A Dynamic Systems Approach to the Representation of Policy Implementation Processes in a Multi-Actor World

Kovalevsky, Dmitry V.; Hewitt, Richard J.; Boer, Cheryl de; Hasselmann, Klaus

doi:10.5890/dnc.2017.09.001

Cited by 4 publications

(11 citation statements)

References 31 publications

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“…To avoid hiding the concepts behind unnecessary technical details, we illustrate the basic ideas of model family development on a simple two-actor model, similar in spirit to (but not identical with) the 'linear model with equal influence weighing' described in detail in (Kovalevsky et al, 2017). The model could be immediately generalized to an n-actor case (as was done in (Kovalevsky et al, 2017)), but, for the sake of simplicity, we stick to a lowdimensional case of just two interacting aggregate actors: a proponent and an opponent of policy implementation under study.…”

Section: Model Assumptionsmentioning

confidence: 99%

“…To implement our approach, we integrate a geographical cellular automata model with a system dynamics model describing the actor dynamics (Hewitt et al, 2017;Kovalevsky et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The system dynamics model is tailored to reproduce the dynamics of interaction (and possible conflicts of interest) of a number of aggregate actors that might influence regional development in general and might affect (either in positive or in negative way) the implementation of policy under study in particular. The state variables of the system dynamics models include actor state variables adopted from Participatory Contextual Interaction Theory (PCIT), the latter being a result of integration of two approaches: Contextual Interaction Theory (CIT) and Participatory Action Research (PAR) (see the references on CIT, PAR, and PCIT in (Kovalevsky et al, 2017)). These state variables include:…”

Section: Introductionmentioning

confidence: 99%

“…In our previous studies (Hewitt et al, 2017;Kovalevsky et al, 2017) we explored the impact of actor dynamics on the deployment of solar energy installations and related land use change in the Navarre region, Spain. The system dynamics model describing local actor dynamics was integrated with the APoLUS model (APoLUS stands for Actor, Policy and Land Use Simulator) (Hewitt et al, 2015, Hewitt, 2018.…”

Section: Introductionmentioning

confidence: 99%

“…APoLUS is an open-source, multi-platform model based on cellular automata (freely downloadable from https://simlander.wordpress.com/apolus/) and designed within the R software environment (The R Project for Statistical Computing, URL: https://www.rproject.org/). Our previous work (Hewitt et al, 2017;Kovalevsky et al, 2017), was focussed around the development of a system dynamic model family for the actor dynamic module integrated with APoLUS. We explored several alternative configurations for the system dynamics model describing the actor dynamics, starting from simple linear models and then developing more complex nonlinear schemes.…”

Section: Introductionmentioning

confidence: 99%

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Integrating actor dynamics with land use cellular automata for modelling climate and environmental policy implementation at regional level

Kovalevsky¹,

Hewitt²

2020

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Successful implementation of environmental policies, including climate adaptation and mitigation policies, requires careful consideration of regional and local conditions. Consequently, there is growing understanding that regional models are needed to support climate and environmental policy making. Such models need to take into account the dynamics of geographical space as well as historic and expected future land use change patterns. One relevant geographical modelling approach is based on cellular automata (CA) which has a prominent track record of successful application to a diverse range of geographical problems. Traditionally, CA models are calibrated to reproduce the footprint of actor decision-making manifested in historical land use dynamics, and then projected forward to explore the effect of the observed dynamics on future periods. However, this is a poor representation of the way the world actually works, since policy decisions reflect current needs and priorities, not historic ones. Such a model cannot help us understand how decision-making actors might respond spontaneously to emerging land use outcomes. For these reasons, we believe there is considerable scope for existing CA-based geographical models to be improved by introducing realistic representations of the dynamic behaviour of decision-making actors. We present a modelling approach which retains the well-attested benefits of CA land use models, but which allows greater flexibility in modelling the dynamic behaviour of actors for particular “policy driven” land uses. To implement our approach, we integrate the APoLUS model (APoLUS stands for Actor, Policy and Land Use Simulator) – an open-source, multi-platform model based on geographical CA – with a system dynamics (SD) model describing the actor dynamics. The SD model is tailored to reproduce the dynamics of interaction (and possible conflicting interests) of a number of aggregate actors that might influence regional development in general and might affect (either in positive or in negative way) the implementation of policy under study in particular. In the present paper, we describe new developments in the actor dynamics model family, progressing beyond earlier work in three key ways: (i) incorporating the 2 possible ‘regime shifts’ that might be related, in particular, to election cycles; (ii) describing in more detail the economic drivers of actor dynamics; (iii) introducing the stochasticity in a SD model.

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Section: Model Assumptionsmentioning

confidence: 99%

“…To implement our approach, we integrate a geographical cellular automata model with a system dynamics model describing the actor dynamics (Hewitt et al, 2017;Kovalevsky et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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