This paper investigates how shifting from Title-Based Regime (TBR) to Equitable Distribution Regime (EDR) alters labor supply and welfare for married couples. Using staggered DiD with PSID/NLSW data, the author finds that EDR raises labor supply for both spouses, with wives contributing about 30% of the total rise in female labor force participation over the past seven decades. The mechanism is framed through precautionary savings: increased post-divorce asset-sharing uncertainty makes households save more and work more during marriage. A dynamic two-period model shows that precautionary savings, governed by relative prudence (RP = 1+γ), determines the sign of the response; in CRRA settings the switch occurs at γ=1. Welfare analysis indicates that while wives may gain post-divorce shares under EDR, overall household welfare can decline during marriage due to higher uncertainty, suggesting community-property-like sharing could yield similar wife shares with less welfare loss [page::1][page::14][page::21][page::25].
This paper analyzes multi-asset portfolio liquidation in dark pools under quadratic adverse selection costs, formulating a multidimensional linear–quadratic stochastic control problem that leads to a matrix-valued BSDE with jumps and a singular terminal condition [page::1]. It establishes both existence and a novel uniqueness result for such matrix-valued BSDEs, a significant theoretical advancement even in simplified settings [page::2]. In a two-asset setting, the work reveals how asset correlation and spillover effects govern diversification: well-diversified portfolios can be protected if hedging dominates spillover, while adverse selection mainly determines whether a poorly diversified portfolio can be rescued via dark-pool trading [page::3]. The analysis hinges on a powerful comparison principle for multidimensional BSDEs with jumps and singular terminals, enabling the minimality argument and a rigorous uniqueness proof for the singular problem [page::4]. By contrasting with models without adverse selection, the paper shows how spillover reshapes diversification benefits and the role of dark pools in cross-asset liquidation, extending the dark-pool literature substantially [page::5].
OpenMarket releases the first public millisecond-level Polymarket BTC/Binance BTC-USDT paired corpus with explicit pairing metadata and reproducible tooling, totaling 727,098,247 deduplicated rows and 2,936,031 lead–lag pairs across 54 Polymarket days and 57 Binance days, plus a Rust-based pipeline for collection, pairing, Parquet export, and walk-forward calibration [page::0]. Central empirical findings are narrowly scoped and include that BTC 15-minute top-of-book spreads are usually one tick wide, source-clock lead–lag has a compact median with heavy tails, Polymarket quotes respond to large Binance moves after several hundred milliseconds on the collector clock, lead–lag timing is stable across disagreement regimes, and multivariate features underperform the naive market mid-prior out-of-sample; the work emphasizes reproducibility and data/methods rather than trading alpha [page::0].
This paper formalizes a macro-socioeconomic stability constraint for AI governance and introduces the Human Utility Factor (HUF), a differentiable welfare metric decomposed into Agency, Wellbeing, and Economic Stability. It links HUF to actionable levers—automation depth ha, redistribution α+β, and employment coverage ρ—and derives a closed-form interior optimum h∗a and a redistribution threshold (α+β)∗. Through three-agent Stackelberg MARL simulations across BAU, Partial, and Nordic regimes, the study shows redistribution is the decisive lever and that Wellbeing-dominant equilibria can arise without a floor on redistribution, highlighting governance risks. Empirical evidence (GAGI divergence, AI layoffs, labor share decline) supports the urgency of a system-level stability constraint. The framework recasts AI governance as a computable constrained optimization problem with enforceable inequalities that regulators can monitor and enforce. [page::0][page::4]
This study investigates how competitive pressure in an idealised AI race shapes safety decisions. Using a framed two-player repeated game, we find that Unsafe development is not primarily driven by risk preferences or exogenous risk levels, but by the evolving strategic state of the race—opponent behavior, race position, and early momentum. An evolutionary four-strategy model (Always Safe, Always Unsafe, Conditionally Safe, Conditionally Antisocial Safe) reproduces the observed patterns, showing why conditional Unsafe actions can dominate under competition. Policy implications emphasize reducing competitive pressure and fostering cooperation in AI development rather than focusing solely on individual risk aversion [page::0]
