Facial expressions are central to human interaction, yet their role in strategic decision-making has received limited attention. We investigate how real-time facial communication influences cooperation in repeated social dilemmas. In a laboratory experiment, participants play a repeated Prisoner's Dilemma game under two conditions: in one, they observe their counterpart's facial expressions via gender-neutral avatars, and in the other no facial cues are available. Using state-of-the-art biometric technology to capture and display emotions in real-time, we find that facial communication significantly increases overall cooperation and, notably, promotes cooperation following defection. This restorative effect suggests that facial expressions help participants interpret defections less harshly, fostering forgiveness and the resumption of cooperation. While past actions remain the strongest predictor of behavior, our findings highlight the communicative power of facial expressions in shaping strategic outcomes. These results offer practical insights for designing emotionally responsive virtual agents and digital platforms that sustain cooperation in the absence of physical presence.

Risk measures such as Conditional Value-at-Risk (CVaR) focus on extreme losses, where scarce tail data makes model error unavoidable. To hedge misspecification, one evaluates worst-case tail risk over an ambiguity set. Using Extreme Value Theory (EVT), we derive first-order asymptotics for worst-case tail risk for a broad class of tail-risk measures under standard ambiguity sets, including Wasserstein balls and $ϕ$-divergence neighborhoods. We show that robustification can alter the nominal tail asymptotic scaling as the tail level $β\to0$, leading to excess risk inflation. Motivated by this diagnostic, we propose a tail-calibrated ambiguity design that preserves the nominal tail asymptotic scaling while still guarding against misspecification. Under standard domain of attraction assumptions, we prove that the resulting worst-case risk preserves the baseline first-order scaling as $β\to0$, uniformly over key tuning parameters, and that a plug-in implementation based on consistent tail-index estimation inherits these guarantees. Synthetic and real-data experiments show that the proposed design avoids the severe inflation often induced by standard ambiguity sets.

We examine the new production function developed by Chilarescu, and prove that under certain restrictions, the values of the elasticity can also be less than one. We will also prove that under certain restrictions on the parameters, the production function satisfies the Inada conditions.

This study introduces a novel framework to model cost overruns associated with four key stakeholders in nuclear power plant construction: equipment suppliers, construction subcontractors, the design and management team, and creditors. The framework estimates the share of overruns caused by each stakeholder and the share of overruns they receive as payment. The results show that the share of cost overruns a given stakeholder causes and the share of overruns they receive as payment are often starkly different, which can lead to profit misallocations and litigation between parties, further exacerbating overruns. The magnitude of these potential profit misallocations is examined under three common contract structures - fixed-price, cost-plus, and performance-based - revealing the advantages and disadvantages of each framework for aligning stakeholder incentives. Regardless of the contract type chosen, strong owner involvement is crucial for project success, and the study concludes with specific recommendations for project owners seeking to minimize cost overruns.

The United States labor market exhibits a persistent coexistence of high job vacancy rates and prolonged unemployment duration, a pattern that standard labor market theory struggles to explain. This paper argues that a non-trivial portion of contemporary frictional unemployment is artificially induced by automated recruitment systems that rely on deterministic keyword-based screening. Drawing on labor economics, information asymmetry theory, and prior work on algorithmic hiring, we formalize this phenomenon as artificial frictional unemployment arising from semantic misinterpretation of candidate competencies. We evaluate this claim using controlled simulations that compare legacy keyword-based screening with semantic matching based on high-dimensional vector representations of resumes and job descriptions. The results demonstrate substantial improvements in recall and overall matching efficiency without a corresponding loss in precision. Building on these findings, the paper proposes a candidate-side workforce operating architecture that standardizes, verifies, and semantically aligns human capital signals while remaining interoperable with existing recruitment infrastructure. The findings highlight the economic costs of outdated hiring systems and the potential gains from improving semantic alignment in labor market matching.

