| Abstract: |
| Quantilized mean-field game models involve quantiles of the population`s distribution. We study a class of such games with a capacity for ranking games, where the performance of each agent is evaluated based on its terminal state relative to the population`s \(\alpha\)-quantile value, where \(\alpha \in [0,1]\). This evaluation criterion is designed to select the top \((1 - \alpha)\%\) performing agents. We then propose an application to early-stage venture investments, where a venture capital firm supports a group of startups competing over a finite horizon, aiming to identify and fund the top-performing fraction at the end of the period. |
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