Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Risk allows the objective function of a linear program to be determined only up to a known probability distribution. In the case studied, hover, the evolution of the distribution over time conforms to a simple learning model reflecting the increasing (decreasing) precision of executive response. If the stochastic extremand is replaced by the truncated minimax-maximax preference functional the optimal dynamic path is shown to be a geodesic in Riemann space with the standard deviation of the objective function as metric. Surprisingly, the path is independent of the risk attitude adopted.
['Paul van Moeseke']
I am writing a research paper on a idea Dynamic risk programming with learning adjustment
[{"from": "human", "value": "I am writing a research paper on a idea Dynamic risk programming with learning adjustment"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Risk allows the objective function of a linear program to be determined only up to a known probability distribution. In the case studied, hover, the evolution of the distribution over time conforms to a simple learning model reflecting the increasing (decreasing) precision of executive response. If the stochastic extremand is replaced by the truncated minimax-maximax preference functional the optimal dynamic path is shown to be a geodesic in Riemann space with the standard deviation of the objective function as metric. Surprisingly, the path is independent of the risk attitude adopted."}]