Activity Status

  • 17-18 March 2010 Project kickoff
  • Development of Decision Support functionality as part of WP4.
  • Demonstration of activities on this web-site
  • 1 April 2011 WP4 model and software development meeting
  • 26-27 May 2011 Project Meeting
  • 22 September 2011 WP4 meeting
  • 1 November 2011 WP4 modeling meeting
  • 2 February WP4 meeting
  • 24 February 2012 Midterm meeting
  • 14 May 2012 WP 4 meeting
  • 20 June 2012 Annual meeting
  • 6 July 2012 WP4 modeling meeting
  • 15 March 2013 WP4 modelling meeting
  • 15 September 2013 Annual meeting

Mildew, Jensen (1996)

In this example, a farmer has to decide on the treatment of a wheat field. Two months before harvest of a wheat field he observes the state of the crop and he observes whether it has been attacked by mildew. If there is an attack he should decide on a level of treatment with fungicides to eliminate the mildew attack.

The domain of this problem is modeled by the (limited-memory) influence diagram (LIMID) in Figure 1. Note the links from the observations to Decision. They specify that when the treatment decision has to be made, the states of the two variables are known to the farmer. If these links are not specified, the computations will be made under the assumption that these observations are not known by the farmer when he has to make the treatment decision.

Figure 1: The Mildew problem as an influence diagram.

Figure 1 above shows the Mildew problem as an influence diagram. Assume the uncertainty and utility of the decision problem can be expressed as the conditional probability distributions as defined in this HUGIN network specification file. The quantification is made for demonstration purposes only.

The profit (or loss) made by the farmer depends on the quality of the decision he makes. Solving the decision problem is a question of weighing the costs and rewards with the probability distributions specifying the uncertainty in the decision problem under the assumption that the decision maker tries to optimize the expected utility. The table below shows the expected cost and reward made by the farmer when he adheres to the (optimal) strategy identified by solving the influence diagram representation shown above.

Expected Cost and Reward

Expected Utility
Cost
Reward
Cost + Reward

The table below shows the probability distribution over the harvest node. The probability distribution changes when observations on the level of mildew attack and observed quality are obtained.

Harvest

State Probability Expected Utility
Rotten
Bad
Poor
Fair
Average
Good
Very Good

The decision considered by the farmer is whether or not to and how much to treat the wheat field before harvesting.

Enter the actual decision on treatment below to investigate the expected impact on the harvest and cost-benefit.

Observed Mildew level
Observed Quality

The decision considered by the farmer is whether or not to and how much to treat the wheat field before harvesting.

Enter the actual decision on treatment below to investigate the expected impact on the harvest and cost-benefit.

Treatment Decision

The expected utility of the decision options and the probability of each option under the (optimal) strategy identified is shown below. The latter is the probability of the treatment decision when no observations are entered and it changes as observations and the decision are made. Thus, initially there is 70.59% probability that the farmer will not treat with fungicides.

Treatment Decision Option Expected Utility Probability
No treatment
Low
Mild
Severe

Enter the actual decision below (and see the expected utility in the table above)

Treatment Decision: