globals [ cycle ;; The simulation time is partitioned in discrete unit (cycle), in each of this latter every agent make the actions; knowtransfer ;; Total measure of the knowledge exchange during the simulation ; network-density ;; The rapport between the real number of interaction and the max number of interaction possible: ;; D = 2 l / ( n * (n - 1)),where l is the real number of interaction and n is the number of nodes; clustering-coefficient ;; the clustering coefficient of the network alone ;; number of th nodes isolated; ] breed [ links link ] breed [ nodes node ] nodes-own [ A-C ;; set of the Absorptive Capacity for each competence knowledge ;; set of the competences activity ;; phases of the production process c1 c2 c3 a-c1 a-c2 a-c3 knowledge-level ;; Knowledge level of the individual firm. ;; This parameter is the average of three level of competences ? K = (c1 + c2 + c3) / 3 node-clustering-coefficient ] to setup ca set cycle 0 set knowtransfer 0 set-default-shape nodes "circle" make-nodes ask nodes [ set knowledge n-values 3 [0] set c1 item 0 knowledge set c2 item 1 knowledge set c3 item 2 knowledge set A-C n-values 3 [0] set a-c1 item 0 knowledge set a-c2 item 1 knowledge set a-c3 item 2 knowledge without-interruption [ establish-activity establish-knowledge establish-A-C ] ] if label? [ ask nodes [set label A-C] ] end to make-nodes create-custom-nodes num-nodes [ fd max-pxcor - 1 ] end ;; There are three types of agent, everyone of them is one expert on one competence: ;; node yellow are specialized in c1 ;; node green are specialized in c2 ;; node blue are specialized in c3 to establish-activity without-interruption [ ask nodes [set color yellow] ask n-of n-g nodes [set color green] ask n-of n-b nodes with [color = green] [set color blue] ] end to establish-knowledge ask nodes with [color = yellow] [set c1 (20 + random 80) set c2 10 set c3 10 set knowledge replace-item 0 knowledge c1 set knowledge replace-item 1 knowledge c2 set knowledge replace-item 2 knowledge c3] ask nodes with [color = green] [set c1 10 set c2 20 + random 80 set c3 10 set knowledge replace-item 0 knowledge c1 set knowledge replace-item 1 knowledge c2 set knowledge replace-item 2 knowledge c3] ask nodes with [color = blue] [set c1 10 set c2 10 set c3 20 + random 80 set knowledge replace-item 0 knowledge c1 set knowledge replace-item 1 knowledge c2 set knowledge replace-item 2 knowledge c3] ask nodes [ set knowledge-level precision ((c1 + c2 + c3) / 3) 1] end ;; The AC is theory or model used to measure a firm's ability to value, assimilate, and apply new knowledge (W. Cohen e D. Levinthal, 1990). ;; The principal element to determine a firm’s absorptive capacity is represented by its available knowledge. ;; Therefore the AC is a function of the knowledge level of the firms. to establish-A-C ask nodes [ set a-c1 precision (c1 / 100) 2 set a-c2 precision (c2 / 100) 2 set a-c3 precision (c3 / 100) 2 set A-C replace-item 0 A-C a-c1 set A-C replace-item 1 A-C a-c2 set A-C replace-item 2 A-C a-c3] end to go if (count nodes <= 1 and count links <= 1) [stop] set cycle cycle + 1 without-interruption [ establish-A-C obsolescence learning-i learning ask nodes [ let node1 self ask node1 [ if (random-float 1 <= collaboration-probability) and (count __link-neighbors < L) [ let partner one-of nodes with [(self != node1) and (not __link-neighbor? node1)] if partner != nobody ;; the exchange of knowledge between two firms take place only if they are complementary competences [if (((c1-of self < c1-of partner) or (c2-of self < c2-of partner) or (c3-of self < c3-of partner)) and ((c1-of self > c1-of partner) or (c2-of self > c2-of partner) or (c3-of self > c3-of partner))) [__create-link-with partner [set color red] learning-e self partner] ] ] ] ] ask nodes [if ((c1 <= 0) and (c2 <= 0) and (c3 <= 0)) [die] set knowledge-level precision ((c1 + c2 + c3) / 3) 1] ] if count links > 0 [set network-density (count links) / ((count nodes * (count nodes - 1)) / 2)] set alone count nodes with [count __link-neighbors = 0] find-clustering-coefficient