Financial Accounting: Reporting, Analysis and Decision Making [5th ed.]

In recent years accounting education has seen numerous changes to the way financial accounting is taught. These changes reflect the demands of an ever-changing business world, opportunities created by new technology and instructional technologies, and an increased understanding of how students learn. The foundation of Financial Accounting is based on a number of unique principles and innovations in accounting education. The objective of Financial Accounting is to provide students with an understanding of those concepts that are fundamental to the preparation and use of accounting information. Most students will forget procedural details within a short period of time. On the other hand, concepts, if well taught, should be remembered for a lifetime. Concepts are especially important in a world where the details are constantly changing. Students learn best when they are actively engaged. The overriding pedagogical objective of Financial Accounting is to provide students with continual opportunities for active learning. One of the best tools for active learning is strategically placed questions. Discussions are framed by questions, often beginning with rhetorical questions and ending with review questions, and our analytical devices, called decision-making toolkits, use key questions to demonstrate the purpose of each.

Linear programming for large-scale Markov decision problems

We consider the problem of controlling a Markov decision process (MDP) with a large state space, so as to minimize average cost. Since it is intractable to compete with the optimal policy for large scale problems, we pursue the more modest goal of competing with a low-dimensional family of policies. We use the dual linear programming formulation of the MDP average cost problem, in which the variable is a stationary distribution over state-action pairs, and we consider a neighborhood of a low-dimensional subset of the set of stationary distributions (defined in terms of state-action features) as the comparison class. We propose a technique based on stochastic convex optimization and give bounds that show that the performance of our algorithm approaches the best achievable by any policy in the comparison class. Most importantly, this result depends on the size of the comparison class, but not on the size of the state space. Preliminary experiments show the effectiveness of the proposed algorithm in a queuing application.

Robust iris recognition using decision fusion and degradation modelling

This thesis investigates the use of fusion techniques and mathematical modelling to increase the robustness of iris recognition systems against iris image quality degradation, pupil size changes and partial occlusion. The proposed techniques improve recognition accuracy and enhance security. They can be further developed for better iris recognition in less constrained environments that do not require user cooperation. A framework to analyse the consistency of different regions of the iris is also developed. This can be applied to improve recognition systems using partial iris images, and cancelable biometric signatures or biometric based cryptography for privacy protection.

Director independence or decision bias? An investigation of alternative sources of agency costs in board decision making

Research into boards traditionally focuses on independent monitoring of management, with studies focused on the effect of board independence on firm performance. This thesis aims to broaden the research tradition by consolidating prior research and investigating how agents may circumvent independent monitoring. Meta-analysis of previous board independence-firm performance studies indicated no systematic relationship between board independence and firm performance. Next, a series of experiments demonstrated that the presentation of recommendations to directors may bias decision making irrespective of other information presented and the independence of the decision maker. Together, results suggest that independence may be less important than the agent's motivation to misdirect the monitoring process.

A simple Bayesian decision-theoretic design for dose-finding trials

A flexible and simple Bayesian decision-theoretic design for dose-finding trials is proposed in this paper. In order to reduce the computational burden, we adopt a working model with conjugate priors, which is flexible to fit all monotonic dose-toxicity curves and produces analytic posterior distributions. We also discuss how to use a proper utility function to reflect the interest of the trial. Patients are allocated based on not only the utility function but also the chosen dose selection rule. The most popular dose selection rule is the one-step-look-ahead (OSLA), which selects the best-so-far dose. A more complicated rule, such as the two-step-look-ahead, is theoretically more efficient than the OSLA only when the required distributional assumptions are met, which is, however, often not the case in practice. We carried out extensive simulation studies to evaluate these two dose selection rules and found that OSLA was often more efficient than two-step-look-ahead under the proposed Bayesian structure. Moreover, our simulation results show that the proposed Bayesian method's performance is superior to several popular Bayesian methods and that the negative impact of prior misspecification can be managed in the design stage.

An Algorithm to Decide Feasibility of Linear Integer Constraints Occurrng in Decision Tables

Abstract-To detect errors in decision tables one needs to decide whether a given set of constraints is feasible or not. This paper describes an algorithm to do so when the constraints are linear in variables that take only integer values. Decision tables with such constraints occur frequently in business data processing and in nonnumeric applications. The aim of the algorithm is to exploit. the abundance of very simple constraints that occur in typical decision table contexts. Essentially, the algorithm is a backtrack procedure where the the solution space is pruned by using the set of simple constrains. After some simplications, the simple constraints are captured in an acyclic directed graph with weighted edges. Further, only those partial vectors are considered from extension which can be extended to assignments that will at least satisfy the simple constraints. This is how pruning of the solution space is achieved. For every partial assignment considered, the graph representation of the simple constraints provides a lower bound for each variable which is not yet assigned a value. These lower bounds play a vital role in the algorithm and they are obtained in an efficient manner by updating older lower bounds. Our present algorithm also incorporates an idea by which it can be checked whether or not an (m - 2)-ary vector can be extended to a solution vector of m components, thereby backtracking is reduced by one component.

