globals [average-path-length VM-WOM TM-WOM] turtles-own [see-VM? see-TM? go-online? VM-interest? VM-persuaded? TM-Persuaded? Seek-Info? Provide-Info? VM-clothes? TM-clothes?] links-own [connected?] ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Setup to setup clear-all set-default-shape turtles "circle" crt population [ set color gray + 2 ] layout-circle (sort turtles) max-pxcor - 1 wire-neighbor ask links [ if (random-float 100) <= Globalization [ ;if (random-float 100) <= 0 [ ;; Use this code if you want to make the model run faster. This setup, however, increase localization within the world. let agentx end1 if [count link-neighbors] of end1 < (count turtles - 1) [ let agenty one-of turtles with [ (self != agentx) and (not link-neighbor? agentx)] ask agentx [ create-link-with agenty [ set color cyan set connected? true ]]]] if connected? = true [ die]] ask links [ if (random-float 100) < Additional-Density [ let agent1 end1 if [count link-neighbors] of end1 < (count turtles - 1)[ let agent2 one-of turtles with [(self != agent1) and (not link-neighbor? agent1)] ask agent1 [ create-link-with agent2 [ set color cyan]]]]] ask turtles [set see-VM? false set see-TM? false] ask turtles [set VM-interest? false set VM-persuaded? false set TM-Persuaded? false] ask turtles [set Seek-Info? false set Provide-Info? false] ask turtles [set VM-clothes? false set TM-clothes? false] end to wire-neighbor let n 0 while [n < count turtles] [link-with-neighbor turtle n turtle ((n + 1) mod count turtles) link-with-neighbor turtle n turtle ((n + 2) mod count turtles) set n n + 1] end to link-with-neighbor [agent1 agent2] ask agent1 [ create-link-with agent2 [set color cyan]] end ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Run Model to go if all? turtles [color != grey + 2] [stop] tick interest dissemination interest final-decision update-plots end to awareness ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Awareness Stage ask turtles [set go-online? false] ask turtles [ if (random-float 100) <= (random-normal (internet-frequency) (internet-frequency * .05)) [ set go-online? true]] ask turtles with [go-online? = true] [ if (random-float 100) <= (random-normal 65 3.25) [ set see-VM? true]] ask turtles with [go-online? = true] [ if (random-float 100) <= (random-normal 90 4.5) [ set see-TM? true]] end to interest ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Interest Stage ;ask turtles with [see-VM? = true] [ ; if (random-float 100) <= (random-normal 34 1.7) [ ; set VM-interest? true]] ask n-of (((random-normal (TM-persuasiveness) (TM-persuasiveness * .05)) / 100) * (count turtles with [see-TM? = true])) turtles with [see-TM? = true] [ ; Use this option to allow oberserver the ability to change the credibility of the TM ad set TM-Persuaded? true] ask n-of (((random-normal (VM-persuasiveness) (VM-persuasiveness * .05)) / 100) * (count turtles with [see-VM? = true])) turtles with [see-VM? = true] [ ; Use this option to allow oberserver the ability to change the credibility of the VM ad set VM-persuaded? true] ask n-of (((random-normal (VM-interest-level) (VM-interest-level * .05)) / 100) * (count turtles with [see-VM? = true])) turtles with [see-VM? = true] [ ; Use this option to allow oberserver the ability to change the credibility of the VM ad set VM-interest? true] ;ask turtles with [see-TM? = true] [ ; if (random-float 100) <= (random-normal 16 .8) [ ; set TM-Persuaded? true]] end to dissemination ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Dissemination Stage (WOM) ask turtles with [VM-interest? = true] [ ask n-of ((random-normal (dissemination-rate / 100) (dissemination-rate * .05 / 100)) * (count link-neighbors)) link-neighbors [ set see-VM? true]] ;ask turtles with [TM-Persuaded? = true and Provide-Info? = true] [ ; if count link-neighbors with [Seek-Info? = true] >= 1 [ ; ask n-of round (random-normal (dissemination-rate / 100) (dissemination-rate * .05 / 100)) link-neighbors with [Seek-Info? = true] [ ; set see-TM? true ; interest]]] ;; This code abandoned becuase I now assume agents will not want to share a traditional advertisement with their peers since such traditional ;;advertisements are not spreadable (they take the form of a web banner or graphic on a web page) ;ask turtles with [go-online? = true] [ ; if (random-float 100) <= (random-normal 80 4) [ ; set see-TM? true]] end ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Final Decision to final-decision let VM1-decision 0 let TM1-decision 0 ifelse ticks = 0 [ask turtles with [VM-persuaded? = true and TM-Persuaded? = false] [ set VM1-decision ((count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false] * 1.43)) set TM1-decision (count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false]) ifelse VM1-decision >= TM1-decision [set VM-clothes? true set TM-clothes? false] [set TM-clothes? true set VM-clothes? false]]] [ask turtles with [VM-persuaded? = true and TM-Persuaded? = false] [ ifelse VM-clothes? = true [set VM1-decision (((count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false and VM-clothes? = true]) + (count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false and VM-clothes? = false] * .20)) * 3.57) set TM1-decision ((count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false and TM-clothes? = true]) + (count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false and TM-clothes? = false] * .20))] [set VM1-decision (((count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false and VM-clothes? = true]) + (count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false and VM-clothes? = false] * .20)) * 1.43) set TM1-decision ((count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false and TM-clothes? = true]) + (count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false and TM-clothes? = false] * .20))] ifelse VM1-decision >= TM1-decision [set VM-clothes? true set TM-clothes? false] [set TM-clothes? true set VM-clothes? false]]] let VM2-decision 0 let TM2-decision 0 ifelse ticks = 0 [ask turtles with [TM-persuaded? = true and VM-Persuaded? = false] [ set TM2-decision ((count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false] * 1.43)) set VM2-decision (count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false]) ifelse TM2-decision >= VM2-decision [set TM-clothes? true set VM-clothes? false] [set VM-clothes? true set TM-clothes? false]]] [ask turtles with [TM-persuaded? = true and VM-Persuaded? = false] [ ifelse TM-clothes? = true [set VM1-decision (((count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false and TM-clothes? = true]) + (count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false and TM-clothes? = false] * .20)) * 3.57) set TM1-decision ((count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false and VM-clothes? = true]) + (count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false and VM-clothes? = false] * .20))] [set VM1-decision (((count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false and TM-clothes? = true]) + (count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false and TM-clothes? = false] * .20)) * 1.43) set TM1-decision ((count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false and VM-clothes? = true]) + (count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false and VM-clothes? = false] * .20))] ifelse TM2-decision >= VM2-decision [set TM-clothes? true set VM-clothes? false] [set VM-clothes? true set TM-clothes? false]]] let VM3-decision 0 let TM3-decision 0 ask turtles with [TM-Persuaded? = true and VM-persuaded? = true and VM-clothes? = true] [ set VM3-decision (((count link-neighbors with [VM-clothes? = true and TM-persuaded? = true] * .8) + (count link-neighbors with [VM-clothes? = true and TM-persuaded? = false]) + (count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false and VM-clothes? = false] * .20)) * 3.03) set TM3-decision (((count link-neighbors with [TM-clothes? = true and VM-persuaded? = true] * .8) + (count link-neighbors with [TM-clothes? = true and VM-persuaded? = false]) + (count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false and TM-clothes? = false] * .20))) ifelse VM3-decision >= TM3-decision [set VM-clothes? true set TM-clothes? false] [set TM-clothes? true set VM-clothes? false]] let VM4-decision 0 let TM4-decision 0 ask turtles with [TM-Persuaded? = true and VM-persuaded? = true and TM-clothes? = true] [ set TM4-decision (((count link-neighbors with [TM-clothes? = true and VM-persuaded? = true] * .8) + (count link-neighbors with [TM-clothes? = true and VM-persuaded? = false]) + (count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false and TM-clothes? = false] * .20)) * 3.03) set VM4-decision ((count link-neighbors with [VM-clothes? = true and TM-persuaded? = true] * .8) + (count link-neighbors with [VM-clothes? = true and TM-persuaded? = false]) + (count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false and VM-clothes? = false] * .20)) ifelse TM4-decision >= VM4-decision [set TM-clothes? true set