Kurt J. Engemann

On the concept of immediate probabilities

The problem of decision making under doubt is described. the concept of immediate probabilities is introduced. It is seen as a modification of typical probabilistic knowledge with information about the payoffs, mediated through dispositional information (optimism/pessimism), resulting in a modified formulation of an agents perception of the probabilities in effect in the current decision. We use the Dempster rule of combination to help obtain an expression for these probabilities. We show how immediate probabilities allows us to explain the Allais paradox. A number of properties of these probabilities are described. the strategic use of these probabilities are explored as a means for effecting other peoples decisions.

MODELLING DECISION MAKING USING IMMEDIATE PROBABILITIES

Abstract In this paper we provide a model of how a decision maker incorporates payoffs, event probabilities, and his level of optimism to select an alternative. We present a new decision making method that combines decision making under uncertainty with decision making under risk. We introduce a new concept of immediate probabilities for use by decision makers in selecting alternatives. We discuss the role of the decision makers level of optimism in transforming probabilities into immediate probabilities to be used for a decision. We show how the Ordered Weighted Averaging (OWA) operators play a central role in this modification. We also provide a procedure to learn OWA structures and the decision makers level of confidence from previous decisions and we show how this information can be used for new decisions.

DECISION MAKING WITH BELIEF STRUCTURES: AN APPLICATION IN RISK MANAGEMENT

This paper examines the problem of selecting an alternative in situations in which there exists uncertainty in our knowledge of the state of the world. We show how the ordered weighted averaging aggregation operators provide a unifying approach to selecting alternatives under uncertainty. In particular, we see how these operators provide a type of probability associated with our degree of optimism. We also show how the Dempster-Shafer belief structure provides a general framework for representing the information a decision maker has regarding relevant events. We then propose a methodology for decision making under uncertainty, integrating the ordered weighted averaging aggregation operators and the Dempster-Shafer belief structure. The proposed methodology is applied to a real world case involving risk management at one of the nation’s largest banks.

A Monte Carlo simulation model of supply chain risk due to natural disasters

In this paper we present a model that simulates the effects of natural disaster risks for a hypothetical three tier supply chain. Drawing on concepts from reliability theory and capacity analysis, the model is structured such that diminutions of service capacity at nodes lower in the supply chain can affect higher tier nodes. The model is used to examine various scenarios, including examining correlation among node locations; the effectiveness of disaster recovery plans; and dual sourcing. In addition, the size of the lower tier of the supply chain is expanded and the ensuing results are compared to those for leaner supply chains.

Critical infrastructure and smart technology risk modelling using computational intelligence

We discuss the criticality of infrastructure in economic development and security, and identify various risks posed by smart technologies as applied to infrastructure. We provide a computational intelligence methodology, using attitudinal and fuzzy modelling, and illustrate its application as a risk modelling decision technology in the selection of smart technology in critical infrastructure.

Decision making with Dempster-Shafer belief structure and the OWAWA operator

AbstractA new decision making model that uses the weighted average and the ordered weighted averaging (OWA) operator in the Dempster-Shafer belief structure is presented. Thus, we are able to represent the decision making problem considering objective and subjective information and the attitudinal character of the decision maker. For doing so, we use the ordered weighted averaging – weighted average (OWAWA) operator. It is an aggregation operator that unifies the weighted average and the OWA in the same formulation. This approach is generalized by using quasi-arithmetic means and group decision making techniques. An application of the new approach in a group decision making problem concerning political management of a country is also developed.

A general methodology for decision making under uncertainty with a risk management application

The authors provide a general formulation for decision making under uncertainty. They discuss the role of the decision makers level of optimism in the selection of an alternative. It is shown that the ordered weighted averaging (OWA) operators play a central role in aggregating the payoffs to determine a value associated with each alternative. It is also shown that the Dempster-Shafer belief structure may provide a suitable framework for representing the information a decision maker has regarding the events. Using OWA operators, a methodology is provided for selecting the optimal alternative in decision making under uncertainty in which the knowledge about the uncertainty can be modeled by the belief structure. The methodology is applied to a case involving risk management at one of the USAs largest banks.<<ETX>>

Using Analytical Methods in Business Continuity Planning

Business continuity focuses on ensuring an organization can continue to provide services when faced with various crisis events. Part of the business continuity planning process involves: Business Impact Analysis; Risk Assessment; and Strategy Development. In practice, these activities often rely on ad hoc methods for collecting and analyzing data necessary for developing the business continuity plan. In this paper we discuss how various analytical methods that have been successfully used for addressing other problems, may be applied to the three phases of business continuity planning mentioned above.

A GENERAL APPROACH TO DECISION MAKING WITH INTERVAL PROBABILITIES

We propose a general method for decision making in situations in which our information about the relevant probabilities is available in the form of intervals. A key component of this approach is our algorithm which is used to resolve the imprecise probabilities. This algorithm makes considerable use of the decision makers attitude as expressed in a scale with optimistic and pessimistic as its poles.

Decision making with attitudinal based expected values

We provide the conceptual framework for attitudinal based expected values for continuous random variables. We introduce a new methodology to allow the decision maker to incorporate his own disposition with objective information in a risk environment. We provide an extension of the OWA operator to the case in which our argument is a continuous valued interval rather than a finite set of values. We look at some examples of this type of aggregation. We propose a method of incorporating the relative value of an alternatives intangible factors into the decision process.