Skip to main content
SpringerLink
Log in

Understanding Simulation Results

  • Chapter
  • First Online:
Simulating Social Complexity

Part of the book series: Understanding Complex Systems ((UCS))

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Confusingly, ‘generalised entropy’ methods are also widely used in econometrics for the estimation of missing data. Routines which provide this capability, e.g. in SAS, are not helpful in the description of simulation model outputs!

References

  • Andrienko N, Andrienko G, Gatalsky P (2003) Exploratory spatio-temporal visualisation: an analytical review. J Vis Lang Comput 14(6):503–541

    Article  Google Scholar 

  • Baird AA et al (2002) Frontal lobe activation during object permanence: data from near-infrared spectroscopy. Neuroimage 16:1120–1126

    Article  Google Scholar 

  • Batty, M (2006) Rank Clocks. Nature, 444: 592–596

    Article  Google Scholar 

  • Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang D-U (2006) Complex networks: structure and dynamics. Phys Rep 424(4–5):175–308

    Article  MathSciNet  Google Scholar 

  • Boroditsky L (2001) Does language shape thought? Mandarin and English Speakers’ conceptions of time. Cogn Psychol 43:1–22

    Article  Google Scholar 

  • Bouvrie JV, Sinha P (2007) Visual object concept discovery: observations in congenitally blind children, and a computational approach. Neurocomputing 70(13–15):2218–2233

    Article  Google Scholar 

  • Casdagli M (1997) Recurrence plots revisited. Phys D 108(1–2):12–44

    Article  MathSciNet  MATH  Google Scholar 

  • Chua HF, Boland JE, Nisbett RE (2005) Cultural variation in eye movements during scene perception. Proc Natl Acad Sci USA 102(35):12629–12633

    Article  Google Scholar 

  • Clark PJ, Evans FC (1954) Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35(4):445–453

    Article  Google Scholar 

  • Clement DE, Sistrunk F, Guenther ZC (1970) Pattern perception among Brazilians as a function of pattern uncertainty and age. J Cross Cult Psychol 1(4):305–313

    Article  Google Scholar 

  • Cleveland WS (1983) Visualising data. Hobart Press, Summit

    Google Scholar 

  • David N (2013) Validating simulations. Chapter 8 in this volume

    Google Scholar 

  • Eckmann JP, Kamphorst SO, Reulle D (1987) Recurrence plots of dynamical systems. Europhys Lett 4(9):973–977

    Article  Google Scholar 

  • Evans AJ (2010) Complex spatial networks in application. Complexity 16(2):11–19

    Article  Google Scholar 

  • Evans AJ (2012) Uncertainty and error. In: Heppenstall AJ, Crooks AT, See LM, Batty M (eds) Agent-based models of geographical systems. Springer, Berlin, chapter 15

    Google Scholar 

  • Fisher N, Lewis T, Embleton B (1987) Statistical analysis of spherical data. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  • Fleming L, Sorenson O (2001) Technology as a complex adaptive system: evidence from patent data. Research Policy 30: 1019–1039

    Article  Google Scholar 

  • Foote J, Cooper M (2001) Visualising music structure and rhythm via self-similarity. In: Proceedings of the international computer music conference, ICMC’01, Havana. ICMA, San Francisco, pp 419–422

    Google Scholar 

  • Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, Chichester

    Google Scholar 

  • Gahegan M (2001) Visual exploration in geography: analysis with light. In: Miller HJ, Han J (eds) Geographic data mining and knowledge discovery. Taylor & Francis, London, pp 260–287

    Chapter  Google Scholar 

  • Gehlke CE, Biehl H (1934) Certain effects of grouping upon the size of correlation coefficients in census tract material. J Am Stat Assoc 29(2):169–170

    Google Scholar 

  • Getis A (2007) Reflections on spatial autocorrelation. Reg Sci Urban Econ 37(4):491–496

    Article  Google Scholar 

  • Graps A (2004) Amara’s wavelet page. Access date 8 Jun 2011. http://web.archive.org/web/20110608005544/http://www.amara.com/current/wavelet.html

  • Greenland S, Pearl J (2006) Causal diagrams (Technical report, R-332). UCLA Cognitive Systems Laboratory, Los Angeles. Access date 8 June 2011 http://ftp.cs.ucla.edu/pub/stat_ser/r332.pdf

