arXiv subjects

Roberto Garrone

Publications and source records attributed to Roberto Garrone.

13 recordsLinked to original sources

arXiv2026

Verification of Adaptive Agentic Controllers through Finite Rule Revision

Industrial agentic AI systems increasingly exhibit a gap between prototype capability and production deployment. In particular, adaptive agents may generate plausible outputs while remaining difficult to verify under non-determinism, confidentiality constraints, limited context, and weak observability. This paper formulates a bounded verification protocol for adaptive agentic controllers represented by finite symbolic rules, explicit diagnostic predicates, explanation logs, and held-out re-evaluation. The central research question is: when an adaptive agentic controller is represented through finite rules, explicit diagnostic predicates, explanation logs, and held-out re-evaluation, which classes of controller failure can be detected, locally repaired, or rejected without relying on unrestricted human-in-the-loop judgment? The proposed framework treats the controller as a finite revisable object. Diagnostic failures are mapped to predefined rule-level edits, including rule addition, rule deletion, and priority revision. Repaired controllers are then evaluated on held-out simulation seeds or cloned initial states. Experiments in a stylized financially constrained inventory-control benchmark show three outcomes: resource-induced failures that remain non-repairable by one rule edit, partial repairs that are rejected because they violate thresholds or guardrails, and a local one-step repair of an order-volatility failure induced by removing a smoothing rule. The contribution is methodological and provides a simulation-compatible procedure for testing whether specific controller-level failures can be made observable, explainable, locally revisable, and empirically re-tested under controlled conditions.

cs.AI↗

arXiv2026

Structural Distinguishability of Static and Adaptive Policy Regimes in Agent-Based Regulatory Simulation

Agent-based models are widely used to evaluate policy interventions in complex socio-technical systems, yet many policy-oriented ABMs represent regulation as a fixed scenario parameter. This limits their ability to distinguish whether regulatory conclusions depend on agent adaptation, policy adaptation, or the interaction between both. Building on a previously proposed four-regime architecture, this paper contributes a controlled simulation benchmark rather than a new general framework. Using a single configurable emissions-regulation ABM, we compare constant policy/constant agents, constant policy/adaptive agents, adaptive policy/constant agents, and adaptive policy/adaptive agents under matched simulation conditions. We evaluate naive fixed policies, tracking-aware calibrated fixed policies, and three adaptive controllers: setpoint, safety-margin, and one-sided control. The benchmark recovers expected controller archetypes: setpoint control tracks the cap but produces frequent boundary crossings, safety-margin control reduces violations through conservatism, and one-sided control can limit violations but may ratchet toward over-conservatism when combined with adaptive agents. The contribution is methodological: scalar indicators, cap-relative symbolic diagnostics, trajectory motifs, and visual inspection jointly reveal how regulatory conclusions can differ even when average outcomes appear similar. Adaptive policy-oriented ABMs should therefore be evaluated through regime distinguishability, not only through average performance.

cs.MA↗

arXiv2026

Machine-Coached Policy Revision in Adaptive Agent-Based Regulatory Simulation: A Controller-Level Contestability Layer

Policy-oriented agent-based models are increasingly used to study regulatory interventions in complex adaptive socio-technical systems. Recent adaptive ABM frameworks distinguish between static and adaptive agents, fixed and adaptive policies, and alternative controller designs. However, most diagnostic workflows remain ex post: trajectories are analysed after simulation, but the resulting evidence is not systematically fed back into the policy controller. This paper proposes a lightweight machine-coached policy-revision layer for adaptive agent-based regulation. The layer represents policy decisions as defeasible rules with explicit conflicts and priorities, generates explanations for controller actions, and allows diagnostic failures to be translated into rule additions, removals, or priority changes. The contribution is not a new optimal controller and does not claim formal guarantees for unrestricted machine coaching. Instead, it provides a simulation-compatible operationalization of controller-level contestability: policy decisions can be explained, challenged, revised, and re-evaluated in held-out simulation runs. A stylized emissions-regulation ABM is used as the experimental component. A controlled simulation experiment focuses on an over-conservatism failure in the VPVA regime. The predefined coaching template adds a relaxation rule to the symbolic controller, reducing over-conservatism recurrence under held-out seeds while preserving violation, overshoot, and volatility guardrails. The paper argues that machine coaching is best understood as a controller-level extension of explainable adaptive ABM, complementary to causal, information-theoretic, and trajectory-based diagnostics.