VM-clothes? false] [set VM-clothes? true set TM-clothes? false]] let VM5-decision 0 let TM5-decision 0 ifelse ticks = 0 [ask turtles with [TM-Persuaded? = true and VM-persuaded? = true and TM-clothes? = false and VM-clothes? = false] [ set TM5-decision (count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false]) set VM5-decision (count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false]) ifelse TM5-decision >= VM5-decision [set TM-clothes? true set VM-clothes? false] [set VM-clothes? true set TM-clothes? false]]] [ask turtles with [TM-Persuaded? = true and VM-persuaded? = true and TM-clothes? = false and VM-clothes? = false] [ set TM5-decision (((count link-neighbors with [TM-clothes? = true]) + (count link-neighbors with [TM-persuaded? = true and VM-Persuaded? = false and TM-clothes? = false] * .20))) set VM5-decision (((count link-neighbors with [VM-clothes? = true]) + (count link-neighbors with [VM-persuaded? = true and TM-Persuaded? = false and VM-clothes? = false] * .20))) ifelse TM5-decision >= VM5-decision [set TM-clothes? true set VM-clothes? false] [set VM-clothes? true set TM-clothes? false]]] ask turtles [ if VM-clothes? = true and TM-clothes? = false [ set color red]] ask turtles [ if TM-clothes? = true and VM-clothes? = false [ set color green]] end to online ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; To Go Online ask turtles [set go-online? false] ask turtles [ if (random-float 100) <= (random-normal (internet-frequency) (internet-frequency * .05)) [ set go-online? true]] end ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Plotting Elements to update-plots if ticks >= 1 [ set-current-plot "ROI Graph" clear-plot plot (count turtles with [VM-clothes? = true]) plot (count turtles with [TM-clothes? = true]) set-current-plot "% Undecided" plot (count turtles with [TM-Persuaded? = false and VM-interest? = false] / count turtles * 100) set-current-plot "% Seen VM" plot (count turtles with [see-VM? = true] / count turtles * 100) set-current-plot "% Wearing VM Clothes v. TM Clothes" set-current-plot-pen "TM" plot ((count turtles with [TM-Clothes? = True]) / (count turtles with [VM-Clothes? = True] + count turtles with [TM-Clothes? = True]) * 100) set-current-plot-pen "VM" plot ((count turtles with [VM-Clothes? = True]) / (count turtles with [VM-Clothes? = True] + count turtles with [TM-Clothes? = True]) * 100) set-current-plot "% Haven't seen the ads" set-current-plot-pen "Both" plot (count turtles with [see-VM? = false and see-TM? = false] / count turtles * 100) set-current-plot-pen "TM" plot (count turtles with [see-TM? = false] / count turtles * 100) set-current-plot-pen "VM" plot (count turtles with [see-VM? = false] / count turtles * 100) set-current-plot "VM Clothes:TM Clothes" plot ((count turtles with [VM-Clothes? = True]) / (count turtles with [TM-Clothes? = True]) * 100) ] end ; *** NetLogo 4.0.3 Model Copyright Notice *** ; ; Copyright 2008 by Tony Wang. All rights reserved. ; ; Permission to use, modify or redistribute this model is hereby granted, ; provided that both of the following requirements are followed: ; a) this copyright notice is included. ; b) this model will not be redistributed for profit without permission ; from Tony Wang. ; Contact Tony Wang for appropriate licenses for redistribution for ; profit at tonywang@wharton.upenn.edu. ; ; To refer to this model in academic publications, please use: ; Wang, Tony. (2008). NetLogo Viral Marketing and Fashion Trends Model. ; Department of Philosophy, Politics, and Economics, ; University of Pennsylvania, Philadelphia, PA. ; ; In other publications, please use: ; Copyright 2008 Tony Wang. All rights reserved. ; ; *** End of NetLogo 4.0.3 Model Copyright Notice *** @#$#@#$#@ GRAPHICS-WINDOW 196 51 555 431 20 20 8.5122 1 10 1 1 1 0 0 0 1 -20 20 -20 20 0 0 1 ticks CC-WINDOW 5 514 1226 609 Command Center 0 BUTTON 6 10 70 43 Setup setup NIL 1 T OBSERVER NIL NIL NIL NIL SLIDER 6 152 178 185 Population Population 0 500 500 1 1 People HORIZONTAL BUTTON 180 10 268 43 Go (Once) Go NIL 1 T OBSERVER NIL NIL NIL NIL SLIDER 7 276 179 309 VM-interest-level VM-interest-level 0 100 16 1 1 % HORIZONTAL SLIDER 7 318 179 351 TM-Persuasiveness TM-Persuasiveness 0 100 16 1 1 % HORIZONTAL SLIDER 5 430 177 463 Dissemination-Rate Dissemination-Rate 0 100 59 1 1 % HORIZONTAL TEXTBOX 8 362 194 418 Standard persuasiveness levels are...