breed [rioter rioters] turtles-own [ threshold ] links-own [ bandwagon ] to setup clear-all setup-rioters set-default-shape turtles "circle" ask turtles [ set label-color white ] end to setup-rioters crt individuals [ setxy random-xcor * 0.95 random-ycor * 0.95 set color green set size .5 ] ask turtles [ ifelse randomize [ set threshold random-normal (individuals / 10) (individuals / 20) ] [ set threshold threshold = who ] ] ;; if not-random, the threshold level is the ask turtle 0 [ set color red ] ;; same as the 'who' number // turtle 0 is always red plotting end to go if (count turtles with [ color = red ]) = ( individuals ) [ stop ] ;; the system does not give you errors, ;; but stops when all turtles are red ask turtle (count turtles with [ color = red ]) [ if randomize = false [ ask turtle (count turtles with [ color = red ]) ;; if not-random threshold, the [ set color red ] ;; model works under Granovetter's ] ] ;; hypotheses open-up_bandwagon ;; this works if randomize is on plotting bandwagon tick end to open-up_bandwagon if randomize [ ask turtles with [ threshold <= (count (turtles with [ color = red ] in-radius vicinity)) ] [ ;; how can I make this work at every tick? if randomize [ set color red ] ]] end to bandwagon if link-rioters [ ask turtles with [ color = red ] [ create-links-with turtles with [ color = red ] with [ self != myself ] ] ] end to layout repeat 10 [ layout-spring turtles links 0.02 .05 .01 ; __layout-magspring turtles links 1 2 0.01 5 10 true ;; the first number attains to the spring relaxation, display ;; the second is a measure of the length of ] ;; the spring, the third is a measure of repulsion ;; between nodes, fourth is the stregth of the mag.field, ; ask turtles with [ color = green ] with [ who != 0 ] [ ;; the last indicates the type of magnetic field... ; set heading towards turtle 0 ] ;; trying to have different movements in place here end to plotting set-current-plot "Bandwagon" set-current-plot-pen "bandwagon diffusion" plot count turtles with [ color = red ] set-current-plot-pen "independent individuals" plot count turtles with [ color = green ] end ; Implemented in NetLogo by Davide Secchi, November 2010. ; please leave comments and/or suggestions here: secchi.davi@uwlax.edu ; Copyright 1999-2010 by Uri Wilensky. All rights reserved. ; The full copyright notice is available in the NetLogo website ; http://ccl.northwestern.edu/netlogo/docs/copyright.html @#$#@#$#@ GRAPHICS-WINDOW 643 10 1097 485 16 16 13.455 1 10 1 1 1 0 1 1 1 -16 16 -16 16 0 0 1 ticks BUTTON 19 22 85 55 NIL setup NIL 1 T OBSERVER NIL NIL NIL NIL BUTTON 98 22 211 55 go (one step) go NIL 1 T OBSERVER NIL NIL NIL NIL BUTTON 19 62 89 95 NIL layout T 1 T OBSERVER NIL NIL NIL NIL PLOT 23 272 608 489 Bandwagon ticks individuals 0.0 10.0 0.0 10.0 true true PENS "bandwagon diffusion" 1.0 0 -2674135 true "independent individuals" 1.0 0 -10899396 true SLIDER 20 126 192 159 individuals individuals 0 200 100 1 1 NIL HORIZONTAL SWITCH 21 171 128 204 randomize randomize 1 1 -1000 SLIDER 26 229 198 262 vicinity vicinity 0 50 5 1 1 NIL HORIZONTAL BUTTON 98 63 225 96 go (continuum) go T 1 T OBSERVER NIL NIL NIL NIL MONITOR 440 156 609 201 # turtles with who = #red count turtles with [ who = count turtles with [color = red ]] 2 1 11 MONITOR 439 216 609 261 turtle joining bandwagon turtle (count turtles with [ color = red ]) 2 1 11 SWITCH 150 176 277 209 link-rioters link-rioters 1 1 -1000 MONITOR 435 103 609 148 turtles subject to bandwagon count turtles with [ color = green ] with [ threshold <= (count turtles with [ color = red ] in-radius vicinity)] 2 1 11 MONITOR 435 51 610 96 NIL max [ threshold ] of turtles 2 1 11 MONITOR 254 51 429 96 NIL min [ threshold ] of turtles 2 1 11 MONITOR 477 330 588 375 red turtles count turtles with [ color = red ] 2 1 11 @#$#@#$#@ Please send comments/suggestions to secchi.davi@uwlax.edu WHAT IS IT? ----------- This simple model reflects Granovetter's (1978) idea of how the bandwagon effect emerges. He takes a riot as an example of the phenomenon. Bandwagon is usually described as the tendency to adopt a technique, innovation, behavior, thought, process, or attitude because of its popularity or, that is the same, because that is what the sheer number of peers are doing. According to Granovetter, bandwagon emerges through the idea of a ÒthresholdÓ level. This is, for the individual, Òthe proportion of the group he would have to see join before he would do soÓ (Granovetter 1978, p. 1422). He also specifies that the Òthreshold is simply that point where the perceived benefits to an individual of doing the thing in question [É] exceed the perceived costsÓ (p. 1422). Imagine 100 individuals and that Òthere is one individual with threshold 0, one with threshold 1, one with threshold 2, and so on up to the last individual with threshold 99. [É] The outcome is clear and could be described as a ÔbandwagonÕ or ÔdominoÕ effectÓ (1424). Default settings of the model (i.e., randomize = off) show a pattern of bandwagon diffusion (or effect) that reflect Granovetter's idea. If you switch 'randomize,' to the 'on' position, random thresholds are assigned to agents and the model significantly deviates from the standard Granovetter's. These "deviated" models are meant to be better approximations of what happens to real bandwagon phenomena. HOW IT WORKS ------------ At the beginning, all agents in the model are green, with one exception: one agent is red. The red agent has threshold 0 and it is the one showing a behavior that can be imitated by other agents. On every tick, the agent with a threshold that equals the number of red agents (i.e., that already joined the bandwagon), automatically joins the bandwagon and