turtles-own [ network-list risk turtle-attitude use positive negative ] patches-own [ user-visits ] to setup ;;All agents get the same initial attitude off the slider and risk is random-normal assigned ;;Also calls connect-the-turtles which sets up social network that follows inverse power law ;;with each agent having a minimum of two links. ca crt 500 ask turtles [ set shape "person" set color 5 set network-list [] set turtle-attitude attitude show-the-attitude set risk int random-normal 50 25 set use 0 set positive 0 set negative 0 rt random 360 jump random screen-edge-x ] ask patches [ set user-visits 0 ] set pcolor-of patch-at 0 0 red connect-the-turtles end to connect-the-turtles ;;connect-the-turles sets up the inverse power law distribution of network links ;;Each turtle has a minimum of two links ;;note the code assumes 500 turtles and power is 1.5 because of earlier trial-and-error experiments by the programmer locals [ links reps buddy ] set links 2 while [any? turtles with[length network-list = 0] ] [ ask turtles [ set reps int ( 500 * (1 / links ^ (1.5) )) repeat reps [ if not any? turtles with [length network-list = 0] [stop] ask random-one-of turtles with [length network-list = 0] [ repeat links [ set buddy random-one-of turtles set network-list lput (who-of buddy) network-list ] ] ] set links (links + 1) ] ] end ;;GO button to go ask turtles [ rt random 360 fd 1 check-the-buzz if pcolor-of patch-here = red and risk > turtle-attitude [set use (use + 1) if use = 1 [set color red] if use = uses-to-habit [set color blue] how-was-it tell-the-network ] ;;keeps turtle-attitude between 1 and 100 if (turtle-attitude > 100) [set turtle-attitude 100] if turtle-attitude < 1 [set turtle-attitude 1] ] show-the-attitude count-up-druggies market-watch end to how-was-it ;;how-was-it uses goodstuff/badstuff slider values, which are likelihood of good and bad ;;experiences, clearly a drug dependent number. Good and bad can both occur. ;;how-was-it evaluates experiences and changes attitude of turtle as a result. Effect diminishes with experience. ;;if using agent is an addict, no evaluation is made if use >= uses-to-habit [stop] if random 101 <= Goodstuff? [ set positive (positive + 1) set turtle-attitude (turtle-attitude - ( (1 / positive) * 20 ) )] if random 101 <= Badstuff? [ set negative (negative + 1) set turtle-attitude (turtle-attitude + ( (1 / negative) * 40) )] end to tell-the-network ;;Tell-the-network talks to all the agents in the network-list of the agent who used. ;;If the agent in the network list is already addicted, the experience of self has no effect. ;;If self is already addicted, it has a strong negative effect on others in its network. ;;Otherwise self sets the network member attitude to the mean of their two attitudes. ;;Whether or not self and network member are addicted, self offers some drug to everyone in the network-list. ;;If network member uses, it evaluates the experience using how-was-it but does not in turn offer it to it's network-list. locals [ n x id ] set n (length network-list) set x 0 repeat n [ set id (item (x) network-list) if use-of turtle id < uses-to-habit [ ifelse use >= uses-to-habit [set turtle-attitude-of turtle id (turtle-attitude-of turtle id + 20)] [set (turtle-attitude-of turtle id) ((turtle-attitude + turtle-attitude-of turtle id) / 2)] ] ] ;;drug is offered, whatever the status of agents, whatever the pos/neg tally ask turtle id [ if risk > turtle-attitude [set use (use + 1) if use = 1 [set color red] if use = uses-to-habit [set color blue] how-was-it ] set x (x + 1) ] end to check-the-buzz ;;check-the-buzz looks at the accumulated positive and negative experiences of neighborhing turtles and influences the attitude ;;of the turtle doing the checking. Note that negative has greater impact. locals [posbuzz negbuzz] ;;A dependent agent doesn't care what the buzz is because it has no choice. if use >= uses-to-habit [stop] ;;A dependent agent in the neighborhood is a strong turn-off if any? turtles in-radius 2 with [use >= uses-to-habit] [set turtle-attitude (turtle-attitude + 20)] set posbuzz (sum values-from (turtles in-radius 2) [positive]) set negbuzz (sum values-from (turtles in-radius 2) [negative]) set turtle-attitude (turtle-attitude + (2 * negbuzz) - (posbuzz)) end to show-the-attitude ;;plots attitude distribution among 500 agents. set-current-plot "attitude" set-plot-pen-mode 1 set-plot-pen-interval 10 histogram-from turtles [turtle-attitude] end to count-up-druggies ;;plots number of agents at risk, number who have used at least once, and number who have become addicted. set-current-plot "users/dependent" set-plot-pen-mode 0 set-current-plot-pen "users" plot count turtles with [use >= 1] set-current-plot-pen "addicts" plot count turtles with [use >= uses-to-habit] set-current-plot-pen "at risk" plot count turtles with [risk > turtle-attitude] end ;;MARKET-WATCH ;;Patches keep track of using at risk turtles in neighborhood and turn red at certain accumulated uses to market-watch ;;adds more patches where drug is available as more users who are still at risk move through the neighborhood. ask patches [ set user-visits (user-visits + count turtles-here with [use > 0 and risk > turtle-attitude]) ;;number that follows indicates how fast market can respond with max of 125 patches if count patches with [pcolor = red] = 125 [stop] ;;originally number of visits was 300. I'm experimenting with different values to see how that works. if user-visits + (sum values-from neighbors [user-visits]) > demand-response [set pcolor red] ] end @#$#@#$#@ GRAPHICS-WINDOW 409 10 891 513 17 17 13.5 1 10 1 1 1 CC-WINDOW 689 528 972 626 Command Center SLIDER 7 49 179 82 Badstuff? Badstuff? 0 100 30 1 1 NIL BUTTON 6 10 87 43 Setup setup NIL 1 T OBSERVER T PLOT 7 149 207 299 attitude attitude agents 0.0 100.0 0.0 500.0 false false PENS "default" 20.0 1 -16776961 true SLIDER 7 116 179 149 attitude attitude 0 100 50 1 1 NIL BUTTON 101 10 182 43 Go go T 1 T OBSERVER NIL MONITOR 193 93 243 142 users count turtles with [use >= 1] 3 1 MONITOR 194 40 244 89 addicts count turtles with [use >= 5] 3 1 SLIDER 6 83 178 116 Goodstuff? Goodstuff? 0 100 70 1 1 NIL PLOT 10 301 334 563 users/dependent ticks u/d 0.0 100.0 0.0 100.0 true false PENS "users" 1.0 0 -65536 true "addicts" 1.0 0 -16776961 true "at risk" 1.0 0 -11352576 true SLIDER 475 548 647 581 uses-to-habit uses-to-habit 0 20 5 1 1 NIL SLIDER 475 514 647 547 demand-response demand-response 0 500 100 25 1 NIL @#$#@#$#@ Drugtalk models how experiences with an illicit drug, evaluations of those experiences transmitted through social and spatial networks, and encounters with addicted agents lead to different rates of use and addiction. The model is described in detail and discussed in the context of ethnographic research and model validity in a forthcoming article in the Journal of Artificial Societies and Social Simulation entitled "Real Agents and Simulated Agents: "Emics" and "Etics" in Artificial Societies by Michael Agar (magar@anth.umd.edu). The current model always uses 500 agents and assigns a "risk" value based on a random-normal distribution. An agent's risk value does not change. "Attitude" is set with a slider on the interface and all agents receive that value initially. Agent attitudes change with experience, those changes shown on the "Attitude" bar graph on the interface. A red patch will appear at the center, that patch representing availability of a new drug. The number of patches will increase with time if use takes off. The rapidity of growth of new patches can be adjusted with the "demand-response" parameter on the interface. An agent is "at-risk" if its risk value is greater than its attitude value. If it is at-risk, and it lands on a red patch, or if an agent in its network offers the drug to it, it will use. The "users/dependent" graph shows the number of agents who are at-risk, who have used at least once, and who are addicted. "Addicts" are agents who have used a certain number of times, that number set as a parameter on the "uses-to-habit" slider on the interface. "Badstuff?" and "Goodstuff?" sliders on the interface vary the average quality of the drug experience. The higher the number, the more likely the experience will be positive or negative. Note that the model allows both results to occur--an experience can be both good and bad. 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@#$#@#$#@ NetLogo 2.0.1 @#$#@#$#@ @#$#@#$#@ @#$#@#$#@