Decision-theoretic designs for dose-finding clinical trials with multiple outcomes

A decision-theoretic framework is proposed for designing sequential dose-finding trials with multiple outcomes. The optimal strategy is solvable theoretically via backward induction. However, for dose-finding studies involving k doses, the computational complexity is the same as the bandit problem with k-dependent arms, which is computationally prohibitive. We therefore provide two computationally compromised strategies, which is of practical interest as the computational complexity is greatly reduced: one is closely related to the continual reassessment method (CRM), and the other improves CRM and approximates to the optimal strategy better. In particular, we present the framework for phase I/II trials with multiple outcomes. Applications to a pediatric HIV trial and a cancer chemotherapy trial are given to illustrate the proposed approach. Simulation results for the two trials show that the computationally compromised strategy can perform well and appear to be ethical for allocating patients. The proposed framework can provide better approximation to the optimal strategy if more extensive computing is available.

An extension of the continual reassessment method using decision theory

The primary goal of a phase I trial is to find the maximally tolerated dose (MTD) of a treatment. The MTD is usually defined in terms of a tolerable probability, q*, of toxicity. Our objective is to find the highest dose with toxicity risk that does not exceed q*, a criterion that is often desired in designing phase I trials. This criterion differs from that of finding the dose with toxicity risk closest to q*, that is used in methods such as the continual reassessment method. We use the theory of decision processes to find optimal sequential designs that maximize the expected number of patients within the trial allocated to the highest dose with toxicity not exceeding q*, among the doses under consideration. The proposed method is very general in the sense that criteria other than the one considered here can be optimized and that optimal dose assignment can be defined in terms of patients within or outside the trial. It includes as an important special case the continual reassessment method. Numerical study indicates the strategy compares favourably with other phase I designs.

A Bayesian decision approach for sample size determination in phase II trials

Stallard (1998, Biometrics 54, 279-294) recently used Bayesian decision theory for sample-size determination in phase II trials. His design maximizes the expected financial gains in the development of a new treatment. However, it results in a very high probability (0.65) of recommending an ineffective treatment for phase III testing. On the other hand, the expected gain using his design is more than 10 times that of a design that tightly controls the false positive error (Thall and Simon, 1994, Biometrics 50, 337-349). Stallard's design maximizes the expected gain per phase II trial, but it does not maximize the rate of gain or total gain for a fixed length of time because the rate of gain depends on the proportion: of treatments forwarding to the phase III study. We suggest maximizing the rate of gain, and the resulting optimal one-stage design becomes twice as efficient as Stallard's one-stage design. Furthermore, the new design has a probability of only 0.12 of passing an ineffective treatment to phase III study.

Bayesian and Decision Tree Approaches for Pattern Recognition Including Feature Measurement Costs

The minimum cost classifier when general cost functionsare associated with the tasks of feature measurement and classification is formulated as a decision graph which does not reject class labels at intermediate stages. Noting its complexities, a heuristic procedure to simplify this scheme to a binary decision tree is presented. The optimizationof the binary tree in this context is carried out using ynamicprogramming. This technique is applied to the voiced-unvoiced-silence classification in speech processing.

Human mating in the informal market for sperm donation: Preferences and decision making

Choosing a mate is one of the largest (economic) decisions humans make. This thesis investigates this large scale decision and how the process is changing with the advent of the internet and the growing market for online informal sperm donation. This research identifies individual factors that influence female mating preferences. It explores the roles of behavioural traits and physical appearance, preferences for homogamy and hypergamy, and personality, and how these impact the decision to choose a donor. Overall, this thesis makes contributions to both the literature on human behaviour, and that on decision-making in extreme and highly important situations.

Patient decision aids in routine maternity care: Benefits, barriers, and new opportunities

Background and aim Participation in decision-making, supported by comprehensive and quality information provision, is increasingly emphasised as a priority for women in maternity care. Patient decision aids are tools that can offer women greater access to information and guidance to participate in maternity care decision-making. Relative to their evaluation in controlled settings, the implementation of patient decision aids in routine maternity care has received little attention and our understanding of which approaches may be effective is limited. This paper critically discusses the application of patient decision aids in routine maternity care and explores viable solutions for promoting their successful uptake. Discussion A range of patient decision aids have been developed for use within maternity care, and controlled trials have highlighted their positive impact on the decision-making process for women. Nevertheless, evidence of successful patient decision aid implementation in real world health care settings is lacking due to practical and ideological barriers that exist. Patient-directed social marketing campaigns are a relatively novel approach to patient decision aid delivery that may facilitate their adoption in maternity care, at least in the short-term, by overcoming common implementation barriers. Social marketing may also be particularly well suited to maternity care, given the unique characteristics of this health context. Conclusions The potential of social marketing campaigns to facilitate patient decision aid adoption in maternity care highlights the need for pragmatic trials to evaluate their effectiveness. Identifying which sub-groups of women are more or less likely to respond to these strategies will further direct implementation.

