RESOURCE-CONSTRAINED POSTERIOR COLLAPSE IN HIGH-DIMENSIONAL DECISION SYSTEMS
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2026
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| _version_ | 1866901682282037248 |
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| author | Amaya, Richard A. |
| author_facet | Amaya, Richard A. |
| contents | <p>We formalize a phenomenon of resource-constrained posterior collapse (RCPC) in high-dimensional inference, where an agent's belief distribution concentrates almost entirely onto a vanishingly small, cost-efficient subset of hypotheses. We demonstrate that under three natural constraints—bounded informational capacity, asymmetric evaluation costs, and Bayesian updating—this collapse is mathematically inevitable, not a pathological bias. This result distinguishes RCPC from standard asymptotic Bayesian convergence, highlighting a fundamental tension between optimal inference and finite computational resources. We provide formal theorems, constructive proofs, a practical algorithm, and discuss falsifiability conditions. Our findings imply that decision-makers operating under bounded rationality will inevitably suffer a loss of representational diversity.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18446386 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | RESOURCE-CONSTRAINED POSTERIOR COLLAPSE IN HIGH-DIMENSIONAL DECISION SYSTEMS Amaya, Richard A. bounded rationality, Bayesian inference, resource constraints, posterior collapse, decision theory, computational rationality, cognitive modeling, hypothesis selection, algorithm, martingale convergence, high-dimensional inference <p>We formalize a phenomenon of resource-constrained posterior collapse (RCPC) in high-dimensional inference, where an agent's belief distribution concentrates almost entirely onto a vanishingly small, cost-efficient subset of hypotheses. We demonstrate that under three natural constraints—bounded informational capacity, asymmetric evaluation costs, and Bayesian updating—this collapse is mathematically inevitable, not a pathological bias. This result distinguishes RCPC from standard asymptotic Bayesian convergence, highlighting a fundamental tension between optimal inference and finite computational resources. We provide formal theorems, constructive proofs, a practical algorithm, and discuss falsifiability conditions. Our findings imply that decision-makers operating under bounded rationality will inevitably suffer a loss of representational diversity.</p> |
| title | RESOURCE-CONSTRAINED POSTERIOR COLLAPSE IN HIGH-DIMENSIONAL DECISION SYSTEMS |
| topic | bounded rationality, Bayesian inference, resource constraints, posterior collapse, decision theory, computational rationality, cognitive modeling, hypothesis selection, algorithm, martingale convergence, high-dimensional inference |
| url | https://doi.org/10.5281/zenodo.18446386 |