Bio-economic Modelling as a Method for Determining Economic Weights for Optimal Multiple-Trait Tree Selection

Modelling Financially Optimal Afforestation and Forest Management Scenarios Using a Bio-Economic Model

The expansion of non-industrial private forests (NIPF) in Ireland is unique in the European context in which the almost doubling of forest cover within the last thirty years has taken place largely on farmland. This is not surprising as Ireland has some of the highest growth rates for conifers in Europe and also has a large proportion of land which is marginal for agriculture but highly product...

A Design For An Islamic Economic System

Extending Instrumental Variable Method for Effective Economic Modelling

Economic modeling that yields practical value must cater for effects caused by exogenous variables. AutoRegressive eXogenous approach (ARX) has been widely used in regional economic studies. Instrumental Variable Method is regarded as a preferential method to parametric estimation in ARX modeling. However, traditional instrumental variable methods can only handle single variable which has limit...

Determining The Optimal Weights In Multiple Objective Function Optimization

An important problem in computer vision is the determination of weights for multiple objective function optimization. This problem arises naturally in many reconstruction problems, where one wishes to reconstruct a function belonging to a constrained class of signals based upon noisy observed data. A common approach is to combine the objective functions into a single total cost function. The pr...

Optimal Rewards for Economic Regulation

Suppose several production units or firms must be regulated when costs and benefits are uncertain. Pollution might be a specific example, although there are many others. Given that firms must bear their own costs, the regulators want to transmit a schedule of revenues to each unit which in some expected value sense elicits an optimal response. What makes this problem intriguing is that while be...