This readme file was generated on 2025-01-17 by Alexandra Eckert # GENERAL INFORMATION * Title of Dataset: Descriptive norm and fraud dynamics (V 1.0.1) simulation results ## Author/Principal Investigator Information Name: Alexandra Eckert ORCID: 0000-0002-7204-3099 Institution: Hamburg University of Technology Address: Am Schwarzenberg-Campus 4, 21149 Hamburg, Germany Email: alexandra.eckert@tuhh.de ## Author/Associate or Co-investigator Information Name: Christian Stindt von Dohm ORCID: 0009-0001-1818-7892 Institution: Hamburg University of Technology Address: Am Schwarzenberg-Campus 4, 21149 Hamburg, Germany Email: alexandra.eckert@tuhh.de ## Author/Associate or Co-investigator Information Name: Matthias Meyer ORCID: 0000-0003-2980-4670 Institution: Hamburg University of Technology Address: Am Schwarzenberg-Campus 4, 21149 Hamburg, Germany Email: alexandra.eckert@tuhh.de * Date of data collection: 2026-01-06 * Geographic location of data collection: Hamburg, Germany * Information about funding sources that supported the collection of the data: not applicable # SHARING/ACCESS INFORMATION * Licenses/restrictions placed on the data: Public Domain Mark 1.0 Universal * Links to publications that cite or use the data: not applicable * Links to other publicly accessible locations of the data: not applicable * Links/relationships to ancillary data sets: not applicable * Was data derived from another source? not applicable * If yes, list source(s): * Recommended citation for this dataset: Eckert, A., Meyer, M., & Stindt, C. (2026). *Descriptive norm and fraud dynamics (V 1.0.1) simulation results* (Version V1) [Data set]. TUHH Open Research (TORE) https://doi.org/10.15480/882.16493 # DATA & FILE OVERVIEW This dataset contains simulation data generated by running the Descriptive Norm and Fraud Dynamics Model (version 1.0.1), archived on CoMSES.net (see below). The simulation experiments explore how descriptive social norms influence fraudulent behavior dynamics over time. Data includes outputs from multiple runs of the model, capturing behavioral variables relevant to norm sensitivity and fraud propagation. ## File List: * 2026-01-06_results_Vignette_1_v1.0.1.csv: Results of simulation experiment run on 2026-01-06 with model input data from "John (Vignette 1)". * 2026-01-06_results_Vignette_2_v1.0.1.csv: Results of simulation experiment run on 2026-01-06 with model input data from "Laura (Vignette 2)". * Relationship between files, if important: Both data sets contain the BehaviorSpace results (NetLogo 6.4.0) generated by running simulation experiments using the Descriptive norm and fraud dynamics model (V 1.0.1), archived on CoMSES.net (see below). * Additional related data collected that was not included in the current data package: not applicable * Are there multiple versions of the dataset? not applicable * If yes, name of file(s) that was updated: * Why was the file updated? * When was the file updated? # METHODOLOGICAL INFORMATION ## Description of methods used for collection/generation of data: ### related software / code Eckert, A., Meyer, M., & Stindt, C. (2025). Descriptive norm and fraud dynamics v1.0.1. CoMSES Computational Model Library. https://doi.org/10.25937/gqxp-7311 ### related article Eckert, A., Meyer, M., & Stindt, C. (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 ## Methods for processing the data: Data set contains raw data. ## Instrument- or software-specific information needed to interpret the data: not applicable * Standards and calibration information, if appropriate: not applicable * Environmental/experimental conditions: not applicable * Describe any quality-assurance procedures performed on the data: not applicable * People involved with sample collection, processing, analysis and/or submission: Alexandra Eckert, Christian Stindt von Dohm # DATA-SPECIFIC INFORMATION FOR: 2026-01-06_results_Vignette_1_v1.0.1.csv ## Table 1 — Simulation Parameters | Parameter | Description | Type | Range / Values | Experimental Setting | |-----------|-------------|------|----------------|----------------------| | **run number** | Number of runs of the simulation | Integer | 1 to 250 | 10 runs per parameter combination | | **update-O?