Projet_14.nlogo_3d.nlogo3d
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## WHAT IS IT?
This software has been designed from the results of work done by the Public Opinion Research Group https://www.grop.ca
This work has focused on modelling the Quebec electorate facing political issues. Surveys have revealed the close relationship among the electorate between the bias level on these issues and the importance of representations underlying the accession of voters to these issues, ie the significance of this adhesion for each of the voters.
The operation of this multi-agent simulator is established on this relationship. It models the transmission in a population of a bipolar view.
Four factors are used to simulate the rules of meme transmission: prevalence, polarization of opinion, influence and social links.
Prevalence simulates the quality and quantity of neural representations an individual could have an opinion or meme. The meme could be more or less prominent or polarize in the mind of its owner. Those two factors act like the role of CO2 attributes to global warming and the profoundness of representations against or in favour of its role. Influence is the ability of individuals to dissipate their own meme and social links simulate the relations of proximity or randomness among individuals sharing similar memes or having antagonistic opinions.
## HOW TO USE IT
By pressing "Setup", the model creates a population of agents according to the size of the population of agents desired in the "pop" window and which can follow two distributions: the first is only random and assigns multiple links to all agents, the second establishes links between agents according to the proximity of the agents with regard to their "opinion" and uses the "update-networks" procedure. The parameters of this procedure can be modified with the sliders "link-removal-threshold" establishing a threshold to eliminate links between agents whose opinions are distant and "link-formation-threshold" creating links between agents whose opinions are closer.It is also possible to multiply the number of links by pressing the "links" button which creates a new call to the "update-networks" procedure.
It is also possible to upload a file containing an agent distribution. This may have been produced using the "File" choice in the "Output" selector. If the file created contains several iterations, the distribution can be chosen according to the iteration number of the "choice_iter" entry. It is also possible to import files created by other software as long as the columns are separated by spaces and the columns from left to right contain the iteration number and the prevalence values (between 0 and 99 ), opinion and influence (between 0 and 1).
The user can also create a proportion of meta-influencers distributed either in the entire population of agents either on the left side or on the right side with the "meta-influencers-selection" selector. The proportion of the whole population is selected using the "meta-influencers" slider. The value of the influence of the selected agents is then set to its maximum value, 1. At any time during the simulation, the user can also insert meta influencers by pressing the "Influential" button.
A modulation of the prevalence according to the change of opinion of the agents can also be activated. A modulation rate is through the "modulation-prevalence" slider. In the same way, it is possible to vary the influence of the agents according to the capacity of agent to modify the opinion of the other agent according to a rate which one can establish in the entry "rate-infl" . It is recommended to add a noise level to the simulation in the "noise" input in order to take into account external variables that may interfere with the simulation.
The "max_iter" entry sets the maximum number of iterations per trial. The "threshold" button establishes a threshold setting the proportion of agents on the right side that must be exceeded to count in the calculation of "Major% right".
The "Output" selector allows the "values" choice to extract the parameters, prevalence, opinion and influence for each of the agents. as well as the proportion of agents on the right side, during each iteration. In the "Statistics" selection, the following results are obtained for each iteration: the average of the opinion parameter for all the agents, the median of the values of the opinion, prevalence and influence parameters of the left and right sides respectively. Finally, the "file" selection allows, as we have seen above, to save the parameters of all the agents (excluding the distribution of links) in order to be able to download them during another simulation.
You can also choose the number of consecutive attempts of the same simulation by indicating this number in the "nb_try" entry (by default 1). The "max_iter" entry gives the maximum number of iterations for each trial. You push on the button "Go" to lunch a simulation.
The simulator can create events that modify the course of the simulation. The contour of the event is established choosing the upper and lower limits for the opinion parameter ("low_meme" and "high_meme") which are between -1 and 1, as well as the upper and lower limits for the prevalence being located between 0 and 99. The button "On/Off to left" allows to choose the movement towards the left side if on "On" and on the right side if on "Off". Once these selections have been made, the "event_size" slider is used to choose the size of the change on the opinion scale and the "prev_change" slider is used to establish the size of the change in prevalence for each of the selected agents. more or less. The "event" button is used to activate the event at the time chosen by the user, while the "On/Off (auto_event)" button is used to automate the launch of the event at the iteration chosen with the entry tick event. It is also possible to select only the agents defined at the starting point as being of the right side type or of the left side type by activating on "On" the "On/off same set" button.
