AutoSimOA: a framework for automated analysis of simulation output

Hoad, Kathryn; Robinson, Stewart; Davies, Ruth

doi:10.1057/jos.2010.22

Cited by 15 publications

(7 citation statements)

References 40 publications

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“…In the first stage, often termed "exploratory data analysis," modellers use a range of statistical and visualisation techniques to recognise patterns in the results (Kornhauser et al 2009;Hoad et al 2011;Evans et al 2013;Dorin and Geard 2014). The outcome of each run is usually described as one (or a set) of summary statistics, for example in dispersal models, the timing of the first arrival at a particular location.…”

Section: Analysing and Interpreting The Resultsmentioning

confidence: 99%

So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals

Romanowska¹

2015

Human Biology

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With the current surge of simulation studies in archaeology there is a growing concern for the lack of engagement and feedback between modellers and domain specialists. To facilitate this dialogue I present a compact guide to the simulation modelling process applied to a common research topic and the focus of this special issue of Human Biology-human dispersals. The process of developing a simulation is divided into nine steps grouped in three phases. ThePre-print version. Visit digitalcommons.wayne.edu/humbiol after publication for the final version.conceptual phase consists of identifying research questions (step 1) and finding the most suitable method (step 2), designing the general framework and the resolution of the simulation (step 3) and then by filling in that framework with the modelled entities and the rules of interactions (step 4). This is followed by the technical phase of coding and testing (step 5), parameterising the simulation (step 6) and running it (step 7). In the final phase the results of the simulation are analysed and re-contextualised (step 8) and the findings of the model are disseminated in publications and code repositories (step 9). Each step will be defined and characterised and then illustrated with examples of published human dispersals simulation studies. While not aiming to be a comprehensive textbookstyle guide to simulation, this overview of the process of modelling human dispersals should arm any non-modeller with enough understanding to evaluate the quality, strengths and weaknesses of any particular archaeological simulation and provide a starting point for further exploration of this common scientific tool.All archaeologists start modelling the moment they step out of the excavation trench. We interpret the individual finds (e.g., pots, skeletons, buildings etc.) within certain frameworks (e.g., pottery typologies, bone taxonomies, architectural types etc.) and analyse sets of finds to detect population level patterns (e.g., cultural similarities, age profiles of bone assemblages, urbanPre-print version. Visit digitalcommons.wayne.edu/humbiol after publication for the final version. development, etc.) with strict analytical rigour. However, the interpretations of these patterns in terms of human behaviour and causality are predominantly built using natural language, i.e. they are constructed in the form of conceptual models that hypothesise which causal mechanisms might have led from actions of individual actors (the owners of pots, users of skeletons and inhabitants of buildings) to the detected population-level patterns. These causal relationships are often described as, for example, "culture A influenced culture B," "the dispersal reached area C," or "population D outcompeted population E." Although inferences like these are made on the basis of rigorously collected and analysed data, and are commonly built upon extensive research and a good understanding of multiple strands of evidence, they nevertheless represent a thought experiment and are therefore limited ...

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Section: Analysing and Interpreting The Resultsmentioning

confidence: 99%

So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals

Romanowska¹

2015

Human Biology

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“…We performed five simulations for each combination of conditions, and used the average values. The number of simulations was chosen based on the Confidence Interval method [60,61], which aims at finding the number of replicates necessary to narrow the confidence interval up to a specified precision d min , defined as half-width of the confidence interval in terms of percentage of the sample mean. Using five simulations yield precision values for dune volume and position in the order of 2-2.5%, which is lower than the suggested threshold precision (i.e., 5%).…”

Section: The Dubeveg Modelmentioning

confidence: 99%

On the Relation between Beach-Dune Dynamics and Shoal Attachment Processes: A Case Study in Terschelling (NL)

Silva

Wijnberg

Hulscher

2020

JMSE

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Inlet-driven processes are capable of modifying the adjacent shoreline. However, few studies have attempted to understand how these changes affect coastal dunes. The present study aims to understand how shoreline changes induced by shoal attachment affect coastal dunes. A barrier island in the Netherlands is used as a case study. Both bathymetric and topographic annual data were analysed, together with the application of a cellular automata model for dune development. The objective of the model is to explore idealised scenarios of inlet-driven shoreline movements. With the model, ten different scenarios were examined regarding beach width increase and rate of alongshore spreading of the shoal. Field data showed that, for the case study, dune volume and shoal attachments could not be directly linked. Instead, rates of dune volume change differed significantly only due to long-term ebb-tidal delta evolution. Such morphological evolution oriented the beach towards the main wind direction, increasing overall aeolian transport potential. Modelling results showed that shoals significantly increased dune volumes only on three out of ten scenarios. This suggests that beach width increase, and rate of alongshore sediment spreading, determine whether the shoal will influence dune growth. Therefore, within the studied time-scale, local rates of dune growth are only increased if shoals are capable of increasing the beach width significantly and persistently.

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“…For example, the user might enter the budget they have for the experiments or the time that they would like the experimentation to take. The user could enter the number of replications they would like to run (or use an estimation technique based on the confidence internals (Hoad, Robinson, & Davies, 2011)). The number of runs could then be calculated by combining the number of replications with the KPIs and parameters.…”

Section: A Future Of Ds In Or?mentioning

confidence: 99%

Distributed simulation: state-of-the-art and potential for operational research

Taylor

2019

European Journal of Operational Research

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AutoSimOA: a framework for automated analysis of simulation output

Cited by 15 publications

References 40 publications

So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals

So You Think You Can Model? A Guide to Building and Evaluating Archaeological Simulation Models of Dispersals

On the Relation between Beach-Dune Dynamics and Shoal Attachment Processes: A Case Study in Terschelling (NL)

Distributed simulation: state-of-the-art and potential for operational research

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