We extend the return extrapolation framework of Atmaz (2022) to incorporate two behaviorally realistic features absent from the linear benchmark: saturation in belief updating and asymmetry between gains and losses. We introduce a smooth, nonlinear, asymmetric extrapolation function and characterize the optimal portfolio of a CRRA investor under Heston (1993) stochastic volatility as the sum of a sentiment-distorted myopic demand, a variance hedging demand, and a sentiment hedging demand. The resulting semilinear Hamilton-Jacobi-Bellman equation is solved by two independent numerical methods, a finite-difference ADI scheme with time-step policy iteration and a deep learning-driven iterative scheme. The model generates four investor-level behavioral anomalies: asymmetric responses to gains and losses, attenuated reactions at extremes, excess trading volume, and welfare loss rising with the strength of extrapolation, each of which maps onto documented empirical patterns. Its central finding is that saturation acts as an endogenous correction mechanism: at the same local slope at the origin, the asymmetric nonlinear extrapolator carries a smaller welfare loss than a linear one.

The ability of spouses to commit to future behavior has important implications for the allocation of resources between them and over time. Using a lifecycle collective model for household behavior, we propose new tests that distinguish between full, limited, and no commitment, based on the dynamic impact of wage shocks on household labor supply. A novelty of our approach is its ability to formally reject limited commitment, in addition to the other two types, exploiting sign restrictions from theory. We implement our tests across 15 European countries, drawing data from the EU-SILC over the years 2005-2019. We find that the elasticity of the Pareto weight with respect to favorable past wages is generally positive, consistent with bargaining under limited commitment. Past wage shocks thus induce bargaining effects on labor supply, empowering the recipient spouse and weakening the partner. Formally, we reject full and no commitment in all but 4 countries, but fail to reject limited commitment.

We present a regime-split Black--Scholes implied volatility solver in which every initial seed is a fully closed-form analytical expression, derived from the asymptotic structure of the Black--Scholes price in its natural domain. At the money, series reversion of an exact Gaussian identity yields a fourth-order seed with error $\mathcal{O}(s^8)$. In the moderate out-of-the-money region, successive Gaussian CDF approximations of increasing order produce explicit initial seed formulas whose accuracy is proved numerically, with no iteration or numerical inversion at the seed stage. In the deep out-of-the-money region, a Gaussian tail cancellation identity -- the Mills ratio -- reveals the asymptotic structure of the Black--Scholes price and motivates a ratio-corrected seed that achieves near-machine-precision initialisation for large moneyness. All regime boundaries are derived analytically from CDF truncation tolerances and numerical solver theoretical error bounds, with no empirically tuned constants. A universal fourth-order Householder polisher then drives all regimes to machine precision, with mean update iterations strictly below two on both standard and granular benchmark grids -- meeting and surpassing the two-iteration target established by the highest-accuracy reference implementation in the literature (Jäckel, 2015). The resulting C implementation achieves a $1.73$--$1.85\times$ throughput gain over the state-of-the-art benchmark (Jäckel, 2015) under identical hardware and compiler conditions, with maximum absolute error $\mathcal{O}(10^{-14})$, stable across grid configurations. A Python/Numba implementation confirms portability. All source code is publicly available.

This paper studies the regularity of finite-maturity American value functions in the Heston model. Although the Heston operator is degenerate when the volatility is zero, we are able to establish C^{1,2} regularity of the American value functions in the exercise domain and the smooth-fit principle, using PDE techniques.

This paper provides a didactic and beginner friendly review of the gravity model and its conceptual translation from classical physics into international economics. After a brief introduction, it begins by establishing the structural and mathematical parallels between Newton's law of universal gravitation and the economic gravity equation, demonstrating how economic mass (GDP) attracts trade flows while geographic distance acts as a source of spatial resistance. The paper then examines the deterministic philosophical framework required to apply rigid natural laws to collective human behavior. Finally, it traces the chronological evolution of the literature, highlighting the historical divergence between the physics rooted approach developed by early demographers and Walter Isard and the utility based econometric adaptations that later emerged. The paper ultimately shows that, despite globalization, spatial friction remains a significant and measurable force shaping international trade and geopolitical interactions.

We release a new dataset of U.S. skill specialization, relatedness, and complexity, derived from 433.6 million job postings between 2010 and 2024. The panel covers 3,194 counties across 15 years and reports 201 variables that describe the volume of job postings (e.g., labor demand), the modality and nature of work (e.g., remote share, internship share), and the structure of employer skill demand by category (e.g., specialized, software, and common). We develop a suite of economic geography variables: skill-based measures of county specialization, relatedness, diversity, complexity, and dynamics. These measures are further decomposed by employer entity type (corporate, university, government, and federal lab), along with entity-pair measures of alignment, overlap, and directional skill gaps. An accompanying interactive dashboard supports both academic research and applied use, with features including spatiotemporal visualization, county rankings and trends, pairwise county comparisons, and individual county profiles.

