The practice of agent-based model visualization

Artificial Life

Volumn 20, Issue 2, 2014, Pages 271-289

  Dorin, Alan ^a   Geard, Nicholas ^b  

^a MONASH UNIVERSITY   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Agent based model; Individual based model; Virtual ecosystem; Visualization

Indexed keywords

AUTONOMOUS AGENTS; ECOLOGY; FLOW VISUALIZATION; SIMULATION PLATFORM; VISUALIZATION;

AGENT-BASED MODEL; INDIVIDUAL BASED MODEL; NON EQUILIBRIUM; ONLINE SOURCES; RESEARCH COMMUNITIES; SIMULATION SOFTWARE; TRANSIENT BEHAVIOR; VIRTUAL ECOSYSTEMS;

COMPUTATIONAL METHODS;

ARTICLE; COMPUTER PROGRAM; COMPUTER SIMULATION; INTERPERSONAL COMMUNICATION; THEORETICAL MODEL;

COMMUNICATION; COMPUTER SIMULATION; MODELS, THEORETICAL; SOFTWARE;

EID: 85047686954     PISSN: 10645462     EISSN: 15309185     Source Type: Journal    
DOI: 10.1162/ARTL_a_00129     Document Type: Article

References (54)

1. Auchincloss, A. H., & Roux, A. V. D. (2008). A new tool for epidemiology: The usefulness of dynamic-agent models in understanding place effects on health. American Journal of Epidemiology, 168(1), 1-8.
2. Bearman, P. S., Moody, J., & Stovel, K. (2004). Chains of affection: The structure of adolescent romantic and sexual networks. American Journal of Sociology, 110 (1), 44-91.
3. Bedau, M. A., Snyder, E., & Packard, N. H. (1998). A classification of long-term evolutionary dynamics. In C. Adami et al. (Eds.), Artificial life VI (pp. 228-237). Cambridge, MA: MIT Press.
4. Berry, R., et al. (2001). Unfinished symphonies-songs of 3.5 worlds. In E. Bilotta et al. (Eds.), Workshop on Artificial Life Models for Musical Applications, Sixth European Conference on Artificial Life (pp. 51-64). Prague: Editoriale Bios.
5. Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the U.S.A., 99(Suppl 3), 7280-7287.
6. Bongard, J. (2002). Evolving modular genetic regulatory networks. In Proceedings of the Congress on Evolutionary Computation (pp. 1872-1877). Piscataway, NJ: IEEE Press.
7. Börner, K., Sanyal, S., & Vespignani, A. (2007). Network science. Annual Review of Information Science and Technology, 41(1), 537-607.
8. Braitenberg, V. (1984). Vehicles: Experiments in synthetic psychology. Cambridge, MA: MIT Press.
9. Bullock, S., Smith, T., & Bird, J. (2006). Picture this: The state of the art in visualization for complex adaptive systems. Artificial Life, 12(2), 189-192.
10. Clement, R. (2002). Visualizing speciation in artificial cichlid fish. Artificial Life, 12(2), 243-257.
11. Dale, K., & Husbands, P. (2010). The evolution of reaction-diffusion controllers for minimally cognitive agents. Artificial Life, 16(1), 1-19.
12. Deneubourg, J. L., Theraulaz, G., & Beckers, R. (1992). Swarm-made architectures. In Toward a practice of autonomous systems, proceedings of the First European Conference on Artificial Life (pp. 123-133). Cambridge, MA: MIT Press/Bradford Books.
13. Doolittle, W. F. (2000). Uprooting the tree of life. Scientific American, 282(2), 90-95.
14. Dorin, A. (2005). Visualisation and sonification of artificial life systems and environments, tutorial notes. In Proceedings of the 8th European Conference on Artificial Life. http://www.csse.monash.edu.au/~aland/TALKS/ ecalTutorial2005.pdf
15. Dorin, A., & Korb, K. B. (2010). Network measures of ecosystem complexity. In Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems (Artificial Life XII) (pp. 323-328). Cambridge, MA: MIT Press.
16. Dwyer, T., Marriott, K., & Wybrow, M. (2009). Dunnart: A constraint-based network diagram authoring tool. In I. G. Tollis & M. Patrignani (Eds.), 16th International Symposium on Graph Drawing GD 2008 (pp. 420-431). Berlin: Springer.
17. Dyer, A. G., Dorin, A., Reinhardt, V., Garcia, J. E., & Rosa, M. G. P. (2014). Bee reverse = learning bahavior and intra-colony differences: Simulations based on behavioral experiments reveal benefits of diversity. Ecological Modelling, 277, 119-131.
18. Eldridge, A. (2002). Issues in auditory display. Artificial Life, 12(2), 259-274.
19. Epstein, J. M., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up. Washington, DC: Brookings Institution.
20. Eren, E. N. (2010). Karl Sims, contemporary art @ Bogaziçi- Interview project (cited 28 September 2011). Available from http:// istanbulmuseum.org/artists/karl%20sims.html.
21. Ferguson, E. S. (1977). The mind's eye: Non-verbal thought in technology. Science, 197(4306), 827-836.
22. Geard, N. L., & Bullock, S. (2010). Competition and the dynamics of group affiliation. Advances in Complex Systems, 13(4), 501.
23. Gómez, J. M., Verdú, M., & Perfectti, F. (2010). Ecological interactions are evolutionarily conserved across the entire tree of life. Nature, 465(7300), 918-921.
24. Grimm, V. (2002). Visual debugging: A way of analyzing, understanding and communicating bottom-up simulation models in ecology. Natural Resource Modeling, 15(1), 23-38.
25. Grimm, V., & Railsback, S. F. (2005). Individual-based modeling and ecology (p. 428). Princeton, NJ: Princeton University Press.
