Encyclopedia of Complexity and Systems Science

Living Edition
| Editors: Robert A. Meyers

Agent-Based Modeling and Artificial Life

  • Charles M. MacalEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-642-27737-5_7-5

Definition of the Subject

Agent-based modeling began as the computational arm of artificial life some 20 years ago. Artificial life is concerned with the emergence of order in nature. How do systems self-organize themselves and spontaneously achieve a higher-ordered state? Agent-based modeling, then, is concerned with exploring and understanding the processes that lead to the emergence of order through computational means. The essential features of artificial life models are translated into computational algorithms through agent-based modeling. With its historical roots in artificial life, agent-based modeling has become a distinctive form of modeling and simulation. Agent-based modeling is a bottom-up approach to modeling complex systems by explicitly representing the behaviors of large numbers of agents and the processes by which they interact. These essential features are all that is needed to produce at least rudimentary forms of emergent behavior at the systems level. To...


Cellular Automaton Cellular Automaton Complex Adaptive System Artificial Life Pheromone Trail 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.


Primary Literature

  1. Adami C (1998) Introduction to artificial life. TELOS, Santa ClarazbMATHCrossRefGoogle Scholar
  2. Alber MS, Kiskowski MA, Glazier JA, Jiang Y (2003) On cellular automaton approaches to modeling biological cells. In: Rosenthal J, Gilliam DS (eds) Mathematical systems theory in biology, communication, and finance, IMA volume. Springer, New York, pp 1–39CrossRefGoogle Scholar
  3. Alpaydın E (2004) Introduction to machine learning. MIT Press, CambridgeGoogle Scholar
  4. Axelrod R (1984) The evolution of cooperation. Basic Books, New YorkGoogle Scholar
  5. Axelrod R (1997) The complexity of cooperation: agent-based models of competition and collaboration. Princeton University Press, PrincetonGoogle Scholar
  6. Azzedine B, Renato BM, Kathia RLJ, Joao Bosco MS, Mirela SMAN (2007) An agent based and biological inspired real-time intrusion detection and security model for computer network operations. Comput Commun 30(13):2649–2660CrossRefGoogle Scholar
  7. Back T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, New YorkGoogle Scholar
  8. Berlekamp ER, Conway JH, Guy RK (2003) Winning ways for your mathematical plays, 2nd edn. AK Peters, NatickGoogle Scholar
  9. Bernaschi M, Castiglione F (2001) Design and implementation of an immune system simulator. Comput Biol Med 31(5):303–331CrossRefGoogle Scholar
  10. Bishop CM (2007) Pattern recognition and machine learning. Springer, New YorkGoogle Scholar
  11. Bobashev GV, Goedecke DM, Yu F, Epstein JM (2007) A hybrid epidemic model: combining the advantages of agent-based and equation-based approaches. In: Henderson SG, Biller B, Hsieh M-H, Shortle J, Tew JD, Barton RR (eds) Proceeding 2007 winter simulation conference, Washington, pp 1532–1537Google Scholar
  12. Bonabeau E (1997) From classical models of morphogenesis to agent-based models of pattern formation. Artif Life 3:191–211CrossRefGoogle Scholar
  13. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, New YorkzbMATHGoogle Scholar
  14. Carley KM, Fridsma DB, Casman E, Yahja A, Altman N, Chen LC, Kaminsky B, Nave D (2006) Biowar: scalable agent-based model of bioattacks. IEEE Trans Syst Man Cybern Part A: Syst Hum 36(2):252–265CrossRefGoogle Scholar
  15. Celada F, Seiden PE (1992) A computer model of cellular interactions in the immune system. Immunol Today 13(2):56–62CrossRefGoogle Scholar
  16. Clerc M (2006) Particle swarm optimization. ISTE Publishing, LondonzbMATHCrossRefGoogle Scholar
  17. Dawkins R (1989) The selfish gene, 2nd edn. Oxford University Press, OxfordGoogle Scholar
  18. DeAngelis DL, Gross LJ (eds) (1992) Individual-based models and approaches in ecology: populations, communities and ecosystems. Proceedings of a symposium/workshop, Knoxville, 16–19 May 1990. Chapman & Hall, New York. ISBN 0-412-03171-XGoogle Scholar
