;Nodes represent the retailers, distributers, and factory. breed [nodes node] ;breed [links link] ;nodes have color of either: green, red, blue ;distances: [ [agent distance][...]] used to calculate min distance to every other agent nodes-own [distances cluster-coefficient] ;components is a list of agents, each a member of a different component globals [changed-distances? characteristic-path-length clustering-coefficient components max-distance size-max-component] to ultralog while [count nodes < num-nodes] [ layout-spring nodes links 0.2 2 1 let partner find-preferential-partner ;apply Pi function: choose stochastically based on degree let new-node-who 0 create-nodes 1 [ set new-node-who who set shape "circle" set color generate-node-color setup-distances if (color = blue) [ ;battalion create-link-with partner [ ;first edge attaches with set color gray ] if (random-float 1.0 < p) [ ;create second link let nodes-at-distance-two nodes with [get-distance-to myself = 2] if (any? nodes-at-distance-two) [ create-link-with (one-of nodes-at-distance-two) [ set color gray ] ] ] ] if (color = red) [; FSB- Forward Support Battalion create-link-with partner [ ;first edge attaches with set color gray ] let nodes-at-distance-lte-3 nodes with [self != myself and get-distance-to myself <= 3 and (not link-neighbor? myself)] ifelse (count nodes-at-distance-lte-3 >= 2) [ create-links-with (n-of 2 nodes-at-distance-lte-3) [ set color gray ] ][ if (any? nodes-at-distance-lte-3) [ ;there is only 1 create-links-with nodes-at-distance-lte-3 [ set color gray ] ] ] ] if (color = green)[ let list-of-neighbors fput self [] repeat 5 [ let n find-preferential-partner-not-in list-of-neighbors if (n != nobody) [ create-link-with n [ set color gray ] ] set list-of-neighbors fput n list-of-neighbors ] ] ] ;update distances. update-distances-new-node (turtle new-node-who) calculate-distances ] end to setup-attachment [find-fun] while [count nodes < num-nodes] [ layout-spring nodes links 0.2 2 1 create-nodes 1 [ set shape "circle" set color generate-node-color let num-attachments 1 setup-distances if (color = blue) [ set num-attachments num-attachments + ifelse-value (random-float 1.0 < p) [1][0] ] if (color = red) [ set num-attachments 3 ] if (color = green) [ set num-attachments 5 ] let list-of-neighbors fput self [] repeat num-attachments [ let n run-result (word find-fun " list-of-neighbors") if (n != nobody) [ create-link-with n [ set color gray ] ] set list-of-neighbors fput n list-of-neighbors ] ] ] end to-report find-preferential-partner let total random-float sum [count my-links] of nodes let partner nobody ;; reporter procedures always run without interruption, ;; so we don't need "without-interruption" in order to ;; make the turtles go one at a time ask nodes [ let nc (count my-links) if partner = nobody [ ifelse nc > total [ set partner self ] [ set total total - nc ] ] ] if (partner = nobody) [ ;this only happens if there are no edges in the graph (initial condition) so just pick one at random report one-of nodes ] report partner end to-report find-preferential-partner-not-in [list-of-agents] let candidates nodes with [not member? self list-of-agents] let total random-float sum [count my-links] of candidates let partner nobody ;; reporter procedures always run without interruption, ;; so we don't need "without-interruption" in order to ;; make the turtles go one at a time ask candidates [ let nc (count my-links) if partner = nobody [ ifelse nc > total [ set partner self ] [ set total total - nc ] ] ] report partner end to-report find-random-partner-not-in [list-of-agents] let candidates nodes with [not member? self list-of-agents] report one-of candidates end to setup-initial-nodes let who-of-first 0 create-nodes 1 [ set who-of-first who set color generate-node-color setup-distances set shape "circle" ] create-nodes 1 [ set color generate-node-color setup-distances set shape "circle" create-link-with (turtle who-of-first) [ set color gray ] ] end ;Reports node color based on ratios specified by user to-report generate-node-color let s ratio-of-red + ratio-of-blue + ratio-of-green let r random s if (r < ratio-of-red) [ report red ] if (r < ratio-of-red + ratio-of-blue) [ report blue ] report green end to preferential-attachment setup-attachment "find-preferential-partner-not-in" end to random-attachment setup-attachment "find-random-partner-not-in" end to setup ;; (for this model to work with NetLogo's new plotting features, ;; __clear-all-and-reset-ticks should be replaced with clear-all at ;; the beginning of your setup