Tootfinder

Rational Miner Behaviour, Protocol Stability, and Time Preference: An Austrian and Game-Theoretic Analysis of Bitcoin's Incentive Environment
Craig Steven Wright
https://arxiv.org/abs/2506.20965

Rational Miner Behaviour, Protocol Stability, and Time Preference: An Austrian and Game-Theoretic Analysis of Bitcoin's Incentive Environment
This paper integrates Austrian capital theory with repeated game theory to examine strategic miner behaviour under different institutional conditions in blockchain systems. It shows that when protocol rules are mutable, effective time preference rises, undermining rational long-term planning and cooperative equilibria. Using formal game-theoretic analysis and Austrian economic principles, the paper demonstrates how mutable protocols shift miner incentives from productive investment to political…

Geometric Shape Modelling and Volume Estimation of Dry Bulk Cargo Piles using a Single Image
Debanshu Ratha, Madhu Koirala, P{\aa}l Gunnar Ellingsen
https://arxiv.org/abs/2505.17896

Geometric Shape Modelling and Volume Estimation of Dry Bulk Cargo Piles using a Single Image
Volume estimation of onshore cargo piles is of economic importance for shipping and mining companies as well as public authorities for real-time planning of logistics, business intelligence, transport services by land or sea and governmental oversight. In remote sensing literature, the volume of pile is estimated by relying on the illumination property of object to construct the geometric shape from a single image, alternatively, stereographic imaging for construction of a digital elevation mod…

Deep Spatial Neural Net Models with Functional Predictors: Application in Large-Scale Crop Yield Prediction
Yeonjoo Park, Bo Li, Yehua Li
https://arxiv.org/abs/2506.13017

Deep Spatial Neural Net Models with Functional Predictors: Application in Large-Scale Crop Yield Prediction
Accurate prediction of crop yield is critical for supporting food security, agricultural planning, and economic decision-making. However, yield forecasting remains a significant challenge due to the complex and nonlinear relationships between weather variables and crop production, as well as spatial heterogeneity across agricultural regions. We propose DSNet, a deep neural network architecture that integrates functional and scalar predictors with spatially varying coefficients and spatial rando…