Assisting Whole-Farm Decision-Making through Stochastic Budgeting

Stochastic budgeting is used to simulate the business and financial risk and the performance over a six-year planning horizon on a Norwegian dairy farm. A major difficulty with stochastic whole-farm budgeting lies in identifying and measuring dependency relationships between stochastic variables. Some methods to account for these stochastic dependencies are illustrated. The financial feasibility of different investment and management strategies is evaluated. In contrast with earlier studies with stochastic farm budgeting, the option aspect is included in the analysis.Farm Management,

The Apriori Stochastic Dependency Detection (ASDD) algorithm for learning Stochastic logic rules

Apriori Stochastic Dependency Detection (ASDD) is an algorithm for fast induction of stochastic logic rules from a database of observations made by an agent situated in an environment. ASDD is based on features of the Apriori algorithm for mining association rules in large databases of sales transactions [1] and the MSDD algorithm for discovering stochastic dependencies in multiple streams of data [15]. Once these rules have been acquired the Precedence algorithm assigns operator precedence when two or more rules matching the input data are applicable to the same output variable. These algorithms currently learn propositional rules, with future extensions aimed towards learning first-order models. We show that stochastic rules produced by this algorithm are capable of reproducing an accurate world model in a simple predator-prey environment

Risk and economic sustainability of crop farming systems

Environmental, social and economic attributes are important for the sustainability of a farming system. Comparing farming systems by considering only expected profitability ignores differences in both sustainability and in the riskiness of system returns. Further, in choosing between farming systems, the ability to survive various risks and shocks and con-tinue in the future is important, i.e., system resilience and persistence are important aspects of sustainabil-ity. Yet resilience and persistence have seldom been directly considered in evaluations of economic sus-tainability. A whole-farm stochastic simulation model over a six-year planning horizon was used to compare organic and conventional cropping systems for a representative farm situation in Eastern Norway. The relative sustainability of alternative systems under changing assumptions about future technology and price regimes was examined in terms of terminal financial position. The risk efficiency of the same alternatives was also compared. The results illustrate possible conflicts between pursuit of risk efficiency versus sustainability. The model used could be useful in supporting farmers’ choice between farming sys-tems as well for policy makers to develop more sharply targeted policies

Economic sustainability and risk efficiency of organic versus conventional cropping systems

Environmental, social and economic attributes are important for the sustainability of a farming system. Resilience is also important yet has seldom been directly considered in evaluations of economic sustainability. In economic terms, resilience has to do with the capacity of the farm business to survive various risks and other shocks. A whole-farm stochastic simulation model over a six-year planning horizon was used to analyse organic and conventional cropping systems using a model of a representative farm in Eastern Norway. The relative economic sustainability of alternative systems under changing assumptions about future technology and price regimes was examined in terms of financial survival to the end of the planning period. The same alternatives were also compared in terms of stochastic efficiency. The results illustrate possible confl icts between pursuit of risk efficiency and sustainability. The model developed could be useful in supporting farmers’ choices between farming systems as well as in helping policy makers to develop more sharply targeted policies

Salience-based selection: attentional capture by distractors less salient than the target

Current accounts of attentional capture predict the most salient stimulus to be invariably selected first. However, existing salience and visual search models assume noise in the map computation or selection process. Consequently, they predict the first selection to be stochastically dependent on salience, implying that attention could even be captured first by the second most salient (instead of the most salient) stimulus in the field. Yet, capture by less salient distractors has not been reported and salience-based selection accounts claim that the distractor has to be more salient in order to capture attention. We tested this prediction using an empirical and modeling approach of the visual search distractor paradigm. For the empirical part, we manipulated salience of target and distractor parametrically and measured reaction time interference when a distractor was present compared to absent. Reaction time interference was strongly correlated with distractor salience relative to the target. Moreover, even distractors less salient than the target captured attention, as measured by reaction time interference and oculomotor capture. In the modeling part, we simulated first selection in the distractor paradigm using behavioral measures of salience and considering the time course of selection including noise. We were able to replicate the result pattern we obtained in the empirical part. We conclude that each salience value follows a specific selection time distribution and attentional capture occurs when the selection time distributions of target and distractor overlap. Hence, selection is stochastic in nature and attentional capture occurs with a certain probability depending on relative salience

Balancing Performance Measures When Agents Behave Competitively in an Environment With Technological Interdependencies

This paper addresses the question, what metrics should be used for performance evaluation and in particular how they should be weighted and combined in the presence of technological interdependencies when the agents exhibit variedly strong developed rivalry. We find that the principal reacts to his agents' competitive preferences through a reallocation of incentive intensity. As a consequence, depending on the underlying sort of technological interdependency, various differences in the balancing of performance measures compared to the case of purely egoistical behavior arise and changes in the agents' basic types of compensation can occur. We further show that the principal does not want both of his agents to behave equally competitively. Instead, he can only profit when the agents are asymmetrical. Then the principal wants the more productive agent to exhibit rivalry while the other ideally should behave completely egoistically