{
  "annotation_id": "83286c78-e93a-479e-a7f6-54afd13b73c8",
  "date_created": "2026-02-17T05:53:24.229000Z",
  "date_modified": "2026-02-17T05:53:24.229000Z",
  "file_hash": "b353045a0b2da9ffe71c07b63cd8dab2f08dad21777076db4c81979838a00681",
  "private": false,
  "record": {
    "abstract": "Understanding how agents coordinate or compete from limited behavioral data is central to modeling strategic interactions in traffic, robotics, and other multi-agent systems. In this work, we investigate the following complementary formulations of inverse game-theoretic learning: (i) a Closed-form Correlated Equilibrium Maximum-Likelihood estimator (CE-ML) specialized for $2\\times2$ games; and (ii) a Logit Best Response Maximum-Likelihood estimator (LBR-ML) that captures long-run adaptation dynamics via stochastic response processes. Together, these approaches span the spectrum between static equilibrium consistency and dynamic behavioral realism. We evaluate them on synthetic \"chicken-dare\" games and traffic-interaction scenarios simulated in SUMO, comparing parameter recovery and distributional fit. Results reveal clear trade-offs between interpretability, computational tractability, and behavioral expressiveness across models.",
    "arxiv_id": "2601.10367",
    "authors": [
      "Daniela Aguirre Salazar",
      "Firas Moatemri",
      "Tatiana Tatarenko"
    ],
    "categories": [
      "cs.GT"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Inverse Learning in $2\\times2$ Games: From Synthetic Interactions to Traffic Simulation",
    "url": "https://arxiv.org/abs/2601.10367",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "e640b850-e343-42df-9f2b-7a04b37f3d55",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}