In this paper, we explore some of the architectural issues associated with architecting high-performance wide-bit multiplier units on programmable logic devices for embedded systems applications. Many applications of reconfigurable computing for arithmetic, such as for DSP and Cryptography, as reported in the literature, have been carried out with relatively small bit-widths, thereby avoiding some of the more difficult design tradeoffs and scheduling control issues associated with larger operand widths. In this paper, we define an Estimator function and present some of the results obtained in exploring the architectural space associated with large-bit width multipliers for cryptography applications. Specifically, we explore topologies for hierarchical, hybrid multiplier architectures

An experiment was conducted to test a rational principle of decision making called dynamic consistency. Decision makers were presented with a sequence of two gambles, and they were asked to make two decisions. Before the first gamble was played, they made a plan for the second gamble contingent on the outcome of the first gamble; then they played the first gamble and were asked to make a final decision regarding the second gamble after experiencing the first outome. Violations of dynamic consistency were observed, e.g., decision makers planned to the gamble a second time if the first gamble was won; but when in fact the first gamble was won, they switched and chose not to play the gamble a second time. Two models of dynamic inconsistency were compared at the individual level: One assumes that experience shifts the reference point and changes the utility associated with the final choice; another assumes that experience changes the subjective probability associated with the final choice. The reference point model provided the best account for the majority of participants. Modeling Dynamic Inconsistency 3 Most real life decisions require decsion makers to make plans for an uncertain future before they can decide how to act in the present. For example, a student must plan for a future job when deciding courses to take now in college; or a CEO must plan ahead for future markets when deciding products to develop now in the company. The generally prescribed procedure for planning a path of future actions entails backwards induction (c.f. Keeny & Raiffa, 1976; Raiffa, 1968; Von Vinterfeldt & Edwards, 1986), and one of the central assumptions of backward induction is a principle called dynamic consistency (Machina, 1989; Sarin & Wakker, 1998). According to the this principle, mere experience with the events upon which a plan was based should not change the preferences used to form the original plan.

Many real world problems involve making repeated decisions over time in an uncertain environment. These decisions often involve a trade-off between some immediate benefit(s) and possible future benefit(s), and also take in to account the impact the decision will have on future decisions and benefits. Stochastic dynamic programming (SDP) is often used to analyse problems of this type and the objective is often to maximise the expected value of benefits, which can imply that the decision-maker is `risk neutral'. But is this appropriate? In this paper a SDP formulation is described which accommodates risk attitudes via a utility function. The approach is discussed and illustrated for stochastic reservoir management and stochastic route choice problems. 1 Introduction There are numerous OR problems which involve making decisions over time in an uncertain environment. These problems can quickly become complex depending on aspects such as the number and form of objectives, the way in whi...

Introduction Data Envelopment Analisys (DEA) and Multicriteria Decision Aid/Making (MCDA/M) are two important research areas in Operational Research and Management Science, which have been receiving considerable attention in OR/MS literature over the past two decades (see [13], for a large DEA bibliography, [12], [14], for a survey of MCDA/M). DEA is a set of models, based on Linear Programming (LP), which is used to evaluate the relative efficiency of Decision Making Units (DMUs) performing similar transformation processes of several inputs , into several outputs . The principal aim is to provide a "measure of efficiency/inefficiency", so as to distinguish between efficient and not efficient DMUs. The measure of inefficiency obtained by DEA gives an assessment of the improvement needed to reach efficiency (i.e. good relative performance). There is an analogy betwe

Abstract. The mean-Gini approach is used to analyze stochastic externalities generated by agricultural production. The model addresses the problem of groundwater pollution caused by excessive fertilizer application. Inherent in the mean-Gini approach to expected utility maximization is a two-fold value: the simplicity of the two-parameter mean-variance model and satisfaction of necessary and sufficient conditions for stochastic dominance. Price and quantity policy recommendations to control externalities are formulated based upon the relative assessment of uncertainty by the regulatory authority and the farmers. Using the Gini as a measure of risk allows for the quantification of control policy measures under differentiated risk aversion and multiple sources of pollution. The model shows that when producers underestimate uncertainty, quota policies restricting fertilizer are more efficient than tax policies in reducing groundwater contamination. Key words. Stochastic externalities, water pollution policies, stochastic dominance. 1.

Abstract—Chemical industry project management involves complex decision making situations that require discerning abilities and methods to make sound decisions. Project managers are faced with decision environments and problems in projects that are complex. In this work, case study is Research and Development (R&D) project selection. R&D is an ongoing process for forward thinking technology-based chemical industries. R&D project selection is an important task for organizations with R&D project management. It is a multi-criteria problem which includes both tangible and intangible factors. The ability to make sound decisions is very important to success of R&D projects. Multiple-criteria decision making (MCDM) approaches are major parts of decision theory and analysis. This paper presents all of MCDM approaches for use in R&D project selection. It is hoped that this work will provide a ready reference on MCDM and this will encourage the application of the MCDM by chemical engineering management.

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