  • Grimm V (1999) Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future? Ecol Model 115(2):129–148

    Article  Google Scholar 

  • Grimm V (2002) Visual debugging: a way of analyzing, understanding, and communicating bottom-up simulation models in ecology. Nat Resour Model 15:23–38

    Article  MATH  Google Scholar 

  • Grimm V et al (2006) A standard protocol for describing individual-based and agent-based models. Ecol Model 198(1–2):115–126

    Article  Google Scholar 

  • Haining R (1990) Spatial data analysis in the social and environmental sciences. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Heppenstall AJ, Evans AJ, Birkin MH (2006) Using hybrid agent-based systems to model spatially-influenced retail markets. J Artif Soc Soc Simul 9(3). http://jasss.soc.surrey.ac.uk/9/3/2.html

  • Heppenstall AJ, Evans AJ, Birkin MH (2007) Genetic algorithm optimisation of a multi-agent system for simulating a retail market. Environ Plann B 34(6):1051–1070

    Article  Google Scholar 

  • Hinneburg A, Keim DA, Wawryniuk M (1999) HD-eye: visual mining of high-dimensional data. IEEE Comput Graph Appl 19(5):22–31

    Article  Google Scholar 

  • Hipp J, Güntzer U, Nakhaeizadeh G (2002) Data mining of association rules and the process of knowledge discovery in databases. In: Perner P (ed) Advances in data mining, vol 2394, Lecture notes in computer science. Springer, Berlin, pp 207–226

    Google Scholar 

  • Isaaks EH, Srivastava RM (1990) Applied geostatistics. Oxford University Press, Cary

    Google Scholar 

  • Kantz H, Schreiber T (1997) Non-linear time series analysis. Cambridge University Press, Cambridge

    Google Scholar 

  • Knudsen DC, Fotheringham AS (1986) Matrix comparison, goodness-of-fit, and spatial interaction modelling. Int Reg Sci Rev 10:127–147

    Article  Google Scholar 

  • Korie S et al (1998) Analysing maps of dispersal around a single focus. Environ Ecol Stat 5(4): 317–344

    Article  Google Scholar 

  • Marwan N, Kruths J (2002) Nonlinear analysis of bivariate data with cross recurrence plots. Phys Lett A 302(5–6):299–307

    Article  MathSciNet  MATH  Google Scholar 

  • Marwan N, Wessel N, Meyerfeldt U, Schirdewan A, Kurths J (2002) Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Phys Rev E 66(2):026702

    Article  Google Scholar 

  • McGarigal K (2002) Landscape pattern metrics. In: El-Shaarawi AH, Piegorsch WW (eds) Encyclopedia of environmentrics, vol 2. Wiley, Chichester, pp 1135–1142

    Google Scholar 

  • Moon I-C, Schneider M, Carley K (2006) Evolution of player skill in the America’s army game. Simulation 82(11):703–718

    Article  Google Scholar 

  • Müller W, Schumann HS (2003) Visualisation methods for time-dependent data: an overview. In: Chick S, Sánchez PJ, Ferrin D, Morrice DJ (eds) Proceedings of winter simulation 2003, New Orleans, 7–10 Dec 2003. http://informs-sim.org/wsc03papers/090.pdf

  • Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256

    Article  MathSciNet  MATH  Google Scholar 

  • Nisbett RE, Masuda T (2003) Culture and point of view. Proc Natl Acad Sci USA 100(19): 11163–11170

    Article  Google Scholar 

  • OED (2010) Oxford English dictionary. Access date 21 Dec 2010 http://www.oed.com/

  • Openshaw S, Craft AW, Charlton M, Birch JM (1988) Investigation of leukaemia clusters by use of a geographical analysis machine. Lancet 331(8580):272–273

    Article  Google Scholar 

  • Orford S, Harris R, Dorling D (1999) Geography: information visualisation in the social sciences. Soc Sci Comput Rev 17(3):289–304

    Article  Google Scholar 

  • Parry H (2005) Effects of Land Management upon Species Population Dynamics: A Spatially Explicit, Individual-based Model. Unpublished PhD thesis, University of Leeds. pp. 281

    Google Scholar 

  • Patel A, Hudson-Smith A (2012) Agent tools, techniques and methods for macro and microscopic simulation. In: Heppenstall AJ, Crooks AT, See LM, Batty M (eds) Agent-based models of geographical systems. Springer, Berlin, chapter 18