26. Haeckel, E. (1897). The evolution of man: A popular exposition of the principal points of human ontogeny and phylogeny. New York: Appleton.
27. Harris, R. L. (1999). Information graphics: A comprehensive illustrated reference. Oxford, UK: Oxford University Press.
28. Huth, A., & Wissel, C. (1992). The simulation of the movement of fish schools. Journal of Theoretical Biology, 156(3), 365-385.
29. Itami, R. M., & Gimblett, H. R. (2001). Intelligent recreation agents in a virtual GIS world. Complexity International, 8, 1-14.
30. Jones, C. G., Lawton, J. H., & Shachak, M. (1997). Positive and negative effects of organisms as physical ecosystem engineers. Ecology, 78(7), 1946-1957.
31. Korb, K. B., & Dorin, A. (2011). Evolution unbound: Releasing the arrow of complexity. Biology and Philosophy, 26(3), 317-338.
32. Kornhauser, D., Wilensky, U., & Rand, W. (2009). Design guidelines for agent based model visualization. Journal of Artificial Societies and Social Simulation, 12(21), 32.
33. Kosonen, I., & Pursula, M. (1990). A simulation tool for traffic signal control planning. In Third International Conference on Road Traffic Control (pp. 72-76). IEEE.
34. McCormack, J., & Dorin, A. (2002). Ways of seeing: Visualisation of artificial life environments. In Proceedings of Beyond Fitness: Visualising Evolution, Artificial Life 8.
35. Miller, G. F., & Todd, P. M. (1993). Evolutionary wanderlust: Sexual selection with directional mate preferences. In J.-A. Meyer, H. L. Roitblat, & S. W. Wilson (Eds.), From animals to animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior (pp. 21-30). MIT Press, Bradford Books.
36. Neutel, A.-M., Heesterbeek, J. A. P., & de Ruiter, P. C. (2002). Stability in real food webs: Weak links in long loops. Science, 296(5570), 1120-1123.
37. Neutel, A.-M., Heesterbeek, J. A. P., van de Koppel, J., Hoenderboom, G., Vos, A., Kaldeway, C., Berendse, F., & de Ruiter, P. C. (2007). Reconciling complexity with stability in naturally assembling food webs. Nature, 449(7162), 599-602.
38. Odum, H. T. (1957). Trophic structure and productivity of silver springs, Florida. Ecological Monographs, 27(1), 55-112.
39. Palla, G., et al. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043), 814-818.
40. Reynolds, C. (2004). OpenSteer: Steering behaviours for autonomous characters. (Cited 20 July 2012.) Available from http://opensteer.sourceforge. net/.
41. Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioural model. In Proceedings of SIGGRAPH '87, 21(4), 25-34.
42. Romesin, H. M. (2002). Autopoiesis, structural coupling and cognition: A history of these and other notions in the biology of cognition. Cybernetics and Human Knowing, 9(3-4), 5-34.
43. Russell, S. J., & Norvig, P. (1991). Artificial intelligence: A modern approach (3rd ed.). Englewood Cliffs, NJ: Prentice Hall.
44. Sims, K. (1991). Artificial evolution for computer graphics. Computer Graphics, 25(4), 319-328.
45. Sims, K. (1994). Evolving 3D morphology and behavior by competition. Artificial Life, 1(4), 353-372.
46. Smith, T., Bullock, S., & Bird, J. (2002). Beyond fitness: Visualising evolution-Workshop overview. In D. G. E. Bilotta, T. Smith, T. Lenaerts, S. Bullock, H. H. Lund, J. Bird, R. Watson, P. Pantano, L. Pagliarini, H. Abbass, R. Standish, & M. Bedau (Eds.), Beyond fitness: Visualising evolution, ALifeVIII Workshop Proceedings (pp. 99-102). Cambridge, MA: MIT Press.
47. Spector, L., & Klein, J. (2002). Evolutionary dynamics discovered via visualization in the BREVE simulation environment. In T. Smith, J. Bird, & S. Bullock (Eds.), Proceedings of Beyond Fitness: Visualising Evolution, Artificial Life 8 (pp. 163-170).
48. Standish, R. K., & Galloway, J. (2002). Visualising Tierra's tree of life using Netmap. In D. G. E. Bilotta, T. Smith, T. Lenaerts, S. Bullock, H. H. Lund, J. Bird, R. Watson, P. Pantano, L. Pagliarini, H. Abbass, R. Standish, & M. Bedau (Eds.), Beyond fitness: Visualising evolution, ALifeVIII Workshop Proceedings (pp. 171-176). Cambridge, MA: MIT Press.
49. Terzopoulos, D., Tu, X., & Grzeszczuk, R. (1994). Artificial fishes: Autonomous locomotion, perception, behavior, and learning in a simulated physical world. Artificial Life, 1(4), 327-351.
50. Tews, J., Ferguson, M. A. D., & Fahrig, L. (2007). Potential net effects of climate change on High Arctic Peary caribou: Lessons from a spatially explicit simulation model. Ecological Modelling, 207(2-4), 85-98.
51. Tufte, E. R. (2001). The visual display of quantitative information. Cheshire, CT: Graphics Press.
52. Victor, B. (2011). Up and down the ladder of abstraction: A systematic approach to interactive visualization. Kill Math. (Cited 17 October 2011.) Available from http://worrydream.com/LadderOfAbstraction/.
53. Withall, M. S., Phillips, I. W., & Parish, D. J. (2007). Network visualization: A review. IET Communications, 1(3), 365-372.
54. Yaeger, L. (1992). Computational genetics, physiology, metabolism, neural systems, learning, vision and behavior or Polyworld: Life in a new context. In C. Langton (Ed.), Artificial Life III (pp. 263-298). Reading, MA: Addison-Wesley.