Generative AI is changing how software is produced and used. In vibe coding, an AI agent builds software by selecting and assembling open-source software (OSS), often without users directly reading documentation, reporting bugs, or otherwise engaging with maintainers. We study the equilibrium effects of vibe coding on the OSS ecosystem. We develop a model with endogenous entry and heterogeneous project quality in which OSS is a scalable input into producing more software. Users choose whether to use OSS directly or through vibe coding. Vibe coding raises productivity by lowering the cost of using and building on existing code, but it also weakens the user engagement through which many maintainers earn returns. When OSS is monetized only through direct user engagement, greater adoption of vibe coding lowers entry and sharing, reduces the availability and quality of OSS, and reduces welfare despite higher productivity. Sustaining OSS at its current scale under widespread vibe coding requires major changes in how maintainers are paid.

We study mixed bundling and competitive price-matching guarantees (PMGs) in a duopoly selling complementary products to heterogeneous customers. One retailer offers mixed bundling while the rival sells only a bundle. We characterize unique pure-strategy Nash equilibria across subgames and compare them to a no-bundling benchmark. Mixed bundling strictly dominates whenever an equilibrium exists. Conditional on bundling, PMG adoption trades off strategic demand capture against margin losses on loyal customers and varies systematically with relative demand responsiveness to prices and complementarities.

While delegating tasks to large language models (LLMs) can save people time, there is growing evidence that offloading tasks to such models produces social costs. We use behavior in two canonical economic games to study whether people have different expectations when decisions are made by LLMs acting on their behalf instead of themselves. More specifically, we study the social appropriateness of a spectrum of possible behaviors: when LLMs divide resources on our behalf (Dictator Game and Ultimatum Game) and when they monitor the fairness of splits of resources (Ultimatum Game). We use the Krupka-Weber norm elicitation task to detect shifts in social appropriateness ratings. Results of two pre-registered and incentivized experimental studies using representative samples from the UK and US (N = 2,658) show three key findings. First, people find that offers from machines - when no acceptance is necessary - are judged to be less appropriate than when they come from humans, although there is no shift in the modal response. Second - when acceptance is necessary - it is more appropriate for a person to reject offers from machines than from humans. Third, receiving a rejection of an offer from a machine is no less socially appropriate than receiving the same rejection from a human. Overall, these results suggest that people apply different norms for machines deciding on how to split resources but are not opposed to machines enforcing the norms. The findings are consistent with offers made by machines now being viewed as having both a cognitive and emotional component.

Market integrity monitoring is difficult because suspicious price/volume behavior can arise from many benign mechanisms, while modern detection systems often rely on opaque models that are hard to audit and communicate. We present AIMM-X, an explainable monitoring pipeline that combines market microstructure-style signals derived from OHLCV time series with multi-source public attention signals (e.g., news and online discussion proxies) to surface time windows that merit analyst review. The system detects candidate anomalous windows using transparent thresholding and aggregation, then assigns an interpretable integrity score decomposed into a small set of additive components, allowing practitioners to trace why a window was flagged and which factors drove the score. We provide an end-to-end, reproducible implementation that downloads data, constructs attention features, builds unified panels, detects windows, computes component signals, and generates summary figures/tables. Our goal is not to label manipulation, but to provide a practical, auditable screening tool that supports downstream investigation by compliance teams, exchanges, or researchers.

In an incomplete financial market with general continuous semimartingale dynamics; we model an investor with log-utility preferences who, in addition to an initial capital, receives units of a non-traded endowment process. Using duality techniques, we derive the fourth-order expansion of the primal value function with respect to the units $ε$, held in the non-traded endowment. In turn, this lays the foundation for expanding the optimal wealth process, in this context, up to second order w.r.t. $ε$. The key processes underpinning the aforementioned results are given in terms of Kunita-Watanabe projections, mirroring the case of lower order expansions of similar nature. Both the case of finite and infinite horizons are treated in a unified manner.

We identify volatility spillovers across commodities, equities, and treasuries using a hybrid HAR-ElasticNet framework on daily realized volatility for six futures markets over 2002--2025. Our two step procedure estimates own-volatility dynamics via OLS to preserve persistence, then applies ElasticNet regularization to cross-market spillovers. The sparse network structure that emerges shows equity markets (ES, NQ) act as the primary volatility transmitters, while crude oil (CL) ends up being the largest receiver of cross-market shocks. Agricultural commodities stay isolated from the larger network. A simple univariate HAR model achieves equally performing point forecasts as our model, but our approach reveals network structure that univariate models cannot. Joint Impulse Response Functions trace how shocks propagate through the network. Our contribution is to demonstrate that hybrid estimation methods can identify meaningful spillover pathways while preserving forecast performance.

