Uncertainty Assesment

Abstract Two important challenges in policy design are better understanding of the design space and consideration of the temporal factors. Moreover, in recent years it has been demonstrated that understanding the complex interactions of policy measures can play an important role in policy design and analysis. In this paper, the advances made in conceptualization and application of networks to policy design in the past decade are highlighted. Specifically, the use of a network-centric policy design approach in better understanding the design space and temporal consequences of design choices are presented. Network-centric policy design approach has been used in classification, visualization, and analysis of the relations among policy measures as well as ranking of policy measures using their internal properties and interactions, and conducting sensitivity analysis using Monte Carlo simulations. Furthermore, through use of a decision support system, network-centric approach facilitates ranking, visualization, and selection of policies using different sets of criteria, and exploring the potential for compromise in policy formulation. The advantage of the network-centric approach is providing the ability to go beyond visualizations and analysis of policies and piecemeal use of network concepts as a
tool for different policy design tasks to moving to a more integrated bottom–up approach to design. Furthermore, the computational advantages of the network-centric policy design in considering temporal factors such as policy sequencing and addressing issues such as layering, drift, policy failure, and delay are presented. Finally, some of the current challenges of network-centric design are discussed, and some potential avenues of exploration in policy design through use of computational methodologies, as well as possible integration with approaches from other disciplines, are highlighted.

Actors in competitive environments are bound to decide and act under conditions of uncertainty because they rarely have accurate foreknowledge of how their opponents will respond and when they will respond. Just as a competitor makes a move to improve their standing on a given variable relative to a target competitor, she should expect the latter to counteract with an iterative lagged asymmetric response, that is, with a sequence of countermoves (iteration) that is very different in kind from its trigger (asymmetry) and that will be launched at some unknown point in the future (time lag). The paper explicates the broad relevance of the newly proposed concept of " iterative lagged asymmetric responses " to the social study of temporality and to fields as diverse as intelligence and counterintelligence studies, strategic management, futures studies, military theory, and long-range planning. By bringing out in the foreground and substantiating the observation that competitive environments place a strategic premium on surprise, the concept of iterative lagged asymmetric responses makes a contribution to the never-ending and many-pronged debate about the extent to which the future can be predicted.
DOI: https://doi.org/10.1177/0961463X17752652

The rising tide of uncertainty concerning the future and the rapid growth of complexity to be coped with have led to more and more sophisticated attempts to systematize as well as computerize decision making. A publication of handy size promising to give an overview on the concepts behind decision making will therefore be warmly welcomed. Granted, there are other books on the market serving this purpose. The particular advantages of this one may be considered to lie in its being well-structured, admirably supported by more than a hundred figures and, with proper concentration, easy to grasp, though by no means trivial in nature. As its title indicates, the book is practice-oriented.

In land cover maps, categories represent a continuum of variation and for this reason, fuzzy set theory, which accepts degrees of membership, has been suggested for land classification. Nevertheless, classical set theory, which only assumes single map categories, is still widely used. The purpose of this study is to develop a methodology to reduce the weakness of land cover maps in which classical theory has been applied. To do so, we propose adding an error relevance step after accuracy assessment, which evaluates how relevant are the classification errors to selected land applications. First, a membership matrix is built based on a linguistic scale associated to land cover rates obtained from literature. Then, two fuzzy measures are calculated and the frequency of categories, that do not pose a problem to the user in light of the land application, is determined. The methodology is demonstrated using two Brazilian tropical coastal regions and two land applications relevant for coastal watershed management. The study presents land cover maps of the Mamanguape and the Paraíba es-tuarine regions, their full accuracy assessment, and the relevance of the classification errors to the land applications. The accuracy assessment step has demonstrated that the land cover maps are reliable. The error relevance step has shown that the map weakness can be reduced. Both steps show that the land cover maps produced are suitable for further land mapping applications. The results on land cover composition point to the importance of future work focused on the environmental sustainability of the studied regions. The new procedure has proven useful to decrease the degree of distrust with which land cover maps are regarded. The framework provided is suitable for virtually any land mapping application.