\nVM-Persuasiveness | 16%\nVM-Interest-Level | 16%\nTM-Persuasiveness | 16% 11 0.0 1 PLOT 581 14 782 162 ROI Graph Clothing Brand (VM then TM) Loyal Consumers 0.0 2.0 0.0 10.0 true false PENS "default" 1.0 1 -16777216 true PLOT 1014 10 1214 160 % Undecided Ticks % Undecided 0.0 10.0 0.0 100.0 true false PENS "default" 1.0 0 -16777216 true PLOT 801 10 1001 160 % Haven't Seen the Ads Ticks % Haven't Seen the Ads 0.0 10.0 0.0 10.0 true true PENS "VM" 1.0 0 -2674135 true "TM" 1.0 0 -10899396 true "Both" 1.0 0 -13345367 true PLOT 586 170 995 500 % Wearing VM Clothes v. TM Clothes Ticks % Wearing VM Clothes 0.0 10.0 0.0 100.0 true false PENS "TM" 1.0 0 -10899396 true "VM" 1.0 0 -2674135 true SLIDER 6 232 178 265 VM-Persuasiveness VM-Persuasiveness 0 100 16 1 1 % HORIZONTAL BUTTON 78 10 171 43 NIL Awareness NIL 1 T OBSERVER NIL NIL NIL NIL SLIDER 6 190 178 223 Additional-Density Additional-Density 0 100 0 1 1 NIL HORIZONTAL SLIDER 6 52 181 85 Internet-Frequency Internet-Frequency 0 100 80 1 1 % HORIZONTAL PLOT 1017 168 1217 318 % Seen VM Ticks % Seen VM 0.0 10.0 0.0 100.0 true false PENS "default" 1.0 0 -16777216 true BUTTON 279 10 400 43 Go (Continuous) Go T 1 T OBSERVER NIL NIL NIL NIL TEXTBOX 8 469 184 497 Standard dissemination rate is 59% 11 0.0 1 TEXTBOX 12 88 196 116 Standard internet use rate is 80% 11 0.0 1 SLIDER 7 109 179 142 Globalization Globalization 0 100 100 1 1 NIL HORIZONTAL PLOT 1014 348 1214 498 VM Clothes:TM Clothes Ticks Odds 0.0 10.0 0.0 10.0 true false PENS "default" 1.0 0 -16777216 true @#$#@#$#@ WHAT IS IT? ----------- The Viral Marketing and Fashion Trends model explores the nature of online viral marketing based advertisements in terms of spread (dissemination rate), conversion (return on investment), and comparison to traditional advertisement. The model is run in a closed world and assumes a competetive setting with a company that only uses online viral ads competing against a company that only uses online traditional ads. The model interface enables users to visualize the the effectiveness of a fashion trend that relies solely on viral marketing in garnering loyal consumers versus the effectiveness of a fashion trend that only utilizes traditional marketing. HOW IT WORKS ------------ The model has three major procedural components, two of which are standard to any NetLogo model: "SETUP" and "GO." The third is called “AWARENESS” In addition to these three commands there are eight sliding scale variables that the observer is given flexibility in choosing. Each slider corresponds to a key variable factor that affects the model. However, the model also has default values for such variables that reflect the value the factor would take on in reality. These default values are based on research done by business experts in the field of word of mouth research and viral marketing. The “SETUP” command creates the social network configuration of the population in consideration. The three sliders that affect this part of the model are “POPULATION” "GLOBALIZATION" and “ADDITIONAL-DENSITY”. "POPULATION” determines the number of people in the model, "POPULATION" affects the level of globalization the world, and “ADDITIONAL-DENSITY” affects how closely connected individuals in the population are to each other in addition to the already pre-configured small world social network. The more globalized the world is, the more a turtles connections will come from unique networks that his other peers won’t be connected with. Since “ADDITIONAL-DENSITY” asks the links to make the additional connections, the most a single agent can increase his connections by is 100%. The “AWARENESS” command initializes the model by having the advertisements for both clothing lines run, one which only uses viral marketing (the red colored fashion trend) and one that only uses traditional marketing (the green colored fashion trend). The consumers then become aware of one, both or neither of the products depending on whether they are online and which advertisements they see on the internet. The slider that determines the rate of people who go online at any given time is the “INTERNET-FREQUENCY” slider. The rate at which those agents that are online see a given advertisement are pre-determined and use figures based on the research done by Dee T. Allsop, Bryce R. Bassett, and James A. Hoskins of the Harris Interactive for the Journal of Advertising Research. The “GO” command runs the model by running through several steps that ultimately result in the subjects of the model making a decision about whether or not to buy one of the two types of clothing. Of the four remaining sliders three affect the nature of the