In a search game, a seeker searches for a hider in some space. The seeker wishes to find the hider as quickly as possible, and the hider wishes to avoid capture as long as possible. In this paper, I will focus on the case where the search space is a star, and the only information the seeker has is the speed of the hider. I will provide algorithms for some cases where the seeker is guaranteed to find the hider and prove optimality for some of these cases. Also, I will look at some cases where the hider can avoid capture indefinitely. I will also present some results for searching on trees.

In everyday life, we often encounter groupings of objects. It could be a group of products kept on a retail shelf, food items in a shop window or depictions of groups of objects or people in an advertisement. However, very little attention has been paid to how these different groupings could influence consumer decisions. The main aim of this article is to study the influence of such groupings on consumer choice and behavior. A new phenomenon is presented that studies the influence of gestalt grouping of objects, such as economy, symmetry, and similarity in different consumer decision domains. Specifically, it is proposed that when each object in two groups has an equal chance of a gain (for instance, one in 10 has a gift coupon), then people prefer to select an object from the group with better gestalt features. However, when each object in two groups has an equal chance of a loss (for instance, one in 10 is defective), then people prefer to select an object from the group with worse gestalt features. Normative theory would predict that people should be indifferent between the two groups. However, this article demonstrates that people utilize spatial grouping, a non-informative factor, as a cue in their preferences. I call the differential influence of groupings on decisions the cooler effect. The cooler effect in the gain and loss domains is demonstrated in experiments 1 and 2. Both experiments use different domains of product choice and a game of chance to test the robustness of the findings. Subsequently, an underlying process utilizing gestalt theory and contagion theory is proposed. Further three alternate accounts are presented - motivational reasoning, contagion by itself being sufficient, and gestalt perception by itself being enough. Experiments 4 and 5 test the proposed account, rule out the alternate accounts, and moderate the differential influence of spatial grouping on choice in the domain of gains and losses. Finally, theoretical and managerial implications are presented.

In many circumstances, predictions elicited from induced classification models are useful to a certain extent, as such predictions provide insight into what the future may hold. Such models, in and of themselves, hold little value beyond making such predictions, as they are unable to inform their user as to how to change a predicted outcome. Consider, for example, a health care domain where a classification model has been induced to learn the mapping from patient characteristics to disease outcome. A patient may want to know how to lessen their probability of developing such a disease. In this document, four different approaches to inverse classification, the process of turning predictions into prescriptions by working backwards through an induced classification model to optimize for a particular outcome of interest, are explored. The first study develops an inverse classification framework, which is created to produce instance-specific, real-world feasible recommendations that optimally improve the probability of a good outcome, while being as classifier-permissive as possible. Real-world feasible recommendations are obtained by imposition of constraints that specify which features can be optimized over and accounts for user-specific preferences. Assumptions are made as to the differentiability of the classification function, permitting the use of classifiers with exploitable gradient information, such as support vector machines (SVMs) and logistic regression. Our results show that the framework produces real-world recommendations that successfully reduce the probability of a negative outcome. In the second study, we further relax our assumptions as to the differentiability of the classifier, allowing virtually any classification function to be used. Correspondingly, we adjust our optimization methodology. To such an end, three heuristic-based optimization methods are devised. Furthermore, non-linear (quadratic) relationships between feature changes and so-called cost, which accounts for user preferences, are explored. The results suggest that non-differentiable classifiers, such as random forests, can be successfully navigated using the specified framework and updated, heuristic-based optimization methodology. Furthermore, findings suggest that regularizers, encouraging sparse solutions, should be used when quadratic/non-linear cost-change relationships are specified. The third study takes a longitudinal approach to the problem, exploring the effects of applying the inverse classification process to instances across time. Furthermore, we explore the use of added temporal linkages, in the form of features representing past predicted outcome probability (i.e., risk), on the inverse classification results. We further explore and propose a solution to a missing data subproblem that frequently arises in longitudinal data settings. In the fourth and final study, a causal formulation of the inverse classification framework is provided and explored. The formulation encompasses a Gaussian Process-based method of inducing causal classifiers, which is subsequently leveraged when the inverse classification process is applied. Furthermore, exploration of the addition of certain dependencies is explored. The results suggest the importance of including such dependencies and the benefits of taking a causal approach to the problem.

The airline industry is facing unprecedented challenges in generating sufficient revenues to stay in business. Airlines must capture the greatest revenue yield from every flight by leaving no seats unsold and not over filling the cabin with discount fares. To succeed in doing the above airlines must be able to accurately forecast each of their market segments, manage product andprice availability to maximize revenue and react quickly to competitive changes in the market place. Thus seat inventory control and ticket pricing form the two major tools of revenue management. The focus of this paper is to consolidate the ideas of seats inventory control and pricing in order to maximize the revenues generated by an airline network. A continuous time yield management model for a network with multiple legs, multiple fare classes and dynamic price changes for all fare classes is considered. Each fare class has a set of fares from which the optimal fare is chosen based upon the Minimum Acceptable Fare (MAF) which performs the critical role in the decision process. A machine Learning based algorithm, EMSRa based and EMSRb based algorithm for obtaining dynamic policies for combined pricing and allocation. The algorithms are implemented for a sample network with eight cities, eleven logs, thirty origin-destinations(ODs), three fare classes, three levels of fares in each class and ninety itineraries.

