arXiv subjects

Marco Pollanen

Publications and source records attributed to Marco Pollanen.

5 recordsLinked to original sources

arXiv2026

The Likelihood Ratio Wall: Structural Limits on Accurate Risk Assessment for Rare Violence

Pretrial risk assessment tools are used on over one million U.S. defendants each year, yet their use for predicting rare violent re-offense faces a basic statistical barrier. We derive a universal precision bound -- the Likelihood Ratio Wall -- showing that when violent re-arrest rates are low (2-5%), achieving even a 50% hit rate among people labeled "high risk" (positive predictive value, or PPV) would require tools far more discriminative than current instruments appear to be. For rare outcomes, a tool can have respectable-looking performance metrics and still be wrong most of the time it flags someone as "high risk for violence." We show that post-hoc score recalibration cannot solve this problem because it does not improve the tool's underlying ability to separate true positives from false positives. We further prove a Surveillance Ceiling: when over-policing inflates recorded "risk factors" among those who would not re-offend, the maximum achievable precision is structurally lower for over-policed groups, even at equal offense rates. We translate these results into the Number Needed to Detain (how many people must be detained to prevent one violent offense), and propose that risk reports should communicate this uncertainty explicitly. Our findings suggest that for rare violent outcomes, debates about fairness metrics alone are incomplete: under current data regimes, the available features may not support high-confidence individualized detention decisions.

cs.CY↗

arXiv2026

When AAA Satisfies Nothing: Impossibility Theorems for Structured Credit Ratings

A credit rating of AAA asserts near-certainty of repayment. This paper asks whether the pre-crisis information environment could have supported that assertion for structured products. Bayes' theorem implies that any reliability target requires a minimum level of statistical discrimination between instruments that will repay and those that will not. At structured-finance base rates, a four-nines reliability target demands discrimination on the order of 10,000 to 1. A three-nines target demands 1,000 to 1. Nothing in the published credit-prediction literature provides an affirmative basis for believing that discrimination of this magnitude was achievable with the data available at rating time. Retrospectively, the realized system fell short of the four-nines benchmark by roughly 90,000-fold. The framework accommodates the historical feasibility of corporate AAA ratings, where high base rates and rich information produce low required discrimination. Illustrative calibrations for contemporary collateralized loan obligations suggest that material tension between the precision target and the information environment persists. The central implication is that the AAA precision claim itself likely exceeded what the available information could support.

q-fin.RM↗

arXiv2026

The Certainty Bound: Structural Limits on Scientific Reliability

Explanations of the replication crisis often emphasize misconduct, questionable research practices, or incentive misalignment, implying that behavioral reform is sufficient. This paper argues that a substantial component is architectural: within binary significance-based publication systems, even perfectly diligent researchers face structural limits on the reliability they can deliver. The posterior log-odds of a finding equal prior log-odds plus log(Lambda), where Lambda = (1-beta)/alpha is the experimental leverage. Interpreted architecturally, this implies a hard constraint: once evidence is coarsened to a binary significance decision, the decision rule contributes exactly log(Lambda) to posterior log-odds. A target reliability tau is feasible iff pi >= pi_crit, and under fixed alpha this generally cannot be rescued by sample size alone. Two mechanisms can drive effective leverage to 1 without bad faith: persistent unmeasured confounding in observational studies and unbounded specification search under publication pressure. These results concern binary significance-based decision architectures and do not bound inference based on full likelihoods or richer continuous evidence summaries. Two collapse results formalize these mechanisms, while the Replication Pipeline Theorem and Minimum Pipeline Depth Corollary identify a quantitative evidentiary standard for escape. Using independently documented parameters for pre-reform psychology (pi about 0.10, power about 0.35), the framework implies a replication rate of 36%, consistent with the Open Science Collaboration. The framework also provides quantitative bridges to Popper, Kuhn, and Lakatos. In low-prior settings below the single-study feasibility threshold, the natural unit of evidence is the replication pipeline rather than the individual experiment.

stat.ME↗

arXiv2026

The Viscosity of Logic: Phase Transitions and Hysteresis in DPO Alignment

Direct Preference Optimization (DPO) is often tuned as if increasing alignment pressure (controlled by $\beta$) yields progressively "better" behavior. We instead treat $\beta$ as a control parameter and densely sweep it for three 7B open-weight families under a fixed DPO recipe. In Mistral, capability is sharply non-monotonic: aggregated logic-probe margins become positive only in a narrow band near $\beta \approx 10^{-2}$ and revert outside it, with boundary points that are seed-sensitive. Across architectures under the same sweep, we observe qualitatively different response modes: sharp reorganization in Mistral, selective changes in Llama, and smooth trade-offs in Qwen. Critically, the DPO preference margin can anticorrelate with reasoning capability (Pearson $r=-0.91$ for Llama logic), so margin-based selection can prefer capability-impaired models. Training path also matters: exposure to high $\beta$ induces capability losses that persist even after $\beta$ is reduced (hysteresis). These findings motivate capability-resolved evaluation across the $\beta$ landscape rather than reliance on margins or aggregate benchmarks.

cs.LG↗

arXiv2025

The Probabilistic Foundations of Surveillance Failure: From False Alerts to Structural Bias

For decades, forensic statisticians have debated whether searching large DNA databases undermines the evidential value of a match. Modern surveillance faces an exponentially harder problem: screening populations across thousands of attributes using threshold rules rather than exact matching. Intuition suggests that requiring many coincidental matches should make false alerts astronomically unlikely. This intuition fails. Consider a system that monitors 1,000 attributes, each with a 0.5 percent innocent match rate. Matching 15 pre-specified attributes has probability $10^{-35}$, one in 30 decillion, effectively impossible. But operational systems require no such specificity. They might flag anyone who matches \emph{any} 15 of the 1,000. In a city of one million innocent people, this produces about 226 false alerts. A seemingly impossible event becomes all but guaranteed. This is not an implementation flaw but a mathematical consequence of high-dimensional screening. We identify fundamental probabilistic limits on screening reliability. Systems undergo sharp transitions from reliable to unreliable with small increases in data scale, a fragility worsened by data growth and correlations. As data accumulate and correlation collapses effective dimensionality, systems enter regimes where alerts lose evidential value even when individual coincidences remain vanishingly rare. This framework reframes the DNA database controversy as a shift between operational regimes. Unequal surveillance exposures magnify failure, making ``structural bias'' mathematically inevitable. These limits are structural: beyond a critical scale, failure cannot be prevented through threshold adjustment or algorithmic refinement.

stat.ME↗