advertisements used by two fashion trends, they are “VM-INTEREST-RATE”, “VM-PERSUASIVENESS” and “TM-PERSUASIVENESS”. In the case of the viral advertisement, we distinguish between the effects of interest and persuasiveness because viral advertisements can be interesting and entertaining, and thus likely to be spread around peer networks, but not exactly persuasive (for an example see http://www.youtube.com/watch?v=CmSCh5ZkMqk ). Traditional advertisements focus on reaching out to the masses but do not focus on peer to peer dissemination, thus there is only an interest variable affecting it that determines how likely a consumer is to buy it. DISSEMINATION-RATE” describes what percentage of an agent’s connections he will share the viral advertisement with. There are 4 steps that the “GO” command runs through for each tick. First is the “INTEREST” stage. In the “INTEREST” stage, subjects in the population become aware of one, both, or neither of the products. In the “DISSEMINATION” stage, subjects in the population share the viral marketing advertisement with their connections (those they are linked with). In the “FINAL-DECISION” step, the agents decide whether or not to buy the product. Lastly, “UPDATE-PLOTS” simply updates the five plots on the right hand side of the interface. HOW TO USE IT ------------- To start the model, you have to use the “SETUP” command to create the world in which the subjects will populate. The “SETUP” command also creates the social network the agents will organize themselves around. Use “POPULATION” to change the number of agents and “ADDITIONAL-DENSITY” to potentially increase the number of connections by twice the amount there are by default (typically four connections). Then, to run both the viral advertisement and the traditional marketing advertisement, press “AWARENESS.” The “INTERNET-FREQUENCY” slider will affect the percentage of the population that uses the internet. Since we assume that all ads are done online, this slider will affect the initial setup of the model in terms of who sees the traditional advertisement and the spread of the viral advertisement. Lastly, to run the actual model and see the spread of the two clothing lines, press “GO”. To change the effect that the ads have on engaging interest and persuading consumers, use the sliders “VM-INTEREST-RATE”, “VM-PERSUASIVENESS” and “TM-PERSUASIVENESS”. Use the slider “DISSEMINATION-RATE” to change the agents’ willingness to share the viral advertisement. The plots on the right hand side of the model help gauge the spread and effectiveness of the two types of marketing over time. Starting from top to bottom, left to right, the first graph is the ROI graph that shows the number of agents that are wearing the VM clothing and the number of agents that are wearing the TM clothing. Next is the % Wearing VM Clothes graph v. TM Clothes; this graph shows what percentage of the population is wearing VM clothing and what percentage is wearing TM clothing. To check the percentage of people who haven’t seen a certain ad, we use the next graph, the % Haven’t Seen the Ads graph. Notice that the green line, the line that corresponds to those who haven’t seen the TM ad, stays constant. This is on purpose as the model only runs through the traditional marketing advertisement once. However, the red line starts out much higher, which means that the traditional ad has a dissemination advantage in the beginning. The % Undecided Graph shows the % of consumers that have yet to decide which clothing they are going to wear. This may be because they haven’t seen the ad or haven’t been persuaded. The next graph is the % Seen VM, which shows the percent of agents that have seen the viral ad. Lastly, we have the VM Clothes: TM Clothes ratio. This graph shows the ratio of turtles wearing VM clothes to those turtles wearing TM clothes. It is another measurement of effectiveness in addition to the % Wearing VM Clothes v. TM Clothes graph. THINGS TO NOTICE ---------------- Try to note what impacts each of the sliding variables have on the model as a whole Note the path dependency of the model. The percentages of users who go online greatly affect the outcome of the model. Also note the importance of the “DISSEMINATION-RATE” in determining the effectiveness of the viral marketing advertisement. Also, try to notice the spatial location of agents wearing like clothing. Notice that agents that are connected to each other tend to wear the same clothing. THINGS TO TRY ------------- Try