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Abstract
As agent-based modelling gains popularity, the demand for transparency in underlying modelling assumptions grows. Behavioural rules guiding agents’ decisions, learning, interactions and possible changes in these should rely on solid theoretical and empirical grounds. This field has matured enough to reach the point at which we need to go beyond just reporting what social theory we base these rules upon. Many social science theories operate with various abstract constructions such as attitudes, perceptions, norms or intentions. These concepts are rather subjective and remain open to interpretation when operationalizing them in a formal model code. There is a growing concern that how modellers interpret qualitative social science theories in quantitative ABMs may differ from case to case. Yet, formal tests of these differences are scarce and a systematic approach to analyse any possible disagreements is lacking. Our paper addresses this gap by exploring the consequences of variations in formalizations of one social science theory on the simulation outcomes of agent-based models of the same class. We ran simulations to test the impact of four differences: in model architecture concerning specific equations and their sequence within one theory, in factors affecting agents’ decisions, in representation of these potentially differing factors, and finally in the underlying distribution of data used in a model. We illustrate emergent outcomes of these differences using an agent-based model developed to study regional impacts of households’ solar panel investment decisions. The Theory of Planned Behaviour was applied as one of the most common social science theories used to define behavioural rules of individual agents. Our findings demonstrate qualitative and quantitative differences in simulation outcomes, even when agents’ decision rules are based on the same theory and data. The paper outlines a number of critical methodological implications for future developments in agent-based modelling.
Highlights
Introduction. The computational social science community has witnessed an exponential growth in agent-based models (ABMs)
This paper aims to make a step in addressing this methodological gap by testing the impact of di erent interpretations of the same theory in agent-based models (ABMs) of the same class – models grounded in the same theory and designed to address the same research problem
The field has matured enough to reach the point that we need to go beyond just reporting what social theory we base these rules upon and listing average values of data used for parameterization
Summary
. The computational social science community has witnessed an exponential growth in agent-based models (ABMs). ABMs are o en used to represent human behaviour in applications beyond pure social sciences, for example to study dynamics of coupled human-natural systems (An ) and regime shi s in those (Filatova et al ). Since there is no single generic social science theory explaining human decisions, academics have explored which theory is best to use for specific research problems (Schlüter et al ). There is a growing concern that how which modellers interpret (qualitative) social science theories in (quantitative) ABMs may di er from case to case (Dressler & Schulze a; Parker ).
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