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    Decision-Making and Sustainable Drainage: Design and Scale

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    Sustainable Drainage (SuDS) improves water quality, reduces runoff water quantity, increases amenity and biodiversity benefits, and can also mitigate and adapt to climate change. However, an optimal solution has to be designed to be fit for purpose. Most research concentrates on individual devices, but the focus of this paper is on a full management train, showing the scale-related decision-making process in its design with reference to the city of Coventry, a local government authority in central England. It illustrates this with a large scale site-specific model which identifies the SuDS devices suitable for the area and also at the smaller scale, in order to achieve greenfield runoff rates. A method to create a series of maps using geographical information is shown, to indicate feasible locations for SuDS devices across the local government authority area. Applying the larger scale maps, a management train was designed for a smaller-scale regeneration site using MicroDrainage® software to control runoff at greenfield rates. The generated maps were constructed to provide initial guidance to local government on suitable SuDS at individual sites in a planning area. At all scales, the decision about which device to select was complex and influenced by a range of factors, with slightly different problems encountered. There was overall agreement between large and small scale models

    Optimal utility and probability functions for agents with finite computational precision

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    When making economic choices, such as those between goods or gambles, humans act as if their internal representation of the value and probability of a prospect is distorted away from its true value. These distortions give rise to decisions which apparently fail to maximize reward, and preferences that reverse without reason. Why would humans have evolved to encode value and probability in a distorted fashion, in the face of selective pressure for reward-maximizing choices? Here, we show that under the simple assumption that humans make decisions with finite computational precision––in other words, that decisions are irreducibly corrupted by noise––the distortions of value and probability displayed by humans are approximately optimal in that they maximize reward and minimize uncertainty. In two empirical studies, we manipulate factors that change the reward-maximizing form of distortion, and find that in each case, humans adapt optimally to the manipulation. This work suggests an answer to the longstanding question of why humans make “irrational” economic choices
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