them more negatively than they value themselves." "Evaluation" of opinion ij is set as follows: Eval_ij = Eval_ij + (w * (aji - aii + noise)), where "w" is the parameter "vanity." In words, agent i's new evaluation of j is the old evaluation plus the difference between j's evaluation of i and i's self-evaluation, plus some noise. Recap: Intensityij = 1 / (1 + exp (-1 * ((aij - aii) / sigma))) aii = aii + (intensityij * rho * (aji - aii + noise)) aij = aij + (intensityij * rho * (ajj - aij + noise)) aij = aij + (w * (aji - aii + noise)) ## HOW TO USE IT (how to use the model, including a description of each of the items in the Interface tab) ## THINGS TO NOTICE (suggested things for the user to notice while running the model) ## THINGS TO TRY (suggested things for the user to try to do (move sliders, switches, etc.) with the model) ## EXTENDING THE MODEL (suggested things to add or change in the Code tab to make the model more complicated, detailed, accurate, etc.) ## NETLOGO FEATURES (interesting or unusual features of NetLogo that the model uses, particularly in the Code tab; or where workarounds were needed for missing features) ## RELATED MODELS (models in the NetLogo Models Library and elsewhere which are of related interest) ## CREDITS AND REFERENCES (a reference to the model's URL on the web if it has 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