Current blockchain research and analytics tend to prioritize observable on-chain transactions, obscuring the processes through which cryptocurrencies are created, publicised, retained, and disposed of. In response, this paper considers distributed ledger technologies from records management principles in ISO 15489-1:2016. Setting off by specifying the parallels -- that is transactions as "records", crypto-asset units as "information assets", and blockchains as "aggregations" -- we introduce a seven-stage lifecycle for blockchain data. We apply the framework to Bitcoin, a fungible token, and a non-fungible token. On this basis, we argue that blockchain systems are not merely transactional infrastructures but record management systems with distinctive characteristics. We discuss how the on-chain/off-chain boundary and privacy-enhancing technologies can complicate lifecycle visibility, with particular relevance for crypto-crime research and investigation. As a meta-level framework, the lifecycle perspective enables positioning existing research, decomposing legal, regulatory, technological, and operational challenges by stage, and informing lifecycle-aware approaches to blockchain governance, analytics, and regulation.

GDP per capita is the default lens through which governibng bodies track the economic prosperity and consequences of economic events , yet it is blind to two first-order determinants of lived prosperity: income/wealth distribution and inflation impact. Inequality-adjusted income measures are themselves not new but What is missing from the macroeconomic monitoring toolkit specifically is not a welfare concept but an operational monitoring trigger: a statistic minimal enough to compute annually from public data, transparent enough to audit without modelling assumptions, and normalised so that year-on-year, cross-country change ? the quantity a regulator needs to act on? is legible. We assemble such an instrument, the Gini- Adjusted GDP per Capita Index (GAGI): a reproducible, publicly computable formulation that rescales each country's GDP per capita by its inequality-adjustment factor (1-G) and its price level, normalised to a 2010 baseline. GAGI is a general-purpose welfare index, not inherently specific to AI automation, applicable wherever welfare-adjusted prosperity needs tracking. Applying GAGI to the G7 economies over 2010-2026, we show that welfare-adjusted prosperity has diverged persistently and increasingly from headline GDP growth, that the divergence widens sharply after 2022, temporally coincident with, though not, on this evidence alone, demonstrated to be caused by the after effects of COVID and the acceleration of generative-AI deployment. We argue that GAGI is a necessary complement to GDP-based monitoring: any macroeconomic monitoring instrument that tracks only aggregate output will systematically miss the distributional harm that automation can cause even while reported growth remains strong.

Recent advances in error-corrected qubits have accelerated the timeline for practical quantum computing. It poses a threat to cryptographic primitives used to secure financial systems, government infrastructure, communication networks, and DeFi (Decentralized Finance) ecosystems. This paper introduces a post-quantum secure federated DeFi framework that enables inter-bank collaboration to improve the inclusivity of individuals underserved by local lenders due to limited financial histories. Multiple banks contribute encrypted information batches to a virtual server, where lattice-based Fully Homomorphic Encryption (FHE) enables end-to-end homomorphic computation. The server fuses local data-driven probabilistic assessments, expert beliefs, and verifiable evidence generated by the NASA-IBM Prithvi Geospatial Foundation Model (GFM), in encrypted format. Decentralized technologies are employed to ensure tamper-proof evidence and auditable accountability for all encrypted data exchanges between institutions and the server. The framework is tested on agricultural lending decisions for rural borrowers in Virginia.

Current scaling trajectories for Generative AI, typified by linear supply-side "stacks," prioritize performance density while externalizing significant thermodynamic and material costs. As the "Twin Transition" of green and digital transformation accelerates, the industry faces technology gaps - including Scope 3 emissions and e-waste recycling - that impede sustainable scaling and lead to social tensions. This study proposes a Regenerative Socio-Technical roadmap that repurposes the Sustainable Production and Consumption system map to reframe artificial intelligence infrastructure as a system-of-systems governed ultimately by planetary limits. By integrating the Institute of Electrical and Electronics Engineers International Roadmap for Devices and Systems (IEEE IRDS) sustainability considerations for semiconductor facilities, the study proposes a metabolic circuit framework that centers "Values and Needs" within production and consumption relationship loops. This study identifies critical gaps in current Nvidia-centric roadmaps and proposes a competing reference architecture. It demonstrates how a spontaneous order of resource parsimony and planetary accountability can provide an actionable pathway for regulatory compliance and industrial resilience in the digital circular economy.

