Investment and the Dynamic Cost of Income Uncertainty: the Case of Diminishing Expectations in Agriculture

This paper studies optimal investment and the dynamic cost of income uncertainty, applying a stochastic programming approach. The motivation is given by a case study in Finnish agriculture. Investment decision is modelled as a Markov decision process, extended to account for risk. A numerical framework for studying the dynamic uncertainty cost is presented, modifying the classical expected value of perfect information to a dynamic setting. The uncertainty cost depends on the volatility of income; e.g. with stationary income, the dynamic uncertainty cost corresponds to a dynamic option value of postponing investment. The numerical investment model also yields the optimal investment behavior of a representative farm. The model can be applied e.g. in planning investment subsidies for maintaining target investments. In the case study, the investment decision is sensitive to risk.Financial Economics,

Rural Investment and the Cost of Income Uncertainty

This paper studies optimal investment decision in agriculture under diminishing income expectations. The goal is to study the cost of income uncertainty and its implications to the efficiency of investment subsidies. Investment decision is modelled as a Markov decision process, extended to account for risk. Applying a stochastic programming approach, the cost of imperfect information is evaluated as the difference between the profitability of investment under stable income and under uncertain income. Computational experiments demonstrate that the cost of imperfect information can be high, deteriorating the efficiency of investment subsidies. Also, examples suggest that the optimal timing of the investment can be sensitive to risk.

Collective modes and the speed of sound in the Fulde-Ferrell-Larkin-Ovchinnikov state

We consider the density response of a spin-imbalanced ultracold Fermi gas in an optical lattice in the Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state. We calculate the collective mode spectrum of the system in the generalised random phase approximation and find that though the collective modes are damped even at zero tempererature, the damping is weak enough to have well-defined collective modes. We calculate the speed of sound in the gas and show that it is anisotropic due to the anisotropy of the FFLO pairing, which implies an experimental signature for the FFLO state.Comment: 13 pages, 9 figures (revised version

Bertini intra-nuclear cascade implementation in Geant4

We present here a intra-nuclear cascade model implemented in Geant4 5.0. The cascade model is based on re-engineering of INUCL code. Models included are Bertini intra-nuclear cascade model with exitons, pre-equilibrium model, nucleus explosion model, fission model, and evaporation model. Intermediate energy nuclear reactions from 100 MeV to 3 GeV energy are treated for proton, neutron, pions, photon and nuclear isotopes. We represent overview of the models, review results achieved from simulations and make comparisons with experimental data.Comment: Computing in High Energy and Nuclear Physics, La Jolla, California, March 24-28, 2003 1 tar fil

Stochastic Dominance Portfolio Analysis of Forestry Assets

We consider the forestry decision-making and harvesting problem from the perspective of financial portfolio management, where harvestable forest stands constitute one of the liquid assets of the portfolio. Using real data from Finnish mixed borealis forests and from the Helsinki stock exchange, we investigate the effect of trading the timber stock together with the forest land, or without the land (i.e., harvesting), on the portfolio efficiency. As our research methodology, we utilize the general Stochastic Dominance (SD) criteria, focusing on the recent theoretical advances in analyzing portfolio diversification within the SD framework. Our findings shed some further light on the question of how to model the forestry planning problem, and provide some comparative evidence of the applicability of the alternative SD test approaches.Forest Management, Portfolio Optimization, Stochastic Dominance, Diversification