Twelve monitors show the following results:
• Agents % left: the proportion of agents on the left side
• Agents% right: the proportion of agents on the right side
• Iter: the current iteration number
• Influence left: the median value of the Influence factor for agents on the left side
• Influence right: the median value of the Influence factor for agents on the right side
• Try: the current test number
• Opinion left: the median value of the Opinion factor for agents on the left side
• Opinion right: The median value of the Opinion factor for agents on the right side
• Inversion %: the proportion of side inversion of the agents at each iteration
• Preval left: the median value of the Prevalence factor for agents on the left side
• Preval right: the median value of the Prevalence factor for agents on the right side
• Major right: The number of times agents exceeded the threshold on the right side for one trial.
The graph presents the curves of the proportion of agents on the right side and the left side, as well as the factors Opinion, Prevalence, Influence for each of these two sides.
## THINGS TO NOTICE
This Netlogo simulation program used GPT-4 to enhance the simulator and add new functionalities. Here is the initial proposal received from this LLM:
To create an agent-based simulation model using Netlogo according to the characteristics described, here is a basic plan for structuring the model:
1. Agents: Create two types of Agents: Partisans and Centrists. Supporters have strongly held opinions and are less likely to change their opinion, while centrists have weaker opinions and are more likely to change their opinion.
2. Agent variables: Each agent should have a variable representing their opinion (values between -1 and 1, for example), a variable representing their salience (measured in percentage) and a variable describing their social network (list of agents connected).
3. Initialization: At the start of the simulation, assign random opinions and levels of pregnance to the agents. Connect agents to form a social network based on desired connectivity settings.
4. Forwarding: Create rules for forwarding opinions between agents. For example, when an agent interacts with another agent of its social network having a higher pregnance, it adopts the opinion of this agent with a probability proportional to the difference in pregnance.
5. Polarization: Modify transmission rules to account for polarization of opinions. Agents can take more extreme or moderate views depending on their interactions.
6. Updating social networks: Agents' social networks may change over time. Create rules to determine how agents form new connections or lose connections.
7. Meta-influencers: Introduce special agents (meta-influencers) who have a greater impact on the opinions of other agents. Their influence can be modeled by increasing their salience or changing the rules of transmission for them.
8. Outside Events: Add random or scheduled events that can influence agent opinions, such as news or political changes.
9. Visualization and analysis: Use Netlogo's visualization features to represent agents and their evolution over time. Analyze the results to study the dynamics of opinion transmission and polarization within the population.
By following these steps, you should be able to create an agent-based simulation model using Netlogo to study the transmission of bipolar opinions in a population. You can then adjust and refine the model based on your specific study goals.
## NETLOGO FEATURES
3D model
## CREDITS AND REFERENCES
email: pacotnoir@gmail.com
Translate from french by Google
Comments and Questions
extensions [sound nw] ;; For using soune and Network package globals [ min-prevalence ;; minimale value of prevalence max-prevalence ;; maximal value of prevalence meta-influencers-droit ;; Method of sélection for meta-influencers right field meta-influencers-gauche ;; Methode of sélection for meta-influencers left field iter change total try major ordonnee abcisse profondeur list_data file-in in_data in repet_data opinion-previous ] turtles-own [ opinion ;; Opinion value of agent, between -1 and 1 prevalence ;; Prevalence of agent, between min-prevalence and max-prevalence agent-type ;; Agent type : "Right side" or "Left side" influence ;; Agent level of influence, between 0 and 1 infl_list ] to setup clear-all set repet_data false set iter 0 set min-prevalence 0 set max-prevalence 99 set-default-shape turtles "person" set try 1 set major 0 create rapport end to create ;; Créer les agents Right sides create-turtles pop / 2 [ set agent-type "Right side" set opinion random-float 1 ;; Agent random opinion attribution between 0 and 1 if opinion > 0 [set color blue] set prevalence random-float (opinion * 100) ;; Agent random prevalence attribution in function of opinion polarization set influence random-float 1 ;; Agent random influence attribution between 0 and 1 set abcisse opinion * 16 set ordonnee prevalence / 5.9 set profondeur influence * 16 setxyz