Decision-making under risk is typically studied through single-shot lottery choices. Yet many real decisions involve combinatorial risk, where risk arises from multiple risky components, so the lottery over outcomes is induced rather than given outright and can be costly to evaluate exactly. We introduce an investment-allocation task to study decision under combinatorial risk, where investing in a component raises its success probability and thereby reshapes the outcome distribution. Participants favor the option with the larger probability increment, and, when increments are equal, the option with the higher initial success probability. Revealing the induced probability mass function (PMF) substantially changes behavior, making participants less responsive to combinatorial-risk features and reducing choice variance. To explain these patterns, we move beyond standard benchmarks and hand-crafted hypotheses with symbolic regression to discover compact descriptive models. The discovered models rely mainly on combinatorial-risk features, such as the after-investment success probability, rather than exact evaluation of the full induced distribution. Behavior under the displayed PMF is then well explained by augmenting this model with a prospect-theoretic residual model. The results show that people navigate combinatorial risk primarily through its core features, shifting toward lottery valuation only when the induced PMF is displayed.

Platform content interventions in recommendation systems are typically evaluated as static "nudges", ignoring that the systems adaptively learn from the resulting user behavior. We investigate this dynamic through a large-scale field experiment on a short-video platform. The experiment involves a "sleep reminder" campaign designed to reduce late-night usage. Paradoxically, the intervention increased late-night engagement by 14.75% and overall platform usage by 2.18%, and the effects persisted for weeks even after the experiment. We explain this through a forced-exploration mechanism, showing that by revealing high latent demand for the promoted content, the intervention triggers a recommendation policy update that routine user behavior would not produce. The data generated by the intervention induced the algorithm to update its post-campaign policy, reinforcing the very engagement loops the campaign aimed to mitigate. Our findings demonstrate that user-facing interventions can effectively retrain the underlying algorithm, triggering durable, system-wide shifts in content distribution that challenge standard evaluation metrics in platform governance and social responsibility initiatives.

Real-world asset (RWA) tokenization has emerged as a prominent application of blockchain technology, enabling off-chain financial and non-financial assets to be represented through blockchain-based instruments. However, deployed RWA systems remain difficult to compare because legal claims, custody arrangements, token mechanics, verification processes, and on-chain integrations are often described separately. This paper develops a systems-level taxonomy of RWA tokenization to classify how off-chain assets are legally, economically, and technically represented on-chain. Following an iterative taxonomy-development method, we organize twenty-three dimensions into five components: governance, asset structure, token properties, distributed ledger technology, and economy. We apply the taxonomy to twenty major RWA systems selected by market capitalization and compare their design choices across asset classes and implementation models. The classification shows that current RWA tokenization is predominantly implemented through hybrid architectures: blockchain tokens support representation, transfer control, redemption workflows, pricing, and composability, while core legal guarantees remain anchored in off-chain legal wrappers, custodial arrangements, compliance processes, and verification mechanisms. The analysis also reveals recurring documentation gaps concerning voting rights, dispute forums, burn mechanics, supply constraints, and reserve verification. Overall, the taxonomy provides a structured basis for comparing RWA systems, identifying design patterns and limitations, and supporting future research on blockchain-based financial infrastructure.

In inventory market making, the running-penalty coefficient $ϕ$ of the Cartea-Jaimungal framework and the risk-aversion parameter $γ$ of the Avellaneda-Stoikov framework are typically treated as independent free parameters, calibrated separately. We show that they are in fact not independent. A small set of axioms on the market maker's dynamic preference functional, namely cash-additivity, normalization, concavity, strong dynamic consistency, and law-invariance, forces the preference functional to be the entropic certainty-equivalent on liquidation-adjusted terminal wealth, parametrized by a single positive scalar $γ$. The Avellaneda-Stoikov framework is the unique representative of this axiom class. The Cartea-Jaimungal framework is its second-order Taylor expansion in inventory magnitude, with the running coefficient forced to $ϕ= γσ^2/2$ and (under a mild regularity condition on the liquidation cost) the terminal coefficient forced to $α= \frac{1}{2}L''(0)$. The two frameworks, typically presented as competing alternatives with the choice between them driven by tractability, are different manifestations of a single underlying object. The forced relation is invertible, $γ= 2ϕ/σ^2$, giving a consistency cross-check on independently calibrated desk parameters.