procedure and reset-ticks at the end ;; of the procedure.) __clear-all-and-reset-ticks ask patches [set pcolor white] set components [] setup-initial-nodes run model layout-spring nodes links 0.2 5 1 set-current-plot "Edge Distribution" ; histogram-from nodes [count my-links] calculate-distances set-components let max-component get-largest-component set characteristic-path-length get-characteristic-path-length max-component set clustering-coefficient get-clustering-coefficient max-component set max-distance get-max-distance max-component set size-max-component get-size-of-largest-component end to run-test [choose] setup repeat (.8 * num-nodes) [ set-current-plot "Characteristic Path Length" plot characteristic-path-length set-current-plot "Largest Component" plot size-max-component set-current-plot "Max Distance" plot max-distance if (choose = "random") [ ask one-of nodes [die] ] if (choose = "targeted") [ ask (max-one-of nodes [count my-links]) [die] ] ask nodes [ setup-distances ] set num-nodes num-nodes - 1 calculate-distances set-components let max-component get-largest-component set characteristic-path-length get-characteristic-path-length max-component set clustering-coefficient get-clustering-coefficient max-component set max-distance get-max-distance max-component set size-max-component get-size-of-largest-component repeat 5 [layout-spring nodes links 0.2 5 1] ] end to layout layout-spring nodes links 0.2 2 1 end ;---------- ;global utility/graph functions ;get all nodes to calculate their distances matrix ;implement dijikstra's algorithm to calculate-distances set changed-distances? true; ; ask nodes [setup-distances] ;probably don't need to do this as we are only adding nodes.... while [changed-distances?] [ set changed-distances? false ask nodes [update-distances] ] end ;Updates everyone's distances by adding a distance to new-node which is 1 + their distance to ; a randomly chosen neighbor of new-node. This might be too big so we still need to calculate-distances after this. ;This function speeds up the later call to calculate-distances (a bit). ;Assumes connected graph. to update-distances-new-node [new-node] let new-node-neighbor [one-of link-neighbors] of new-node ask nodes with [self != new-node][ set-distance-to new-node (get-distance-to new-node-neighbor + 1) ] end ;Report a list of all pairs (n choose 2) from the-list. Does not contain duplicates (permutations) to-report get-all-pairs [the-list] let result [] foreach the-list [ let a ? foreach the-list [ if (? != a) [ set result fput list a ? result ] ] ] report result end to-report get-all-pairs-agents [agent-set] report get-all-pairs [self] of agent-set end ;Calculates the characteristic path length, which is the smallest number of links it takes to connect one node to another, averaged ;over all pairs of nodes in the network. ;Do this by first having each node calculate its minimun distance to every other node and then we average over all pairs to-report get-characteristic-path-length [component] let pairs get-all-pairs-agents (nodes with [get-component = component]) if (length pairs = 0) [report 0] let sum-distances 0 foreach pairs [ ask (first ?) [ ;since Im assuming bidirections it does not matter which one I ask. ; show get-distance-to (item 1 ?) set sum-distances sum-distances + get-distance-to (item 1 ?) ] ] report sum-distances / length pairs end to-report get-max-distance [component] report max [max map [item 1 ?] distances] of (nodes with [get-component = component]) end to-report get-clustering-coefficient [component] ask nodes with [get-component = component] [ set cluster-coefficient get-node-clustering-coefficient ] report mean [cluster-coefficient] of (nodes with [get-component = component]) end to-report closest-xy [x y agent-set] ; Return closest agent to x, y report min-one-of agent-set [distancexy-nowrap x y] end to set-components ask nodes [ without-interruption [ if (get-component = nobody) [ set components fput self components ] ] ] end ;reports the agent that is the representative for the largest component to-report get-largest-component let whole-component reduce [ifelse-value (count ?1 > count ?2)[?1][?2]] (map [nodes with [get-component = ?]] components) report [get-component] of (one-of whole-component) end to-report get-size-of-largest-component report max map [count ?] (map [nodes with [get-component = ?]] components) end ;------------------------------------------------------------- ;node functions ;------------- ;graph calculations ; ; The second measure is the clustering coefficient. This measures the