abcisse ordonnee profondeur ] ;; Créer les agents Left sides create-turtles pop / 2 [ set agent-type "Left side" set opinion random-float 1 - 1 ;; Agent random opinion attribution between -1 and 0 if opinion < 0 [set color red] set prevalence random-float ((abs(opinion)) * 100) ;; Agent random prevalence attribution in function of opinion polarization set influence random-float 1 ;; Agent random influence attribution between 0 and 1 set abcisse opinion * 16 set ordonnee prevalence / 5.9 set profondeur influence * 16 setxyz abcisse ordonnee profondeur ] influenceurs ;; create metas-influencers reset-ticks ;; Créer un réseau social if random_links = true [ask turtles [ ask other turtles [ if random-float 1 < 0.1 [ create-link-with myself ] ] ask links [hide-link] ]] if random_links = false [ update-networks update-networks] set total 0 set change 0 end to rapport ;; titles for Statistics or Values inside compute-statistics if output = "Statistics" [ output-print (word "Try ; " "Iter ; " "Opinion global ; " "Opinion right side ; " " Opinion left side ; " "Prevalence right side ; " "Prevalence left side ; " "Influence right side ; " "Influence left side ; " "Major") ] if output = "Values" [output-print (word "Try ; " "Ticks ; " "Agents ; " "Prevalence ; " "Opinion ; " "Influence ;" " meme droit") ] if output = "File" [ ask turtles [ let pre prevalence let mem opinion let infl influence let ti ticks output-print (word ti " " pre " " mem " " infl ) ] ] end to influenceurs ;; create status of meta-influencers if meta-influencers-selection = "All" [ ask n-of (round (count turtles * meta-influencers)) turtles [ set influence 1 ;; maximal influence (1) to X % of agents set color yellow ] ] if meta-influencers-selection = "Right side" [ set meta-influencers-droit round (count turtles * meta-influencers) ask n-of meta-influencers-droit turtles with [opinion > 0 ] [ set influence 1 ;; maximal influence (1) to X % of agents from type "Right side" set color yellow ] ] if meta-influencers-selection = "Left side" [ set meta-influencers-gauche round (count turtles * meta-influencers) ask n-of meta-influencers-gauche turtles with [opinion < 0] [ set influence 1 ;; maximal influence (1) to X % of agents from type "Left side" set color yellow ] ] end to go ifelse iter < max_iter [set iter iter + 1 if auto_event = true [if tick-event = iter [event]] update-opinions if output = "Statistics" [ let avg-opinion mean [opinion] of turtles let positive-opinion median [opinion] of turtles with [opinion >= 0] let negative-opinion median [opinion] of turtles with [opinion < 0] let positive-prevalence median [prevalence] of turtles with [opinion >= 0] / 100 let negative-prevalence median [prevalence] of turtles with [opinion < 0] / 100 let negative-influence median [influence] of turtles with [opinion < 0] let positive-influence median [influence] of turtles with [opinion >= 0] Let Maj Major let ti iter output-print (word try " ; " ti " ; " avg-opinion " ; " positive-opinion " ; " negative-opinion " ; " positive-prevalence " ; " negative-prevalence " ; " positive-influence " ; " negative-influence " ; " Maj) ] tick if cumulative = False [set change 0 set total 0] colorer ;;rafraichir le graphique if refresh = true [if ticks > 200 [reset-ticks clear-plot]] ;set iter iter + 1 if threshold <= (count turtles with [opinion > 0]) / (pop / 100) [set major major + 1] ] [ifelse try < nb_try [set try try + 1 set major 0 clear-turtles clear-plot ifelse repet_data = true [data] [create] set iter 0 set Major 0 set min-prevalence 0 set max-prevalence 99] [sound:play-note "Tubular Bells" 60 64 1 stop]] end to update-opinions ask turtles [ let target one-of link-neighbors ;; Selecting a neighbor randomly in the social network if target != nobody [ ;; verifying that this neighbor exist let difference-in-prevalence [prevalence] of self - [prevalence] of target ;; Calculer la différence de prégnance entre les deux agents ;; If the prevalence of the agent is lower than that of the neighbour, the agent adopts the opinion of the neighbor with a probability proportional to the difference of prevalence if difference-in-prevalence < 0 [ let probability-of-adoption abs difference-in-prevalence / max-prevalence ;; Calculate the probability of adoption based on the difference in prevalence let opinion-difference abs (opinion - [opinion] of target) ;; Calculate the difference of opinion between the agent and the neighbor set opinion-difference opinion-difference * [influence] of target ;; Change adoption likelihood based on bias and influence set probability-of-adoption probability-of-adoption * (1 - polarization-factor * opinion-difference) ;filter the probability of adoption based on neighbor influence and agent influence. if ([influence] of target > probability-of-adoption or [influence] of self < probability-of-adoption ) [set opinion-previous opinion ;random-float 1 set opinion [opinion] of target ;Adopt the neighbor's opinion set total total + 1 ;print total ;make the influence of the