One Theory - Many Formalizations: Testing Different Code Implementations of the Theory of Planned Behaviour in Energy Agent-Based Models
Authored by Hannah Muelder, Tatiana Filatova
Date Published: 2018
DOI: 10.18564/jasss.3855
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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.
Tags
Decision Making
Climate
Simulation
Households
Theory
Decision-Making
diffusion
Technology adoption
systems
Methodology
Energy
behaviour
Consumption
Micro-foundations
Empirical integration
Migration
flows