We investigate whether risk and time preferences differ when individuals make decisions for others compared to making decisions for themselves. We introduce a novel ``skin in the game'' experimental design, where choices for others incur a direct cost to the decision-maker, ensuring a genuine trade-off between self-interest and surrogate allocation. The modal outcome is that participants are more risk-averse and impatient when choosing for others than for themselves. Our methodology reveals significant heterogeneity, successfully identifying selfish types often missed by the more standard ``no skin in the game'' approaches. The message is nuanced, as even non-selfish participants behave differently when they have skin in the game. Furthermore, our framework yields more consistent behavior and superior out-of-sample predictive power.

In a brief reflection on the principles of human society, P. A. M. Dirac articulated a structural tension between two widely affirmed norms: that it is good and natural for parents to improve the prospects of their own children, and that justice requires that all children have equal opportunities in life. These principles, each compelling on its own, cannot be fully realized together. This paper reconstructs Dirac's dilemma, connects it to the dynamics of compounding advantage and inheritance, and situates it within the broader history of political philosophy, including the work of Rawls, Dworkin, Cohen, Brighouse and Swift, Nozick, Murphy and Nagel, and others. The paper argues that attempts to eliminate the resulting injustices entirely risk damaging the non--zero--sum structures that generate general prosperity, and defends a position of "managed inequality": a robust social floor and real mobility, combined with limits on extreme dynastic accumulation and an explicit acceptance of some residual, but constrained, inherited advantage.

A recent Douglas fir management investigation is repeated in terms of accounting measures. The rotation times become much shorter than in earlier results. Thinnings do not become feasible, provided the thinning effects on the volumetric yield function do not evolve in time. Evolving prices and expenses break the periodic boundary condition in monetary quantities: profit rates and capitalizations evolve with prices. The periodic boundary condition is, however, retained in time derivatives of dimensionless quantities, as well as in physical characteristics of rotations optimized for the rate of return. Then, optimal rotations do not depend on the evolution of prices and expenses. Relatively high timber prices shorten rotations, as relatively high expenses extend them.

The shale revolution, driven by advances in horizontal drilling, multi-stage hydraulic fracturing, and cyclic gas injection, has reshaped the oil and gas industry over the past two decades. In the United States, these technologies transformed ultra-low permeability shale formations into commercially viable resources, increasing crude oil production from 5 million bpd in 2008 to more than 12 million by 2019. Horizontal drilling became the standard after 2010, with nearly 200,000 horizontal wells completed by the end of 2023, accounting for more than 80% of all new wells in the US. This success is now being replicated in Argentina making the country a leading unconventional producer. The common driver in both cases is the mass manufacturing approach, which industrializes oil production through standardized well designs, repeatable workflows, multi-well pad drilling, and continuous process optimization. Supported by a competitive oilfield service market and a skilled technical workforce, this model has reduced drilling times from months to weeks and cut well costs by half over the past decade. New technologies such as precision geosteering, advanced completion designs, real-time drilling analytics, and cyclic gas injection continue to improve efficiency and productivity. Applying these methods and technologies to conventional reservoirs could significantly expand global production capacity and place sustained downward pressure on crude oil prices. This paper argues that in a period of slowing economic growth and potential financial deleveraging, abundant supply from both unconventional and conventional developments could extend a prolonged phase of relatively low oil prices. Such a scenario would have far-reaching implications for energy policy, investment strategies, and the competitive position of oil-producing nations.

The integration of cryptocurrencies into institutional portfolios necessitates the adoption of robust risk modeling frameworks. This study is a part of a series of subsequent works to fine-tune model risk analysis for cryptocurrencies. Through this first research work, we establish a foundational benchmark by applying the traditional industry-standard Geometric Brownian Motion (GBM) model. Popularly used for non-crypto financial assets, GBM assumes Lognormal return distributions for a multi-asset cryptocurrency portfolio (XRP, SOL, ADA). This work utilizes Maximum Likelihood Estimation and a correlated Monte Carlo Simulation incorporating the Cholesky decomposition of historical covariance. We present our stock portfolio model as a Minimum Variance Portfolio (MVP). We observe the model's structural shift within the heavy-tailed, non-Gaussian cryptocurrency environment. The results reveal limitations of the Lognormal assumption: the calculated Value-at-Risk at the 5% confidence level over the one-year horizon. For baselining our results, we also present a holistic comparative analysis with an equity portfolio (AAPL, TSLA, NVDA), demonstrating a significantly lower failure rate. This performance provides conclusive evidence that the GBM model is fundamentally the perfect benchmark for our subsequent works. Results from this novel work will be an indicator for the success criteria in our future model for crypto risk management, rigorously motivating the development and application of advanced models.

