Abstract
The patterns we see in the growth of a plant or the behaviour of animals can appear very complex, but there are often simple rules that underlie what we see. Systems of rules, called L-systems can capture the organisation of branching patterns and other features of growing plants. Simple rules of behaviour can explain many features of animal behaviour; multi-agent simulations use these rules to model community organisation and interaction with the environment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The process often involves using several different rules in parallel, not just one at a time.
- 2.
This simple drawing procedure uses turtle graphics, which we look at later in the chapter.
- 3.
Cellular automata, which we discuss in Chap. 3, have context-sensitive rules.
- 4.
This process, known as stigmergy, involves positive feedback (see Sect. 5.1).
- 5.
We will discuss this kind of model, which is known as a cellular automaton, in the next chapter.
References
Anselme P, Güntürkün O (2019) How foraging works: uncertainty magnifies food-seeking motivation. Behav Brain Sci 42(e35):1–59
Bartumeus F, Campos D, Ryu WS, Lloret-Cabot R, Méndez V, Catalan J (2016) Foraging success under uncertainty: search tradeoffs and optimal space use. Ecol Lett 19(11):1299–1313
Bode NW, Wood AJ, Franks DW (2011) The impact of social networks on animal collective motion. Anim Behav 82(1):29–38
Charles-Edwards DA, Doley D, Rimmington GM (1986) Modeling plant growth and development. Academic, North Ryde
Chmait N, Dowe DL, Green DG, Li YF (2019) Simulating exploration versus exploitation in agent foraging under different environment uncertainties. Behav Brain Sci 42:e39
Chmait N, Dowe DL, Li YF, Green DG, Insa-Cabrera J (2016) Factors of collective intelligence: How smart are agent collectives? Proceedings of the Twenty-second European Conference on Artificial Intelligence IOS Press, Amsterdam, p 542–550
Dunbar RIM (1998) Grooming, gossip and the evolution of language. Harvard University Press, Cambridge
Dunbar RIM (2013) Primate social systems. Springer, Berlin
Fiorucci AS, Fankhauser C (2017) Plant strategies for enhancing access to sunlight. Curr Biol 27(17):931–940
Hernández-Orallo J, Dowe DL (2010) Measuring universal intelligence: towards an anytime intelligence test. Artif Intell 174(18):1508–1539
Hogeweg P, Hesper B (1983) The ontogeny of the interaction structure in bumblebee colonies: a MIRROR model. Behav Ecol Sociobiol 12(4):271–283
Larralde H, Trunfio P, Havlin S, Stanley HE, Weiss GH (1992) Territory covered by N diffusing particles. Nature 355(6359):423–426
Lindenmayer A (1968) Mathematical models for cellular interaction in development. J Theor Biol 18(3):280–315
Mehlhorn K, Newell BR, Todd PM, Lee MD, Morgan K, Braithwaite VA, Hausmann D, Fiedler K, Gonzalez C (2015) Unpacking the exploration–exploitation tradeoff: a synthesis of human and animal literatures. Decision 2(3):191–237
Monk CT, Barbier M, Romanczuk P, Watson JR, Alós J, Nakayama S, Rubenstein DI, Levin SA, Arlinghaus R (2018) How ecology shapes exploitation: a framework to predict the behavioural response of human and animal foragers along exploration–exploitation trade-offs. Ecol Lett 21(6):779–793
Papert S (1973) Uses of technology to enhance education. LOGO Memo No 8 MIT Artificial Intelligence Laboratory, Cambridge
Pinter-Wollman N, Hobson EA, Smith JE, Edelman AJ, Shizuka D, de Silva S, Waters JS, Prager SD, Sasaki T, Wittemyer G, Fewell J, McDonald DB (2014) The dynamics of animal social networks: analytical conceptual and theoretical advances. Behav Ecol 25(2):242–255
Poskanzer J (1991) Xantfarm – simple ant farm for X11. http://www.acme.com/software/xantfarm/ Accessed 27 Dec 2019
Prusinkiewicz P, Lindenmayer A (1990) The algorithmic beauty of plants. Springer, Berlin
Quadros AL, Barros F, Blumstein DT, Meira VH, Nunes JAC (2019) Structural complexity but not territory sizes influences flight initiation distance in a damselfish. Mar Biol 166(5):65–71
Reynolds CW (1987) Flocks herds and schools: a distributed behavioral model. Comp Grap 21(4):25–34
Rimmington GM, Alagic M (2007) From Modeling foliage with L-systems to digital art. In: Bridges Donostia: mathematics, music, art, architecture, culture. Tarquin Publications, pp 269–276
Tan Y, Zheng ZY (2013) Research advance in swarm robotics. Def Technol 9(1):18–39
Tao Y, Börger L, Hastings A (2016) Dynamic range size analysis of territorial animals: an optimality approach. Am Nat 188(4):460–474
Viswanathan GM, Buldyrev SV, Havlin S, Da Luz MGE, Raposo EP, Stanley HE (1999) Optimizing the success of random searches. Nature 401(6756):911–914
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Green, D.G., Klomp, N.I., Rimmington, G., Sadedin, S. (2020). Seeing the Wood for the Trees: Emergent Order in Growth and Behaviour. In: Complexity in Landscape Ecology. Landscape Series, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-030-46773-9_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-46773-9_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-46772-2
Online ISBN: 978-3-030-46773-9
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)