Combining Experiments with Agent-Based Modeling: Benefits for Experimental Management Accounting Research

Abstract

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.