Exploring the use of agent-based modelling in mixed methods research

Abstract

Agent-based modeling (ABM) is in many ways an outsider in social research methods, but I will here argue that it has an important role to play in combination with other methods and approaches. ABM can be used in ways that offer unique benefits to social science researchers, but due to the abstract nature of the gained insights, this requires the employment of various methodologies in combination with ABM. Mixed methods (MM) is an approach that has become quite popular, and I show that even if ABM naturally may seem like a natural ingredient in research where methods are mixed, there exists some peculiarities within MM that may make it less accommodating to ABM that one may assume. KEYWORDSAgent-based modeling; mixed methods; triangulation; multi method research; social research methods. RESUMENLos modelos basados en agentes (MBA) son, en muchos sentidos, métodos poco utilizados en la investigación social pero, en el presente artículo, se tratará de demostrar que pueden jugar un papel importante en combinación con otros métodos y enfoques. Los MBA pueden ser utilizados en modos que aportan unos beneficios excepcionales a los investigadores de las ciencias sociales si bien, dada la naturaleza abstracta de las diferentes perspectivas que se obtienen, esto requiere el uso de varias metodologías en combinación con los MBA. Los métodos mixtos (MM) son una estrategia de investigación que se ha hecho muy popular y aquí se muestra que, incluso siendo vistos los MBA como un ingrediente natural en las investigaciones en las que se utilizan varios métodos, se dan una serie de peculiaridades, dentro de los mismos MM, que los hacen menos acomodaticios a los MBA de lo que se podría presumir en un primer momento.

Matching Behavioral Theories and Rules with Research Methods in Spatial Planning-Related Fields

“…The planning and governance sector is interested in a wide range of behavior such as travel, land-related (e.g., land leasing), and location choice behavior of households and industries. Many experts pointed to the importance of examining the behavior of planners, policymakers, and citizens (e.g., participation, compliance) as important actors/agents (e.g., Scholz and Stiftel 2005; Campbell 2006; Crawford et al 2008; Podagrosi, Vojnovic and Pigozzi 2011; Cooper et al 2014; Cvetinovic, Nedovic-Budic and Bolay 2017; Salet 2018; Qiao, Wong and Zheng 2019) and pointed to planning theories such as “communicative/collaborative planning theory” and “incrementalism” as well as economic and community development theories. In geography and spatial analysis, experts mainly look at spatial behavior, for example, the spillover of pro-environmental behavior using “behavioral spillover theory” and “social identity theory” as well as walking and crowd movement using “social force model,” “graph theory,” and concepts like collective norm (e.g., Morgado and Costa 2011).…”

“…However, this paper's literature review and expert survey suggested that behavioral modeling in planning-related fields tends to be focused on individual behavior and that more consideration can be given to the aspect of institutions and governance. As highlighted by Donaghy and Hopkins (2006, p. 177), effective planning can be supported by “good theories of both planning behavior … and the workings of interdependent systems … within which planning activities are being considered.” Different theories can be applicable to different scales of behavior, for example, theories such as the rational actor model, organizational process model, and governmental politics model can be useful for government behavior (Allison, 1971; van Riel et al, 2016), and institutional theories such as collective action theory for collective behavior (Olson, 1965; Scholz and Stiftel, 2005; Zellner et al, 2009; Salet, 2018). Also, for behavioral intervention, theories like nudge theory tend to focus on targeting individual behavioral change while theories like the theory of change can be applied at a larger level beyond individuals.…”

“…Third, dynamic simulation such as ABM, CA, and NN is a particularly useful tool for spatial planners for its ability to link with geographic information systems (GIS) and work with spatial data (Brown et al, 2005;Lu et al, 2020;Xu et al, 2020) as demonstrated in many existing models including SLEUTH (Silva and Clarke, 2002), UrbanSim (Waddell, 2007) and LEAM (Deal and Sun, 2006). Simulation of the interaction between a-spatial agents (e.g., residents, firms) and spatial agents (e.g., land parcels) with a set of assumptions (Benenson and Torrens, 2004) can not only deductively generate results that can be statistically studied but also generate emergent patterns that require inductive and ethnographic observations, which is often called "abductive reasoning" (Saetra, 2017).…”

Fish provision in a changing environment: The buffering effect of regional trade networks

“…The iteration between the conceptualization of the trading processes in the model and the analysis of the qualitative data, was key to answering the first research question and to identifying the key processes that characterize fish trading in the case study. The analysis of qualitative data often requires a process of abstraction and interpretation that can be facilitated by the modelling framework itself [ 46 ], and iteration between the analysis of empirical data and model simulation has been described as a useful process for understanding empirical phenomena [ 47 , 48 ]. In this way, the qualitative analysis of semi-structured questions consisted of thematic coding based on a preliminary conceptualization of the model processes.…”