{
  "annotation_id": "b2389719-60d1-419f-9ef8-a1d8e96457e8",
  "date_created": "2026-02-17T05:53:15.775000Z",
  "date_modified": "2026-02-17T05:53:15.775000Z",
  "file_hash": "2263bae6c6d70a6e9841e79ec4cdb25c58fb32ad71219ac2a40d67cf0622f10b",
  "private": false,
  "record": {
    "abstract": "Financial markets are influenced by human behavior that deviates from rationality due to cognitive biases. Traditional reinforcement learning (RL) models for financial decision-making assume rational agents, potentially overlooking the impact of psychological factors. This study integrates cognitive biases into RL frameworks for financial trading, hypothesizing that such models can exhibit human-like trading behavior and achieve better risk-adjusted returns than standard RL agents. We introduce biases, such as overconfidence and loss aversion, into reward structures and decision-making processes and evaluate their performance in simulated and real-world trading environments. Despite its inconclusive or negative results, this study provides insights into the challenges of incorporating human-like biases into RL, offering valuable lessons for developing robust financial AI systems.",
    "arxiv_id": "2601.08247",
    "authors": [
      "Liu He"
    ],
    "categories": [
      "cs.LG",
      "econ.EM"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Incorporating Cognitive Biases into Reinforcement Learning for Financial Decision-Making",
    "url": "https://arxiv.org/abs/2601.08247",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "5cf1cd26-36ec-46b6-868e-36d720ad9508",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}