Creative industries are acknowledged to offer opportunities for socio-economic development of neighbourhoods (Booyens, 2012). Research into creative industries and creative city strategies has often focused on Global-North contexts, and how policy making can spur the promotion of such industries and strategies, i.e. commodification. Less attention has been paid to creative industries development in Global South contexts and how these industries can best be used to support future local economic growth.
Current research is focused on identifying links between the different actors involved in the development and implementation of creative industries strategies. However, it is of yet unclear how the behaviour of these actors is influenced by the different current and potential strategies of the local government to stimulate commodification. In other domains, the influence of government strategies on the interactions between actors has been studies with Agent-Based Models (ABMs) (e.g. Moncada et al. 2018).
As a Global-South country, Indonesia offers the opportunity to study creative industries because several cities have been embracing creative industries and creative city discourses (Fahmi et al., 2017). Indonesian cities also host specific types of neighbourhoods – kampungs – where often craft-based and creative-based industries are found.
The goal of this project is to develop and apply an ABM to understand commodification in creative industries, in Indonesian kampungs, in response to different local government strategies.
This project is a collaboration with Mafalda Mudureira, Ana Bustamante, and Karin Pfeffer (University of Twente, The Netherlands). These researchers have (sensitive!) data available that can be used to set up the ABM.
- To formalize the actors and interactions between them in Indonesian kampungs into ABM rules.
- To implement these rules in a selected ABM programming language, such as NetLogo
- To device and run a set of future scenarios to project the dynamics in Indonesian kampungs in response to government strategies.
- Booyens I. (2012). Creative industries, inequality and social development: developments, impacts and challenges in Cape Town. Urban Forum 23(1), 43-60. DOI: 10.1007/s12132-012-9140-6.
- Moncada J.A., Verstegen J.A., Posada J.A., Junginger M., Lukszo Z., Faaij A.P.C., Weijnen M. (2018). Exploring policy options to spur the expansion of ethanol production and consumption in Brazil: An agent-based modeling approach. Energy Policy 123, 619-641. DOI: 10.1016/j.enpol.2018.09.015.
- Fahmi F. Z., McCann P., Koster S. (2017). Creative economy policy in developing countries: The case of Indonesia. Urban Studies, 54(6), 1367-1384. DOI: 10.1177/0042098015620529.
- Passed the course Spatial Modelling & Statistics (30306), or another course in which agent-based modelling is taught.
- Conceptual-thinking skills.
Theme(s): Modelling & visualisation; Empowering & engaging communities