Policy and Complex Systems
Figure 8 . Time average behavioral intent .
a result , messaging intended to increase the evaluation of particular outcomes may , in fact , have the opposite effect or no impact at all . Behavioral theories , such as TPB , offer a common framework for representing the inherent heterogeneity in society , but they are still dependent on the experiences individuals have in a dynamic and often unpredictable environment . Fortunately , agent-based models provide us with a starting point for representing not only the heterogeneous nature of populations , but also the ever-changing environment they occupy . By moving away from static approximations of behavioral action and intent , we can begin to model more complex phenomena such as negative back-lash or “ going viral .” In this work , we have operationalized one of the most heavily cited theories of human behavior , TPB . Our goal was to illustrate the complexity involved in translating a long-standing theoretical framework into a working agent-based model and to underscore how such a model can be used to test various hypotheses pertaining to both behavior and the underlying theory . It is our contention that scientific advancement in this field will come from first embracing the merits of a particular theory and then rigorously testing any faults or shortcomings it presents . TPB is a theory designed to connect behavioral action to behavioral intent through the individual ’ s current set of beliefs . As a result , it makes no mention of how those underlying beliefs change over time . To operationalize TPB in an agentbased model , the evolution of attitude , SN , and perceived behavioral control over time in a changing environment must be estimated . We chose to build on the existing structures of consumer behavior and the Rescorla – Wagner equations to represent this more dynamic setting for TPB . Our implementation of TPB in an agent-based framework offers a first step in this process . Our experiments qualitatively replicate the idea that
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