if plot? and (count nodes > 0 and count links > 0) [ do-plotting ] if label? [ ask nodes [set label A-C] ] if cycle >= stop-simulation [stop] end to learning ask nodes [ let node1 self ask node1 [ without-interruption [ __remove-links-with __link-neighbors with [(c1-of self <= c1-of node1) and (c2-of self <= c2-of node1) and (c3-of self <= c3-of node1)] if __link-neighbor? node1 != nobody [ ask __link-neighbors [ if (((c1-of self < c1-of node1) or (c2-of self < c2-of node1) or (c3-of self < c3-of node1)) and ((c1-of self > c1-of node1) or (c2-of self > c2-of node1) or (c3-of self > c3-of node1))) [learning-e self node1] ] ] ] ] ] end to learning-i ask nodes with [color = yellow] [let node1 self ask node1 [set c1 c1 + a-c1 if c1 > 100 [set c1 100] set knowledge replace-item 0 knowledge c1]] ask nodes with [color = green] [let node1 self ask node1 [set c2 c2 + a-c2 if c2 > 100 [set c2 100] set knowledge replace-item 1 knowledge c2]] ask nodes with [color = blue] [let node1 self ask node1 [set c3 c3 + a-c3 if c3 > 100 [set c3 100] set knowledge replace-item 2 knowledge c3]] ask nodes [set knowledge-level precision ((c1 + c2 + c3) / 3) 1] end ;; The external learning depends by the absorptive capacity and by the knowledge gap. When the gap is too marked, the learning efficacy decrease. to learning-e [A B] ask A [ if c1-of self < c1-of B [set c1-of A c1-of A + ((a-c1) * (((-0.004) * ((c1-of B - c1-of A)^ 2)) + (0.4 * (c1-of B - c1-of A)))) set knowtransfer knowtransfer + ((a-c1) * (((-0.004) * ((c1-of B - c1-of A)^ 2)) + (0.4 * (c1-of B - c1-of A)))) ] if c2-of self < c2-of B [set c2-of A c2-of A + ((a-c2) * (((-0.004) * ((c2-of B - c2-of A)^ 2)) + (0.4 * (c2-of B - c2-of A)))) set knowtransfer knowtransfer + ((a-c2) * (((-0.004) * ((c2-of B - c2-of A)^ 2)) + (0.4 * (c2-of B - c2-of A)))) ] if c3-of self < c3-of B [set c3-of A c3-of A + ((a-c3) * (((-0.004) * ((c3-of B - c3-of A)^ 2)) + (0.4 * (c3-of B - c3-of A)))) set knowtransfer knowtransfer + ((a-c3) * (((-0.004) * ((c3-of B - c3-of A)^ 2)) + (0.4 * (c3-of B - c3-of A))))] set knowledge replace-item 0 knowledge c1 set knowledge replace-item 1 knowledge c2 set knowledge replace-item 2 knowledge c3 ] ask B [ if c1-of self < c1-of A [set c1-of B c1-of B + ((a-c1) * (((-0.004) * ((c1-of A - c1-of B)^ 2)) + (0.4 * (c1-of A - c1-of B)))) set knowtransfer knowtransfer + ((a-c1) * (((-0.004) * ((c1-of A - c1-of B)^ 2)) + (0.4 * (c1-of A - c1-of B))))] if c2-of self < c2-of A [set c2-of B c2-of B + ((a-c2) * (((-0.004) * ((c2-of A - c2-of B)^ 2)) + (0.4 * (c2-of A - c2-of B)))) set knowtransfer knowtransfer + ((a-c2) * (((-0.004) * ((c2-of A - c2-of B)^ 2)) + (0.4 * (c2-of A - c2-of B))))] if c3-of self < c3-of A [set c3-of B c3-of B + ((a-c3) * (((-0.004) * ((c3-of A - c3-of B)^ 2)) + (0.4 * (c3-of A - c3-of B)))) set knowtransfer knowtransfer + ((a-c3) * (((-0.004) * ((c3-of A - c3-of B)^ 2)) + (0.4 * (c3-of A - c3-of B))))] set knowledge replace-item 0 knowledge c1 set knowledge replace-item 1 knowledge c2 set knowledge replace-item 2 knowledge c3 ] ask nodes [set knowledge-level precision ((c1 + c2 + c3) / 3) 1] end ;; The knowledge possessed lose progressively value as it is subject to the obsolescence. to obsolescence ask nodes [ let node1 self ask node1[ without-interruption [ set c1 c1 - obs set c2 c2 - obs set c3 c3 - obs if c1 < 0 [set c1 0] if c2 < 0 [set c2 0] if c3 < 0 [set c3 0] set knowledge replace-item 0 knowledge c1 set knowledge replace-item 1 knowledge c2 set knowledge replace-item 2 knowledge c3 ask nodes [if ((c1 <= 0) and (c2 <= 0) and (c3 <= 0)) [die] ] ] ] ] ask nodes [set knowledge-level precision ((c1 + c2 + c3) / 3) 1] end to do-plotting ;; plotting procedure if count links > 0 [ let max-degree max values-from nodes [count __link-neighbors] set-current-plot "Degree Distribution" plot-pen-reset ;; erase what we plotted before set-plot-x-range 1 (max-degree + 1) histogram-from nodes [count __link-neighbors] set-current-plot "Degree Distribution (log-log)" plot-pen-reset let degree 1 while [degree <= max-degree] [ let matches nodes with [count __link-neighbors = degree] if any? matches [ plotxy log degree 10 log (count matches) 10 ] set degree degree + 1 ] ] set-current-plot "Average-Knowledge&n" set-current-plot-pen "A-K" plot mean values-from nodes [knowledge-level] set-current-plot-pen "n" plot count nodes set-current-plot "variance-knowledge" plot variance values-from nodes [knowledge-level] set-current-plot "network-density" plot network-density set-current-plot "A-C" plot mean values-from nodes [(a-c1 + a-c2 + a-c3) / 3] set-current-plot "knowtransfer" plot knowtransfer set-current-plot "CC" plot clustering-coefficient end to plot-node clear-all-plots ask nodes[ set-current-plot "knowledge-nodes" set-current-plot-pen "K" plotxy who knowledge-level set-current-plot-pen "A-C" plotxy who (c1 + c2 + c3) / 3 set-current-plot "Degree-centrality" plotxy who (count __link-neighbors) ] ask nodes with [node-clustering-coefficient != "undefined"] [set-current-plot "C-C" plotxy who (node-clustering-coefficient)] end to-report in-neighborhood? [ hood ] report ( member? __end1 hood and member? __end2 hood ) end to find-clustering-coefficient set clustering-coefficient 0 ask nodes [set node-clustering-coefficient 0] ifelse not any? nodes with [count __link-neighbors > 1] [ set clustering-coefficient 0 ] [ ask nodes with [ count __link-neighbors <= 1] [ set node-clustering-coefficient "undefined" ] ask nodes with [ count __link-neighbors > 1] [ let hood __link-neighbors set node-clustering-coefficient (2 * count links with [ in-neighborhood? hood ] / ((count hood) * (count hood - 1)) ) ] set clustering-coefficient (sum values-from nodes [node-clustering-coefficient]) / count nodes with [count __link-neighbors > 1] ] end @#$#@#$#@ GRAPHICS-WINDOW 188 51 618 502 17 17 12.0 1 10 1 1 1 0 0 0 1 -17 17 -17 17 CC-WINDOW 5 900 1994 995 Command Center 0 BUTTON 6 10 69 43 NIL setup NIL 1 T OBSERVER T NIL BUTTON 71 10 134 43 NIL go T 1 T OBSERVER T NIL SLIDER 2 174 182 207 collaboration-probability collaboration-probability 0 1 1.0 0.1 1 NIL MONITOR 3 224 60 273 NIL cycle 3 1 SWITCH 78 224 181 257 plot? plot? 0 1 -1000 SWITCH 140 10 243 43 label? label? 1 1 -1000 SLIDER 4 50 176 83 num-nodes num-nodes 0 100 90 1 1 NIL SLIDER 5 86 177 119 n-g n-g 0 100 60 1 1 NIL SLIDER 6 123 178 156 n-b n-b 0 100 20 1 1 NIL PLOT 624 10 918 228 Degree Distribution degree n of nodes 1.0 10.0 0.0 10.0 true false PENS "default" 1.0 1 -16777216 true PLOT 624 230 918 447 Degree Distribution (log-log) log (degree) log (n of nodes) 0.0 0.3 0.0 0.3 true false PENS "default" 1.0 2 -16777216 true PLOT 920 10 1291 228 network-density ciclo NIL 0.0 0.5 0.0 0.1 true false PENS "D-R" 1.0 0 -16777216 true PLOT 920 230 1286 447 Average-Knowledge&n ciclo k 0.0 10.0 0.0 100.0 true false PENS "A-K" 1.0 0 -16777216 true "n" 1.0 0 -13345367 true SLIDER 9 398 181 431 stop-simulation stop-simulation 0 10000 10000 100 1 NIL PLOT 1531 10 1984 255 knowledge-nodes nodes l/k 0.0 90.0 0.0 10.0 true false PENS "A-C" 1.0 1 -13345367 true "k" 1.0 1 -16777216 true PLOT 1531 255 1985 508 Degree-centrality node L 0.0 90.0 0.0 10.0 true false PENS "default" 1.0 1 -2674135 true BUTTON 1352 51 1437 84 NIL plot-node NIL 1 T OBSERVER T NIL MONITOR 99 279 180 328 NIL count nodes 3 1 PLOT 920 448 1287 657 variance-knowledge ciclo variance 0.0 10.0 0.0 10.0 true false PLOT 189 506 617 651 CC ciclo c-c 0.0 10.0 0.0 0.1 true true PENS "CC" 1.0 0 -2674135 true PLOT 1531 524 1984 757 C-C NIL NIL 0.0 90.0 0.0 1.0 true false PENS "default" 1.0 1 -16777216 true PLOT 624 449 916 658 A-C NIL NIL 0.0 10.0 0.0 1.0 true false PENS "default" 1.0 0 -2674135 true PLOT 920 659 1287 886 knowtransfer ciclo k-t 0.0 10.0 0.0 10.0 true false PENS "default" 1.0 0 -13840069 true MONITOR 4 485 61 534 NIL alone 3 1 SLIDER 9 364 181 397 obs obs 0 1 0.7 0.1 1 NIL SLIDER 10 330 182 363 L L 0 20 3 1 1 NIL @#$#@#$#@ WHAT IS IT? ----------- The objective of this model is to study the dynamics of learning processes within industrial clusters, in relation to the structural properties of network relations. Industrial