Communicating Social Simulation Models to Sceptical Minds

Waldherr, Annie; Wijermans, Nanda

doi:10.18564/jasss.2247

Cited by 43 publications

(27 citation statements)

References 8 publications

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“…However, Richiardi et al's [94] approach, involving the meta-study of ABMs and practices, would be a promising way of eliciting emergent standards and methodologies, and their applicability in different contexts, should funding be made available enabling such a study. A similar approach would also apply to addressing issues with 'arbitrariness' in the ways in which models are built (see [101], para. 3.9), and in synthesizing and generalizing knowledge from the application of ABMs.…”

Section: Developing Methodsologymentioning

confidence: 99%

Crossing the chasm: a ‘tube-map’ for agent-based social simulation of policy scenarios in spatially-distributed systems

Polhill

Hare

et al. 2019

Geoinformatica

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Agent based models (ABMs) simulate actions and interactions of autonomous agents/groups and their effect on systems as a whole, accounting for learning without assuming perfect rationality or complete knowledge. ABMs are an increasingly popular approach to studying complex, spatially distributed socio-environmental systems, but have still to become an established approach in the sense of being one that is expected by those wanting to explore scenarios in such systems. Partly, this is an issue of awareness -ABM is still new enough that many people have not heard of it; partly, it is an issue of confidence -ABM has more to do to prove itself if it is to become a preferred method. This paper will identify advances in the craft and deployment of ABM needed if ABM is to become an accepted part of mainstream science for policy or stakeholders. The conduct of ABM has, over the last decade, seen a transition from using abstracted representations of systems (supporting theory-led thought experiments) to more accessible representations derived empirically (to deliver more applied analysis). This has enhanced the perception of potential users of ABM outputs that the latter are salient and credible. Empirical ABM is not, however, a panacea, as it demands more computing and data resources, limiting applications to domains where data exist along with suitable environmental models where these are required. Further, empirical ABM is still facing serious questions of validation and the ontology used to describe the system in the first place. Using Geoffrey A. Moore's Crossing the Chasm as a lens, we argue that the way ahead for ABM lies in identifying the niches in which it can best demonstrate its advantages, working with collaborators to demonstrate that it can deliver on its promises. This leads us to identify several areas where work is needed.

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Section: Developing Methodsologymentioning

confidence: 99%

Crossing the chasm: a ‘tube-map’ for agent-based social simulation of policy scenarios in spatially-distributed systems

Polhill

Hare

et al. 2019

Geoinformatica

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“…Some reasons why center on the fact that results from ABM often do not match the standard terminology, scope of research questions, complexity of results, or validation by established research methods. There is also a common skepticism that questions whether science-based computation is compatible with fields of research closely tied to the humanities (18). Even for steadfastly skeptical research areas, ABM meshes well with the focus on individual agency or action emphasized in such areas of social theory as feminist theory, critical theory, and poststructuralism (5).…”

Section: No Panaceamentioning

confidence: 99%

Building a better past with the help of agent-based modeling

Rogers

Cegielski

2017

Proc. Natl. Acad. Sci. U.S.A.

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With a few exceptions, the distant past is an anonymous land occupied by people who made things and left behind a cryptic record of broken pieces. Even when the written word comes into play in human history and we can read the hieroglyphs naming an Egyptian pharaoh and describing royal achievements, the majority of human experience remains unknown and unrecoverable. Archaeologists usually excavate to gather physical evidence from ruins, monuments, and artifacts, which become data streams for the interpretation of past societies. Basic excavation is supplemented by a variety of scientific and historical research methods, all of which are far removed from observing the actual experiences of individuals in the past.The result is that aggregated categories such as households, neighborhoods, cities, nations, and civilizations become the units of analysis. These categories are incredibly important, but they are often used because finer analytical levels are unavailable. Furthermore, the aggregate categories are typically implemented under expectations of classical science, such as equilibrium outcomes that assume normal distributions as well as Fig. 1. Agent-based modeling is a powerful means for archeologists to explore the connections among individual action, intangible social entities, and broader interpretations at any time scale. If used more widely, ABM could free practitioners from many traditional limitations and make findings translatable to the general scientific public. Image courtesy of Wendy Cegielski and Jay Etchings

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“…Communication challenges ABM offers a more comprehensive conceptual framework than many other computational approaches. Nonetheless, as has been documented in other fields, ABM users need to communicate methods and results to a tourism audience that is largely unfamiliar with ABM language or modelling process (Waldherr & Wijermans, 2013). For example, the information required by tourism researchers differs from that of ABM programmers.…”

Section: Technical Challengesmentioning

confidence: 99%