Exploring Creativity and Urban Development with Agent-Based Modeling

Malik, Ammar; Crooks, Andrew; Root, Hilton L.; Swartz, Melanie

doi:10.18564/jasss.2722

Cited by 23 publications

(14 citation statements)

References 62 publications

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“…Thus how one should evaluate models with respect to verification, validation and calibration will vary. For example with abstract models such as [47], which explores urban creativity and development, often the verification and validation strategy is theoretical comparisons to others works and the ability to replicate existing patterns. Such styles of models are in contrast to models based on real world locations which attempt to explain and project what might happen in the future.…”

Section: Abm For City Simulationmentioning

confidence: 99%

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

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Cities are complex systems, comprising of many interacting parts. How we simulate and understand causality in urban systems is continually evolving. Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of cities. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using urban spaces. These data raise several questions: can we effectively use them to understand and model cities as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of urban processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate cities? What is the appropriate level of spatial analysis and time frame to model urban phenomena? Within this paper we discuss these questions using several examples of ABM applied to urban geography to begin a dialogue about the utility of ABM for urban modeling. The arguments that the paper raises are applicable across the wider research environment where researchers are considering using this approach.

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Section: Abm For City Simulationmentioning

confidence: 99%

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Heppenstall

Malleson

Crooks

2016

Systems

Self Cite

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“…The urban development simulations in DAnCE4Water currently focus strongly on the processes of urban densification of Australian cities building on observations over the last decades. However, densification may well lead to the evolution of different urban forms and a more compact city as noted by (Vermeer, 2014) and, other than assumed in our work, land use regulations are unlikely to remain static over the planning horizon and assumptions need to be made on their modification as demonstrated by (Malik et al, 2015).…”

Section: Urban Development Modellingmentioning

confidence: 78%

Assessment of urban pluvial flood risk and efficiency of adaptation options through simulations – A new generation of urban planning tools

Löwe

Urich

Domingo

et al. 2017

Journal of Hydrology

158

109

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“…(3) Run the model and analyze the output of the model that has been made. This method was chosen based on benchmarking research such as 10 resources common pool sustainability by Schill et al (2016), creativity in context of rural development (Malik et al, 2015) and entrepreneurship behaviour by Alvarez & Busenitz (2001) and Audretsch & Keilbach (2004). The ABM, tools of analysis used for this research using the net logo (Wilensky & Rand, 2015).…”

Section: Methodsmentioning

confidence: 99%

An agent based model of Creative Social Entrepreneurship Behaviour in the context of creative economy

Permatasari

Dhewanto²,

Dellyana³

2021

jmi

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This paper presents an agent-based model that illustrates creative-social entrepreneurial behaviour and its impact on socio-economic development and local resources sustainability. This study conducted an agent-based model simulation test to demonstrate the potential of the model developed through a literature review. The model approach assumes the interactions between agents are influenced by three purposes, which are profit entrepreneur, social entrepreneur and hybrid entrepreneur. The process is captured from the ability of entrepreneurial creativity in exploiting and conserving local resources. The results show the success of a dynamic model in integrating characteristics and creative-social entrepreneurial behaviour. The final model used as a reference to validate the impact and the relationship of creative-social entrepreneurial action on the socio-economic development and sustainability of a region's local resources. Keywords—Creative-social Entrepreneurship, Agent Based Model, Hybrid Entrepreneurs, Socio-Economic Development Local Resources Sustainability

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Exploring Creativity and Urban Development with Agent-Based Modeling

Cited by 23 publications

References 62 publications

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

“Space, the Final Frontier”: How Good are Agent-Based Models at Simulating Individuals and Space in Cities?

Assessment of urban pluvial flood risk and efficiency of adaptation options through simulations – A new generation of urban planning tools

An agent based model of Creative Social Entrepreneurship Behaviour in the context of creative economy

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