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A decision structure for teaching machines. by Richard D Smallwood

By Richard D Smallwood

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Using this architecture has several benefits: it allows to forecast several time series, that is, all the observables, at once. There is no dedicated input or output. It offers the possibility to achieve multi step forecasts by repeatedly applying equation 1. By training several hundred networks we also get a distribution of possible outcomes. This allows to gauge the risk of the resulting forecast, see also the application in figure 2. This architecture also leads to robust models. Trying to match several time series reduces the risk of overfitting to a specific time series.

95). When calculating Value at Risk for all assets in our portfolio we notice that the violations are within the permitted range. Tracking is far better than popular benchmarks like, e. , historical simulation. We present a comparison to statistical methods in [2]. Our second application concerns transaction decision support. The situation is the following: on an ongoing basis an asset has to be purchased. For example the task could involve buying a certain amount of a currency or a commodity every month.

Smola, editors, Advances in kernel methods: support vector learning, pages 293–305. MIT Press, Cambridge, MA, USA, 1999. 2 Game Theory and Experimental Economics Chair: Prof. Dr. Karl Morasch (Universität der Bundeswehr München) Game Theory analyzes individual decision making in strategic situations. Unlike to a decision theoretic setting the outcome for an individual is then affected by actions of other players as well. Making sensible decisions therefore requires predicting the likely behavior of one’s opponents.

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