Defines resilience evaluation D^ρ π as the L1-limit of scaled dynamic risk measure applied to process increments, and derives its dual representation as worst-case conditional expectation of an effective drift when ρ arises from BSDEs with Lipschitz or quadratic drivers.
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
5 Pith papers cite this work. Polarity classification is still indexing.
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Agent-based model with multi-agent RL shows market-maker profitability trends upward overall with rising aggregate market informedness as price-discovery benefits offset adverse-selection costs.
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
Proposes using timing synchronicity and statistical surprise to detect per-action price impact, assuming fast adverse events indicate causation.
Cross-sectional survey of Jordanian nurses shows positive effects of green leadership and ethical climate on sustainable behaviors, partially mediated and moderated by hypocrisy perception, explaining 35.7% variance.
citing papers explorer
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Financial Resilience Evaluation: From Conditional Expectations to Dynamic Convex Risk Measures
Defines resilience evaluation D^ρ π as the L1-limit of scaled dynamic risk measure applied to process increments, and derives its dual representation as worst-case conditional expectation of an effective drift when ρ arises from BSDEs with Lipschitz or quadratic drivers.
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Market Informedness and Market-Maker Profitability: The Trade-Off Between Adverse Selection and Price Discovery
Agent-based model with multi-agent RL shows market-maker profitability trends upward overall with rising aggregate market informedness as price-discovery benefits offset adverse-selection costs.
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Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
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Realtime price impact detection
Proposes using timing synchronicity and statistical surprise to detect per-action price impact, assuming fast adverse events indicate causation.
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Green Transformational Leadership and Sustainable Nursing Practices: Evidence from the Healthcare Sector
Cross-sectional survey of Jordanian nurses shows positive effects of green leadership and ethical climate on sustainable behaviors, partially mediated and moderated by hypocrisy perception, explaining 35.7% variance.