We investigate algorithms for different steps in the decision making process, focusing on systems where we are uncertain about the outcomes but can quantify how probable they are using random variables. Any decision one makes in such a situation leads to a distribution of outcomes and requires a way to evaluate a decision. The standard approach is to marginalise the distribution of outcomes into a single number that tries in some way to summarise the value of each decision. After selecting a marginalisation approach, mathematicians and decision makers focus their analysis on the marginalised value but ignore the distribution. We argue that we should also be investigating the implications of the chosen mathematical approach for the whole distribution of outcomes. We illustrate the effect different mathematical formulations have on the distribution with one-stage and sequential decision problems. We show that different ways to marginalise the distributions can result in very similar decisions but each way has a different complexity and computational cost. It is often computationally intractable to approximate optimal decisions to high precision and much research goes into developing algorithms that are suboptimal in the marginalised sense, but work within the computational budget available. If the performance of these algorithms is evaluated they are mainly judged based on the marginalised values, however, comparing the performance using the full distribution provides interesting information: We provide numerical examples from dynamic pricing applications where the suboptimal algorithm results in higher profit than the optimal algorithm in more than half of the realisations, which is paid for with a more significant underperformance in the remaining realisations. All the problems discussed in this thesis lead to continuous optimisation problems. We develop a new algorithm that can be used on top of existing optimisation algorithms to reduce the cost of approximating solutions. The algorithm is tested on a range of optimisation problems and is shown to be competitive with existing methods.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Chemical and Biochemical Engineering." Includes bibliographical references (p. 268-270).

Made available in DSpace on 2016-04-25T18:40:19Z (GMT). No. of bitstreams: 1 ROGERIO GUEDES COSTA.pdf: 1050139 bytes, checksum: bda4d1f492cd6fa5080b445771017339 (MD5) Previous issue date: 2006-06-30 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In a contemporary world, the success of the companies is linked up to several integrated factors that support the businesses administration. By looking at the context, the importance given to the Costs Management has been a matter of studying by different authors. Aiming at knowing how the Group Medicine Companies uses the Cost Strategic Management while being a tool of managing and supporting the decision made. The research was done in the metropolitan region of São Paulo, with 13 private companies of supplementary health. Talking about the method, it was done a qualitative research due to the lack of published material. The collection of data was accomplished by questionnaire prepared and developed in order to evaluate the use of many cost tools according to the approach of Costs Management. The results showed that even the companies have the knowledge of cost techniques, they believe they have the control over its costs using different tools to get the economic results. They believe in using tools for the management of their businesses but, as always, they do not use the best techniques to attain their objectives, which can harm the information analysis, the control of its operation and, as a result, the decision made / No mundo contemporâneo o sucesso das empresas está vinculado a diversos fatores integrados que sustentam a administração dos negócios. Neste contexto, a importância dada à Gestão de Custos tem sido objeto de estudo por diferentes autores. O presente trabalho tem como objetivo principal investigar como as empresas de Medicina de Grupo utilizam a Gestão Estratégica de Custos enquanto ferramenta de gerenciamento e suporte a Tomada de Decisão. A pesquisa foi realizada na região metropolitana de São Paulo, com 13 empresas privadas de saúde suplementar. Quanto à linha metodológica, foi utilizada uma pesquisa qualitativa de natureza exploratória devido a inexistência de material publicado. A coleta de dados efetivou-se por meio de questionário estruturado, desenvolvido de forma a avaliar a utilização das diversas ferramentas de custeio de acordo com o enfoque da Gestão de Custos. Os resultados encontrados demonstraram que embora as empresas tenham conhecimento das técnicas de custeio, acreditam possuir controle de seus custos utilizando os mais variados instrumentos para obter resultados econômicos. Acreditam adotar ferramentas para a gestão de seus negócios, porém, com freqüência não utilizam as técnicas mais adequadas dentro dos objetivos pretendidos, as quais podem comprometer a análise das informações, o controle das operações e conseqüentemente as tomadas de decisões

The human brain is equipped with the ability to plan ahead, i.e. to mentally simulate the expected consequences of candidate actions to select the one with the most desirable expected long-term outcome. Insufficient planning can lead to maladaptive behaviour and may even be a contributory cause of important societal problems such as the depletion of natural resources or man-made climate change. Understanding the cognitive and neural mechanisms of forward planning and its regulation are therefore of great importance and could ultimately give us clues on how to better align our behaviour with long-term goals. Apart from its potential beneficial effects, planning is time-consuming and therefore associated with opportunity costs. It is assumed that the brain regulates the investment into planning based on a cost-benefit analysis, so that planning only takes place when the perceived benefits outweigh the costs. But how can the brain know in advance how beneficial or costly planning will be? One potential solution is that people learn from experience how valuable planning would be in a given situation. It is however largely unknown how the brain implements such learning, especially in environments with large state spaces. This dissertation tested the hypothesis that humans construct and use so-called control contexts to efficiently adjust the degree of planning to the demands of the current situation. Control contexts can be seen as abstract state representations, that conveniently cluster together situations with a similar demand for planning. Inferring context thus allows to prospectively adjust the control system to the learned demands of the global context. To test the control context hypothesis, two complex sequential decision making tasks were developed. Each of the two tasks had to fulfil two important criteria. First, the tasks should generate both situations in which planning had the potential to improve performance, as well as situations in which a simple strategy was sufficient. Second, the tasks had to feature rich state spaces requiring participants to compress their state representation for efficient regulation of planning. Participants’ planning was modelled using a parametrized dynamic programming solution to a Markov Decision Process, with parameters estimated via hierarchical Bayesian inference. The first study used a 15-step task in which participants had to make a series of decisions to achieve one or multiple goals. In this task, the computational costs of accurate forward planning increased exponentially with the length of the planning horizon. We therefore hypothesized that participants identify ‘distance from goal’ as the relevant contextual feature to guide their regulation of forward planning. As expected we found that participants predominantly relied on a simple heuristic when still far from the goal but progressively switched towards forward planning when the goal approached. In the second study participants had to sustainably invest a limited but replenishable energy resource, that was needed to accept offers, in order to accumulate a maximum number of points in the long run. The demand for planning varied across the different situations of the task, but due to the large number of possible situations (n = 448) it would be difficult for the participants to develop an expectation for each individual situation of how beneficial planning would be. We therefore hypothesized, that to regulate their forward planning participants used a compressed tasks representation, clustering together states with similar demands for planning. Consistent with this, reaction times (operationalising planning duration) increased with trial-by-trial value-conflict (operationalising approximate planning demand), but this increase was more pronounced in a context with generally high demand for planning. We further found that fMRI activity in the dorsal anterior cingulate cortex (dACC) increased with conflict, but this increase was more pronounced in a context with generally high demand for planning as well. Taken together, the results suggest that the dACC integrates representations of planning demand on different levels of abstraction to regulate prospective information sampling in an efficient and situation-appropriate way. This dissertation provides novel insights into the question how humans adapt their planning to the demands of the current situation. The results are consistent with the view that the regulation of planning is based on an integrated signal of the expected costs and benefits of planning. Furthermore, the results of this dissertation provide evidence that the regulation of planning in environments with real-world complexity critically relies on the brain’s powerful ability to construct and use abstract hierarchical representations.

Thesis (Doctora).