arXiv subjects

Isaak Mengesha

Publications and source records attributed to Isaak Mengesha.

7 recordsLinked to original sources

arXiv2026

Capability-Based Planning for AI Crisis Preparedness

Capability-based planning drives preparedness in defense and homeland security, but has yet to be applied seriously to AI. Government AI preparations follow a predict-then-act paradigm: rank risks by likelihood and impact, then prepare for the highest expected harm. AI resists prediction: expert timelines disagree by orders of magnitude, and official reviews concede that likelihood-based risk assessment fails for exactly this class of risk. Drawing on principles of decision making under deep uncertainty, we propose a methodological framework in three parts: a scenario library sampled systematically across declared axes; a rating procedure that assesses each government capability against each scenario on coarse, gated criteria; and a prioritization step that maps the resulting matrix onto decision rules a government might adopt. Through a pilot across the four most severe AI-enabled threat classes, we illustrate the kind of insight the instrument yields and provide a proof of concept for capability-based planning as a practical tool for AI crisis preparedness.

cs.CY↗

arXiv2026

A pragmatic classification framework for AI incident monitoring

Incident monitoring can drive safety improvements in high-reliability industries and population-scale technologies, but remains underdeveloped in AI governance. Public databases catalog thousands of AI incidents, but simple incident counts conflate media reporting propensity, system deployment ("exposure"), and harm frequency per unit exposure. We propose a methodological framework that accounts for these factors and calibrates confidence to available evidence in analyzing how AI incidents change over time. The framework comprises three components: a structured monitoring question that defines the scope of the analysis; a tiered estimation process that separately derives harm and exposure trends, including through LLM-assisted filtering of public incident databases; and a classification scheme that maps the resulting trend estimates onto actionable governance categories (Escalating, Mitigating, Concentrating, Receding or Unclassifiable). Through case studies, we examine the framework's clarifying power and limitations, demonstrate governance insight despite real-world data constraints, and provide a proof of concept for AI incident monitoring as a practical governance tool.

cs.CY↗

arXiv2026

Poverty traps are rare, but trappedness isn't

The persistence of poverty is not well explained by who is poor. We argue the relevant object of measurement is trappedness--expected escape time from deprivation--which varies systematically across institutional environments and is invisible to standard poverty indices. Using Markov chains estimated on twenty years of longitudinal data from 27 European countries, we show that countries with identical deprivation rates differ in escape times by up to fourfold. These differences are not explained by household characteristics alone: exogenous shocks reshape welfare landscapes differently across countries, with divergence tracking welfare regime architecture rather than household composition. The mechanism is behavioural: health constrains a household's capacity to convert income gains into durable welfare improvement. Income transfers without health improvement fail to reduce poverty-return risk; combined interventions are super-additive across 28 countries, and the gap widens with transfer size. These findings dissolve the long-running poverty trap debate--studies that rejected traps measured the wrong dimension; studies that found them captured one projection of a multidimensional dynamic process. Trappedness is continuous, multidimensional, and institutionally shaped.

econ.GN↗

arXiv2026

Cities cluster into growth regimes that propagate shocks

Economic growth is conventionally analyzed at the national level, yet cities generate the bulk of global output. Here we construct GDP trajectories for 8,808 functional urban areas (FUAs) across 165 countries over 1993-2019 using satellite-derived nighttime light data and identify 17 distinct, persistent growth regimes through clustering of full temporal trajectories. Rather than converging toward a common frontier, FUAs inhabit distinct economic niches-analogous to ecological niches-defined by shared volatility profiles, shock responses, and long-run dynamics that transcend national boundaries. Cities within the same country frequently belong to different regimes, while structurally similar cities on different continents share the same one; regime membership explains 16% of within-country growth variance beyond country fixed effects. National-level convergence emerges as an aggregation artifact: conditional convergence operates within regimes, not globally. A directed propagation network reveals that shocks transmit along lines of structural similarity rather than geographic proximity, with advanced economies exporting disturbances and emerging economies absorbing or amplifying them. Within-country spatial inequality declines with industrialization maturity, consistent with growth initially concentrating in leading cities before diffusing across the urban system. The global economy is better understood as an ecology of heterogeneous urban growth regimes than as a collection of nations on a shared development path.

econ.GN↗

arXiv2026

The coordination gap in frontier AI safety policies

Frontier AI Safety Policies concentrate on prevention: capability evaluations, deployment gates, and usage constraints, while neglecting the capacity to coordinate responses when prevention fails. We argue this coordination gap is structural: investments in ecosystem robustness yield diffuse benefits but concentrated costs, generating systematic underinvestment. Drawing on risk regimes in nuclear safety, pandemic preparedness, and critical infrastructure, we propose that similar mechanisms (precommitment, shared protocols, standing coordination venues) could be adapted to frontier AI governance. Closing the gap requires cross-actor "note-exchange" of ex ante if-then response logic, exposing not only triggers but the decision processes that convert signals into actions. Without such architecture, institutions cannot learn from failures at the pace of relevance.

cs.CY↗

arXiv2026

Measuring growth and convergence at the mesoscale

Global inequality has shifted inward, with rising dispersion increasingly occurring within countries rather than between them. Using 8,790 newly harmonised Functional Urban Areas (FUAs) - micro-founded labour-market regions encompassing 3.9 billion people and representing approximately 80% of global GDP - we show that national aggregates systematically, and increasingly, misrepresent the dynamics of growth, convergence, and structural change. Holding the underlying nighttime-lights GDP raster (1992-2019) fixed while varying the unit of aggregation (ADM0-ADM3, FUA), we isolate the contribution of the Modifiable Areal Unit Problem directly in the growth literature. Three results follow. First, where inequality is located is unit-dependent: FUAs recover the most stable income-inequality relationship, corroborated by independent wealth data. Second, estimated beta-convergence is scale-sensitive, and FUAs exhibit a discrete jump in convergence strength relative to administrative units of comparable population. Third, we find no poverty trap at the urban scale: expected growth remains positive throughout the income distribution, while the middle-income acceleration flattens over time. The cross-country convergence debate has been conducted on national aggregates, yet nations are political containers rather than economic units, and measured relationships, including the convergence coefficient, depend on the spatial unit of analysis.

econ.GN↗

arXiv2025

Cognitive biases shape the evolution of zero-sum norms

Why do maladaptive perceptions and norms, such as zero-sum interpretations of interaction, persist even when they undermine cooperation and investment? We develop a framework where bounded rationality and heterogeneous cognitive biases shape the evolutionary dynamics of norm coordination. Extending evolutionary game theory with quantal response equilibria and prospect-theoretic utility, we show that subjective evaluation of payoffs systematically alters population-level equilibrium selection, generating stable but inefficient attractors. Counterintuitively, our analysis demonstrates that the benefit of rationality and the cost of risk aversion on welfare behave in nonmonotone ways: intermediate precision enhances coordination, while excessive precision or strong loss aversion leads to persistent lock-in at low-payoff and zero-sum equilibria. These dynamics produce an endogenous equity-efficiency trade-off: parameter configurations that raise aggregate welfare also increase inequality, while more equal distributions are associated with lower efficiency. The results highlight how distorted payoff perceptions can anchor societies in divergent institutional trajectories, offering a behavioral-evolutionary explanation for persistent zero-sum norms and inequality.

econ.GN↗