** | Indicates whether agents’ opportunity state variable is updated | Boolean | TRUE or FALSE | FALSE | | **update-M?** | Indicates whether agents’ motive state variable is updated | Boolean | TRUE or FALSE | FALSE | | **beta** | Controls influence between close colleagues only vs. entire population when forming empirical expectations | Decimal | 0 to 1 | (0, 0.25, 0.5, 0.75, 1) | | **emp-norm-sensitivity-distribution** | Input dataset of norm sensitivities (vignette survey) | String | "John (Vignette 1)" or "Laura (Vignette 2)" | "John (Vignette 1)" | | **start-num-of-fraudsters** | Number of fraudsters at simulation start | Integer | 0 to 100 | (0, 25, 50, 75, 100) | | **probability-to-connect** | Influences number of connections created during network setup | Decimal | 0 to 1 | 0.1| | **opportunity-likelihood** | Probability that agents perceive an opportunity to commit fraud | Decimal | 0 to 1 | 1 | * Number of variables: 250 * Number of cases/rows: 3000 * Variable List: ## Table 2 — Measurement Variables per Simulation Run | Variable | Description | Type | Range | |----------|-------------|-------|--------| | **steps** | Simulation period | Integer | 0 to 2099 | | **number-of-fraudsters** | Fraudsters per simulation period | Integer | 0 to 100 | | **mean [emp-exp-value] of employees \* 100** | Mean empirical expectation × 100 (x 100 - to avoid BehaviorSpace errors) | Decimal | 0 to 101 | | **mean [NS-EE] of employees** | Mean norm sensitivity (behavioral threshold) | Decimal | 0 to 1 | | **standard-deviation [NS-EE] of employees** | Standard deviation of norm sensitivity | Decimal | 0 to 1 | * Missing data codes: not applicable * Specialized formats or other abbreviations used: not applicable # DATA-SPECIFIC INFORMATION FOR: 2026-01-06_results_Vignette_2_v1.0.1.csv ## Table 2 — Simulation Parameters | Parameter | Description | Type | Range / Values | Experimental Setting | |-----------|-------------|------|----------------|----------------------| | **run number** | Number of runs of the simulation | Integer | 1 to 250 | 10 runs per parameter combination | | **update-O?** | Indicates whether agents’ opportunity state variable is updated | Boolean | TRUE or FALSE | FALSE | | **update-M?** | Indicates whether agents’ motive state variable is updated | Boolean | TRUE or FALSE | FALSE | | **beta** | Controls influence between close colleagues only vs. entire population when forming empirical expectations | Decimal | 0 to 1 | (0, 0.25, 0.5, 0.75, 1) | | **emp-norm-sensitivity-distribution** | Input dataset of norm sensitivities (vignette survey) | String | "John (Vignette 1)" or "Laura (Vignette 2)" | "Laura (Vignette 2)" | | **start-num-of-fraudsters** | Number of fraudsters at simulation start | Integer | 0 to 100 | (0, 25, 50, 75, 100) | | **probability-to-connect** | Influences number of connections created during network setup | Decimal | 0 to 1 | 0.1| | **opportunity-likelihood** | Probability that agents perceive an opportunity to commit fraud | Decimal | 0 to 1 | 1 | * Number of variables: 250 * Number of cases/rows: 3000 * Variable List: ## Table 4 — Measurement Variables per Simulation Run | Variable | Description | Type | Range | |----------|-------------|-------|--------| | **steps** | Simulation period | Integer | 0 to 2099 | | **number-of-fraudsters** | Fraudsters per simulation period | Integer | 0 to 100 | | **mean [emp-exp-value] of employees \* 100** | Mean empirical expectation × 100 (x 100 - to avoid BehaviorSpace errors) | Decimal | 0 to 101 | | **mean [NS-EE] of employees** | Mean norm sensitivity (behavioral threshold) | Decimal | 0 to 1 | | **standard-deviation [NS-EE] of employees** | Standard deviation of norm sensitivity | Decimal | 0 to 1 | * Missing data codes: not applicable * Specialized formats or other abbreviations used: not applicable