TY - JOUR
T1 - Model histories
T2 - Narrative explanation in generative simulation modelling
AU - Millington, James D.A.
AU - O'Sullivan, David
AU - Perry, George L. W.
PY - 2012/11
Y1 - 2012/11
N2 - The increasing use of computer simulation modelling brings with it epistemological questions about the possibilities and limits of its use for understanding spatio-temporal dynamics of social and environmental systems. These questions include how we learn from simulation models and how we most appropriately explain what we have learnt. Generative simulation modelling provides a framework to investigate how the interactions of individual heterogeneous entities across space and through time produce system-level patterns. This modelling approach includes individual- and agent-based models and is increasingly being applied to study environmental and social systems, and their interactions with one another. Much of the formally presented analysis and interpretation of this type of simulation resorts to statistical summaries of aggregated, system-level patterns. Here, we argue that generative simulation modelling can be recognised as being ‘event-driven’, retaining a history in the patterns produced via simulated events and interactions. Consequently, we explore how a narrative approach might use this simulated history to better explain how patterns are produced as a result of model structure, and we provide an example of this approach using variations of a simulation model of breeding synchrony in bird colonies. This example illustrates not only why observed patterns are produced in this particular case, but also how generative simulation models function more generally. Aggregated summaries of emergent system-level patterns will remain an important component of modellers’ toolkits, but narratives can act as an intermediary between formal descriptions of model structure and these summaries. Using a narrative approach should help generative simulation modellers to better communicate the process by which they learn so that their activities and results can be more widely interpreted. In turn, this will allow non-modellers to foster a fuller appreciation of the function and benefits of generative simulation modelling.
AB - The increasing use of computer simulation modelling brings with it epistemological questions about the possibilities and limits of its use for understanding spatio-temporal dynamics of social and environmental systems. These questions include how we learn from simulation models and how we most appropriately explain what we have learnt. Generative simulation modelling provides a framework to investigate how the interactions of individual heterogeneous entities across space and through time produce system-level patterns. This modelling approach includes individual- and agent-based models and is increasingly being applied to study environmental and social systems, and their interactions with one another. Much of the formally presented analysis and interpretation of this type of simulation resorts to statistical summaries of aggregated, system-level patterns. Here, we argue that generative simulation modelling can be recognised as being ‘event-driven’, retaining a history in the patterns produced via simulated events and interactions. Consequently, we explore how a narrative approach might use this simulated history to better explain how patterns are produced as a result of model structure, and we provide an example of this approach using variations of a simulation model of breeding synchrony in bird colonies. This example illustrates not only why observed patterns are produced in this particular case, but also how generative simulation models function more generally. Aggregated summaries of emergent system-level patterns will remain an important component of modellers’ toolkits, but narratives can act as an intermediary between formal descriptions of model structure and these summaries. Using a narrative approach should help generative simulation modellers to better communicate the process by which they learn so that their activities and results can be more widely interpreted. In turn, this will allow non-modellers to foster a fuller appreciation of the function and benefits of generative simulation modelling.
U2 - 10.1016/j.geoforum.2012.06.017
DO - 10.1016/j.geoforum.2012.06.017
M3 - Article
SN - 0016-7185
VL - 43
SP - 1025
EP - 1034
JO - GEOFORUM
JF - GEOFORUM
IS - 6
ER -