scholarly journals From Agent-Based Models to Network Analysis (and Return): The Policy-Making Perspective

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Magda Fontana ◽  
Pietro Terna
Keyword(s):  
Network Analysis ◽  
Policy Making ◽  
Agent Based Models ◽  
Agent Based

scholarly journals Agent-Based Models for Assessing Complex Statistical Models: An Example Evaluating Selection and Social Influence Estimates from SIENA

2019 ◽  
Author(s):  
Sebastian Daza ◽  
L. Kurt Kreuger
Keyword(s):  
Social Sciences ◽  
Substance Use ◽  
Network Analysis ◽  
Social Influence ◽  
Empirical Research ◽  
Statistical Models ◽  
Analysis Method ◽  
Agent Based Models ◽  
Agent Based ◽  
Highly Sensitive

Although Agent-based models (ABM) have been increasingly accepted in social sciences as a valid tool to formalize theory, propose mechanisms able to recreate regularities, and guide empirical research, we are not aware of any research using ABMs to assess the robustness of our statistical methods. We argue that ABMs can be extremely helpful to assess models when the phenomena under study is complex. As an example, we create an ABM to evaluate the estimation of selection and influence effects by SIENA, a stochastic actor-oriented model proposed by Tom A. B. Snijders and colleagues. It is a prominent network analysis method that has gained popularity during the last 10 years and been applied to estimate selection and influence for a broad range of behaviors and traits such as substance use, delinquency, violence, health, and educational attainment. However, we know little about the conditions for which this method is reliable or the particular biases it might have. The results from our analysis show that selection and influence are estimated by SIENA asymmetrically, and that with very simple assumptions, we can generate data where selection estimates are highly sensitive to mis-specification, suggesting caution when interpreting SIENA analyses.

scholarly journals Epidemiological models and COVID-19: a comparative view

2021 ◽  
Vol 43 (3) ◽  
Author(s):  
Valeriano Iranzo ◽  
Saúl Pérez-González
Keyword(s):  
Policy Making ◽  
Contextual Factors ◽  
Target Population ◽  
Compartmental Models ◽  
Epidemiological Models ◽  
Agent Based Models ◽  
Policy Makers ◽  
Agent Based

AbstractEpidemiological models have played a central role in the COVID-19 pandemic, particularly when urgent decisions were required and available evidence was sparse. They have been used to predict the evolution of the disease and to inform policy-making. In this paper, we address two kinds of epidemiological models widely used in the pandemic, namely, compartmental models and agent-based models. After describing their essentials—some real examples are invoked—we discuss their main strengths and weaknesses. Then, on the basis of this analysis, we make a comparison between their respective merits concerning three different goals: prediction, explanation, and intervention. We argue that there are general considerations which could favour any of those sorts of models for obtaining the aforementioned goals. We conclude, however, that preference for particular models must be grounded case-by-case since additional contextual factors, as the peculiarities of the target population and the aims and expectations of policy-makers, cannot be overlooked.

scholarly journals Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review

2020 ◽  
Vol 2 ◽  
pp. 16325 ◽  
Author(s):  
Meike Will ◽  
Jürgen Groeneveld ◽  
Karin Frank ◽  
Birgit Müller
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Human Interaction ◽  
Extensive Literature ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
The Impact

Agent-based modelling (ABM) and social network analysis (SNA) are both valuable tools for exploring the impact of human interactions on a broad range of social and ecological patterns. Integrating these approaches offers unique opportunities to gain insights into human behaviour that neither the evaluation of social networks nor agent-based models alone can provide. There are many intriguing examples that demonstrate this potential, for instance in epidemiology, marketing or social dynamics. Based on an extensive literature review, we provide an overview on coupling ABM with SNA and evaluating the integrated approach. Building on this, we identify current shortcomings in the combination of the two methods. The greatest room for improvement is found with regard to (i) the consideration of the concept of social integration through networks, (ii) an increased use of the co-evolutionary character of social networks and embedded agents, and (iii) a systematic and quantitative model analysis focusing on the causal relationship between the agents and the network. Furthermore, we highlight the importance of a comprehensive and clearly structured model conceptualization and documentation. We synthesize our findings in guidelines that contain the main aspects to consider when integrating social networks into agent-based models.

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