clusters are geographically localized production systems, characterized by a large number of small and medium sized firms that are involved at various phases of the production of a homogeneous product family. These firms are highly specialized in a few phases of the production process, and integrated through a complex network of interorganizational relationships (Becattini, 1990; Porter, 1998). Industrial clusters can be view as complex systems composed by many elements (firms) that interact among thems in a non-linear way. The system shows behaviors and patterns which emerge from the interactions among the system parts. HOW IT WORKS ------------ The agents represent the firms of the cluster. Every agent is in possession of a well defined a set of 3 competencies: K = [c1 , c2 , c3] where every dimension indicates the level of one competence. The level of knowledge is represented by a number in a scale from 0 to 100. Three types of agent are available, everyone of them is one expert on one competence. Out of 90 agents, 30 agents (yellow color)are specialized in c1; 40 agents (green color) are specialized in c2; 20 firms (of blue color) are specialized in c3. In addition every agent has its own absorptive capacity (AC). The AC is a construct used to measure a firm's ability to value, assimilate, and apply new knowledge (W. Cohen e D. Levinthal, 1990). The principal element to determine a firm’s absorptive capacity is represented by its available knowledge. In the model to every agent AC a three dimensional vector is associated: A-C = [a-c1, a-c2, a-c3] where each dimension indicates the level of AC in one competence. The AC is a function of the knowledge level of the firms: • a-c1 = c1 / 100 • a-c2 = c2 / 100 • a-c3 = c3 / 100 At the beginning of every simulation, any agent is supplied with a set competencies and the related AC. Later on, in the course of simulation, the set of competencies (and therefore the AC) is subject to continues changes, caused by two conflicting strength: learning and obsolescence. There are two types of learning modes: internal and external. The internal learning is related to R&D activities and to the process of ”learning by doing” and “learning by using”. The external learning, instead, is related to process of “learning by interacting”, therefore firms can increase their knowledge trough continues inter-exchange within the relational network. The exchange of knowledge between two firms occurs only if they have complementary knowledge. Furthermore, the exchange is effective if the cognitive gap is not too high. In addition to the set of competencies and to the absorptive capacity there is a third parameter which characterizes the agents: the number of interaction that any agent try to realize. This variable (that is signed in the model with the letter “ L”) is the same for every agent and it is established externally from the researcher. For example, if the L = 3, every agent, in any cycle, will continue to search a partner for cooperate until it doesn’t succeed to establish three relations. If on one hand every firm keeps on learning during the simulation, on the other hand knowledge lose progressively value as it is subject to the obsolescence. This phenomenon competes to determinate the possibility of survival of the firm. If the firm doesn’t contrast knowledge decrease through a combination of the different ways of learning, it will risk to exit from the cluster. Therefore, in the model the obsolescence is a progressive decrease of knowledge levels . The decrease rate (indicated in the model as “obs”) affects in the same way every firm and it is established from the researcher, which can select a value between 0 and 1. HOW TO USE IT ------------- After choosing the number of firms to create, and setting the model variables, press the GO button. Through model simulation it is to possible observe the formation of networks that evolve slowly until a stable configuration is reached. For different initial conditions, the cluster reacts spontaneously making a