experimenting with the sliders previously mentioned in the “Things to Notice Section.” Further experiment between different scenarios and compare the effects. For example: try to compare the effect of a viral ad that is highly persuasive but not interesting. Then, create a viral ad that is extremely interesting but not very persuasive. See the effects that these two sliders have on the viral ad as a whole. Also, see what happens when “VM-INTEREST-RATE”, “VM-PERSUASIVENESS” and “TM-PERSUASIVENESS” are all at the same rate (by default, “VM-PERSUASIVENESS” and “TM-PERSUASIVENESS” are the same). Try to see what happens when they are all at a low rate then at a high rate. Does this change anything even though both ads have the same effect in terms of engaging interest and persuasion? EXTENDING THE MODEL ------------------- Try to use BehaviorSpace and a statistical analytics program such as JMP to see is there is a significant difference between a viral ad and traditional ad in garnering customers based on several initial settings. Then try to see what settings will create the best situation for a viral ad and the best situation for a traditional ad. Then try to find the situation that is the worst. Also, consider the implications of time in this model. While this model iterates through each step in a lienar fashion, in real life, all these steps are going at once and might follow different steps than what this code models. For example, I originally repeated “INTEREST” twice, once after “DISSEMINATION” to code for the possibility that after the dissemination of information, more people would want to view the advertisement again before making a purchasing decision. Indeed, I don’t just see an ad usually then immediately make a decision. Likewise, the model assumes that all friends have equal influence. However, as we know, this is untrue. Try to change the model to better reflect reality or to better study a different aspect of viral marketing. See if adding another competitor changes the outcome or if changing the social network setup from small worlds to another configuration makes a difference. NETLOGO FEATURES ---------------- Links and sliders are heavily used in this model. Links are used to create the social networks and sliders are used as a way to give the observer flexibility in the model. RELATED MODELS -------------- The social network configuration of this model is based on the small worlds / six degrees of separation concept (Watts). There is a code that models the small world network in the NetLogo models library appropriately entitled "Small Worlds" (Wilensky). CREDITS AND REFERENCES ---------------------- Bampo, Mauro, et al. "The Effects of the Social Structure of Digital Networks on Viral Marketing Performance." Information Systems Research 19.3 (2008): 273-90. De Bruyn, Arnaud, and Gary L. Lilien. "A Multi-Stage Model of Word-of-Mouth Influence through Viral Marketing." International Journal of Research in Marketing 25.3 (2008): 151-63. Duncan J. Watts, Six Degrees: The Science of a Connected Age. (W.W. Norton & Company, New York, 2003), pages 83-100. Eccleston, Derek, and Luca Griseri. "How does Web 2.0 Stretch Traditional Influencing Patterns?" International Journal of Market Research 50.5 (2008): 591-161. Ferguson, Rick. "Word of Mouth and Viral Marketing: Taking the Temperature of the Hottest Trends in Marketing." Journal of Consumer Marketing 25.3 (2008): 179-82. Gladwell, Malcolm. The Tipping Point: How Little Things Can Make a Big Difference. Boston: Little, Brown and Company, 2000. Masker, Stephen J., and Andrew Somosi. "Marketing to Teens Online." McKinsey Quarterly.4 (2004): 12-. Smith, Ted, et al. "Reconsidering Models of Influence: The Relationship between Consumer Social Networks and Word-of- Mouth Effectiveness." Journal of Advertising Research 47.4 (2007): 387-97. Thackeray, Rosemary, et al. "Enhancing Promotional Strategies within Social Marketing Programs: Use of Web 2.0 Social Media." Health Promotion Practice 9.4 (2008): 338-43. Wilensky, U. (2005). NetLogo Small Worlds Model. . Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL ========== Permission to use, modify or redistribute this model is hereby granted, provided that both of the following requirements are followed: a) this copyright notice is included. b) this model will not be redistributed for profit without permission from Tony Wang. Contact Tony Wang for appropriate licenses for redistribution for profit at tonywang@wharton.upenn.edu To refer to this model in academic publications, please use: Wang, Tony. (2008). NetLogo Viral Marketing and Fashion Trends Model. Department of Philosophy, Politics, and Economics, University of Pennsylvania, Philadelphia, PA. In other publications, please use: Copyright 2008 Tony Wang. All rights reserved. @#$#@#$#@ default true 0 Polygon -7500403 true true 150 5 40 250 150 205 260 250 airplane true 0 Polygon -7500403 true true 150 0 135 15 120 60 120 105 15 165 15 195 120 180 135 240 105 270 120 285 150 270 180 285 210 270 165 240 180 180 285 195 285 165 180 105 180 60 165 15 arrow true 0 Polygon -7500403 true true 150 0 0 150 105 150 105 293 195 293 195 150 300 150 box false 0 Polygon -7500403 true true 150 285 285 225 285 75 150 135 Polygon -7500403 true true 150 135 15 75 150 15 285 75 Polygon -7500403 true true 15 75 15 225 150 285 150 135 Line -16777216 false 150 285 150 135 Line -16777216 false 150 135 15 75 Line -16777216 false 150 135 285 75 bug true 0 Circle -7500403 true true 96 182 108 Circle -7500403 true true 110 127 80 Circle -7500403 true true 110 75 80 Line -7500403 true 150 100 80 30 Line -7500403 true 150 100 220 30 butterfly true 0 Polygon -7500403 true true 150 165 209 199 225 225 225 255 195 270 165 255 150 240 Polygon -7500403 true true 150 165 89 198 75 225 75 255 105 270 135 255 150 240 Polygon -7500403 true true 139 148 100 105 55 90 25 90 10 105 10 135 25 180 40 195 85 194 139 163 Polygon -7500403 true true 162 150 200 105 245 90 275 90 290 105 290 135 275 180 260 195 215 195 162 165 Polygon -16777216 true false 150 255 135 225 120 150 135 120 150 105 165 120 180 150 165 225 Circle -16777216 true false 135 90 30 Line -16777216 false 150 105 195 60 Line -16777216 false 150 105 105 60 car false 0 Polygon -7500403 true true 300 180 279 164 261 144 240 135 226 132 213 106 203 84 185 63 159 50 135 50 75 60 0 150 0 165 0 225 300 225 300 180 Circle -16777216 true false 180 180 90 Circle -16777216 true false 30 180 90 Polygon -16777216 true false 162 80 132 78 134 135 209 135 194 105 189 96 180 89 Circle -7500403 true true 47 195 58 Circle -7500403 true true 195 195 58 circle false 0 Circle -7500403 true true 0 0 300 circle 2 false 0 Circle -7500403 true true 0 0 300 Circle -16777216 true false 30 30 240 cow false 0 Polygon -7500403 true true 200 193 197 249 179 249 177 196 166 187 140 189 93 191 78 179 72 211 49 209 48 181 37 149 25 120 25 89 45 72 103 84 179 75 198 76 252 64 272 81 293 103 285 121 255 121 242 118 224 167 Polygon -7500403 true true 73 210 86 251 62 249 48 208 Polygon -7500403 true true 25 114 16 195 9 204 23 213 25 200 39 123 cylinder false 0 Circle -7500403 true true 0 0 300 dot false 0 Circle -7500403 true true 90 90 120 face happy false 0 Circle -7500403 true true 8 8 285 Circle -16777216 true false 60 75 60 Circle -16777216 true false 180 75 60 Polygon -16777216 true false 150 255 90 239 62 213 47 191 67 179 90 203 109 218 150 225 192 218 210 203 227 181 251 194 236 217 212 240 face neutral false 0 Circle -7500403 true true 8 7 285 Circle -16777216 true false 60 75 60 Circle -16777216 true false 180 75 60 Rectangle -16777216 true false 60 195 240 225 face sad false 0 Circle -7500403 true true 8 8 285 Circle -16777216 true false 60 75 60 Circle -16777216 true false 180 75 60 Polygon -16777216 true false 150 168 90 184 62 210 47 232 67 244 90 220 109 205 150 198 192 205 210 220 227 242 251 229 236 206 212 183 fish false 0 Polygon -1 true false 44 131 21 87 15 86 0 120 15 150 0 180 13 214 20 212 45 166 Polygon -1 true false 135 195 119 235 95 218 76 210 46 204 60 165 Polygon -1 true false 75 45 83 77 71 103 86 114 166 78 135 60 Polygon -7500403 true true 30 136 151 77 226 81 280 119 292 146 292 160 287 170 270 195 195 210 151 212 30 166 Circle -16777216 true false 215 106 30 flag false 0 Rectangle -7500403 true true 60 15 75 300 Polygon -7500403 true true 90 150 270 90 90 30 Line -7500403 true 75 135 90 135 Line -7500403 true 75 45 90 45 flower false 0 Polygon -10899396 true false 135 120 165 165 180 210 180 240 150 300 165 300 195 240 195 195 165 135 Circle -7500403 true true 85 132 38 Circle -7500403 true true 130 147 38 Circle -7500403 true true 192 85 38 Circle -7500403 true true 85 40 38 Circle -7500403 true true 177 40 38 Circle -7500403 true true 177 132 38 Circle -7500403 true true 70 85 38 Circle -7500403 true true 130 25 38 Circle -7500403 true true 96 51 108 Circle -16777216 true false 113 68 74 Polygon -10899396 true false 189 233 219 188 249 173 279 188 234 218 Polygon -10899396 true false 180 255 150 210 105 