  19. Dorigo M, Stützle T (2004) Ant colony optimization. MIT Press, CambridgezbMATHCrossRefGoogle Scholar
  20. Dreyfus HL (1979) What computers can’t do: the limits of artificial intelligence. Harper & Row, New YorkGoogle Scholar
  21. Eiben AE, Smith JE (2007) Introduction to evolutionary computing, 2nd edn. Springer, New YorkGoogle Scholar
  22. Eigen M, Schuster P (1979) The hypercycle: a principle of natural self-organization. Springer, BerlinCrossRefGoogle Scholar
  23. Emonet T, Macal CM, North MJ, Wickersham CE, Cluzel P (2005) AgentCell: a digital single-cell assay for bacterial chemotaxis. Bioinformatics 21(11):2714–2721CrossRefGoogle Scholar
  24. Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. Wiley, HobokenGoogle Scholar
  25. Epstein JM (2007) Generative social science: studies in agent-based computational modeling. Princeton University Press, PrincetonGoogle Scholar
  26. Epstein JM, Axtell R (1996) Growing artificial societies: social science from the bottom up. MIT Press, CambridgeGoogle Scholar
  27. Ermentrout GB, Edelstein-Keshet L (1993) Cellular automata approaches to biological modeling. J Theor Biol 160(1):97–133CrossRefGoogle Scholar
  28. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, HobokenzbMATHGoogle Scholar
  29. Folcik VA, An GC, Orosz CG (2007) The basic immune simulator: an agent-based model to study the interactions between innate and adaptive immunity. Theor Biol Med Model 4(39):1–18. http://www.tbiomed.com/content/4/1/39
  30. Fontana W (1992) Algorithmic chemistry. In: Langton CG, Taylor C, Farmer JD, Rasmussen S (eds) Artificial life II: proceedings of the workshop on artificial life, Santa Fe, Feb 1990, Santa Fe Institute studies in the sciences of the complexity, vol X. Addison-Wesley, Reading, pp 159–209Google Scholar
  31. Gardner M (1970) The fantastic combinations of John Conway’s new solitaire game life. Sci Am 223:120–123CrossRefGoogle Scholar
  32. Gilbert N (2002) Varieties of emergence. In: Macal C, Sallach D (eds) Proceedings of the agent 2002 conference on social agents: ecology, exchange and evolution, Chicago, 11–12 Oct 2002, pp 1–11. Available on CD and at www.agent2007.anl.gov
  33. Gilbert N, Troitzsch KG (1999) Simulation for the social scientist. Open University Press, BuckinghamGoogle Scholar
  34. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, ReadingzbMATHGoogle Scholar
  35. Goldberg DE (1994) Genetic and evolutionary algorithms come of age. Commun ACM 37(3):113–119CrossRefGoogle Scholar
  36. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan, Ann ArborGoogle Scholar
  37. Holland J (1995) Hidden order: how adaptation builds complexity. Addison-Wesley, ReadingGoogle Scholar
  38. Holland JH, Booker LB, Colombetti M, Dorigo M, Goldberg DE, Forrest S, Riolo RL, Smith RE, Lanzi PL, Stolzmann W, Wilson SW (2000) What is a learning classifier system? In: Lanzi PL, Stolzmann W, Wilson SW (eds) Learning classifier systems, from foundations to applications. Springer, London, pp 3–32CrossRefGoogle Scholar
  39. Kauffman SA (1993) The origins of order: self-organization and selection in evolution. Oxford University Press, OxfordGoogle Scholar
  40. Koza JR (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, Cambridge, 840 ppzbMATHGoogle Scholar
  41. Langton CG (1984) Self-reproduction in cellular automata. Physica D 10:135–144CrossRefADSGoogle Scholar
  42. Langton CG (1986) Studying artificial life with cellular automata. Physica D 22:120–149MathSciNetCrossRefADSGoogle Scholar
  43. Langton CG (1989a) Preface. In: Langton CG (ed) Artificial life: proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems, Los Alamos, Sept 1987, Addison-Wesley, Reading, pp xv–xxviGoogle Scholar
  44. Langton CG (1989b) Artificial life. In: Langton CG (ed) Artificial life: the proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems, Los Alamos, Sept 1987, Santa Fe Institute studies in the sciences of complexity, vol VI. Addison-Wesley, Reading, pp 1–47Google Scholar
