Determinants of Historic and Cultural Landmark Designation: Why We Preserve What We Preserve

There is much interest among cultural economists in assessing the effects of heritage preservation policies. There has been less interest in modeling the policy choices made in historic and cultural landmark preservation. This paper builds an economic model of a landmark designation that highlights the tensions between the interests of owners of cultural amenities and the interests of the neighboring community. We perform empirical tests by estimating a discrete choice model for landmark preservation using data from Chicago, combining the Chicago Historical Resources Survey of over 17,000 historic structures with property sales, Census, and other geographic data. The data allow us to explain why some properties were designated landmarks (or landmark districts) and others were not. The results identify the influence of property characteristics, local socio-economic factors, and measures of historic and cultural quality. The results emphasize the political economy of implementing preservation policies.heritage preservation policy, landmark designation

Sliding-mode neuro-controller for uncertain systems

In this paper, a method that allows for the merger of the good features of sliding-mode control and neural network (NN) design is presented. Design is performed by applying an NN to minimize the cost function that is selected to depend on the distance from the sliding-mode manifold, thus providing that the NN controller enforces sliding-mode motion in a closed-loop system. It has been proven that the selected cost function has no local minima in controller parameter space, so under certain conditions, selection of the NN weights guarantees that the global minimum is reached, and then the sliding-mode conditions are satisfied; thus, closed-loop motion is robust against parameter changes and disturbances. For controller design, the system states and the nominal value of the control input matrix are used. The design for both multiple-input-multiple-output and single-input-single-output systems is discussed. Due to the structure of the (M)ADALINE network used in control calculation, the proposed algorithm can also be interpreted as a sliding-mode-based control parameter adaptation scheme. The controller performance is verified by experimental results