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By R Cooke, K L. Lockett, J A Bellman

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4 What Is A Neural Network? 25 Following the same example, as interest rates begin to increase from low levels, consumers will judge the probability of a sharp uptick or downtick in the interest rate based on the currently advertised financing packages. The more experience they have, up to some level, the more apt they are to interpret this signal as the time to take advantage of the current interest rate, or the time to postpone a purchase. The results are markedly different from those experienced at other points on the temporal history of interest rates.

Moreover, within critical changes, such as [−2, 0] and [0, 2], the slope of the cumulative Gaussian function is much steeper. 34) 28 2. What Are Neural Networks? 3 Radial Basis Functions The radial basis network function (RBF) network makes use of the radial basis or Gaussian density function as the activation function, but the structure of the network is different from the feedforward or MLP networks we have discussed so far. The input neuron may be a linear combination of regressors, as in the other networks, but there is only one input signal, only one set of coefficients of the input variables x.

What if decision makers have to learn about their environment, about the nature of the shocks and underlying production, the policy objectives and feedback rules of the government, or the ways other players formulate their plans? It is not too hard to imagine that economic agents have to use approximations to capture and learn the way key variables interact in this type of environment. From this perspective, the black box attack could be turned around. Should not fundamental theory take seriously the fact that economic decision makers are in the process of learning, of approximating their environment?

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