  45. Langton CG (1992) Life at the edge of chaos. In: Langton CG, Taylor C, Farmer JD, Rasmussen S (eds) Artificial life II: proceedings of the workshop on artificial life, Santa Fe, Feb 1990, Santa Fe Institute studies in the sciences of the complexity, vol X. Addison-Wesley, Reading, pp 41–91Google Scholar
  46. Le Novere N, Shimizu TS (2001) Stochsim: modelling of stochastic biomolecular processes. Bioinformatics 17(6):575–576CrossRefGoogle Scholar
  47. Lindenmeyer A (1968) Mathematical models for cellular interaction in development. J Theor Biol 18:280–315CrossRefGoogle Scholar
  48. Lucas JR (1961) Minds, machines and godel. Philosophy 36(137):112–127CrossRefGoogle Scholar
  49. Manson SM (2006) Bounded rationality in agent-based models: experiments with evolutionary programs. Int J Geogr Inf Sci 20(9):991–1012CrossRefGoogle Scholar
  50. Mehrotra K, Mohan CK, Ranka S (1996) Elements of artificial neural networks. MIT Press, CambridgeGoogle Scholar
  51. Mitchell M, Forrest S (1994) Genetic algorithms and artificial life. Artif Life 1(3):267–289CrossRefGoogle Scholar
  52. Ofria C, Wilke CO (2004) Avida: a software platform for research in computational evolutionary biology. Artif Life 10(2):191–229CrossRefGoogle Scholar
  53. Olariu S, Zomaya AY (eds) (2006) Handbook of bioinspired algorithms and applications. Chapman, Boca Raton, p 679zbMATHGoogle Scholar
  54. Padgett JF, Lee D, Collier N (2003) Economic production as chemistry. Ind Corp Chang 12(4):843–877CrossRefGoogle Scholar
  55. Peacor SD, Riolo RL, Pascual M (2006) Plasticity and species coexistence: modeling food webs as complex adaptive systems. In: Pascual M, Dunne JA (eds) Ecological networks: linking structure to dynamics in food webs. Oxford University Press, New York, pp 245–270Google Scholar
  56. Penrose R (1989) The emperor’s new mind: concerning computers, minds, and the laws of physics. Oxford University Press, OxfordGoogle Scholar
  57. Poundstone W (1985) The recursive universe. Contemporary Books, Chicago, 252 ppGoogle Scholar
  58. Preziosi L (ed) (2003) Cancer modelling and simulation. Chapman, Boca RatonzbMATHGoogle Scholar
  59. Ray TS (1991) An approach to the synthesis of life (tierra simulator). In: Langton CG, Taylor C, Farmer JD, Rasmussen S (eds) Artificial life Ii: proceedings of the workshop on artificial life. Wesley, Redwood City, pp 371–408Google Scholar
  60. Rechenberg I (1973) Evolutionsstrategie: optimierung Technischer Systeme Nach Prinzipien Der Biologischen evolution. Frommann-Holzboog, StuttgartGoogle Scholar
  61. Sakoda JM (1971) The checkerboard model of social interaction. J Math Soc 1:119–132CrossRefGoogle Scholar
  62. Schelling TC (1971) Dynamic models of segregation. J Math Soc 1:143–186CrossRefGoogle Scholar
  63. Searle JR (1990) Is the brain a digital computer? Presidential Address to the American Philosophical AssociationGoogle Scholar
  64. Taub AH (ed) (1961) John Von Neumann: collected works. vol V: Design of computers, theory of automata and numerical analysis (Delivered at the Hixon Symposium, Pasadena, Sept 1948). Pergamon Press, OxfordGoogle Scholar
  65. Turing AM (1938) On computable numbers with an application to the entscheidungsproblem. Process Lond Math Soc 2(42):230–265MathSciNetGoogle Scholar
  66. Turing AM (1952) The chemical basis of morphogenesis. Philos Trans Royal Soc B 237:37–72CrossRefADSGoogle Scholar
  67. von Neumann J (1966) In: Burks AW (ed) Theory of self-reproducing automata. University of Illinois Press, ChampaignGoogle Scholar
  68. Wilke CO, Adami C (2002) The biology of digital organisms. Trends Ecol Evol 17(11):528–532CrossRefGoogle Scholar
  69. Wilke CO, Chow SS (2006) Exploring the evolution of ecosystems with digital organisms. In: Pascual M, Dunne JA (eds) Ecological networks: linking structure to dynamics in food webs. Oxford University Press, New York, pp 271–286Google Scholar
  70. Wolfram S (1984) Universality and complexity in cellular automata. Physica D 1–35Google Scholar
  71. Wolfram S (1999) The mathematica book, 4th edn. Wolfram Media/Cambridge University Press, ChampaignzbMATHGoogle Scholar
  72. Wolfram S (2002) A new kind of science. Wolfram Media, ChampaignzbMATHGoogle Scholar

