En lagändring har gjorts i Sverige vars syfte är att små och medelstora företag har frivillig revision om de ligger under eller endast uppfyller ett av följande tre gränsvärden: (1) 3 miljoner kronor i nettoomsättning, (2) 1,5 miljoner kronor i balansomslutning eller (3) fler än tre anställda. Eftersom lagändringen är relativt ny, medför det att företag i dagsläget precis har avslutat eller är inne i en beslutsprocess angående den frivilliga revisionen. Beslutsfattande är något som ständigt pågår i alla former av organiserad mänsklig verksamhet och dagligen förekommer det i alla organisationer ett beslutsfattande både när det gäller rutinfrågor till mer komplexa beslut. Genom mänsklighetens historia har människan strävat efter att få kontroll över resultaten av sina beslut, öka förutsägbarheten samt minska riskerna. När beslut fattas i företag måste även hänsyn tas till en rad förhållanden som är direkt kopplat till den organisatoriska kontexten för handlandet. Syftet med denna uppsats är att undersöka hur och på vilka grunder företagen har fattat beslut vid valet av att anlita en revisor eller inte, samt analysera beslutsprocessen ur ett beslutsteoretiskt perspektiv. Följande problemformulering ligger till grund för denna studie: Vad kännetecknar ett företag som anser sig ha behov av revision trots att kravet på revision inte är tvingande? På vilka grunder är beslutet fattat? Undersökningen ska bidra till en djupare kunskap och förståelse om hur besluten fattats och hur beslutsprocessen gått till. Studien har en kvalitativ forskningsansats och bygger på telefonintervjuer med ett antal företag som står inför beslutet att välja mellan att anlita en revisor eller inte. I studien framkom det inga specifika kännetecken på de företag som ansåg att de hade behov av revision trots att kravet på revision inte längre är tvingande. Dock ansåg majoriteten att kostnaden för revision var mer värt än tiden det krävs för att kontinuerligt vara uppdaterad inom lagar, regler och praxis. Resultatet av undersökningen visar att den trygghet som revisionen ger är avgörande vid valet att behålla revisorn, eftersom den skapar bättre trovärdighet åt företaget inför kreditgivare och andra intressenter. Flertalet av respondenterna har gjort ett försök att agera och tänka rationellt då de beslutat efter vad de ansåg vara bäst för företagen. Studien visar även att respondenterna som var i en pågående beslutsprocess valde att rådfråga personer med kunskap om problemet för att därefter fatta ett kollektivt beslut. En sammanställning hur undersökningen kan se ut när alla respondenterna är klara med besluten tyder på ett resultat där 85 procent av deltagarna valt att behålla revision, 10 procent som väljer bort revision samt 5 procent av respondenterna som inte vet vilket beslut de ska fatta. / There has been a change in the law that allows small and medium-sized companies in Sweden to have voluntary audit if they are below or only exceed one of the following three limits: (1) 3 million in net sales, (2) 1, 5 million in total assets or (3) more than three employees. Since the change in the law about voluntary audit is relatively new, many companies are either in the middle of their decision-making process or have just made a decision. The process of making decision is a continuous process that every company faces every day, which regards every level of the organization. The main purpose of this paper is to analyze how and on what basis the companies have made a decision regarding the voluntary audit. The study will contribute to a deeper knowledge and understanding of how decisions are taken, in general, and how this particular decision about audit has been made. The questions this study aims to examine is: What characterizes a company that believes that they need an audit?  What basis is the decision made on?  The study has not shown any common and clear characteristics of the companies that decided that they need audit, however the majority of the researched companies thought that the cost of the audit was worth more than the time the organization needed to be updated on the new laws, rules and practices. The results of the research have shown that the safety audit gives is the most important factor when deciding whether to keep auditor or not. The reason for this is the fact that audit adds credibility and reliability in front of their creditors and other stakeholders.