    Google Scholar 

  • Pearl J, Verma TS (1991) A theory of inferred causation. In: Allen JA, Fikes R, Sandewall E (eds) Proceedings of the 2nd international conference on principles of knowledge representation and reasoning (KR'91), Cambridge, MA, 22–25 Apr 1991. Morgan Kaufmann, San Mateo, pp 441–452

    Google Scholar 

  • Pohlheim H (2006) Multidimensional scaling for evolutionary algorithms: visualisation of the path through search space and solution space using Sammon mapping. Artif Life 12(2):203–209

    Google Scholar 

  • Ramsey JB (2002) Wavelets in economics and finance: past and future. Stud Nonlinear Dynam Econ 6(3):1–27

    MathSciNet  Google Scholar 

  • Roberson D, Davidoff J, Davies IRL, Shapiro LR (2004) The development of color categories in two languages: a longitudinal study. J Exp Psychol Gen 133(4):554–571

    Article  Google Scholar 

  • Robinson WS (1950) Ecological correlations and the behaviour of individuals. Am Sociol Rev 15: 351–357

    Article  Google Scholar 

  • Ross AN, Vosper SB (2003) Numerical simulations of stably stratified flow through a mountain pass. Q J R Meteor Soc 129:97–115

    Article  Google Scholar 

  • Schelling TC (1969) Models of segregation. Am Econ Rev 59(2):88–493

    Google Scholar 

  • Schreinemachers P, Berger T (2006) Land-use decisions in developing countries and their representation in multi-agent systems. J Land Use Sci 1(1):29–44

    Article  Google Scholar 

  • Tobler WR (1970) A computer model simulation of urban growth in the Detroit region. Econ Geogr 46(2):234–240

    Article  Google Scholar 

  • Vasconcelos DB, Lopes SR, Kurths J, Viana RL (2006) Spatial recurrence plots. Phys Rev E 73(5): 056207

    Article  MathSciNet  Google Scholar 

  • Viboud C et al (2006) Synchrony, waves, and spatial hierarchies in the spread of influenza. Science 312(5772):447–451

    Article  Google Scholar 

  • Wagner HH, Fortin M-J (2005) Spatial analysis of landscapes: concepts and statistics. Ecology 86:1975–1987

    Article  Google Scholar 

  • Worboys MF (2005) Event-oriented approaches to geographic phenomena. Int J Geogr Info Sci 19(1):1–28

    Article  Google Scholar 

  • Wyszomirski T, Wyszomirska I, Jarzyna I (1999) Simple mechanisms of size distribution dynamics in crowded and uncrowded virtual monocultures. Ecol Model 115(2–3):253–273

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Spatial Analysis and Policy, University of Leeds, Leeds, Britain, LS2 9JT

    Andrew Evans, Alison Heppenstall & Mark Birkin

Authors
  1. Andrew Evans
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alison Heppenstall
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark Birkin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew Evans .

Editor information

Editors and Affiliations

  1. CPM - Centre for Policy Modelling, Manchester Metropolitan University, Manchester, M1 3GH, United Kingdom

    Bruce Edmonds

  2. , Center for Policy Modelling, Manchester Metropolitan University, Aytoun Street, Manchester, M1 3GH, United Kingdom

    Ruth Meyer

Further Reading

Further Reading

Statistical techniques for spatial data are reviewed by McGarigal (2002) while for network statistics good starting points are (Newman 2003) and (Boccaletti et al. 2006), with more recent work reviewed by Evans (2010). For information on coping with auto/cross-correlation in spatial data, see (Wagner and Fortin 2005). Patel and Hudson-Smith (2012) provide an overview of the types of simulation tool (virtual worlds and virtual reality) available for visualising the outputs of spatially explicit agent-based models. Evans (2012) provides a review of techniques for analysing error and uncertainty in models, including both environmental/climate models and what they can bring to the agent-based field. He also reviews techniques for identifying the appropriate model form and parameter sets.

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Evans, A., Heppenstall, A., Birkin, M. (2013). Understanding Simulation Results. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93813-2_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-93813-2_9

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-93812-5

  • Online ISBN: 978-3-540-93813-2

  • eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)

Publish with us

Policies and ethics

Search

Navigation