The Sleeping Beauty problem is a problem of imperfect recall that has received considerable attention. One approach to solving the Sleeping Beauty problem is to allow Sleeping Beauty to make decisions based on her beliefs, and then characterize what it takes for her decisions to be "rational". In particular, she can be allowed to make monetary bets based on her beliefs, with the assumption that she wants to gain wealth rather than lose it. However, this approach is often coupled with the assumption that Sleeping Beauty should maximize the expected value of her bets. Here, show that Sleeping Beauty maximizes the expected growth rate of her wealth as a "thirder" sizing bets using the Kelly Criterion under multiplicative dynamics. Furthermore, this position is shown to be impervious to Dutch books. By contrast, the "halfer" position is shown to be vulnerable to Dutch books under similar circumstances.

Modern deep learning for asset allocation typically separates forecasting from optimization. We argue this creates a fundamental mismatch where minimizing prediction errors fails to yield robust portfolios. We propose the Signature Informed Transformer to address this by unifying feature extraction and decision making into a single policy. Our model employs path signatures to encode complex path dependencies and introduces a specialized attention mechanism that targets geometric asset relationships. By directly minimizing the Conditional Value at Risk we ensure the training objective aligns with financial goals. We prove that our attention module rigorously amplifies signature derived signals. Experiments across diverse equity universes show our approach significantly outperforms both traditional strategies and advanced forecasting baselines. The code is available at: https://anonymous.4open.science/r/Signature-Informed-Transformer-For-Asset-Allocation-DB88

We investigate whether large language models can discover and analyze U.S. tax-minimization strategies. This real-world domain challenges even seasoned human experts, and progress can reduce tax revenue lost from well-advised, wealthy taxpayers. We evaluate the most advanced LLMs on their ability to (1) interpret and verify tax strategies, (2) fill in gaps in partially specified strategies, and (3) generate complete, end-to-end strategies from scratch. This domain should be of particular interest to the LLM reasoning community: unlike synthetic challenge problems or scientific reasoning tasks, U.S. tax law involves navigating hundreds of thousands of pages of statutes, case law, and administrative guidance, all updated regularly. Notably, LLM-based reasoning identified an entirely novel tax strategy, highlighting these models' potential to revolutionize tax agencies' fight against tax abuse.

While optimal taxation theory provides clear prescriptions for tax design, translating these insights into actual tax codes remains difficult. Existing work largely offers theoretical characterizations of optimal systems, while practical implementation methods are scarce. Bridging this gap involves designing tax rules that meet theoretical goals, while accommodating administrative, distributional, and other practical constraints that arise in real-world reform. We develop a method casting tax reform as a constrained optimization problem by parametrizing the entire income tax code as a set of piecewise linear functions mapping tax-relevant inputs into liabilities and marginal rates. This allows users to impose constraints on marginal rate schedules, limits on income swings, and objectives like revenue neutrality, efficiency, simplicity, or distributional fairness that reflect both theoretical and practical considerations. The framework is computationally tractable for complex tax codes and flexible enough to accommodate diverse constraints, welfare objectives and behavioral responses. Whereas existing tools are typically used for ex-post `what-if' analysis of specific reforms, our framework explicitly incorporates real-world reform constraints and jointly optimizes across the full tax code. We illustrate the framework in several simulated settings, including a detailed reconstruction of the Dutch income tax system. For the Dutch case, we generate a family of reforms that smooth existing spikes in marginal tax rates to any desired cap, reduce the number of rules, and impose hard caps on income losses households can experience from the reform. We also introduce \texttt{TaxSolver}, an open-source package, allowing policymakers and researchers to implement and extend the framework.

Despite the growing importance of the digital sector, research on economic complexity and its implications continues to rely mostly on administrative records, e.g. data on exports, patents, and employment, that have blind spots when it comes to the digital economy. In this paper we use data on the geography of programming languages used in open-source software to extend economic complexity ideas to the digital economy. We estimate a country's software economic complexity index (ECIsoftware) and show that it complements the ability of measures of complexity based on trade, patents, and research to account for international differences in GDP per capita, income inequality, and emissions. We also show that open-source software follows the principle of relatedness, meaning that a country's entries and exits in programming languages are partly explained by its current pattern of specialization. Together, these findings help extend economic complexity ideas and their policy implications to the digital economy.

We study the expected utility maximization problem of a large investor who is allowed to make transactions on tradable assets in an incomplete financial market with endogenous permanent market impacts. The asset prices are assumed to follow a nonlinear price curve quoted in the market as the utility indifference curve of a representative liquidity supplier. Using generalized subgradients, we show that optimality can be fully characterized via a system of coupled forward-backward stochastic differential equations (FBSDEs) which corresponds to a non-linear backward stochastic partial differential equation (BSPDE). We show existence of solutions to the optimal investment problem and the FBSDEs in the case where the driver function of the representative market maker grows at least quadratically or the utility function of the large investor falls faster than quadratically or is exponential. Furthermore, we derive smoothness results for the existence of solutions of BSPDEs. Examples are provided when the market is complete, the driver is positively homogeneous or the utility function is exponential.