globals [ p-heard ;; how many patches have heard the rumor total-positive ;; number of positive reactions total-negative ;; number of negative reactions indifferent-factor ;; an equation that describes the ratio between the offensiveness and the precentage of indifferent agents in the graph. distributer-factor ;; an equation that describes the ratio between the offensiveness and the precentage of ditributeing agents in the graph. positive-factor ;; an equation that describes the ratio between the offensiveness and the precentage of positive agents in the graph. negative-factor ;; an equation that describes the ratio between the offensiveness and the precentage of negative agents in the graph. ] patches-own [ times-heard ;; tracks times the rumor has been heard first-heard ;; clock tick when first heard the rumor just-heard? ;; tracks whether rumor was heard this round -- resets each round opinion ;; Indifferent Dispacher Positive or Negative ] ;;; setup procedures to setup [seed-one?] clear-all set p-heard 0 set total-positive 0 set total-negative 0 ;; set indifferent-factor (offensiveness * -0.002 + 0.019) ;; The factor as calculated based on the model "Social conception of content" set distributer-factor (offensiveness * -0.002 + 0.163) ;; The factor as calculated based on the model "Social conception of content" set positive-factor (offensiveness * -0.006 + 0.585) ;; The factor as calculated based on the model "Social conception of content" set negative-factor (offensiveness * 0.01 + 0.061) ;; The factor as calculated based on the model "Social conception of content" ask patches [ set first-heard -1 set times-heard 0 set just-heard? false ;; The more unique a content is the less agents are indifferent to it. ;;(The factor of 0.012 and the 0.01 constant is made up due to the fact that even the most non-unique contents gets to be distributed sometimes) ifelse random-float 1 > uniqueness * 0.012 + 0.01 [ set opinion ["I"] ] [ let chance random-float 1 ;; Creates the graph according to the precentage that came up from the various opinion factor (Indifferent, Distributer, Positive and Negative) if chance <= negative-factor [ set opinion ["N"] ] if chance > negative-factor and chance <= negative-factor + positive-factor [ set opinion ["P"] ] ;;output-print positive-factor if chance > negative-factor + positive-factor and chance <= negative-factor + positive-factor + distributer-factor [ set opinion ["D"] ] if chance > negative-factor + positive-factor + distributer-factor and chance <= negative-factor + positive-factor + distributer-factor + indifferent-factor[ set opinion ["I"] ] ] recolor ] seed-one reset-ticks end to seed-one ;; tell the center patch the rumor ask patch 0 0 [ hear-rumor 0 ] end to go if all? patches [times-heard > 0] [ stop ] ask patches [ if times-heard > 0 [ spread-rumor ] ] update tick end to spread-rumor ;; patch procedure let neighbor nobody ifelse eight-mode? [ set neighbor one-of neighbors ] [ set neighbor one-of neighbors4 ] if opinion = ["D"] and random-float 1 < uniqueness * 0.01 [ ask neighbor [ set just-heard? true ] ] if opinion = ["P"] and random-float 1 < uniqueness * 0.02 [ ask neighbor [ set just-heard? true ] if times-heard < 2 [ set total-positive total-positive + 1 ] ] if opinion = ["N"] and random-float 1 < uniqueness * 0.03 [ ask neighbor [ set just-heard? true ] if times-heard < 2 [ set total-negative total-negative + 1 ] ] end to hear-rumor [when] ;; patch procedure if first-heard = -1 [ set first-heard when set just-heard? true ] set times-heard times-heard + 1 recolor end to update ask patches with [just-heard?] [ set just-heard? false hear-rumor ticks ] set p-heard count patches with [times-heard > 0] end ;;; coloring procedures to recolor ;; patch procedure if opinion = ["I"] [ set pcolor white ] if opinion = ["D"] [ set pcolor blue ] if opinion = ["P"] [ set pcolor green ] if opinion = ["N"] [ set pcolor red ] if first-heard >= 0 and pcolor != white [ set pcolor black ] end ;;; mouse handling to spread-rumor-with-mouse if mouse-down? [ ask patch mouse-xcor mouse-ycor [ hear-rumor ticks ] display ] end to block-rumor-with-mouse if mouse-down? [ ask patch mouse-xcor mouse-ycor [ set opinion ["I"] hear-rumor ticks ] display ] end @#$#@#$#@ GRAPHICS-WINDOW 195 10 631 447 -1 -1 4.24 1 10 1 1 1 0 1 1 1 -50 50 -50 50 1 1 1 ticks 30.0 PLOT 640 10 1069 203 Rumor Spread time percent 0.0 20.0 0.0 100.0 true false "" "" PENS "default" 1.0 0 -2674135 true "" "plot (p-heard / count patches) * 100" BUTTON 82 373 164 406 Go once go NIL 1 T OBSERVER NIL NIL NIL NIL 0 SWITCH 4 205 176 238 eight-mode? eight-mode? 0 1 -1000 BUTTON 5 32 184 65 Setup setup true NIL 1 T OBSERVER NIL NIL NIL NIL 1 BUTTON 5 373 74 406 Go go T 1 T OBSERVER NIL NIL NIL NIL 0 MONITOR 970 209 1070 254 % Heard (p-heard / count patches)\n * 100 3 1 11 BUTTON 4 68 185 101 NIL spread-rumor-with-mouse T 1 T OBSERVER NIL NIL NIL NIL 0 MONITOR 750 208 850 253 Positive total-positive 17 1 11 MONITOR 641 207 741 252 Negative total-negative 17 1 11 MONITOR 860 208 960 253 Heard count patches with [pcolor = black] 17 1 11 BUTTON 2 105 186 138 NIL block-rumor-with-mouse T 1 T OBSERVER NIL NIL NIL NIL 1 SLIDER 4 241 176 274 offensiveness offensiveness 0 100 16.0 1 1 NIL HORIZONTAL SLIDER 4 277 176 310 uniqueness uniqueness 0 100 83.0 1 1 NIL HORIZONTAL TEXTBOX 8 11 158 29 Setup 11 0.0 1 TEXTBOX 7 348 157 366 Running the model 11 0.0 1 TEXTBOX 7 179 157 197 Key Variables 11 0.0 1 @#$#@#$#@ ## WHAT IS IT? This model studies how a content is spread in a network with regard to its uniqueness and offensiveness. once the initial parameters were set, the centered member of the graph starts spreading the content (if he isn't indifferent to it) to one of his neighbors , the neighbor then spreads it to one of his neighbors, and so on. If the agent has either a positive or negative opinion on the content, his or hers opinion will be taken to account (meaning, it will be counted). Thus we can simulate content spreading and the reactions of the network to it. ## HOW IT WORKS After initial parameters, namely, the offensiveness and the uniqueness of the content are set, the network can be covered by agents that holds one of 4 opinions regarding the content - Indifferent, Distributer, Positive or Negative (correlates to the colors white, blue, green and red respectively). the percentage of each opinion is in correlation with the offensiveness factor according to model of "Social conception of content" (a full credit will appear in next sections). in addition to the offensiveness factor, the indifference of the agents is also correlated with the uniqueness factor by the function P(indifferent)=0.012U+0.01 (this function was made up and could be revisited) - the function's purpose is to define a more subtle connection between uniqueness of content and the probability of an agent being indifferent to it (even "boring" content isn't totally ignored sometimes). Once the graph was setup, we can run the model. on the first tick, the centered agent (in case he isn't indifferent) is choosing one of 4 or 8 (depends on the user's choice) neighbors and spreads the content to him/her. if the agent isn't indifferent he then will also choose a neighbor to spread the content to, and so will the other neighbors. if the agent is just a distributer, the content will simply spread, if, the agent has a positive or negative opinion on the content, his/hers opinion will be counted. Once the content stopped spreading, we could see how the network responded to it by the Positive/Negative counters. ## HOW TO USE IT First, one must define the initial parameters - offensiveness, uniqueness and eight-mode. Once defined, the user can hit "Setup" to create the initial graph of the network. at all times of the simulation, the user can manually turn agents which didn't receive the content to indifferent using the "block-rumor-with-mouse" button, or spread the content to specific agents using the "spread-rumor-with-mouse" button. Each non-indifferent agent that hears the rumor turns black on the graph (looks like he disappeared), and each agent that didn't receive the content remains with its color. indifferent agents stays white all along. The simulation never stops, but the progress is visualized both on the Network-graph with the agents and on the grapth describing the rumor spread as a function of time. Monitors that shows the number of agents that already recieved the content, their precentage, and the number of positive and negative responses appear as well. EIGHT-MODE? is a switch that determines whether at each time step the rumor spreads to one of four randomly chosen neighbors, or one of eight such neighbors. ## EXTENDING THE MODEL Here are some suggestions for ways to extend the model. 1. A more scholarred functions can desribe the ration between offensiveness and uniqueness and the precentage of each opinion. 2. A stability factor could be added to the system the stops the simulation. 3. Probabilities could be assigned to each tick of the simulation depending on the opinion of the agent. 4. An idea of the RumorMill model that could be taken into account here: Can you make a rumor model that does not spread the rumor through spatial proximity? ## NETLOGO FEATURES Note the use of the `neighbors` and `neighbors4` primitives to implement 8-mode and 4-mode, respectively. ## RELATED MODELS RumorMill by Uri Wilensk (Wilensky, U. (1997). NetLogo Rumor Mill model. http://ccl.northwestern.edu/netlogo/models/RumorMill. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.) 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