Chapter 31 Computational Laboratories for Spatial Agent-Based Models

Dibble, Catherine

doi:10.1016/s1574-0021(05)02031-9

Cited by 25 publications

(14 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spatial and dynamic behaviour of complex system can then be investigated, which many other modelling approaches find intractable [20]. ABM techniques have been applied to a wide range of areas and disciplines, these include those involving human decision-making and those that do not [21], from vigilance patterns in gulls [22], through epidemiology [23], to representing contingent behaviours [24]. Within the agricultural sector, ABM has been commonly used for modelling of land-use and land-cover changes [15,[25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Modelling the perennial energy crop market: the role of spatial diffusion

Alexander

Moran

Rounsevell

et al. 2013

J. R. Soc. Interface.

View full text Add to dashboard Cite

Biomass produced from energy crops, such as Miscanthus and short rotation coppice is expected to contribute to renewable energy targets, but the slower than anticipated development of the UK market implies the need for greater understanding of the factors that govern adoption. Here, we apply an agentbased model of the UK perennial energy crop market, including the contingent interaction of supply and demand, to understand the spatial and temporal dynamics of energy crop adoption. Results indicate that perennial energy crop supply will be between six and nine times lower than previously published, because of time lags in adoption arising from a spatial diffusion process. The model simulates time lags of at least 20 years, which is supported empirically by the analogue of oilseed rape adoption in the UK from the 1970s. This implies the need to account for time lags arising from spatial diffusion in evaluating land-use change, climate change (mitigation or adaptation) or the adoption of novel technologies.

show abstract

Section: Introductionmentioning

confidence: 99%

Modelling the perennial energy crop market: the role of spatial diffusion

Alexander

Moran

Rounsevell

et al. 2013

J. R. Soc. Interface.

View full text Add to dashboard Cite

show abstract

“…In the following part, the ideas and theory are connected to each other and an Agent-based model to simulate the movement of firms in the geographic space is designed. [18] ЭКОНОМИКА РЕГИОНА Т. 13, вып.…”

Section: The Model In Detailmentioning

confidence: 99%

Spatial Planning and Policy Evaluation in an Urban Conurbation: a Regional Agent-Based Economic Model

Stricker¹,

Baruffini²

2017

EoR

View full text Add to dashboard Cite

1 This paper studies different functions and relations between 45 agglomerated municipalities in southern Switzerland (Ticino), using a territorial agent-based model. Our research adopts a bottom-up approach to urban systems, considering the agglomeration mechanism and effects of different regional and urban policies, and simulates the individual actions of diverse agents on a real city using an Agent-based model (ABM). Simulating the individual actions of diverse agents on a real city and measuring the resulting system behaviour and outcomes over time, they effectively provide a good test bed for evaluating the impact of different policies. The database is created merging the Swiss official secondary data for one reference year (2011) with Eurostat and OECD-Regpat. The results highlight that the understanding of municipalities' functions on the territory appears to be essential for designing a solid institutional agglomeration (or city). From a methodo-

show abstract

“…The framework then facilitated the rapid implementation and refinement of these theories, as we enforced and relaxed assumptions at will, making it possible to isolate the sufficient conditions for generating jagged preferences in the probabilistic response model. It allowed us to investigate the sensitivity of the various models to their parameters; while a full sensitivity analysis in a Computational Laboratory (Dibble, 2006) would provide a more thorough and rigorous picture, these basic tests have helped guide our thinking. Finally, the agent-based model has identified the circumstances under which the theories provide divergent predictions, thus improving our leverage with small tests.…”

Section: Insight Into Real-world Behaviormentioning

confidence: 99%