Modelling for the Social Sciences

Silverman, Eric; Bryden, John

doi:10.1007/978-3-319-72408-9_5

Cited by 4 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The constant innovation of software then also allows for increasing potential in what these models can represent (Skår, 2003, p. 1055; cf. Silverman & Bryden, 2018, p. vii, chap. 1).…”

Section: Self‐organisation In Social Sciencesmentioning

confidence: 99%

“…Formalising the system theory could highlight and better promote its potential. As Silverman and Bryden (2018) also argue, formalising Luhmann's theory provides a different perspective than the social simulations used in the social sciences. In contrast to their narrow focus on a specific problem, system theory focuses on the interactions of agents that shape the order of society without being determined by pre‐existing assumptions (Silverman & Bryden, 2018, pp.…”

Section: Self‐organisation In Luhmann's System Theorymentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy‐logical self‐organisation system modelling—Illustrating case of self‐organisation Czech restaurants during the Covid‐19 pandemic

Šerá,

Doubravský,

Schüller

2023

Syst Res Behav Sci

View full text Add to dashboard Cite

The aim of this paper is to show how Niklas Luhmann's concept of self‐organization can be formalized using fuzzy logic. This approach is based on the shared assumptions of systems theory and fuzzy logic and focuses on natural language expression and the complexity of social processes. It involves modelling the operations of the system in relation to the fuzziness of the environment, that is, the transformation of environmental stimuli in gradual uncertainty to binary codes of communication. Based on these operations, it is possible to formalize their unified pattern, which shows the observation of past operations and the simultaneous observation of the environment. An example is given of the formalization of restaurant self‐organization in the Czech Republic during the Covid‐19 pandemic. The model illustrates and explains how the restaurant system reduces lockdown as an environment, in other words, how the system stabilizes its behaviour in a complex pandemic period.

show abstract

“…The constant innovation of software then also allows for increasing potential in what these models can represent (Skår, 2003, p. 1055; cf. Silverman & Bryden, 2018, p. vii, chap. 1).…”

Section: Self‐organisation In Social Sciencesmentioning

confidence: 99%

Section: Self‐organisation In Luhmann's System Theorymentioning

confidence: 99%

Fuzzy‐logical self‐organisation system modelling—Illustrating case of self‐organisation Czech restaurants during the Covid‐19 pandemic

Šerá,

Doubravský,

Schüller

2023

Syst Res Behav Sci

View full text Add to dashboard Cite

show abstract

“…Personal preferences, social influences, temporal considerations, and environmental or spatial influences all also play roles ( Lund et al, 2020b ). There are important considerations to the interpretation of emergent patterns of life, such as possible interactions among scale levels (e.g., some community-level properties may not be reducible to the properties of individuals) or that the data underlying the modeling may be incomplete ( Silverman and Bryden, 2018 ). We thus propose that it is important to carefully describe contextual factors.…”

Section: Human Behavior and Agent-based Modelingmentioning

confidence: 99%

Simulating patterns of life: More representative time-activity patterns that account for context

Hubal

2023

Environment International

View full text Add to dashboard Cite

“…This is not a new phenomenon; one of the very first ABMs was a social model—a simple model of residential housing segregation designed by Thomas Schelling [ 5 ]. Since the 1980s and Axelrod’s The Evolution of Cooperation [ 6 ], this synergy with the social sciences has led to the development of the field of social simulation , in which this variety of computational social science is used to examine the development and evolution of human society in a wide variety of circumstances [ 7 ]. In recent years, more applied areas of social science, such as public health, have proposed agent-based modelling as a means to investigate societal responses to new social or economic policies [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Using machine learning as a surrogate model for agent-based simulations

2022

Self Cite

View full text Add to dashboard Cite

In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as surrogate models for use in the analysis of agent-based models (ABMs). Analysing agent-based modelling outputs can be challenging, as the relationships between input parameters can be non-linear or even chaotic even in relatively simple models, and each model run can require significant CPU time. Surrogate modelling, in which a statistical model of the ABM is constructed to facilitate detailed model analyses, has been proposed as an alternative to computationally costly Monte Carlo methods. Here we compare multiple machine-learning methods for ABM surrogate modelling in order to determine the approaches best suited as a surrogate for modelling the complex behaviour of ABMs. Our results suggest that, in most scenarios, artificial neural networks (ANNs) and gradient-boosted trees outperform Gaussian process surrogates, currently the most commonly used method for the surrogate modelling of complex computational models. ANNs produced the most accurate model replications in scenarios with high numbers of model runs, although training times were longer than the other methods. We propose that agent-based modelling would benefit from using machine-learning methods for surrogate modelling, as this can facilitate more robust sensitivity analyses for the models while also reducing CPU time consumption when calibrating and analysing the simulation.

show abstract

Modelling for the Social Sciences

Cited by 4 publications

References 26 publications

Fuzzy‐logical self‐organisation system modelling—Illustrating case of self‐organisation Czech restaurants during the Covid‐19 pandemic

Fuzzy‐logical self‐organisation system modelling—Illustrating case of self‐organisation Czech restaurants during the Covid‐19 pandemic

Simulating patterns of life: More representative time-activity patterns that account for context

Using machine learning as a surrogate model for agent-based simulations

Contact Info

Product

Resources

About