turtles-own [ turtle-state ;; turtle-state of this turtle, taking value of +/-1 mean-received ;; the mean of all the turtle-states from all connected turtles (including itself) ] globals [ min-noise-level-4-agreement ;; the minimum level of noise that can guarantee the entire network to reach agreement/consensus max-noise-level-4-agreement ;; the maximum level of noise that can guarantee the entire network to reach agreement, above which the network generates symmetric behavior / disagreement prediction-msg ;; the message displayed to predict agreement or disagreement instant-sum-states ;; for statistics, the sum of all turtle states at a time instant accumulated-sum-states ;; for statistics, the sum of all turtle-states over all the time instants ] to setup clear-all crt population [ set size 5 ;; easier to see set shape "dot" ;; set label who ;; if uncommented, this will put labels by the turtles setxy random-xcor random-ycor set turtle-state ( random 2 ) * 2 - 1 ;; uniformly distributed over +/-1 recolor ;; color the turtles in red or blue ] reset-ticks update-links ;; establish and show links among the turtles ifelse noise-level <= max-noise-level-4-agreement and noise-level > min-noise-level-4-agreement [ ifelse random-connection? [ set prediction-msg "Network will reach consensus (agreement/symmetry)!" ] [ set prediction-msg "All connected sub-graphs will reach symmetry (disagreement)!" ] ] [ ifelse noise-level > min-noise-level-4-agreement [ set prediction-msg "Network will reach symmetry (disagreement)!" ] [ set prediction-msg "Network behavior cannot be predicted!" ] ] end to go update-links ; re-establish and show links among the turtles ask turtles [ receive-states ] ask turtles [ update-state ] set instant-sum-states ( sum [ turtle-state ] of turtles ) set accumulated-sum-states ( accumulated-sum-states + instant-sum-states ) tick end to update-links ;; to initialize links or update links ifelse random-connection? = true [ update-random-graph set min-noise-level-4-agreement 0 set max-noise-level-4-agreement 1 ] [ initial-fixed-graph let max-degree max [count link-neighbors] of turtles set min-noise-level-4-agreement 1 - 2 / max-degree set max-noise-level-4-agreement 1 ] end to update-random-graph ;; to initialize or update the random graph according to random graph process let n count turtles let n-total-links n * ( n - 1 ) / 2 let n-links n-total-links * p-for-random-connection clear-links ;; remove all existing links and will create completely new links for each tick repeat n-links [ ask one-of turtles [ create-link-with one-of other turtles ] ] end to initial-fixed-graph ;; to initialize the fixed graph based on nearest neighbors if ticks = 0 [ ask turtles [ create-links-with ( other turtles in-radius vision ) ;; one way to create a fixed graph is to connect to nearest neighbors only ] ] end to receive-states ;; turtle procedure if any? link-neighbors [ set mean-received average-turtle-state-link-neighbors ;; a turtle receives the turtle-states from all neighbors (inclusive) ] end to-report average-turtle-state-link-neighbors ;; turtle procedure let neighbor-states [ turtle-state ] of ( turtle-set ( link-neighbors ) ( turtles-here ) ) let mean-value mean neighbor-states ;; find the average of the turtle-states from all neighbors (inclusive) report mean-value end to update-state ;; turtle procedure if any? link-neighbors [ perturb ;; the received information is perturbed by a random noise and determine the next state of this turtle recolor ;; update the color according to new state ] end to perturb ;; turtle procedure. The received information is perturbed by a random noise and determine the next state of this turtle set turtle-state sign ( mean-received + ( ( random-float 1 ) * 2 - 1 ) * noise-level ) end to recolor ifelse turtle-state = -1 [ set color blue ] [ set color red ] end to-report sign [number] ifelse number >= 0 [ report 1 ] [ report -1 ] end ;;Copyright 2013 J. Liu. @#$#@#$#@ GRAPHICS-WINDOW 289 15 783 530 60 60 4.0 1 10 