One Theory - Many Formalizations: Testing Different Code Implementations of the Theory of Planned Behaviour in Energy Agent-Based Models

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.