agent evolve according to the change of opinion of the agent if vary-influence = true [if abs(opinion-previous) > abs (opinion) [set influence (influence + rate-infl) if influence > 1 [set influence 1]] if abs(opinion-previous) < abs (opinion) [set influence (influence - rate-infl) if influence < 0 [set influence 0]]] ; faire varier l'influence if (opinion < 0 and opinion-previous > 0) or (opinion > 0 and opinion-previous < 0) [set change change + 1] ]] ] ;;modulation of the prevalence according to the difference of opinion if modulation-prevalence = true [ if prevalence > abs opinion * 100 [set prevalence prevalence - abs (opinion - opinion-previous) * [influence] of self * Rate-modulation] if prevalence < abs opinion * 100 [set prevalence prevalence + abs (opinion - opinion-previous) * [influence] of self * Rate-modulation] ] ;; add stochastic noise to each iteration if random-float 1 < noise [ ;; Change the agent's opinion by adding or subtracting a random value less than noise set opinion opinion + (random-float 0.4 - 0.2) ;; Make sure the opinion stays in the interval [-1, 1] if opinion > 1 [ set opinion 1 ] if opinion < -1 [ set opinion -1 ] ] ;;position agents on the chart based on opinion and influence set abcisse opinion * 16 set ordonnee prevalence / 5.9 * 4 / 5 set profondeur influence * 16 setxyz abcisse ordonnee profondeur if output = "Values" or output = "File" [compute-statistics] ] end to colorer ; change the color of agents who have changed sides ask turtles [ifelse opinion >= 0 [ set color blue ] [ set color red ] if influence = 1 [set color yellow] ] end to update-networks ;; Remove links between agents with opinions that are too different ask links [ if abs ([opinion] of end1 - [opinion] of end2) > link-removal-threshold [ die ] ] ;; Form bonds between like-minded agents ask turtles [ let potential-friends other turtles with [abs (opinion - [opinion] of myself) <= link-formation-threshold] let friend one-of potential-friends if friend != nobody and not link-neighbor? friend [ create-link-with friend ]] end to compute-statistics if output = "Values" [ ;ask turtles [ let pre prevalence let mem opinion let infl influence let ag who let ti ticks let ess try let memed (count turtles with [opinion > 0]) / (pop / 100) let maj major output-print (word ess " ; " ti " ; " ag " ; " pre " ; " mem " ; " infl " ; " memed) ;] ] if output = "File" [ ;ask turtles [ let pre prevalence let mem opinion let infl influence let ti ticks output-print (word ti " " pre " " mem " " infl ) ;] ] end ;;reading an input file of list of agents to in_file ;File d'entrée carefully [ set file-in user-file if (file-in != false) [ set list_data [] file-open file-in while [not file-at-end?] [ set list_data sentence list_data (list (list file-read file-read file-read file-read)) ] file-close ;; Add this line to close the File after reading it user-message "File uploaded!" set in true ] ] [ user-message "File read error" ] data end to data clear-turtles clear-links let tick_to_load choice_iter ifelse (is-list? list_data) [ let filtered_data filter [row -> first row = tick_to_load] list_data create-turtles length filtered_data [ let my_index who let agent_data item my_index filtered_data set prevalence item 1 agent_data set opinion item 2 agent_data set influence item 3 agent_data setxy opinion prevalence if opinion < 0 [set color red set agent-type "Left side"] if opinion > 0 [set color blue set agent-type "Right side"] if influence = 1 [set color yellow] ] ] [ set in false user-message "Read error" ] if random_links = true [ask turtles [ ask other turtles [ if random-float 1 < 0.1 [ create-link-with myself ] ] ]] if random_links = false [update-networks update-networks] influenceurs update-opinions set repet_data true end to event ; moving agents to the right or left side by increasing or decreasing the prevalence ask turtles [ ifelse meme_set = true [ if to_left = false [if agent-type = "Right side" [if opinion < 0 [set opinion opinion + event_size if opinion > 1 [set opinion 1]]]] if to_left = true [if agent-type = "Left side" [if opinion > 0 [set opinion opinion - event_size if opinion < -1 [set opinion -1]]]]] [if to_left = false [if opinion < high_meme and opinion > low_meme and prevalence < high-prev and prevalence > low-prev [set opinion opinion + event_size if prev_change != 0 [set prevalence prevalence + prev_change] if opinion > 1 [set opinion 1]]] if to_left = true [if opinion > low_meme and opinion < high_meme and prevalence > low-prev and prevalence < high-prev [set opinion opinion - event_size if prev_change != 0 [set prevalence prevalence + prev_change] if opinion < -1 [set opinion -1]]] ]] end
There is only one version of this model, created 8 months ago by Pierre-Alain Cotnoir.
Attached files
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Projet_14.nlogo_3d.nlogo3d.png | preview | Preview for 'Projet_14.nlogo_3d.nlogo3d' | 8 months ago, by Pierre-Alain Cotnoir | Download |
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