We introduce BallotRank, a ranked preference aggregation method derived from a modified PageRank algorithm. It is a Condorcet-consistent method without damping, and empirical examination of nearly 2,000 ranked choice elections and over 20,000 internet polls confirms that BallotRank always identifies the Condorcet winner at conventional values of the damping parameter. We also prove that the method satisfies many of the same social choice criteria as other well-known Condorcet completion methods, but it has the advantage of being a natural social welfare function that provides a full ranking of the candidates.

A new wave of work on covariance cleaning and nonlinear shrinkage has delivered asymptotically optimal analytical solutions for large covariance matrices. The same framework has been generalized to empirical cross-covariance matrices, whose singular value decomposition identifies canonical comovement modes between two asset sets, with singular values quantifying the strength of each mode and providing natural targets for shrinkage. Existing analytical cross-covariance cleaners are derived under strong stationarity and large-sample assumptions, and they typically rely on mesoscopic regularity conditions such as bounded spectra; macroscopic common modes (e.g., a global market factor) violate these conditions. When applied to real equity returns, where dependence structures drift over time and global modes are prominent, we find that these theoretically optimal formulas do not translate into robust out-of-sample performance. We address this gap by designing a random-matrix-inspired neural architecture that operates in the empirical singular-vector basis and learns a nonlinear mapping from empirical singular values to their corresponding cleaned values. By construction, the network can recover the analytical solution as a special case, yet it remains flexible enough to adapt to non-stationary dynamics and mode-driven distortions. Trained on a long history of equity returns, the proposed method achieves a more favorable bias-variance trade-off than purely analytical cleaners and delivers systematically lower out-of-sample cross-covariance prediction errors. Our results demonstrate that combining random-matrix theory with machine learning makes asymptotic theories practically effective in realistic time-varying markets.

This article examines the law of the tendency of the rate of profit to fall in the Spanish economy between 1960 and 2024, considering the organic composition of capital and the rate of surplus value as central variables. Its aim is to determine whether this law, formulated by Marx in Capital (Vol. III), continues to operate in the contemporary context. The methodology consists of transforming orthodox macroeconomic categories derived from the Spanish National Accounts (CNE), available in BDMACRO, into Marxist variables: constant capital ($c$), variable capital ($v$), and surplus value ($pv$). Based on these, historical series of the organic composition of capital ($q$), the rate of surplus value ($pv'$), and the rate of profit ($g'$) are constructed, adjusted to constant prices to ensure temporal coherence and comparability. The results show a sustained increase in $q$ and a slight decrease in $pv'$, generating a tendential decline in $g'$ with cyclical fluctuations associated with specific crises. The conclusions empirically confirm the validity of the law in Spain, highlighting the historical limits of capitalism and providing quantitative evidence on the structural dynamics of profitability.

We propose a quantum unstructured search algorithm to find the extrema or roots of discrete functions, $f(\mathbf{x})$, such as the objective functions in combinatorial and other discrete optimisation problems. The first step of the Quantum Search for Extrema and Roots Algorithm (QSERA) is to translate conditions of the form $f(\mathbf{x}_*) \simeq f_*$, where $f_*$ is the extremum or zero, to an unstructured search problem for $\mathbf{x}_*$. This is achieved by mapping $f(\mathbf{x})$ to a function $u(z)$ to create a quantum oracle, such that $u(z_*) = 1$ and $u(z \neq z_*) = 0$. The next step is to employ Grover's algorithm to find $z_*$, which offers a quadratic speed-up over classical algorithms. The number of operations needed to map $f(\mathbf{x})$ to $u(z)$ determines the accuracy of the result and the circuit depth. We describe the implementation of QSERA by assembling a quantum circuit for portfolio optimisation, which can be formulated as a combinatorial problem. QSERA can handle objective functions with higher order terms than the commonly-used Quadratic Unconstrained Binary Optimisation (QUBO) framework. Moreover, while QSERA requires some a priori knowledge of the extrema of $f(\mathbf{x})$, it can still find approximate solutions even if the conditions are not exactly satisfied.

