Behavioral Biases Against the Scalper Bots

This paper explores the problem of scalper bots in the e-commerce industry, focusing on the challenges posed by limited supply and high demand for certain products. Previous solutions, including CAPTCHAs and clustering methods, have failed to effectively prevent scalper bots from purchasing rare items. To address this issue, the paper proposes leveraging human behavioral biases as a defense mechanism against scalper bots. By analyzing economic decision-making biases, a new approach is suggested to differentiate between genuine human users and bots, allowing the former to have a fair chance at purchasing rare products. The paper argues that while bots excel in calculations and analysis, their ability to predict and navigate complex decision-making scenarios, particularly in economic choices, may be limited. By incorporating human biases into the assessment of users, the proposed mechanism aims to reduce the impact of scalper bots and enhance the opportunities for genuine customers to acquire in-demand products. This paper provides a foundation for further research on behavioral-based tests, addressing limitations, exploring different scenarios, and developing a mathematical model for the proposed mechanism.

Keywords:

Scalper bots , e-commerce , limited supply , high demand , behavioral biases , economic decision-making , genuine users , rare products