Descriptive Norm and Fraud Dynamics

Model Discussion
1

Descriptive Norm and Fraud Dynamics (1.0.1)

The “Descriptive Norm and Fraud Dynamics” model demonstrates how fraudulent behavior can either proliferate or be contained within non-hierarchical organizations, such as peer networks, through social influence taking the form of a descriptive norm. This model expands on the fraud triangle theory, which posits that an individual must concurrently possess a financial motive, perceive an opportunity, and hold a pro-fraud attitude to engage in fraudulent activities (red agent). In the absence of any of these elements, the individual will act honestly (green agent).

The model explores variations in a descriptive norm mechanism, ranging from local distorted knowledge to global perfect knowledge. In the case of local distorted knowledge, agents primarily rely on information from their first-degree colleagues. This knowledge is often distorted because agents are slow to update their empirical expectations, which are only partially revised after one-to-one interactions. On the other end of the spectrum, local perfect knowledge is achieved by incorporating a secondary source of information into the agents’ decision-making process. Here, accurate information provided by an observer is used to update empirical expectations.

The model shows that the same variation of the descriptive norm mechanism could lead to varying aggregate fraud levels across different fraud categories. Two empirically measured norm sensitivity distributions associated with different fraud categories can be selected into the model to see the different aggregate outcomes.

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Release Notes

Changelog

[1.0.1] - 2026-01-15

Fixed

  • Removed an extra agent loop in the update-attitude procedure.

Documentation

  • Updated ODD documentation to reflect the change.
  • Added a new plot to the ODD.

Notes

  • Simulation results were not affected by this fix.
  • All simulation experiments were re-run for consistency

Associated Publications

Eckert, Alexandra, Matthias Meyer and Christian Stindt. 2025. “Combining vignette surveys with agent-based modeling: Insights on fraud dynamics with empirically calibrated norm sensitivities.” In M. Czupryna, B. Kamiński, & H. Verhagen (Eds.), Proceedings of the 19th Social Simulation Conference, Cracow, Poland, 16–20 September 2024 (pp. 83–98). Springer International Publishing. https://doi.org/10.1007/978-3-031-91782-0_6

Eckert, Alexandra, Christian Stindt, and Matthias Meyer. 2024. “Combining Vignette Surveys with Agent-Based Modeling: Insights on Fraud Dynamics with Empirically Calibrated Norm Sensitivities.” Conference presentation at the 19th Annual Social Simulation Conference (16-20 September 2024), Cracow, September 16.

This is a companion discussion topic for the original entry at https://www.comses.net/codebases/521839c0-7542-4324-b50d-e04e8f6d7a97/releases/1.0.1