cs.MA↗

arXiv2026

Transfer Learning Across Policy Regimes in Adaptive Multi-Agent Systems

Policy models often assume that the relationship between a policy instrument and its outcome remains stable across institutional conditions. In adaptive socio-technical systems this assumption may fail: regulatory change can alter incentives, agents can respond strategically, and the mapping from policy variables to aggregate outcomes can change. This paper studies such regime change as a transfer-learning problem in adaptive multi-agent systems. A policy regime is represented as a learning problem induced by an observable input distribution and a target function mapping policy variables to outcomes. We compare a blank-slate learner that searches a flexible hypothesis class in the new regime with a transfer learner whose effective hypothesis class is restricted by structural knowledge from the previous regime. Transfer is beneficial when this restriction preserves the new target function while reducing effective complexity; it is harmful when the restriction excludes the new target and creates misspecification. A stylized emissions-regulation experimental environment and a dynamic ABM robustness experiment support the claim. When the target regime preserves an affine monotone tax-emissions relation, transfer improves empirical small-sample performance. When the target regime introduces a threshold break, the same transferred structure produces negative transfer: held-out error remains high, online prediction generates more mistakes, and repeated online streams show larger cumulative and final-window error under misspecification. The contribution is methodological: previous regulatory experience should be reused when it captures stable structural invariants, but treated cautiously when policy change alters the policy-outcome relationship.

cs.MA↗

arXiv2026

A Real-Options-Aware Multi-Criteria Framework for Ex-Ante Real Estate Redevelopment Use Selection

A growing share of the existing real estate stock exhibits persistent underperformance that can no longer be explained by cyclical market phases or inadequate maintenance alone. In many cases, technically recoverable assets located in non-marginal contexts fail to generate economic value consistent with the capital immobilized. This condition reflects a structural misalignment between intended use and effective demand rather than episodic market weakness, and calls for a decision framework capable of integrating value, risk, complexity, and irreversibility in strategic use selection. This study proposes a decision-analytic framework for the ex-ante selection of intended use in real estate redevelopment processes. The framework integrates real-options logic on irreversibility and managerial flexibility with a multi-criteria decision-analysis structure, enabling comparative evaluation of expected economic value, market and operational risk, technical and managerial complexity, and time-to-income. By treating redevelopment primarily as a problem of strategic option selection rather than design or financial optimization, the framework operationalizes option value preservation through disciplined ex-ante screening. Illustrative cases demonstrate how this integration of real options reasoning and MCDA reduces over-complexification and misalignment across different asset types and urban contexts.

q-fin.GN↗

arXiv2026

Feasibility-First Satellite Integration in Robust Portfolio Architectures

The integration of thematic satellite allocations into core-satellite portfolio architectures is commonly approached using factor exposures, discretionary convictions, or backtested performance, with feasibility assessed primarily through liquidity screens or market-impact considerations. While such approaches may be appropriate at institutional scale, they are ill-suited to small portfolios and robustness-oriented allocation frameworks, where dominant constraints arise not from return predictability or trading capacity, but from fixed costs, irreversibility risk, and governance complexity. This paper develops a feasibility-first, non-predictive framework for satellite integration that is explicitly scale-aware. We formalize four nested feasibility layers (physical, economic, structural, and epistemic) that jointly determine whether a satellite allocation is admissible. Physical feasibility ensures implementability under concave market-impact laws; economic feasibility suppresses noise-dominated reallocations via cost-dominance threshold constraints; structural feasibility bounds satellite size through an explicit optionality budget defined by tolerable loss under thesis failure; and epistemic feasibility limits satellite breadth and dispersion through an entropy-based complexity budget. Within this hierarchy, structural optionality is identified as the primary design principle for thematic satellites, with the remaining layers acting as robustness lenses rather than optimization criteria. The framework yields closed-form feasibility bounds on satellite size, turnover, and breadth without reliance on return forecasts, factor premia, or backtested performance, providing a disciplined basis for integrating thematic satellites into small, robustness-oriented portfolios.

q-fin.PM↗

arXiv2026

Geopolitical and Institutional Constraints on Adaptive Market Efficiency -- A Feasibility Diagnostic for Robust Portfolio Construction