Ordered response scales are ubiquitous in economics, but their interpretation rests on an untested assumption: that numerical labels reflect equal psychological intervals. We develop a framework to quantify how relaxing this assumption affects empirical results. Using new experimental evidence, we show that scale use is only mildly non-linear. Replicating over 40,000 estimates from more than 80 papers, we find that coefficient signs and significance are largely robust, but relative magnitudes are not. Even modest non-linearities generate substantial variation in implied trade-offs.

We report the results of a study to identify and quantify drivers of inventory record inaccuracy (IRI) in a grocery retailing environment, a context where products are often subject to promotion activity and a substantial share of items are perishable. The analysis covers ~24,000 stock keeping units (SKUs) sold in 11 stores. We find that IRI is positively associated with average inventory level, restocking frequency, and whether the item is perishable, and negatively associated with promotional activity. We also conduct a field quasi-experiment to assess the marginal effect of stockcounts on sales. While performing an inventory audit is found to lead to an 11% store-wide sales lift, the audit has heterogeneous effects with all the sales lift concentrated on items exhibiting negative IRI (i.e., where system inventory is greater than actual inventory). The benefits of inventory audits are also found to be more pronounced on perishable items, that are associated with higher IRI levels. Our findings inform retailers on the appropriate allocation of effort to improve IRI and reframes stock counting as a sales-increasing strategy rather than a cost-intensive necessity.

Preference heterogeneity massively increases the social cost of carbon. We call this the Weitzman premium. Uncertainty about an exponential discount rate implies a hyperbolic discount rate, which in the near term is equal to the average discount rate but in the long term falls to the minimum discount rate. We generalise Weitzman's (2001, AER) gamma discounting to zero-inflation and two dimensions but find that the analytical solution is a poor approximation of the non-parametric heterogeneity. We calibrate the pure rate of time preference and the inverse of the elasticity of intertemporal substitution of 79,273 individuals from 76 countries and compute the corresponding social cost of carbon. Compared to the social cost of carbon for average time preferences, the average social cost of carbon is 6 times as large in the base calibration, and up to 200 times as large in sensitivity analyses.

This paper proposes the second-order Esscher transform as a tractable extension of the classical Esscher framework for option pricing and risk management in Lévy-driven markets. For a general Lévy process, we derive the associated densities and equivalent pricing measures, characterize the martingale condition in closed form, and obtain FFT-based valuation formulas for European call options. For jump-diffusion models, we establish explicit pricing formulas under the second-order Esscher measure and show that the resulting option prices lie in an interval bounded below by the Black--Scholes price and above by the underlying asset value. For the constant jump-diffusion model, we further prove monotonicity of option prices with respect to the second-order Esscher parameter. An empirical analysis based on market data shows that this additional parameter provides a tractable tool for stress testing, delta-hedging evaluation, and the construction of interval-valued risk measures in incomplete markets. We further document a strong association between the estimated second-order Esscher parameter and standard indicators of market stress, including the VIX, news sentiment, and crisis regimes. The proposed framework preserves analytical tractability while enlarging the class of admissible pricing measures, thereby supporting pricing, hedging, and stress-based risk assessment in incomplete markets with jump and general Lévy dynamics.

The trapping probability, $ψ$, as defined in Kovacevic and Pflug (2011), is modelled by assuming proportional capital losses, both in the case where there is no insurance and in the case where insurance is purchased by the household. Insurance coverage is likewise proportional, mirroring the structure of quota-share contracts, which are both prevalent in practice and analytically convenient. New closed formulae for $ψ$ are obtained in the case of no insurance when the distribution of the remaining proportion of capital is a power law, extending the results in Kovacevic and Pflug (2011). When proportional insurance is acquired and the remaining proportion of capital is uniformly distributed on $[0,1]$, $ψ$ satisfies a non-local differential equation whose analysis is based on the properties of diffusion processes. The non-local nature of the equation can be addressed using iterative solution methods, leading to a constructive determination of the trapping probability. Constraints on the parameters governing the capital process are derived in both the uninsured and insured cases to prevent the certainty of trapping. Numerical calculations are used to determine the trapping probability for the insured process and to illustrate the impact of different parameters. Consequences on the trapping probability for vulnerable non-poor populations with initial capital slightly above the poverty line are discussed.

We analyze historic S&P500 multi-day returns: from daily returns to those accumulated over up to ten days. Despite symmetry breaking between gains and losses in the distribution of returns, resulting in its positive mean and negative skew, realized variance (volatility squared) exhibits remarkably good linear dependence on the number of days of accumulation. Mean of the distribution also shows near perfect linear dependence as well. We analyze this phenomenon both analytically and numerically using a modified Jones-Faddy skew t-distribution.