210 75 240 135 240 house false 0 Rectangle -7500403 true true 45 120 255 285 Rectangle -16777216 true false 120 210 180 285 Polygon -7500403 true true 15 120 150 15 285 120 Line -16777216 false 30 120 270 120 leaf false 0 Polygon -7500403 true true 150 210 135 195 120 210 60 210 30 195 60 180 60 165 15 135 30 120 15 105 40 104 45 90 60 90 90 105 105 120 120 120 105 60 120 60 135 30 150 15 165 30 180 60 195 60 180 120 195 120 210 105 240 90 255 90 263 104 285 105 270 120 285 135 240 165 240 180 270 195 240 210 180 210 165 195 Polygon -7500403 true true 135 195 135 240 120 255 105 255 105 285 135 285 165 240 165 195 line true 0 Line -7500403 true 150 0 150 300 line half true 0 Line -7500403 true 150 0 150 150 pentagon false 0 Polygon -7500403 true true 150 15 15 120 60 285 240 285 285 120 person false 0 Circle -7500403 true true 110 5 80 Polygon -7500403 true true 105 90 120 195 90 285 105 300 135 300 150 225 165 300 195 300 210 285 180 195 195 90 Rectangle -7500403 true true 127 79 172 94 Polygon -7500403 true true 195 90 240 150 225 180 165 105 Polygon -7500403 true true 105 90 60 150 75 180 135 105 plant false 0 Rectangle -7500403 true true 135 90 165 300 Polygon -7500403 true true 135 255 90 210 45 195 75 255 135 285 Polygon -7500403 true true 165 255 210 210 255 195 225 255 165 285 Polygon -7500403 true true 135 180 90 135 45 120 75 180 135 210 Polygon -7500403 true true 165 180 165 210 225 180 255 120 210 135 Polygon -7500403 true true 135 105 90 60 45 45 75 105 135 135 Polygon -7500403 true true 165 105 165 135 225 105 255 45 210 60 Polygon -7500403 true true 135 90 120 45 150 15 180 45 165 90 square false 0 Rectangle -7500403 true true 30 30 270 270 square 2 false 0 Rectangle -7500403 true true 30 30 270 270 Rectangle -16777216 true false 60 60 240 240 star false 0 Polygon -7500403 true true 151 1 185 108 298 108 207 175 242 282 151 216 59 282 94 175 3 108 116 108 target false 0 Circle -7500403 true true 0 0 300 Circle -16777216 true false 30 30 240 Circle -7500403 true true 60 60 180 Circle -16777216 true false 90 90 120 Circle -7500403 true true 120 120 60 tree false 0 Circle -7500403 true true 118 3 94 Rectangle -6459832 true false 120 195 180 300 Circle -7500403 true true 65 21 108 Circle -7500403 true true 116 41 127 Circle -7500403 true true 45 90 120 Circle -7500403 true true 104 74 152 triangle false 0 Polygon -7500403 true true 150 30 15 255 285 255 triangle 2 false 0 Polygon -7500403 true true 150 30 15 255 285 255 Polygon -16777216 true false 151 99 225 223 75 224 truck false 0 Rectangle -7500403 true true 4 45 195 187 Polygon -7500403 true true 296 193 296 150 259 134 244 104 208 104 207 194 Rectangle -1 true false 195 60 195 105 Polygon -16777216 true false 238 112 252 141 219 141 218 112 Circle -16777216 true false 234 174 42 Rectangle -7500403 true true 181 185 214 194 Circle -16777216 true false 144 174 42 Circle -16777216 true false 24 174 42 Circle -7500403 false true 24 174 42 Circle -7500403 false true 144 174 42 Circle -7500403 false true 234 174 42 turtle true 0 Polygon -10899396 true false 215 204 240 233 246 254 228 266 215 252 193 210 Polygon -10899396 true false 195 90 225 75 245 75 260 89 269 108 261 124 240 105 225 105 210 105 Polygon -10899396 true false 105 90 75 75 55 75 40 89 31 108 39 124 60 105 75 105 90 105 Polygon -10899396 true false 132 85 134 64 107 51 108 17 150 2 192 18 192 52 169 65 172 87 Polygon -10899396 true false 85 204 60 233 54 254 72 266 85 252 107 210 Polygon -7500403 true true 119 75 179 75 209 101 224 135 220 225 175 261 128 261 81 224 74 135 88 99 wheel false 0 Circle -7500403 true true 3 3 294 Circle -16777216 true false 30 30 240 Line -7500403 true 150 285 150 15 Line -7500403 true 15 150 285 150 Circle -7500403 true true 120 120 60 Line -7500403 true 216 40 79 269 Line -7500403 true 40 84 269 221 Line -7500403 true 40 216 269 79 Line -7500403 true 84 40 221 269 x false 0 Polygon -7500403 true true 270 75 225 30 30 225 75 270 Polygon -7500403 true true 30 75 75 30 270 225 225 270 @#$#@#$#@ NetLogo 4.0.3 @#$#@#$#@ @#$#@#$#@ @#$#@#$#@ setup awareness go count turtles with [color = grey + 2] = 0 count turtles with [VM-clothes? = true] count turtles with [TM-clothes? = true] setup awareness go count turtles with [color = grey + 2] = 0 count turtles with [VM-clothes? = true] count turtles with [TM-clothes? = true] @#$#@#$#@ @#$#@#$#@ default 0.0 -0.2 0 0.0 1.0 0.0 1 1.0 0.0 0.2 0 0.0 1.0 link direction true 0 Line -7500403 true 150 150 90 180 Line -7500403 true 150 150 210 180 @#$#@#$#@