Determining rules for closing customer service centers: A public utility company's fuzzy decision

In the present work, we consider the general problem of knowledge acquisition under uncertainty. A commonly used method is to learn by examples. We observe how the expert solves specific cases and from this infer some rules by which the decision was made. Unique to this work is the fuzzy set representation of the conditions or attributes upon which the decision make may base his fuzzy set decision. From our examples, we infer certain and possible rules containing fuzzy terms. It should be stressed that the procedure determines how closely the expert follows the conditions under consideration in making his decision. We offer two examples pertaining to the possible decision to close a customer service center of a public utility company. In the first example, the decision maker does not follow too closely the conditions. In the second example, the conditions are much more relevant to the decision of the expert

Certain and possible rules for decision making using rough set theory extended to fuzzy sets

Uncertainty may be caused by the ambiguity in the terms used to describe a specific situation. It may also be caused by skepticism of rules used to describe a course of action or by missing and/or erroneous data. To deal with uncertainty, techniques other than classical logic need to be developed. Although, statistics may be the best tool available for handling likelihood, it is not always adequate for dealing with knowledge acquisition under uncertainty. Inadequacies caused by estimating probabilities in statistical processes can be alleviated through use of the Dempster-Shafer theory of evidence. Fuzzy set theory is another tool used to deal with uncertainty where ambiguous terms are present. Other methods include rough sets, the theory of endorsements and nonmonotonic logic. J. Grzymala-Busse has defined the concept of lower and upper approximation of a (crisp) set and has used that concept to extract rules from a set of examples. We will define the fuzzy analogs of lower and upper approximations and use these to obtain certain and possible rules from a set of examples where the data is fuzzy. Central to these concepts will be the idea of the degree to which a fuzzy set A is contained in another fuzzy set B, and the degree of intersection of a set A with set B. These concepts will also give meaning to the statement; A implies B. The two meanings will be: (1) if x is certainly in A then it is certainly in B, and (2) if x is possibly in A then it is possibly in B. Next, classification will be looked at and it will be shown that if a classification will be looked at and it will be shown that if a classification is well externally definable then it is well internally definable, and if it is poorly externally definable then it is poorly internally definable, thus generalizing a result of Grzymala-Busse. Finally, some ideas of how to define consensus and group options to form clusters of rules will be given

A useful savagery: The invention of violence in nineteenth-century England

‘A Useful Savagery: The Invention of Violence in Nineteenth-Century England’ considers a particular configuration of attitudes toward violence that emerged in the early decades of the nineteenth century. As part of a longer-term process of emerging ‘sensibilities,’ violence was, seemingly paradoxically, ‘invented’ as a social issue while concurrently relocated in the ‘civilised’ imagination as an anti-social feature mainly of ‘savage’ working-class life. The dominant way this discourse evolved was through the creation of a narrative that defined ‘civilisation’ in opposition to the presumed ‘savagery’ of the working classes. Although the refined classes were often distanced from the physical experience of violence, concern with violence and brutality became significant parts of social commentary aimed at a middle-class readership. While stridently redefining themselves in opposition to ‘brutality,’ one of the purposes of this literature was to create a new middle class and justify the expansion of state power. By the closing decades of the nineteenth century, as the working classes adopted tenets of Victorian respectability, a proliferating number of social and psychological ‘others’ were identified against which ‘civilised’ thought could define itself

Biological Earth observation with animal sensors

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change

A Fuzzy Rule-Based Model for Artificial Reef Placement Related to Managing Red Snapper (Lutjanus Campechanus) Ecosystems in Alabama Waters

A rough set theory model utilising fuzzy sets was developed to investigate artificial reef placement based on fish ecosystem components. The model incorporates consumption estimates and presumed foraging behavior to provide a rule-based approach to determine how far apart artificial reefs must be placed to eliminate density-dependent competition for prey resources. Simulation of the ecosystem parameters and potential reef distances as triangularly defined fuzzy sets generates input into the rules. Then, based upon the strength of belief in a rule, the artificial reef placement location can be accepted or rejected as being conducive to consumption at the reef and foraging behaviour of the species. Ease of utilisation of the model is highlighted by spreadsheet application to a red snapper (Lutjanus campechanus) ecosystem in Gulf of Mexico waters off the coastal shelf of Alabama. Implications exist for similar applications to other ecosystems and different fish species. Further applications are relevant beyond fish management when viewed as a general managerial decision-making process involving fuzzy sets and simulation.Knowledge management, Rough set theory, fuzzy logic, fisheries management, ecosystem modeling, science-based management

A fuzzy approach for selecting project membership to achieve cognitive style goals

Decision makers select employees for a project to match a particular set of goals pertaining to the multiple criteria mix of skills and competencies needed. Cognitive style influences how a person gathers and evaluates information and consequently, provides skills and competencies toward problem solving. The proposed fuzzy set-based model facilitates the manager's selection of employees who meet the project goal(s) for the preferred cognitive style. The paper presents background information on cognitive styles and fuzzy logic with an algorithm developed based on belief in the fuzzy probability of a cognitive style fitting a defined goal. An application is presented with analysis and conclusions stated.Fuzzy sets Project management Team member selection Cognitive learning