{
  "abstract": "The collective-risk dilemma has become a paradigm for studying cooperation in a wide variety of social experiments, initiatives that generate increasingly rich datasets describing individual actions over successive decision-making processes. Beyond the purely descriptive analysis of such empirical datasets, a methodological framework is required to interpret and reproduce the observed dynamics, a scheme that may be skewed by exogenous biases. Here, we introduce a data-driven, non-parametric method for simulating individual decisions together with a validation protocol for assessing the fidelity level of the outcomes they generate. Our methodological contribution is based on an empirical heuristic ensemble: agents are represented by a finite set of behavioural heuristics, probabilities conditioned on different observable features of the game state, endowed with a hard-voting arbitration that determines their per-round final decisions. This reproducible generative framework allows for behavioural inference in different collective-risk dilemma setups: Behavioural heuristics are seeded directly from empirical data assessing conditional probabilities, without a priori assumptions; a plurality vote with uniform random tie-breaking resolves between competing heuristics each round, adding no free parameters; validation follows a pattern-oriented approach, comparing synthetic and empirical macro-indicators against single-heuristic and uniform-random baselines; synthetic data generated allow exploration of scenarios or experimental setups that have not been empirically realised; as a worked example, we instantiate our methodological approach on the xAire citizen science collective-risk dilemma experiment run in Barcelona.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.13595",
  "author_total": 4,
  "authors": [
    {
      "name": "Marc Sadurní"
    },
    {
      "name": "Martín F. Díaz"
    },
    {
      "name": "Josep Perelló"
    },
    {
      "name": "Miquel Montero"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dszpwj6n2hx8d.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.13595",
  "fulltext_url": null,
  "id": "dszpwj6n2hx8d",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.13595",
  "language": null,
  "licence": "CC-BY-NC-SA-4.0",
  "licence_label": "CC BY-NC-SA 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.CY"
    ],
    [
      "arxiv",
      "physics.data-an"
    ],
    [
      "arxiv",
      "physics.soc-ph"
    ],
    [
      "openalex",
      "3311|Experimental Behavioral Economics Studies"
    ]
  ],
  "openalex": "W7221556535",
  "pdf_url": "https://arxiv.org/pdf/2610.13595",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dszpwj6n2hx8d.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:28:36Z",
      "native_id": "2610.13595",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.13595"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.data-an",
      "subfield": "computational"
    },
    {
      "field": "physics",
      "origin": "arxiv:physics.soc-ph",
      "subfield": "statistical-nonlinear"
    }
  ],
  "title": "A data-driven heuristic-ensemble method for behavioural inference in collective-risk dilemma experiments",
  "title_text": "A data-driven heuristic-ensemble method for behavioural inference in collective-risk dilemma experiments",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:36Z",
  "url": "https://research.draconitic.com/paper/dszpwj6n2hx8d",
  "venue": "arXiv",
  "versions": []
}