Combining experiments with agent-based modeling : benefits for experimental management accounting research

Start Page
371
End Page
382
Citation
17th Social Simulation Conference (SSC 2022)
Contribution to Conference
17th Social Simulation Conference, SSC 2022  
Publisher DOI
10.1007/978-3-031-34920-1_30
Scopus ID
2-s2.0-85174495869
Publisher
Springer Nature Switzerland
ISBN
978-3-031-34919-5
978-3-031-34920-1
978-3-031-34921-8
978-3-031-34922-5
Laboratory experiments are among the most frequently used methods in management accounting research because they offer high internal validity, enabling the examination of causal relationships. However, experiments often struggle with providing support for a specific proposed causal mechanism, given the abundance of psychological and behavioral theories that predict similar outcomes. In this paper, we argue that agent-based modeling is well suited to complement experiments because agent-based modeling is a powerful method to increase confidence in the proposed causal mechanism. As a showcase project, we conduct an experiment to explain antecedents of honest reporting behavior in a participative budgeting setting and propose that a social norm of honesty is the underlying causal mechanism. Next, we adapt an agent-based model to our participative budgeting setting and create two submodels incorporating alternative causal mechanisms. Finally, we assess the capability of the two submodels to reproduce the experiment’s results to evaluate whether the observed behavior in the experiment can be better explained with the causal mechanism representing social norm theory.
Subjects
Agent-based modeling
Experiment
Mixed-method
Participative budgeting
Social norm
DDC Class
004: Computer Sciences