network of stable relations able to balance the two paired forces : learning and obsolescence. THINGS TO NOTICE ---------------- For various values of obsolescence (0 ÷ 0,9) the cluster in any case tend to a high level of knowledge. Increasing the obsolescence the system seem to react by self – organizing exchanges and contrast the effect of obsolescence. This phenomenon can be interpreted as a consequence of the high flexibility of the system that allows firm to adapt efficiently to environmental changes. This ability of reacting to changes by reconfiguring the network of relationships is indeed one of the main properties of industrial clusters. Usually the cluster forms a random network that, during the simulation, evolve toward a scale-free network, i.e. a networks with few leaders. So in the initial phase of the simulations the network is random type with a connective distribution that follows a bell curve. This means that, at the start of the simulation, every firms have more or less the same number of the connections. In the course of the simulation the network may evolve until it turns into a scale – free network that is characterized from a connective distribution which follow a power law. This mean that in the cluster there are few firms (firms -hub) whit a high number of links. To understand the mechanism that generates this phenomenon it was observed which nodes succeed at become hubs. The result was very interesting: the firms that, at the beginning, had a slightly greater number of connections were able to make a increasingly number of connections. Thus the model creates a mechanism similar to preferential attachment proposed by Barabasi. This result can be explained because in the cluster the firms with many links succeed to learn quickly and on all the 3 competences. This imply two outcomes: 1) the firm – hub is able to interacting with any type of the cluster firm 2) All the cluster firms wants to interact with the firms – hub because they have a elevated knowledge level (due to the high number of connections) When the obsolescence value is 1 the firms do not succeed to learn enough to survive. Therefore in this condition all the cluster inevitably dies. The interesting thighs is that for obs = 0.9 the cluster still reaches a high average knowledge level. This threshold effects is the demonstration that the cluster is a non – linear system. When a non –linear system is in a critical condition little causes can generate devastating effect (in this case the death of all firms). This phenomenon can represent a point of weakness of the cluster which in given conditions may experience inability to innovate. To this end there is to say that the cluster firms are strongly interdependent among themselves. This causes on one hand high flexibility to market changes, but on the other hand, the network of relationships can represent an obstacle to best performing firms who are actually braked by the slower performances of other firms in the cluster. The analysis shows that the system tends either toward high innovative potential or toward low innovative potential. No room is left for an intermediate behavior. This reflects another characteristics of real industrial clusters which can be easily classified in highly innovative or low innovative districts. THINGS TO TRY ---------------- Change the number of the firms Change obsolescence rate. Change the number of links that each firm tries to create (L). Change collaboration-probability. If you want to freeze the network in a given instant of the simulation, set the collaboration - probability value equal to 0. EXTENDING THE MODEL ------------------- The model is a closed system since no new firms are allowed to enter the cluster. Try to include entrance of new firms and collaboration with external knowledge source (university, research center, etc..) that can influence the dynamics learning of the industrial cluster. RELATED MODELS -------------- See the models in the Networks section of the Models Library, such as Small World and Preferential