1 1 1 0 0 0 1 -60 60 -60 60 1 1 1 ticks 30.0 BUTTON 10 95 87 128 NIL setup NIL 1 T OBSERVER NIL NIL NIL NIL 1 BUTTON 103 95 184 128 NIL go T 1 T OBSERVER NIL NIL NIL NIL 1 SLIDER 9 51 274 84 population population 1.0 100.0 100 1.0 1 NIL HORIZONTAL SLIDER 27 230 268 263 vision vision 0.0 30.0 16.5 0.5 1 patches HORIZONTAL SLIDER 11 135 272 168 noise-level noise-level 0.0 2.0 1.01 0.01 1 NIL HORIZONTAL PLOT 803 52 1003 202 sum sequence ticks NIL 0.0 500.0 0.0 10.0 true false "" "" PENS "default" 1.0 0 -16777216 true "" "plot instant-sum-states" PLOT 805 216 1005 366 sum of sum sequence ticks values 0.0 10.0 0.0 10.0 true false "" "" PENS "default" 1.0 0 -16777216 true "" "plot accumulated-sum-states" PLOT 807 379 1007 529 average of sum sequence NIL NIL 0.0 10.0 0.0 10.0 true false "" "" PENS "default" 1.0 0 -16777216 true "" "plot accumulated-sum-states / ( 1 + ticks )" SWITCH 11 184 160 217 random-connection? random-connection? 0 1 -1000 SLIDER 28 285 268 318 p-for-random-connection p-for-random-connection 0 .5 0.04 0.001 1 NIL HORIZONTAL BUTTON 199 95 274 128 go once go NIL 1 T OBSERVER NIL NIL NIL NIL 1 MONITOR 6 342 272 387 min/max noise levels guaranteeing agreement list precision min-noise-level-4-agreement 3 \nprecision max-noise-level-4-agreement 3 2 1 11 MONITOR 27 400 84 445 #red count turtles with [ color = red ] 2 1 11 MONITOR 161 399 218 444 #blue count turtles with [ color = blue ] 2 1 11 MONITOR 7 458 274 503 Prediction prediction-msg 17 1 11 @#$#@#$#@ ## WHAT IS IT? This simple model demonstrates a phase transition behavior emerging from the interactions among multiple agents in the presence of noise. At a noise level higher than a certain threshold, the system generates symmetric behavior (e.g. vapor or melt of magnetization) or disagreement; whereas at a noise level lower than the threshold, the system exhibits spontaneous symmetry breaking (solid or magnetization) or consensus. This provides some simple intuitions for understanding the impact of noise in communication networks on network behavior and the impact of noise (temperature) in phase transitions. NOTE: For this simulator, the following terms are used interchangeably: Consensus, agreement, asymmetry, and all agents taking the same value. Disagreement, asymmetry, and approximately half of the agents taking +1 (colored as red) and the other half taking -1 (colored as blue). ## HOW IT WORKS A group of non-mobile agents form a network, and each agent, taking bipolar value either +1 or -1, updates its value according to its previous value and the noisy measurements of the values of the agents connected to it. Update rule: At each moment, each turtle receives all values from all turtles connected to it (including the turtle itself); these values are then averaged at the turtle and perturbed by a noise. The noise is uniformly distributed in interval [-z, z], where z is called the noise level. If the perturbed average value is positive, then the turtle will take value +1, otherwise it will take value -1. In other words, each turtle first aggregates the opinions of all its connections and itself (subject to communication/observation noise perturbation), and then follows the aggregated opinion. ## HOW TO USE IT To simulate, press SETUP to create the turtles and links, and then press GO to see the network evoluation or GO ONCE to go one step. The following may be set before SETUP: POPULATION: Use the slider to set the value for the number of turtles in the simulation. A very high POPULATION may slow down the simulation. NOISE-LEVEL: Use the slider to set the noise level. RANDOM-CONNECTIONS?: If ON is chosen, then the network forms a random graph process, with probability p (set by P-FOR-RANDOM-CONNECTIONS slider) to form a link between any two turtles. If OFF is chosen, then the network is over a fixed graph formed by connecting the nearest turtles within a distance set by VISION slider. VISION: For the fixed graph case (RANDOM-CONNECTIONS?