So those are the three strategies.

Maximin, maximax, and expected value maximizing strategies.

The maxi-min strategy was to chose supplier S, and

it had a maxi-min value of 150,000 Euros.

That is, that was the strategy that would ensure that the worst that could happen

would be IDEA would earn 150,000 Euros.

The maxi-max strategy was to chose supplier P,

and it had a maxi-max value of 450,000 Euros.

The maxi-max strategy ensures

that idea has the chance of earning up to 450,000 euros.

Finally the risk-neutral strategy was to choose supplier S.

And again, it had an expected value of 150,000 euros.

So you can see the maxi-min strategy and

the risk-neutral strategy have the same value.

That is the expected value maximizing strategy is also

a very safe strategy because it's also maximizing the minimum payout.

We've completed the building and the analysis of the decision tree, and

it's a good point to review the mechanics of what we do to analyse decision trees.

First we construct a decision tree.

A decision tree has three parts.

It has decision nodes, those are points at which you make choices among options.

It has event nodes, those are moments in time when there's a random occurrence.

And finally, there are outcomes.

They capture all the costs and rewards leading up to each leaf of the tree.

Having built the decision tree, we just take a look at it.

And looking at the range of outcomes and the probabilities,

itself can be instructive.

But for a very big tree, it's useful to have

a more systematic way of taking a look at the range of possibilities.

And to do that we use three classic decision making strategies,

to look at risk seeking, risk avoiding and risk neutral strategies.

For all three of them, we started at the end with the outcomes, and

worked backwards to the root.

At event notes, we then calculated either the minimum,

the maximum value or the expected value, and that differed with the max/min,

max/max, or expected value maximizing strategy.

And finally,

at decision nodes, we cut away the decision that did not maximize the value.