We introduce a new class of first passage time optimization driven by threshold resetting, inspired by many natural processes where crossing a critical limit triggers failure, degradation or transition. In here, search agents are collectively reset when a threshold is reached, creating event-driven, system-coupled simultaneous resets that induce long-range interactions. We develop a unified framework to compute search times for these correlated stochastic processes, with ballistic- and diffusive searchers as key examples uncovering diverse optimization behaviors. A cost function, akin to breakdown penalties, reveals that optimal resetting can forestall larger losses. This formalism generalizes to broader stochastic systems with multiple degrees of freedom.

Entity resolution (probabilistic record linkage, deduplication) is a key step in scientific analysis and data science pipelines involving multiple data sources. The objective of entity resolution is to link records without common unique identifiers that refer to the same entity (e.g., person, company). However, without identifiers, researchers need to specify which records to compare in order to calculate matching probability and reduce computational complexity. One solution is to deterministically block records based on some common variables, such as names, dates of birth or sex or use phonetic algorithms. However, this approach assumes that these variables are free of errors and completely observed, which is often not the case. To address this challenge, we have developed a Python package, BlockingPy, which uses blocking using modern approximate nearest neighbour search and graph algorithms to reduce the number of comparisons. The package supports both CPU and GPU execution. In this paper, we present the design of the package, its functionalities and two case studies related to official statistics. The presented software will be useful for researchers interested in linking data from various sources.

We introduce a model for the loss distribution of a credit portfolio considering a contagion mechanism for the default of names which is the result of two independent components: an infection attempt generated by defaulting entities and a failed defence from healthy ones. We then propose an efficient recursive algorithm for the loss distribution. Then we extend the framework with more flexible distributions that integrate a contagion component and a systematic factor to better fit real-world data. Finally, we propose an empirical application in which we price synthetic CDO tranches of the iTraxx index, finding a good fit for multiple tranches.

Existing deep learning-based calibration scheme for rough volatility models predominantly rely on supervised learning frameworks, which incur significant computational costs due to the necessity of generating massive synthetic training datasets. In this work, we propose a novel unsupervised learning-based calibration scheme for rough volatility models that eliminates the data generation bottleneck. Our approach leverages the backward stochastic differential equation (BSDE) representation of the pricing function derived by Bayer et al. \cite{bayer2022pricing}. By treating model parameters as trainable variables, we simultaneously approximate the BSDE solution and optimize the parameters within a unified neural network training process, with the terminal misfit as the loss. We theoretically establish that the mean squared error between the model-implied prices and market data is bounded by the loss function. Furthermore, we prove that the loss can be minimized to an arbitary degree, depending on the model's market fitting capacity and the universal approximation capability of neural networks. Numerical experiments for both simulated and historical S\&P 500 data based on rough Bergomi (rBergomi) model demonstrate the efficiency and accuracy of the proposed scheme.

Starting from the global financial crisis to the more recent disruptions brought about by geopolitical tensions and public health crises, the volatility of risk in financial markets has increased significantly. This underscores the necessity for comprehensive risk measures capable of capturing the complexity and heightened fluctuations in market volatility. This need is crucial not only for new financial assets but also for the traditional financial market in the face of a rapidly changing financial environment and global landscape. In this paper, we consider the risk measures on a special space $L^{p(\cdot)}$, where the variable exponent $p(\cdot)$ is no longer a given real number as in the conventional risk measure space $L^{p}$, but rather a random variable reflecting potential fluctuations in volatility within financial markets. Through further development of axioms related to this class of risk measures, we also establish dual representations for them.