Levins and the Lure of Artificial Worlds

Bullock, Seth

doi:10.5840/monist201497320

Cited by 12 publications

(11 citation statements)

References 27 publications

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“…More realism in a simulation is not a problem in itself. However, the higher the number of variables the better chance of introducing unintentional errors, the more difficult it is to check the code and the more challenging it is to recognise which elements of the model caused the "interesting" results (Railsback and Grimm 2011;Bullock 2014). Although this issue is not inherent to ABM, it is nevertheless more common than in other techniques (Ropella et al 2002;O'Sullivan et al 2012;Galán et al 2013).…”

Section: Agent-based Modellingmentioning

confidence: 91%

“…In EBMs, the role of each factor is known as it appears in the equations, so the interpretation of the model's results is usually straightforward (Bullock 2014). However, as the model departs from the analytical rigour of mathematics and strives for realistic representation of the studied real-world system, the results become increasingly opaque because "the behaviour of a simulation is not understandable by simple inspection, on the contrary, effort towards the results of a simulation must be expended, since there is no guarantee that what goes on in it is going to be obvious" (Di Paolo et al…”

Section: Analysing and Interpreting The Resultsmentioning

confidence: 99%

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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: Agent-based Modellingmentioning

confidence: 91%

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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“…Furthermore, CS tools can provide projections or predictions for the systems' planning. This is particularly important since predictability in symbiotic networks is inherently limited, complicated, and complex due to reflexive, complex, and adaptive nature of social systems, which are one of their key dimensions . However, complexity of networks needs to be harnessed and not limited.…”

Section: Conclusion Toward Practical Considerationsmentioning

confidence: 99%

“…This is particularly important since predictability in symbiotic networks is inherently limited, complicated, and complex due to reflexive, complex, and adaptive nature of social systems, which are one of their key dimensions. 50 However, complexity of networks needs to be harnessed and not limited. The value of the complexity of networks can be harnessed in two ways: the first involves mobilizing, developing, utilizing complexity science techniques, and the second tries to frame and efficiently follow social influences such as cooperation and trust.…”

Section: Conclusion Toward Practical Considerationsmentioning

confidence: 99%

Role of computer software tools in industrial symbiotic networks and the examination of sociocultural factors

Fric

Rončević

Uršič

2019

Env Prog and Sustain Energy

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This article represents some principal dilemmas and challenges that need to be undertaken when conducting future research on Industrial Symbiotic Networks (ISN) using existing and efficient Computer Software Tools (CS tools). Multidimensional networks in industrial symbiosis have involved two or more stakeholders in sharing services, utility, or by‐products/waste (material, energy, and water) in order to increase value and reduce financial and environmental costs. Multidimensional networks operate as dynamic complex networks, crossing the boundaries and are formed by natural, technical, social, and computer systems. For a better understanding, there is an outline and review of the benefits of using CS tools within managing industrial symbiotic networks.

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“…Simulationism, or pseudo-empiricism regards the study of ALife systems as having a sort of empirical 'flavor', but does not perceive such systems as direct sources of experimental data. On this view, ALife systems may be perceived as rich 'artificial worlds' worthy of scientific investigation in their own right (Bullock, 2014). They may provide insights into the dynamics which govern living systems, but do not themselves qualify as experimental systems from which to draw conclusions about the living systems themselves.…”

Section: Three Perspectivesmentioning

confidence: 99%

Artificial Life and the Philosophy of Science

Fahmy

2014

Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems

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What is the ontological status of Artificial Life systems? This question admits two interpretations. The first asks whether and to what degree Artificial Life systems could potentially be considered 'alive' in the sense that we take naturally occuring biological systems to be. The second has to do with what sort of role Artificial Life systems occupy in scientific inquiry. In this paper, we investigate both facets of the question, and explore the philosophical and practical implications of possible answers for scientific practice.

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Levins and the Lure of Artificial Worlds

Cited by 12 publications

References 27 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

Role of computer software tools in industrial symbiotic networks and the examination of sociocultural factors

Artificial Life and the Philosophy of Science

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