The main motivation behind this thesis is the lack of belief subjectivity in problems of contracting, and especially in problems of demand for insurance. The idea that an underlying uncertainty in contracting problems (e.g. an insurable loss in problems of insurance demand) is a given random variable on some exogenously determined probability space is so engrained in the literature that one can easily forget that the notion of an objective uncertainty is only one possible approach to the formulation of uncertainty in economic theory. On the other hand, the subjectivist school led by De Finetti and Ramsey challenged the idea that uncertainty is totally objective, and advocated a personal view of probability (subjective probability). This ultimately led to Savage's approach to the theory of choice under uncertainty, where uncertainty is entirely subjective and it is only one's preferences that determine one's probabilistic assessment. It is the purpose of this thesis to revisit the "classical" insurance demand problem from a purely subjectivist perspective on uncertainty. To do so, we will first examine a general problem of contracting under heterogeneous subjective beliefs and provide conditions under which we can show the existence of a solution and then characterize that solution. One such condition will be called "vigilance". We will then specialize the study to the insurance framework, and characterize the solution in terms of what we will call a "generalized deductible contract". Subsequently, we will study some mathematical properties of collections of vigilant beliefs, in preparation for future work on the idea of vigilance. This and other envisaged future work will be discussed in the concluding chapter of this thesis. In the chapter preceding the concluding chapter, we will examine a model of contracting for innovation under heterogeneity and ambiguity, simply to demonstrate how the ideas and techniques developed in the first chapter can be used beyond problems of insurance demand.

Since the transformation from Political Economy to Economics and from Classical to Neoclassical theory in the late nineteenth century, a theory of human behavior has constituted the initial foundation upon which all economic theory is based and developed. Two main theories of human behavior developed by William Stanley Jevons and Carl Menger have been generally accepted to have ushered in this Marginalist Revolution. Jevons marginal utility theory popularized by Alfred Marshall is still extensively used today, while the Austrian approach of Menger was effectively removed from academic discussion in the nineteen thirties; mainly as a result of the annexation of Austria and the dissolution of the Austrian School of Economics. Given the inability of economists to fully operationalize the marginal utility theory and realistically explain and resolve a broad range of behavioral anomalies using Neoclassical and Post-Neoclassical Economics, this thesis attempts to examine and address the most fundamental issues of human behavior in economics to explain how utility theory and modern Neoclassical and Post-Neoclassical Economics are flawed and how a realistic theory of human behavior, developed from the scholarly work of the early Austrian Economists, can be used to develop the basis of a scientific economics, derived from observation, that holds the potential to both expand the scope of economic understanding, redirect the focus of the discipline, and possibly unify the many disparate theories in the field.

There has been an almost 60 percent increase in health care expenditures in the US in the past seven years. Employer-sponsored health coverage premiums have increased significantly (87 percent) in this same period. Besides the cost of care for chronic conditions such as migraine, arthritis and diabetes, absenteeism linked to these diseases also adds financial strain. Current health financial models focus on past spending instead of modeling based on current health burdens and future trends. This approach leads to suboptimal health maintenance and cost management. Identifying the diseases which affect the most employees and are also the most costly (in terms of productivity, work-loss-days, treatment etc) is necessary, since this allows the employer to identify which combination of policies may best address the health burdens. The current predictive health model limits the amount of diseases it models since it ignores incomplete data sets. This research investigated if by using Bayesian methodology it will be possible to create a comprehensive predictive model of the health burdens being faced by corporations, allowing for health decision makers to have comprehensive information when choosing policies. The first specific aim was to identify which diseases were the most costly to employers both directly and indirectly, and the pathogenesis of these diseases. Co-morbidity of diseases was also taken into account as in many cases these diseases are not treated independently. This information was taken into account when designing the models as the inference was disease specific. One of the contributions of this thesis is coherent incorporation of prior information into the proposed expert model. The Bayesian models were able to estimate the predicted disease burdens for corporations, including predicting the percentage of individuals with multiple diseases. The model was also comparable to, or better than current estimators on the market with limited input. The outputs of the model were also able to give further insight into the disease interactions which creates an avenue for further research in disease management.

