The Management of Urban Complexity through a Multi-Agent Participatory Simulation

Semboloni, Ferdinando

doi:10.1080/02513625.2007.10556989

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The model starts with roughly 770,000 household agents and runs in yearly time steps. Extensive literature about past and current attempts in modelling urban residential mobility exist (for example Wegener and Wagner, 2007 ; Putman, 2010 ; Simmonds, 2010 ; Semboloni, 2007 ; Waddell, 2010 ; Benenson, 2004 ). We observe a trend to develop more detailed models in terms of number of household categories, agent types beside households and fine-grained spatial units (based on grid cell level) as well as more sophisticated technical approaches.…”

Section: Model Structure and Parameterizationmentioning

confidence: 99%

“…Transaction costs in terms of moving costs are included using a stochastic residential satisfaction threshold modifier value on the individual household level. During a model time unit (one year), the household may consider only a certain number of dwellings to reflect the incomplete knowledge of the household ( Semboloni, 2007 , p. 61). If a more suitable dwelling can be found, the household will relocate and the old dwelling will subsequently be free on the household market for other households.…”

Section: Model Structure and Parameterizationmentioning

confidence: 99%

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Impact of urban planning on household's residential decisions: An agent-based simulation model for Vienna

Gaube¹,

Remesch²

2013

Environmental Modelling & Software

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Interest in assessing the sustainability of socio-ecological systems of urban areas has increased notably, with additional attention generated due to the fact that half the world's population now lives in cities. Urban areas face both a changing urban population size and increasing sustainability issues in terms of providing good socioeconomic and environmental living conditions. Urban planning has to deal with both challenges. Households play a major role by being affected by urban planning decisions on the one hand and by being responsible – among many other factors – for the environmental performance of a city (e.g. energy use). We here present an agent-based decision model referring to the city of Vienna, the capital of Austria, with a population of about 1.7 million (2.3 million within the metropolitan area, the latter being more than 25% of Austria's total population). Since the early 1990s, after decades of negative population growth, Vienna has been experiencing a steady increase in population, mainly driven by immigration. The aim of the agent-based decision model is to simulate new residential patterns of different household types based on demographic development and migration scenarios. Model results were used to assess spatial patterns of energy use caused by different household types in the four scenarios (1) conventional urban planning, (2) sustainable urban planning, (3) expensive centre and (4) no green area preference. Outcomes show that changes in preferences of households relating to the presence of nearby green areas have the most important impact on the distribution of households across the small-scaled city area. Additionally, the results demonstrate the importance of the distribution of different household types regarding spatial patterns of energy use.

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Section: Model Structure and Parameterizationmentioning

confidence: 99%

Section: Model Structure and Parameterizationmentioning

confidence: 99%

Impact of urban planning on household's residential decisions: An agent-based simulation model for Vienna

Gaube¹,

Remesch²

2013

Environmental Modelling & Software

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“…In a participatory simulation we would expect more direct interaction with system entities. For example, Semboloni's online CityDev software (2007) uses a comprehensive agent‐based model as the basis for a participatory simulation in which a human can generate new agents and control them. Its application to the development of a new suburb of Florence emphasizes the role of private investors in determining the resulting urban structure.…”

Section: Approaches For Structuring User Participation In Simulationsmentioning

confidence: 99%

Using Participatory Computer Simulation to Explore the Process of Urban Evolution

Dearden

Wilson

2011

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When planners intervene in urban systems they seek in part to generate a positive response from those parts of a city outside of their control, namely the private market and private individuals. This response is difficult to predict due to the fact that the city is a nonlinear system of organized complexity. Models of cities which seek to explain this response are necessarily complicated and dynamic. Where an analytical solution is not possible we turn to computer simulation and interactive visualization in order to understand their output. Allowing human participation in such simulations provides a sandbox in which to experiment with the dynamic behaviour of an urban model and play a part in its evolution. Two possible options for structuring this participation are: (1) toy retail systems, which allow unconstrained experimentation, and (2) games, which impose rules and involve role‐play and competition. To explore these ideas we construct a toy retail system and a two‐player retail game, both of which are derived from an existing agent‐based retail model. We explore the application of these systems to the metropolitan county of South Yorkshire in the UK.

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Planning, Residential Decisions and Energy Use in Vienna

2016

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Urban planning must address both a changing urban population size and increasing sustainability issues in terms of providing good socioeconomic and environmental living conditions. Households play a major role in that they are affected by urban planning decisions but are partly responsible for the environmental performance (e.g., energy use) of a city. Here, we present an agent-based decision model of the city of Vienna, the capital of Austria, with a population of approximately 1.7 million. The model results are used to assess the spatial patterns of energy use caused by different household types. The outcomes show that changes in households' preferences regarding the presence of nearby green areas have the most important impact on the distribution of households across the smallscaled city area. Additionally, the results demonstrate the importance of the distribution of different household types regarding spatial patterns of energy use.

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The Management of Urban Complexity through a Multi-Agent Participatory Simulation

Cited by 4 publications

References 13 publications

Impact of urban planning on household's residential decisions: An agent-based simulation model for Vienna

Impact of urban planning on household's residential decisions: An agent-based simulation model for Vienna

Using Participatory Computer Simulation to Explore the Process of Urban Evolution

Planning, Residential Decisions and Energy Use in Vienna

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