amount of cliquishness ; of the network, that is, the fraction of neighbouring nodes that are also connected to one another. For example, in an all-to-all ; connected network, the clustering coefficient is one. ; ;Calculate distances to every other node using Dijistra's max-flow algorithm ; ;set distance to myself to be 0 to setup-distances set distances fput (list self 0) [] end to-report assoc [element lst] foreach lst [ if (first ? = element) [ report ?] ] report [] end to-report get-component foreach (components) [ if (get-distance-to ? > -1) [ report ? ] ] report nobody end to-report get-distance-to [the-other] foreach distances [ if (first ? = the-other) [ report (item 1 ?) ] ] report -1 end to set-distance-to [the-other num] let already-there false set changed-distances? true foreach distances [ if (first ? = the-other) [ without-interruption [ ;don't want someone else reading after delete but before addition set distances remove ? distances set distances fput (list the-other num) distances set already-there true ] ] ] if (not already-there) [ set distances fput (list the-other num) distances ] end ;node updates its "distances" variable by adding 1 to neighbors' distances, if that is smaller. to update-distances let n-distances [distances] of link-neighbors set n-distances ifelse-value (empty? n-distances) [ [] ] [reduce [sentence ?1 ?2] n-distances] ; show n-distances foreach n-distances [ let mine get-distance-to (first ?) if (mine = -1 or item 1 ? + 1 < mine) [ set-distance-to (first ?) (item 1 ? + 1) ] ] end ;report my clustering coefficient: the fraction of my neighbors who are also connected to each other ;requires: 'distances' matrix must already be calculated ;assume bidirection, but this should probably change to-report get-node-clustering-coefficient let neighbors-list remove-duplicates [self] of link-neighbors let pairs get-all-pairs neighbors-list let num-pairs-connected 0 foreach pairs [ ask (first ?) [ if (get-distance-to (item 1 ?) = 1) [ set num-pairs-connected num-pairs-connected + 1 ] ] ] if (length pairs = 0) [report 1.0] report num-pairs-connected / length pairs end @#$#@#$#@ GRAPHICS-WINDOW 243 10 673 461 17 17 12.0 1 10 1 1 1 0 1 1 1 -17 17 -17 17 0 0 1 ticks 30.0 BUTTON 7 16 79 49 NIL setup NIL 1 T OBSERVER NIL NIL NIL NIL 1 CHOOSER 7 53 230 98 model model "ultralog" "preferential-attachment" "random-attachment" 0 SLIDER 10 153 182 186 ratio-of-blue ratio-of-blue 1 50 25 1 1 NIL HORIZONTAL SLIDER 10 188 182 221 ratio-of-red ratio-of-red 1 50 4 1 1 NIL HORIZONTAL SLIDER 10 224 182 257 ratio-of-green ratio-of-green 1 50 1 1 1 NIL HORIZONTAL SLIDER 11 110 183 143 num-nodes num-nodes 10 100 23 1 1 NIL HORIZONTAL SLIDER 10 269 182 302 p p 0 1 0.5 0.01 1 NIL HORIZONTAL MONITOR 186 99 243 152 nodes count nodes 0 1 13 BUTTON 82 16 159 49 NIL layout T 1 T OBSERVER NIL NIL NIL NIL 1 PLOT 11 308 211 458 Edge Distribution # edges NIL 1.0 10.0 0.0 10.0 true false "" "" PENS "default" 1.0 1 -16777216 true "" "" MONITOR 678 116 881 169 Characteristic Path Length characteristic-path-length 2 1 13 MONITOR 676 176 848 229 Clustering Coefficient clustering-coefficient 2 1 13 MONITOR 679 239 834 292 Largest Component get-size-of-largest-component 0 1 13 PLOT 289 462 576 632 Characteristic Path Length Nodes Removed CPL 0.0 10.0 0.0 1.0 true false "" "" PENS "default" 1.0 0 -16777216 true "" "" BUTTON 677 25 827 58 Random Attacks run-test \"random\" NIL 1 T OBSERVER NIL NIL NIL NIL 1 PLOT 8 462 280 632 Largest Component Nodes Removed Size 0.0 10.0 0.0 10.0 true false "" "" PENS "default" 1.0 0 -16777216 true "" "" PLOT 582 462 838 632 Max Distance Nodes Removed Distance 0.0 10.0 0.0 10.0 true false "" "" PENS "default" 1.0 0 -16777216 true "" "" MONITOR 681 300 825 353 Max Distance in LC max-distance 0 1 13 BUTTON 677 74 832 107 Targeted Attacks run-test \"targeted\" NIL 1 T OBSERVER NIL NIL NIL NIL 1 @#$#@#$#@ # Supply Network Survivability ## CREDITS Jose M Vidal ## WHAT IS IT? This model shows how various supply-chain network topologies fare under attack. The original model was developed to study the military's supply chain vulnerability to terrorist or military attacks, part of the Ultralog project. * Hari Prasad Thadakamalla, Usha Nandini Raghavan, Soundar Kumara, and Reka Albert. [Survivability of Multiagent-Based Supply Networks: A Topological Perspectiv](http://jmvidal.cse.sc.edu/lib/thadakamalla04a.html). _IEEE Intelligent Systems,_ 19(5):24--31, 2004. ## CHANGES 20100623 @#$#@#$#@ 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 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