This thesis work describes the formulation of a Bayesian approach along with new tools to systematically reduce uncertainty in Research&Development (R&D) alternatives. During the initial stages of R&D many alternatives are considered and high uncertainty exists for all the alternatives. The ideal approach in addressing the many R&D alternatives is to find the one alternative which is stochastically dominant i.e. the alternative which is better in all possible scenarios of uncertainty. Often a stochastically dominant alternative does not exist. This leaves the R&D manager with two alternatives, either to make a selection based on user defined utility function or to gather more information in order to reduce uncertainty in the various alternatives. From the decision makers perspective the second alternative has more intrinsic value, since reduction of uncertainty will improve the confidence in the selection and further reduce the high downside risk involved with the decisions made under high uncertainty. The motivation for this work is derived from our preliminary work on the evaluation of biorefiney alternatives, which brought into limelight the key challenges and opportunities in the evaluation of R&D alternatives. The primary challenge in the evaluation of many R&D alternatives was the presence of uncertainty in the many unit operations within each and every alternative. Additionally, limited or non-existent experimental data made it infeasible to quantify the uncertainty and lead to inability to develop an even simple systematic strategy to reduce it. Moreover, even if the uncertainty could be quantified, the traditional approaches (scenario analysis or stochastic analysis), lacked the ability to evaluate the key group of uncertainty contributors. Lastly, the traditional design of experiment approaches focus towards reduction in uncertainty in the parameter estimates of the model, whereas what is required is a design of experiment approach which focuses on the decision (selection of the key alternative). In order to tackle all the above mentioned challenges a Bayesian framework along with two new tools is proposed. The Bayesian framework consists of three main steps: a. Quantification of uncertainty b. Evaluation of key uncertainty contributors c. Design of experiment strategies, focussed on decision making rather than the traditional parameter uncertainty reduction To quantify technical uncertainty using expert knowledge, existing elicitation methods in the literature (outside chemical engineering domain) are used. To illustrate the importance of quantifying technical uncertainty, a bio-refinery case study is considered. The case study is an alternative for producing ethanol as a value added product in a Kraft mill producing pulp from softwood. To produce ethanol, a hot water pre-extraction of hemi-cellulose is considered, prior to the pulping stage. Using this case study, the methodology to quantify technical uncertainty using experts' knowledge is demonstrated. To limit the cost of R&D investment for selection or rejection of an R&D alternative, it is essential to evaluate the key uncertainty contributors. Global sensitivity analysis (GSA) is a tool which can be used to evaluate the key uncertainties. But quite often global sensitivity analysis fails to differentiate between the uncertainties and assigns them equal global sensitivity index. To counter this failing of GSA, a new method conditional global sensitivity (c-GSA) is presented, which is able to differentiate between the uncertainties even when GSA fails to do so. To demonstrate the value of c-GSA many small examples are presented. The third and the last key method in the Bayesian framework is the decision oriented design of experiment. Traditional 'Design of Experiment' (DOE) approaches focus on minimization of parameter error variance. In this work, a new "decision-oriented" DOE approach is proposed that takes into account how the generated data, and subsequently, the model developed based on them will be used in decision making. By doing so, the parameter variances get distributed in a manner such that its adverse impact on the targeted decision making is minimal. Results show that the new decision-oriented DOE approach significantly outperforms the standard D-optimal design approach. The new design method should be a valuable tool when experiments are conducted for the purpose of making R&D decisions. Finally, to demonstrate the importance of the overall Bayesian framework a bio-refinery case study is considered. The case study consists of the alternative to introduce a hemi-cellulose pre-extraction stage prior to pulping in a thermo-mechanical pulp mill. Application of the Bayesian framework to address this alternative, results in significant improvement in the prediction of the true potential value of the alternative.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 75-77). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 97-98). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 122-127). Also available on the Internet.

Originally presented as the author's thesis (Ph. D.)--WHU, Otto-Beisheim School of Management, Vallendar, Germany. / Description based on print version record. Includes bibliographical references (p. 195-205) and index.

Thesis (Ph. D.)--University of Missouri-Columbia, 1999. / Typescript. Vita. Includes bibliographical references (leaves 122-127). Also available on the Internet.