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.

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.

Against the backdrop of e-commerce restructuring consumption patterns, last-mile delivery stations have substantially fulfilled the function of community retail distribution. However, the current tax system only levies a low labor service tax on delivery fees, resulting in a tax contribution from the massive circulating goods value that is significantly lower than that of retail supermarkets of equivalent scale. This disparity not only triggers local tax base erosion but also fosters unfair competition. Based on the "substance over form" principle, this paper proposes a tax rate calculation method using "delivery fee plus insurance premium" as the base, corrected through "goods value conversion." This method aims to align the substantive tax burden of e-commerce with that of community retail at the terminal stage, effectively internalizing the high negative externalities of delivery stations through fiscal instruments, addressing E-commerce Deterritorialization.

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 this paper I derive a set of testable implications for econometric models defined by three assumptions: (i) the existence of strictly exogenous discrete instruments, (ii) restrictions on how the instruments affect adoption of a finite number of treatment types (such as monotonicity), and (iii) the assumption that the instruments only affect outcomes through their effect on treatment adoption (i.e. an exclusion restriction). The testable implications aggregate (via integration) an otherwise potentially infinite set of inequalities that must hold for every measurable subset of the outcome's support. For binary instruments the testable implications are sharp. Furthermore, I propose an implementation that links restrictions on latent response types to a generalization of first-order stochastic dominance and random utility models, allowing to distinguish violations of the exclusion restriction from violations of monotonicity-type assumptions. The testable implications extend naturally to the many instruments case.

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.

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.

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.

Instrumental variable regression is a common approach for causal inference in the presence of unobserved confounding. However, identifying valid instruments is often difficult in practice. In this paper, we propose a novel method based on possibility theory that performs posterior inference on the treatment effect, conditional on a user-specified set of potential violations of the exogeneity assumption. Our method can provide informative results even when only a single, potentially invalid, instrument is available, offering a natural and principled framework for sensitivity analysis. Simulation experiments and a real-data application indicate strong performance of the proposed approach.

Blockchain-based smart contracts offer a new take on credible commitment, where players can commit to actions in reaction to actions of others. Such reaction-function games extend on strategic games with players choosing reaction functions instead of strategies. We formalize a solution concept in terms of fixed points for such games, akin to Nash equilibrium, and prove equilibrium existence. Reaction functions can mimic "trigger" strategies from folk theorems on infinitely repeated games -- but now in a one-shot setting -- for instance to support Pareto-improvements on Nash equilibrium outcomes. In some games, this can even be done through risk-free, safe reaction functions. We apply our theoretical framework to symmetric investment games, which includes two prominent classes of games, namely weakest-link and public-good games. In both cases, we highlight a particular safe and optimal reaction function. In this way, our findings highlight how blockchain-based commitment can help overcome trust and free-riding barriers.

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 study the design of information acquisition games-environments where a designer contracts their action on Sender's choice of experiment and the realized signals about some state-and identify which predictions can be made absent knowledge about the prior. To do so, we characterize robust mechanisms: those which induce the same allocation rule (mappings from the state to actions) for all priors. These mechanisms take a simple form: they (1) incentivize fully revealing experiments, (2) depend only on the induced posterior, and (3) maximally punish pooling deviations. In binary action problems, all (and only) ordinally monotone allocation rules are robust. We apply our model to school choice and uncover a novel informational justification for deferred acceptance when school preferences depend on students' unknown ability. For general good allocation problems, we show all efficient allocations are robust, even when agent preferences feature state-dependent outside options and allocation externalities.

Semidiscrete optimal transport is a challenging generalization of the classical transportation problem in linear programming. The goal is to design a joint distribution for two random variables (one continuous, one discrete) with fixed marginals, in a way that minimizes expected cost. We formulate a novel variant of this problem in which the cost functions are unknown, but can be learned through noisy observations; however, only one function can be sampled at a time. We develop a semi-myopic algorithm that couples online learning with stochastic approximation, and prove that it achieves optimal convergence rates, despite the non-smoothness of the stochastic gradient and the lack of strong concavity in the objective function.