How can agent-based models accurately predict tax evasion behavior in individuals?
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Agent-based models (ABMs) can accurately predict tax evasion behavior in individuals by incorporating Machine Learning (ML) techniques . These models consider factors like personal evaluations, social influences, risk aversion, penalties, and satisfaction to simulate tax compliance decisions . By utilizing ABMs, researchers can emulate complex behaviors like tax evasion by defining simple rules for each agent based on their interactions within a given space . Additionally, ABMs can learn latent micro-variables from data, enhancing predictive power by estimating agent-specific variables and improving forecasting capabilities . Incorporating ML in ABMs allows for a standardized description of ML workflows, facilitating transparent communication and reproducibility in investigations . This integration of ABMs with ML techniques enables a comprehensive understanding of tax evasion dynamics and the impact of various factors on compliance behavior.
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| Papers (5) | Insight |
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| Agent-based models can accurately predict tax evasion behavior by incorporating machine learning techniques for model specification, improving error rates significantly and analyzing the impact of tax-related parameters. | |
| Agent-based models predict tax evasion by simulating interactions between individuals, considering factors like neighborhood influence. This approach can provide insights for creating effective tax compliance strategies. | |
| Agent-based models predict tax evasion by incorporating personal evaluations, social influences, risk aversion, penalties, and imitation dynamics, capturing individual behavior responses to economic and social factors accurately. | |
| Agent-based models, such as the Ising model with spin S = 1, simulate tax evasion dynamics by considering individual states (honest, evader, undecided) and punishment rules, aiding in accurate prediction. |
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