# Cheating in Ranking Systems

## TL;DR

This paper analyzes the strategic interactions between app developers and online platforms regarding dishonest review manipulation and platform detection, revealing how detection accuracy influences developer incentives and platform policies.

## Contribution

It provides a game-theoretic model of cheating and detection in ranking systems, offering insights into equilibrium behaviors and policy implications.

## Key findings

- Detection accuracy impacts developer cheating incentives
- Platforms can influence developer behavior through fee and detection policies
- Equilibrium analysis shows strategic interactions between developers and platforms

## Abstract

Consider an application sold on an on-line platform, with the app paying a commission fee and, henceforth, offered for sale on the platform. The ability to sell the application depends on its customer ranking. Therefore, developers may have an incentive to promote their applications ranking in a dishonest manner. One way to do this is by faking positive customer reviews. However, the platform is able to detect dishonest behavior (cheating) with some probability and then proceeds to decide whether to ban the application. We provide an analysis and find the equilibrium behaviors of both the applications developers (cheat or not) and the platform (setting of the commission fee). We provide initial insights into how the platforms detection accuracy affects the incentives of the app developers.