The Reorganisation of Provincial Territories in Light of the Imperial Decision-Making Process : Later Roman Arabia and Tres Palaestinae as Case Studies

The dissertation examines Roman provincial administration and the phenomenon of territorial reorganisations of provinces during the Imperial period with special emphasis on the provinces of Arabia and Palaestina during the Later Roman period, i.e., from Diocletian (r. 284 305) to the accession of Phocas (602), in the light of imperial decision-making. Provinces were the basic unit of Roman rule, for centuries the only level of administration that existed between the emperor and the cities of the Empire. The significance of the territorial reorganisations that the provinces were subjected to during the Imperial period is thus of special interest. The approach to the phenomenon is threefold: firstly, attention is paid to the nature and constraints of the Roman system of provincial administration. Secondly, the phenomenon of territorial reorganisations is analysed on the macro-scale, and thirdly, a case study concerning the reorganisations of the provinces of Arabia and Palaestina is conducted. The study of the mechanisms of decision-making provides a foundation through which the collected data of all known major territorial reorganisations is interpreted. The data concerning reorganisations is also subjected to qualitative comparative analysis that provides a new perspective to the data in the form of statistical analysis that is sensitive to the complexities of individual cases. This analysis of imperial decision-making is based on a timeframe stretching from Augustus (r. 30 BC AD 14) to the accession of Phocas (602). The study identifies five distinct phases in the use of territorial reorganisations of the provinces. From Diocletian s reign there is a clear normative change that made territorial reorganisations a regular tool of administration for the decision-making elite for addressing a wide variety of qualitatively different concerns. From the beginning of the fifth century the use of territorial reorganisations rapidly diminishes. The two primary reasons for the decline in the use of reorganisations were the solidification of ecclesiastical power and interests connected to the extent of provinces, and the decline of the dioceses. The case study of Palaestina and Arabia identifies seven different territorial reorganisations from Diocletian to Phocas. Their existence not only testifies to wider imperial policies, but also shows sensitivity to local conditions and corresponds with the general picture of provincial reorganisations. The territorial reorganisations of the provinces reflect the proactive control of the Roman decision-making elite. The importance of reorganisations should be recognised more clearly as part of the normal imperial administration of the provinces and especially reflecting the functioning of dioceses.

Leibniz on Rational Decision-Making

In this study I discuss G. W. Leibniz's (1646-1716) views on rational decision-making from the standpoint of both God and man. The Divine decision takes place within creation, as God freely chooses the best from an infinite number of possible worlds. While God's choice is based on absolutely certain knowledge, human decisions on practical matters are mostly based on uncertain knowledge. However, in many respects they could be regarded as analogous in more complicated situations. In addition to giving an overview of the divine decision-making and discussing critically the criteria God favours in his choice, I provide an account of Leibniz's views on human deliberation, which includes some new ideas. One of these concerns is the importance of estimating probabilities in making decisions one estimates both the goodness of the act itself and its consequences as far as the desired good is concerned. Another idea is related to the plurality of goods in complicated decisions and the competition this may provoke. Thirdly, heuristic models are used to sketch situations under deliberation in order to help in making the decision. Combining the views of Marcelo Dascal, Jaakko Hintikka and Simo Knuuttila, I argue that Leibniz applied two kinds of models of rational decision-making to practical controversies, often without explicating the details. The more simple, traditional pair of scales model is best suited to cases in which one has to decide for or against some option, or to distribute goods among parties and strive for a compromise. What may be of more help in more complicated deliberations is the novel vectorial model, which is an instance of the general mathematical doctrine of the calculus of variations. To illustrate this distinction, I discuss some cases in which he apparently applied these models in different kinds of situation. These examples support the view that the models had a systematic value in his theory of practical rationality.

Formative evaluation to benchmark and improve climate-based decision support for graziers in Western Queelsland

Researchers developing climate-based forecasts, workshops, software tools and information to aid grazier decisions undertook an evaluation study to enhance planning and benchmark impact. One hundred graziers in Western Queensland were randomly selected from 7 shires and surveyed by mail and telephone (43 respondents) to explore levels of knowledge and use of climate information, practices and information needs. We found 36% of respondents apply the Southern Oscillation Index to property decisions but 92% were unaware El Niño Southern Oscillation’s predictive signal in the region is greater for pasture growth than rainfall, suggesting they may not recognise the potential of pasture growth forecasts. Almost 75% of graziers consider they are conservative or risk averse in their attitude to managing their enterprise. Mail respondents (n= 20) if given a 68%, on average, probability of exceeding median rainfall forecast may change a decision; almost two-thirds vary stocking rate based on forage available, last year’s pasture growth or the Southern Oscillation Index; the balance maintain a constant stocking rate strategy; 90% have access to a computer; 75% to the internet and 95% have a fax. This paper presents findings of the study and draws comparisons with a similar study of 174 irrigators in the Northern Murray-Darling Basin (Aust. J. Exp. Ag. 44, 247-257). New insights and information gained are helping the team better understand client needs and plan, design and extend tools and information tailored to grazier knowledge, practice, information needs and preferences. Results have also provided a benchmark against which to measure project impact and have influenced the team to make important changes to their project planning, activities and methods for transferring technology tailored to grazier preferences.