Attachment. The model was inspired from the article of Journal of Economic Dynamics & Control “Network structure and the diffusion of knowledge” , by Robin Cowan and Nicolas Jonard (2004). Here however the logic is reversed: Cowan and Jonard test the ability of several network structures to influence the learning performance. In this model instead it is the knowledge exchange that generate the network and its topological properties. CREDITS AND REFERENCES ---------------------- This model was development by Luca Iandoli and David Rosario Lubrano Lavadera at the University of Naples Federico II, Italy. This research activity has been carried out within CLOE, the Computational Laboratory of Organizational Engineering Research Group (www. coinor.unina.it/cloe) at the University of Naples Federico II References ALBINO V., CARBONARA N. and GIANNOCCARO I. (2003), Coordination mechanisms based on cooperation and competition within Industrial Districts: An agent-based computational approach, Journal of Artificial Societies and Social Simulation vol. 6, no. 4 AXELROD R. (2005), “Agent-based Modeling as a Bridge between Disciplines”, in Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland. AXTELL R., EPSTEIN J. M. (1996), Growing Artificial Societies: Social Science from the Bottom Up, MIT Press, Cambridge, Mass. BARABASI A., (2002), link, Einaudi. BECATTINI G., 1990, the Marshiallian industrial district as socio - economic notion, , in Pyke, F., G. Becattini, W. Sengenberger (eds.), Industrial districts and inter-firm cooperation in Italy. International Institute for Labour Studies, Geneva, 37-51. BOERO R., SQUAZZONI F. (2002), Economic Performance, Inter-Firm Relations and Local Institutional Engineering in a Computational Prototype of Industrial Districts, Journal of Artificial Societies and Social Simulation, vol. 5 (1). BORRELLI F., PONSIGLIONE C., IANDOLI L. and ZOLLO G. (2005), Inter-Organizational Learning and Collective Memory in Small Firms Clusters: an Agent-Based Approach, Journal of Artificial Societies and Social Simulation vol. 8, no. 3 BRUSCO S., MINERVA T., POLI I e SOLINAS G. (2001), Un automa cellulare per lo studio del distretto industriale, Università degli Studi di Modena e Reggio Emilia, Dipartimento di Economia Politica COHEN W., LEVINTHAL D. (1989), Innovation and Learning: The Two Faces of R&D, Economic Journal, vol. 99 (397), pp. 569-596. COWAN R., JONARD N., (2004), Network structure and the diffusion of knowledge Journal of Economic Dynamics & Control. EPSTEIN J. M., 2005, “Remarks on the foundations of agent-based generative social science”, in Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland. FIORETTI G. (2001), Information structure and behavior of a textile industrial district, Journal of Artificial Societies and Social Simulation. vol. 4, no. 4. FIORETTI G., 2005, "Agent-Based Models of Industrial Clusters and Districts", Contemporary Issues in Urban and Regional Economics, Frank Columbus ed., Nova Science Publishers. HOLLAND H. (1998), Emergence from chaos to order, Oxford University Press, Oxford. PORTER M., 1998, Clusters and the new economics of competition Harvard Business Review; Boston. TERNA P., BOERO R., MORINI M., SONNESSA M. (2006), Modelli per la complessità - La simulazione ad agenti in economia, Ed. Il Mulino, Bologna. TESFATSION L., 2005, “Agent-Based Computational Economics: A Constructive Approach to Economic Theory”, in: Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, Handbooks in Economics Series, North-Holland. @#$#@#$#@ default true 0 Polygon -7500403 true true 150 5 40 250 150 205 260 250 link true 0 Line -7500403 true 150 0 150 300 link direction true 0 Line -7500403 true 150 150 30 225 Line -7500403 true 150 150 270 225 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 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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 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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 3.1.4 @#$#@#$#@ @#$#@#$#@ @#$#@#$#@ @#$#@#$#@