THEORETICAL BASIS 3.1 Introduction
3.2 Discussion of various theoretical foundations for this research
3.2.2 Other relevant theoretical foundations
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approaches that can better incorporate complexity and dynamic elements as its central pillars and as a result acts as a more appropriate choice.
Undoubtedly, the above mentioned economic schools of thought are rooted in a vast array of reputable theory, such as Diacon et al. (2013); Sutton et al. (2016) and Sandmo (2015). The contention of this research is that the highlighted schools of thought have been developed during simpler times, without the inherent dynamism and complexity that we are currently rooted in. This research also acknowledges that theories and schools of thought evolve over time, with new research. However, the inherent fundamentals of most established economic schools of thought are limited and made tractable to simplify reality (LeBaron and Tesfatsion, 2008). Currently, the world is experiencing multiple unprecedented challenges, all seeming to converge at the same time, this includes climate change and growing nationalist movements (World Economy Forum, 2017). When analysing a current economy, particularly a local green economy, that is made up of economic, environmental and social elements – it is vitally important that theoretical foundations that can incorporate an unprecedented level of data, complexity, dynamism and feedback loops are adopted (Dolores Sánchez-Fernández et al., 2014; Armiger, 2015).
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United Nations Environment Programme (2013, pg. 14) describes system dynamics as
“The method uses a stock and flow representation of systems and is well suited to jointly present the economic, social and environmental aspects of the development process”. Jay Forrester developed system dynamics in the 1960’s (Feng et al., 2013;
Marzouk and Azab, 2014). System dynamics models first seek to understand the system in question and then improve on it (Forrester, 1994). While system dynamics have been utilised in economic research before (United Nations Environment Programme, 2013; Musango et al., 2015; Marzouk and Azab, 2014), it does not take the individual behaviour of heterogeneous agents into account (Maidstone, 2012). A major shortcoming of system dynamics is that all data need to be input into the model and cannot be generated by the model itself (Feng et al., 2013; Sjöstedt, 2015). This implies that the models do not have the ability to evolve of their own accord. Due to the inability to incorporate heterogeneous agents and the requirement to input all data into the model, as opposed to setting the initial parameters and letting the model evolve without any external input, system dynamics is ruled out for this research but would have been the immediate alternate to the chosen theoretical underpinning. It is important to note that research utilising system dynamics has been previously utilised in the Western Cape (Musango et al., 2015; Oosthuizen et al., 2016) and at a national level (United Nations Environment Programme, 2013).
While game theory and systems dynamics incorporate elements of complexity and dynamism. The extrapolation of the theory to develop a framework was not viewed as feasible by the researcher for both theories, as the mapping processes did not lend itself to identify leverage points, specifically for the eThekwini green economy.
Furthermore, once the framework is eventually developed further into an actual simulation model, both game theory and system dynamics do not allow for emergence to the scale required. If a simpler explanation of the manner in which the eThekwini green economy was being pursued, system dynamics would have been more than appropriate.
The solution to the shortcomings and inability of existing methods of taking into account the complex dynamic nature of economies can be found in ACE.
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Before we delve into the details of ACE it would be beneficial to first briefly discuss CAS, which can be investigated utilising ACE (Kiose and Voudouris, 2015;
Tesfatsion, 2002). CAS is a specific type of system that has more than two agents (components) which have the ability to learn and adapt to stimuli from other agents and the entire system, while having an impact on other agents and the entire system (Holland, 1992; McKenzie, 2014; Wollmann and Steiner, 2017).
Discussing the various characteristics of CAS normally provides a good overview of the concept. Some of the characteristics include: Self-organisation: a CAS cannot be controlled or managed by one agent and as a result the system is self-managed in a decentralised manner (Bristow and Healy, 2014; Bale et al., 2015). Co-evolution:
when any learning takes place, due to feedback loops the various agents and the system develop simultaneously as a result of the learning (Ellis, 2011; Brady, 2014).
Agents with schemata: each agent is unique in that they cannot be considered homogeneous in terms of their behaviour and characteristics (Ellis, 2011; Filotas et al., 2014). Sensitive dependence: even with the smallest deviation in a starting point, or any subsequent change, the result will likely be exponentially amplified at a later stage (Grus et al., 2010; Speakman, 2017). Path dependence: certain options or opportunities and threats will only be available upon making specific initial decisions (Bale et al., 2015; Held et al., 2014). It should be noted that this implies that other opportunities and threats, for different decisions, will then not be available.
Emergence: this tenet states that through the interaction of agents a ‘unique variable or situation’ arises, the variable or situation could not come into being without the interaction of the agents (Bale et al., 2015; Held et al., 2014). This is one of the fundamentals of a CAS, a situation or outcome that cannot be arrived at by merely aggregating the system’s parts to get the sum.
Balint et al. (2017, pg. 22) “The consequences of climate change for human welfare are likely to be enormous, and the intellectual challenges presented by the economics of climate change are daunting. Complex systems science offers flexible tools to analyse the relationship between the physical and the socio-economic system”.
Furtado et al. (2015) further support this by classifying social systems, the economy, environment, cities, education, transportation and legislation as being complex in nature. It is important to note the earlier definition of the green economy, which
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highlighted the three aspects that needs to be positively impacted by pursuing the green economy, these are: social equity, economic development and environmental sustainability, all of which can be classified as being complex in nature.
It is the view of the researcher that any complexity based theory is a prerequisite for the understanding of the eThekwini green economy. The interconnectedness, feedback loops, and inherent heterogeneous characteristics of all components of the eThekwini green economy require a foundation that has the ability to incorporate such.