This paper develops a structural framework for characterizing the informational feasibility of financial markets under heterogeneous institutional and geopolitical conditions. Departing from the assumption of uniform and time-invariant market efficiency, adaptive efficiency is conceptualized as a localized and state-dependent property emerging from the interaction between economic scale, institutional enforcement, and geopolitical embedding. To operationalize this perspective, the paper introduces the Geopolitical-Adaptive Efficiency Ratio (GAER), a descriptive cross-sectional indicator measuring the concentration of adaptive-efficiency-supporting mass within institutionally and geopolitically central assets. GAER is not a return-predictive signal, factor, or regime classifier. Instead, it functions as a diagnostic boundary condition, delimiting the domain in which ranking-based and robustness-oriented portfolio construction methods are plausibly applicable. The framework integrates insights from adaptive market theory, institutional economics, and political economy, linking disclosure continuity, liquidity provision, and enforcement credibility to the persistence of informational signals in asset prices. GAER is formalized, its theoretical properties are discussed, and its interpretation is illustrated using a global equity snapshot based on publicly observable information. The contribution separates informational feasibility from portfolio construction and execution, providing a conceptual foundation for constraint-aware financial modeling without reliance on forecast-driven assumptions or parametric optimization.

q-fin.PM↗

arXiv2026

Dynamic Inclusion and Bounded Multi-Factor Tilts for Robust Portfolio Construction

This paper proposes a portfolio construction framework designed to remain robust under estimation error, non-stationarity, and realistic trading constraints. The methodology combines dynamic asset eligibility, deterministic rebalancing, and bounded multi-factor tilts applied to an equal-weight baseline. Asset eligibility is formalized as a state-dependent constraint on portfolio construction, allowing factor exposure to adjust endogenously in response to observable market conditions such as liquidity, volatility, and cross-sectional breadth. Rather than estimating expected returns or covariances, the framework relies on cross-sectional rankings and hard structural bounds to control concentration, turnover, and fragility. The resulting approach is fully algorithmic, transparent, and directly implementable. It provides a robustness-oriented alternative to parametric optimization and unconstrained multi-factor models, particularly suited for long-horizon allocations where stability and operational feasibility are primary objectives.

math.OC↗

arXiv2025

An Allele-Centric Pan-Graph-Matrix Representation for Scalable Pangenome Analysis

Population-scale pangenome analysis increasingly requires representations that unify single-nucleotide and structural variation while remaining scalable across large cohorts. Existing formats are typically sequence-centric, path-centric, or sample-centric, and often obscure population structure or fail to exploit carrier sparsity. We introduce the H1 pan-graph-matrix, an allele-centric representation that encodes exact haplotype membership using adaptive per-allele compression. By treating alleles as first-class objects and selecting optimal encodings based on carrier distribution, H1 achieves near-optimal storage across both common and rare variants. We further introduce H2, a path-centric dual representation derived from the same underlying allele-haplotype incidence information that restores explicit haplotype ordering while remaining exactly equivalent in information content. Using real human genome data, we show that this representation yields substantial compression gains, particularly for structural variants, while remaining equivalent in information content to pangenome graphs. H1 provides a unified, population-aware foundation for scalable pangenome analysis and downstream applications such as rare-variant interpretation and drug discovery.

q-bio.GN↗

arXiv2025

Complementary Characterization of Agent-Based Models via Computational Mechanics and Diffusion Models

This article extends the preprint "Characterizing Agent-Based Model Dynamics via $\epsilon$-Machines and Kolmogorov-Style Complexity" by introducing diffusion models as orthogonal and complementary tools for characterizing the output of agent-based models (ABMs). Where $\epsilon$-machines capture the predictive temporal structure and intrinsic computation of ABM-generated time series, diffusion models characterize high-dimensional cross-sectional distributions, learn underlying data manifolds, and enable synthetic generation of plausible population-level outcomes. We provide a formal analysis demonstrating that the two approaches operate on distinct mathematical domains -- processes vs. distributions -- and show that their combination yields a two-axis representation of ABM behavior based on temporal organization and distributional geometry. To our knowledge, this is the first framework to integrate computational mechanics with score-based generative modeling for the structural analysis of ABM outputs, thereby situating ABM characterization within the broader landscape of modern machine-learning methods for density estimation and intrinsic computation. The framework is validated using the same elder-caregiver ABM dataset introduced in the companion paper, and we provide precise definitions and propositions formalizing the mathematical complementarity between $\epsilon$-machines and diffusion models. This establishes a principled methodology for jointly analyzing temporal predictability and high-dimensional distributional structure in complex simulation models.

cs.MA↗

arXiv2025

An Adaptive, Data-Integrated Agent-Based Modeling Framework for Explainable and Contestable Policy Design

Multi-agent systems often operate under feedback, adaptation, and non-stationarity, yet many simulation studies retain static decision rules and fixed control parameters. This paper introduces a general adaptive multi-agent learning framework that integrates: (i) four dynamic regimes distinguishing static versus adaptive agents and fixed versus adaptive system parameters; (ii) information-theoretic diagnostics (entropy rate, statistical complexity, and predictive information) to assess predictability and structure; (iii) structural causal models for explicit intervention semantics; (iv) procedures for generating agent-level priors from aggregate or sample data; and (v) unsupervised methods for identifying emergent behavioral regimes. The framework offers a domain-neutral architecture for analyzing how learning agents and adaptive controls jointly shape system trajectories, enabling systematic comparison of stability, performance, and interpretability across non-equilibrium, oscillatory, or drifting dynamics. Mathematical definitions, computational operators, and an experimental design template are provided, yielding a structured methodology for developing explainable and contestable multi-agent decision processes.

cs.MA↗

arXiv2025

Proportionate Cybersecurity for Micro-SMEs: A Governance Design Model under NIS2

Micro and small enterprises (SMEs) remain structurally vulnerable to cyber threats while facing capacity constraints that make formal compliance burdensome. This article develops a governance design model for proportionate SME cybersecurity, grounded in an awareness-first logic and informed by the EU Squad 2025 experience. Using a qualitative policy-analysis and conceptual policy-design approach, we reconstruct a seven-dimension preventive architecture: awareness and visibility, human behaviour, access control, system hygiene, data protection, detection and response, and continuous review, and justify each dimension's contribution to proportionality and risk reduction. We then map the model's regulatory scope and limits against the NIS2 Directive, Commission Implementing Regulation (EU) 2024/2690, the Digital Operational Resilience Act (DORA), the Cyber Resilience Act (CRA), and the EU Action Plan on Cybersecurity for Hospitals, clarifying which obligations are supported and which require complementary governance (e.g. role accountability, incident timelines, statements of applicability, sector-specific testing and procurement). The analysis argues that raising awareness is the fastest, scalable lever to increase cyber-risk sensitivity in micro-SMEs and complements, rather than replaces, formal compliance. We conclude with policy implications for EU and national programmes seeking practical, proportionate pathways to SME cyber resilience under NIS2.

cs.CR↗

arXiv2025

Characterizing Agent-Based Model Dynamics via $\epsilon$-Machines and Kolmogorov-Style Complexity

We propose a two-level information-theoretic framework for characterizing the informational organization of Agent-Based Model (ABM) dynamics within the broader paradigm of Complex Adaptive Systems (CAS). At the macro level, a pooled $\varepsilon$-machine is reconstructed as a reference model summarizing the system-wide informational regime. At the micro level, $\varepsilon$-machines are reconstructed for each caregiver--elder dyad and variable, complemented by algorithm-agnostic Kolmogorov-style measures, including normalized LZ78 complexity and bits per symbol from lossless compression. The resulting feature set, $\{h_{\mu}, C_{\mu}, E, \mathrm{LZ78}, \mathrm{bps}\}$, enables distributional analysis, stratified comparisons, and unsupervised clustering across agents and scenarios. Empirical results show that coupling $\varepsilon$-machines with compression diagnostics yields a coherent picture of where predictive information resides in the caregiving ABM. Global reconstructions provide a memoryless baseline ($L{=}0$ under coarse symbolizations), whereas per-dyad models reveal localized structure, particularly for walkability under ordinal encodings ($m{=}3$). Compression metrics corroborate these patterns: dictionary compressors agree on algorithmic redundancy, while normalized LZ78 captures statistical novelty. Socioeconomic variables display cross-sectional heterogeneity and near-memoryless dynamics, whereas spatial interaction induces bounded temporal memory and recurrent regimes. The framework thus distinguishes semantic organization (predictive causation and memory) from syntactic simplicity (description length) and clarifies how emergence manifests at different system layers. It is demonstrated on a caregiver--elder case study with dyad-level $\varepsilon$-machine reconstructions and compression-based diagnostics.

cs.MA↗