turn red. When using default setting (randomize = off), thresholds are 'unique;' every agent has a threshold that is different from that of other agents. To get this easyly done, these thresholds are the same as the agent 'who' number. Therefore, default always repeats itself even when you click the layout button (that allows agents to move on your screen). A significantly different pattern may be observed when the default is abandoned. This is as simple as switching the randomize to 'on.' This assignes turtles with random thresholds (via normal distribution that has mean and standard deviation that derive from the number of agents in the system). Why randomize? Simply because in a real-world situation (a) we do not know threshold levels (even our own threshold is somehow a mistery althought social psychology may help us understand), and (b) it may be that some have very similar threshold levels. Anthough relevant, these are not the only differences from the default/Granovetter's. Each agent joins the bandwagon depending on how many agents they are able to observe. The slider 'vicinity' let agents vary their perception of what is going on around them. The assumption for this setting is that people are not always aware of everything that is goin on around them, or there are only few behaviors/ideas/innovations that are interesting or relevant to them (i.e., levels of attention vary). These differences count for a significant number of deviations from the initial hypotheses. Agents turning red are heavily affected by original assumptions. HOW TO USE IT ------------- Here is a short description of the buttons, sliders, etc.: Setup: let's get started! [sometimes, you have to hit that twice to see circles instead of turtlesÉ I do not know whyÉ] Go: there are two go buttons. The button 'go (one step)' takes the model one step further (one tick), while the 'go (continuum)' repeats the go command foreverÉ or until all agents turn red, or until you click the button again. Layout: this makes agents move. Individuals: the number of individuals in the model (everybody is green but one). Randomize: when it is 'off,' agent's threshold is the same as the who number for that agent. When it is 'on,' thresholds are assigned randomly to agents. Vicinity: how far goes the 'sight' or 'observational ability' of the agent. Link-rioters: when it is 'on' red turtles link (and group) together. Monitors have been inserted to check whether the commands are doing what they are supposed to do and to give an idea of the numbers behind the model. THINGS TO NOTICE ---------------- While running the model, users may notice that agents on the default settings (Granovetter's) always repeat themselves although their location in the space is different. This is one of the points that makes that example unrealistic. Randomization helps noticing fairly different behaviors. It may be interesting that randomization brings more uncertainty as bandwagon will not always emerge (as it is in the standard/default settings). What does it take for bandwagon to emerge? What is that triggers the bandwagon effect? The model helps to show that the level of connectedness, or the social awareness of single agents, is necessary for any social imitation mechnisms to spread in a given system. The more individuals are aware of what others are doing/thinking, the more likely it is that bandwagons emerge. Of course, this is a mere statistical concept, since we are not making any assumptions on the motives that lead agents to social imitation. THINGS TO TRY ------------- Three basic things that could be done are: (a) change the number of individual in the system and observe what happens, (b) turn the 'link-rioters' on, (c) move the 'vicinity' slider to set the level of social 'awareness.' EXTENDING THE MODEL ------------------- We can modify this model to include possibilities for different starting conditions. For example, we can: (a) have more than one agent with threshold = 0 in the standard/default setting, maybe with a slider that sets the initial number of red agents; (b) have different conditions on which thresholds are associated to agents; (c) set different rules for how the turtles move in the space; (d) create utilitarian motives for joining the bandwagon, and study how thresholds and motives interact/depend on other conditions of the system; (e) É NETLOGO FEATURES ---------------- Features that are in this model are very simple and they come from a slight modification of existing models. Am I victim of bandwagon myself?!? RELATED MODELS -------------- Models related to this are: Virus on a network (Library) WSN by Qasim Siddique (Community) CREDITS AND REFERENCES ---------------------- Here are some of the references that inspired the model's logic: Abrahamson E, Rosenkopf L (1997) Social network effects on the extent of innovation diffusion: A computer simulation. Organization Science 8(3):289Ð309. Chiang YS (2007) Birds of moderately different feathers: Bandwagon dynamics and the threshold heterogeneity of network neighbors. Journal of Mathematical Sociology 31:47Ð69. Fiol CM, OÕConnor EJ (2003) Waking up! mindfulness in the face of bandwagon. Academy of Management Review 28(1):54Ð70. Granovetter M (1978) Threshold models of collective behavior. American Journal of Sociology 83(6):1420Ð1443. Secchi D (2010). Extendable Rationality. New York: Springer. Secchi D, Bardone E (2009) An organizational model of bandwagon. Working Paper No. 1, College of Business Administration, University of WisconsinÐLa Crosse. Wilensky, U (1998) NetLogo Wolf Sheep Predation model. http://ccl.northwestern.edu/netlogo/models/WolfSheepPredation. 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