=OFF) only. Two turtles are connected by a link if they are within the distance set by VISION sider. P-FOR-RANDOM-CONNECTIONS: For the random graph case (RANDOM-CONNECTIONS?=ON) only. For any turtle pair, the probablity of forming a connection (i.e. a link) is given by P-FOR-RANDOM-CONNECTIONS slider. MIN/MAX NOISE LEVEL GUARANTEEING AGREEMENT: This displays, for a network after SETUP button is pressed, a pair of numbers such that if NOISE-LEVEL slider chooses any value between the min and max, then agreement is guaranteed. The following are output: \#RED, #BLUE: These display the number of red turtles (with state value +1) and the number of blue turtles (with state value -1) at the current time instant. PREDCTION: This displays the prediction message. After SETUP button is preseed, the simulator makes a prediction of the behavior based on some analytic results. The prediction message may be: "Network will reach consensus (agreement/symmetry)!" "Network will reach symmetry (disagreement)!" "All connected sub-graphs will reach symmetry (disagreement)!" "Network behavior cannot be predicted!" SUM SEQUENCE: Each data point of the sequence corresponds to a tick in time and the sum of all turtles' state values at that time. SUM OF SUM SEQUENCE: Each data point of the sequence corresponds to a tick in time and the accumulated sum of the SUM SEQUENCE by that time. AVERAGE OF SUM SEQUENCE: Each data point of the sequence corresponds to a tick in time and the mean value of the SUM SEQUENCE by that time. ## THINGS TO NOTICE If the noise level set by NOISE-LEVEL slider is higher than the displayed MAX NOISE LEVEL GUARANTEEING AGREEMENT, the system generates symmetric behavior (e.g. vapor or melt of magnetization) or disagreement; whereas at a noise level lower than MAX NOISE LEVEL GUARANTEEING AGREEMENT but above MIN NOISE LEVEL GUARANTEEING AGREEMENT, the system exhibits symmetry breaking (solid or magnetization) or consensus. This holds for both the fixed graph (RANDOM-CONNECTIONS?=OFF) and random graph process (RANDOM-CONNECTIONS?=ON). If agreement is predicted, #RED, #BLUE, SUM SEQUENCE, SUM OF SUM SEQUENCE, and AVERAGE OF SUM SEQUENCE will reach/converge to some constants. If disagreement is predicted, #RED, #BLUE, and SUM SEQUENCE will oscillate, SUM OF SUM SEQUENCE will exhibit a random walk behavior, and AVERAGE OF SUM SEQUENCE will tend to zero. ## THINGS TO TRY For the random graph case, sweep the noise level (NOISE-LEVEL) and connection probability (P-FOR-RANDOM-CONNECTIONS), and observe network behavior. For the fixed graph case, sweep the noise level (NOISE-LEVEL) and vision (VISION), and observe network behavior. ## EXTENDING THE MODEL This model assumes no leader yet. What if there is one or more leaders of a same color? What if there are several leaders with different colors? What if there are more than two states or more than two colors for each turtle? What if other noise distributions are used? ## NETLOGO FEATURES ## RELATED MODELS * Flocking * Flocking Vee Formation * Ising * Boiling ## CREDITS AND REFERENCES This model is based on: * J. Liu, V. Yadav, H. Sehgal, J. M. Olson, H. Liu, N. Elia: Phase Transitions on Fixed Connected Graphs and Random Graphs in the Presence of Noise. IEEE Trans. Automat. Contr. 53(8): 1817-1825 (2008) * J. Liu, V. Yadav, H. Sehgal, J. M. Olson, H. Liu, N. Elia: Phase Transitions on Fixed Connected Graphs and Random Graphs in the Presence of Noise. Proc. 44th IEEE Conference on Decision and Control (CDC) and 2005 European Control Conference (ECC): 734-739 (2005) ## HOW TO CITE If you mention this model in a publication, we ask that you include these citations for the model itself and for the NetLogo software: * J. Liu, V. Yadav, H. Sehgal, J. M. Olson, H. Liu, N. Elia: Phase Transitions on Fixed Connected Graphs and Random Graphs in the Presence of Noise. IEEE Trans. Automat. Contr. 53(8): 1817-1825 (2008) * Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. ## COPYRIGHT AND LICENSE Copyright 2013 J. 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