Pitfalls in the Development of Agent-Based Models in Social Sciences: Avoiding Them and Learning from Them

2019 ◽  
pp. 41-66
Author(s):  
Carlos M. Lemos
Keyword(s):  
Social Sciences ◽  
Agent Based Models ◽  
Agent Based

scholarly journals GETTING AWAY FROM NUMBERS: USING QUALITATIVE OBSERVATION FOR AGENT-BASED MODELING

2008 ◽  
Vol 11 (02) ◽  
pp. 175-185 ◽  
Author(s):  
LU YANG ◽  
NIGEL GILBERT
Keyword(s):  
Social Sciences ◽  
Quantitative Data ◽  
Qualitative Data ◽  
Participant Observation ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Qualitative Observation

Although in many social sciences there is a radical division between studies based on quantitative (e.g. statistical) and qualitative (e.g. ethnographic) methodologies and their associated epistemological commitments, agent-based simulation fits into neither camp, and should be capable of modelling both quantitative and qualitative data. Nevertheless, most agent-based models (ABMs) are founded on quantitative data. This paper explores some of the methodological and practical problems involved in basing an ABM on qualitative participant observation and proposes some advice for modelers.

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 Case Studies for a Markov Chain Approach to Analyze Agent-Based Models

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Florian Kitzler ◽  
Martin Bicher
Keyword(s):  
Social Sciences ◽  
Markov Chain ◽  
Case Studies ◽  
Care Management ◽  
Dynamic Models ◽  
Agent Based Models ◽  
Agent Based ◽  
Heterogeneous Structures ◽  
High Flexibility ◽  
Opinion Dynamic

Agent-Based Models have become a widely used tool in social sciences, health care management and other disciplines to describe complex systems from a bottom-up perspective. Some reasons for that are the easy understanding of Agent-Based Models, the high flexibility and the possibility to describe heterogeneous structures. Nevertheless problems occur when it comes to analyzing Agent-Based Models. This paper shows how to describe Agent-Based Models in a macroscopic way as Markov Chains, using the random map representation. The focus is on the implementation of this method for chosen examples of a Random Walk and Opinion Dynamic Models. It is also shown how to use Markov Chain tools to analyze these models. Our case studies imply that this method can be a powerful tool when it comes to analyzing Agent-Based Models although some further research in practice is still necessary.

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