Stepping out: a computer simulation of hominid dispersal from Africa

Mithen, Steven; Reed, Melissa

doi:10.1006/jhev.2002.0584

Cited by 39 publications

(36 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On third instance, parallel simulation offers a solution when exploring emergent properties that a small-scale variant of the model is not able to cope with. For instance, parallel simulation might be the only solution in the case the emergent property is linked to the number of interactions at a given time step (Mithen and Reed, 2002;Rubio-Campillo et al, 2012). On fourth instance, it is important to take into account that we are dealing with non-linear, dynamic systems with high uncertainty and notable degree of stochasticity.…”

Section: Challenges Of Parallel Social Simulationmentioning

confidence: 99%

Approaching parallel computing to simulating population dynamics in demography

et al. 2016

View full text Add to dashboard Cite

Agent-based modelling and simulation is a promising methodology that can be applied in the study of population dynamics. The main advantage of this technique is that it allows representing the particularities of the individuals that are modeled along with the interactions that take place among them and their environment. Hence, classical numerical simulation approaches are less adequate for reproducing complex dynamics. Nowadays, there is a rise of interest on using distributed computing to perform large-scale simulation of social systems. However, the inherent complexity of this type of applications is challenging and requires the study of possible solutions from the parallel computing perspective (e.g., how to deal with fine grain or irregular workload). In this paper, we discuss the particularities of simulating populating dynamics by using parallel discrete event simulation methodologies. To illustrate our approach, we present a possible solution to make transparent the use of parallel simulation for modeling demographic systems: Yades tool. In Yades, modelers can easily define models that describe different demographic processes with a web user interface and transparently run them on any computer architecture environment thanks to its demographic simulation library and code generator. Therefore, transparency is provided by by two means: the provision of a web user interface where modelers and policy makers can specify their agent-based models with the tools they are familiar with, and the automatic generation of the simulation code that can be executed in any platform (cluster or supercomputer). A study is conducted to evaluate the performance of our solution in a High Performance Computing environment. The main benefit of this outline is that our findings can be generalized to problems with similar characteristics to our demographic simulation model.

show abstract

Section: Challenges Of Parallel Social Simulationmentioning

confidence: 99%

Approaching parallel computing to simulating population dynamics in demography

et al. 2016

View full text Add to dashboard Cite

show abstract

“…It has inspired a relatively high number of simulation studies, from models of the first "Out of Africa" dispersal ca. 1.8 Mya (e.g., Mithen and Reed 2002;Nikitas and Nikita 2005;Romanowska 2015), and simulations of the spread of the Neolithic (e.g., Wirtz and Lemmen 2003;Ackland et al 2007;Fort et al 2012;Isern and Fort 2012), to models of the more recent peopling of Oceania (e.g., Di Piazza et al 2007;Davies and Bickler 2015). Although no two dispersal events were the same, they share enough common characteristics to be pulled out of their "time periods" and be treated as one "research problem" for the purposes of this review.…”

Section: Past Human Dispersals Offer Good Examples Of Processes In Whmentioning

confidence: 99%

“…A classic example is the study of the Neolithic spread into Europe through equation-based models (Wirtz and Lemmen 2003;Hazelwood and Steele 2004;Ackland et al 2007;Lemmen et al 2011;Baggaley et al 2012;Fort 2012;Fort et al 2012;Isern and Fort 2012). Other common research topics include the Palaeolithic dispersals (Mithen and Reed 2002;Scherjon 2013;Callegari et al Pre-print version. Visit digitalcommons.wayne.edu/humbiol after publication for the final version.…”

Section: The Purpose Of the Model And Research Questionsmentioning

confidence: 99%

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

Romanowska¹

2015

Human Biology

View full text Add to dashboard Cite

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 ...

show abstract

“…Simulation models designed to explore problems in human evolution do not have the kind of ethnographic-scale realism of agent-based models of societal change in later prehistory, either because they model much larger geographical areas, or because they eschew realism in favour of the simplicity that comes with greater abstraction. Two simulation models which exhibit the kind of coarse-grained realism that comes with a continental or even global scale are Mithen and Reed's (2002) and Nikitas and Nikita's (2005) cellular automata models of hominin dispersal from Africa. Both model the Old World as a lattice in which individual cells are differentiated in terms of basic environmental characteristics such as aridity and altitude.…”

Section: Expansion (2001 Onwards)mentioning

confidence: 99%

Trends in Archaeological Simulation

Lake

2013

J Archaeol Method Theory

116

View full text Add to dashboard Cite

This paper provides an up-to-date history of archaeological computer simulation, starting with the early 1970s simulation models, but paying particular attention to those developed over the past 20-25 years. It revises earlier accounts of archaeological simulation by proposing an alternation between programmatic phases, in which published work tends to be about simulation as a method, and mature phases in which there is greater emphasis on the substantive results of simulation experiments. The paper concludes that the burgeoning interest in computer simulation since circa 2000 is largely characterized by mature application in areas where it fits naturally into existing inferential frameworks (e.g., certain strands of evolutionary archaeology) but that explicitly "sociological" simulation remains a challenge.

show abstract

Stepping out: a computer simulation of hominid dispersal from Africa

Cited by 39 publications

References 30 publications

Approaching parallel computing to simulating population dynamics in demography

Approaching parallel computing to simulating population dynamics in demography

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

Trends in Archaeological Simulation

Contact Info

Product

Resources

About