Books and Reviews

  1. Artificial Life (journal) web page (2008) http://www.mitpressjournals.org/loi/artl. Accessed 8 Mar 2008
  2. Banks ER (1971) Information processing and transmission in cellular automata. PhD dissertation, Massachusetts Institute of TechnologyGoogle Scholar
  3. Batty M (2007) Cities and complexity: understanding cities with cellular automata, agent-based models, and fractals. MIT Press, CambridgeGoogle Scholar
  4. Bedau MA (2002) The scientific and philosophical scope of artificial life. Leonardo 35:395–400CrossRefGoogle Scholar
  5. Bedau MA (2003) Artificial life: organization, adaptation and complexity from the bottom up. TRENDS Cognit Sci 7(11):505–512CrossRefGoogle Scholar
  6. Copeland BJ (2004) The essential turing. Oxford University Press, Oxford, 613 ppzbMATHGoogle Scholar
  7. Ganguly N, Sikdar BK, Deutsch A, Canright G, Chaudhuri PP (2008) A survey on cellular automata. www.cs.unibo.it/bison/publications/CAsurvey.pdf
  8. Griffeath D, Moore C (eds) (2003) New constructions in cellular automata, Santa Fe Institute studies in the sciences of complexity proceedings. Oxford University Press, New York, 360 ppzbMATHGoogle Scholar
  9. Gutowitz H (ed) (1991) Cellular automata: theory and experiment. Special issue of Physica D. 499 ppGoogle Scholar
  10. Hraber T, Jones PT, Forrest S (1997) The ecology of echo. Artif Life 3:165–190CrossRefGoogle Scholar
  11. International Society for Artificial Life web page (2008) www.alife.org. Accessed 8 Mar 2008
  12. Jacob C (2001) Illustrating evolutionary computation with mathematica. Academic, San Diego, 578 ppGoogle Scholar
  13. Michael CF, Fred WG, Jay A (2005) Simulation optimization: a review, new developments, and applications. In: Proceedings of the 37th conference on Winter simulation, OrlandoGoogle Scholar
  14. Miller JH, Page SE (2007) Complex adaptive systems: an introduction to computational models of social life. Princeton University Press, PrincetonGoogle Scholar
  15. North MJ, Macal CM (2007) Managing business complexity: discovering strategic solutions with agent-based modeling and simulation. Oxford University Press, New YorkCrossRefGoogle Scholar
  16. Pascual M, Dunne JA (eds) (2006) Ecological networks: linking structure to dynamics in food webs, Santa Fe Institute studies on the sciences of complexity. Oxford University Press, New YorkGoogle Scholar
  17. Simon H (2001) The sciences of the artificial. MIT Press, CambridgeGoogle Scholar
  18. Sims K (1991) Artificial evolution for computer graphics. ACM SIGGRAPH ′91, Las Vegas, July 1991, pp 319–328Google Scholar
  19. Sims K (1994) Evolving 3D morphology and behavior by competition. Artif Life IV:28–39Google Scholar
  20. Terzopoulos D (1999) Artificial life for computer graphics. Commun ACM 42(8):33–42CrossRefGoogle Scholar
  21. Toffoli T, Margolus N (1987) Cellular automata machines: a new environment for modeling. MIT Press, Cambridge, 200 ppGoogle Scholar
  22. Tu X, Terzopoulos D (1994) Artificial fishes: physics, locomotion, perception, behavior. In: Proceedings of SIGGRAPH`94, 24–29 July 1994, Orlando, pp 43–50Google Scholar
  23. Weisbuch G (1991) Complex systems dynamics: an introduction to automata networks, translated from French by Ryckebusch S. Addison-Wesley, Redwood CityGoogle Scholar
  24. Wiener N (1948) Cybernetics, or control and communication in the animal and the machine. Wiley, New YorkGoogle Scholar
  25. Wooldridge M (2000) Reasoning about rational agents. MIT Press, CambridgezbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Center for Complex Adaptive Agent Systems Simulation (CAS2), Decision and Information Sciences DivisionArgonne National LaboratoryArgonneUSA