Este trabalho trata do comportamento de jovens estudantes de uma escola da rede estadual paulista no que concerne as estratégias e heurísticas observáveis que praticam diante de determinados problemas lógico-matemáticos. Identifica algumas das heurísticas e estratégias clássicas que os educandos praticam e não praticam. A investigação se dá por meio de apresentação de problemas contextualizados que prescindem de conhecimentos matemáticos elaborados, a estudantes do último ano do ensino médio e análise dos processos de resolução deflagrados pelos estudantes na tentativa de resolver os problemas. Também é apresentado subsídio teórico e problemas adequados à reprodução parcial dessa investigação que podem interessar ao professor de matemática da educação básica ou ao pesquisador em resolução de problemas. Por fim, são problematizadas as informações observadas sobre o comportamento intelectual dos educandos no sentido de estabelecer hipóteses sobre as conquistas ou não dos educandos quanto à competência em resolução de problemas. / Este trabalho trata do comportamento de jovens estudantes de uma escola da rede estadual paulista no que concerne as estratégias e heurísticas observáveis que praticam diante de determinados problemas lógico-matemáticos. Identifica algumas das heurísticas e estratégias clássicas que os educandos praticam e não praticam. A investigação se dá por meio de apresentação de problemas contextualizados que prescindem de conhecimentos matemáticos elaborados, a estudantes do último ano do ensino médio e análise dos processos de resolução deflagrados pelos estudantes na tentativa de resolver os problemas. Também é apresentado subsídio teórico e problemas adequados à reprodução parcial dessa investigação que podem interessar ao professor de matemática da educação básica ou ao pesquisador em resolução de problemas. Por fim, são problematizadas as informações observadas sobre o comportamento intelectual dos educandos no sentido de estabelecer hipóteses sobre as conquistas ou não dos educandos quanto à competência em resolução de problemas.

The purpose of this study was to gain understanding about the influence of technology, specifically cellular phones, on decision making during potentially risky situations in the backcountry. Previous research in this area is contradictory and some studies indicate technology is influencing people to take more risks while others suggest it is not. Further confounding the relationship is the fact that previous studies have found people may be taking more risk in the presence of technology were based largely on respondent perceptions, not observation data. The current study used a scenario-based decision model to examine the difference in decision making between those who carried a cell phone on a hike and those who did not. A one-way ANCOVA revealed there was no statistical difference (F=2.18, p=.0898) between the two groups. This indicated people did not take more risks because they carried a cell phone into the backcountry. Risk tolerance and experience hiking long mountain hikes similar to the mountain used in the scenario were the only two variables that influenced decision making in this model. Because cell phones do not adversely influence decision making, it is proposed cell phones and other wireless communication devices be added as a recommended piece of gear to the 10 essentials to help reduce the time it takes for search and rescue to arrive on scene when help is needed.

This project is a creative production that combines image, sound, and narrative. It uses these elements to employ a twist as a device for a paradigm and genre shift in a short film text. My short film introduces a plot that is seemingly linear. Yet, the twist presents a piece of information that reveals the actual non-linear nature of the story, which in turn necessitates the re-interpretation of the plot. The aim of this project is to explore how a twist in a narrative-based short film can alter the cues and perceptions the audience receive from the plot. The project is predominantly practice-based (80%).

<p>This Master’s Thesis deals with a special problem that may be of importance when planning a frequency hopping mobile communication network. In normal cases the Frequency Assignment Problem is solved, in order to plan the use of frequencies in a network. The special case discussed in this thesis occurs when the network operator requires that the frequencies must be arranged into groups. In this case the Frequency Assignment Problem must be solved with respect to the groups, i.e. a Group assignment Problem. </p><p>The thesis constitutes the final part of the Master of Science in Communication and Transport Systems Engineering education, at Linköping University, Campus Norrköping. The Group Arrangement Problem was presented by ComOpt, a company that has specialized in solving the Frequency Assignment Problem for network operators. </p><p>This thesis does not deal with solutions for the Frequency Assignment Problem, with respect to the groups. The main issue in the thesis is to construct a computer based algorithm that solves the Group Arrangement Problem, i.e. creating the groups. The goal is to construct an algorithm that creates groups which imply a better solution for the Frequency Assignment Problem than manually created groups. </p><p>Two algorithms are presented and tested on two cases. Their respective results for both cases are compared with the results from a manual grouping. The two computer based algorithms creates better groups than the manual grouping strategy, according to an artificial quality measure. As of spring 2003 a variant of one of the presented algorithms was implemented in ComOpt’s product for solving the Frequency Assignment Problem.</p>

<p>This thesis contributes to theoretical and quantitative aspects of machine scheduling. In fact, it is dedicated to the issue of scheduling n jobs on one single machine. The scope is limited to deterministic problems - i.e. those with all data available and known with certainty in advance - with tardiness involved objectives; hence, the common denominator of all problems addressed are jobs with a predetermined due date assigned to. A job is finished on time as long as it is completed before its due date, otherwise it is said to be tardy. Since the single machine utilized is assumed to be restricted to process at most one job at a time, the aim is to find a proper sequence - a schedule - of how to process the jobs in order to best fulfill a certain objective. The contribution of this thesis aims at giving a state of the art survey and detailed review of research effort considering the objectives "minimizing the number of tardy jobs" and "minimizing the weighted number of tardy jobs". Further, the objectives of "minimizing the total tardiness", "minimizing the total weighted tardiness" and "minimizing the maximum tardiness" are adumbrated but reduced to a rough overview of research effort made.</p>

This thesis contributes to theoretical and quantitative aspects of machine scheduling. In fact, it is dedicated to the issue of scheduling n jobs on one single machine. The scope is limited to deterministic problems - i.e. those with all data available and known with certainty in advance - with tardiness involved objectives; hence, the common denominator of all problems addressed are jobs with a predetermined due date assigned to. A job is finished on time as long as it is completed before its due date, otherwise it is said to be tardy. Since the single machine utilized is assumed to be restricted to process at most one job at a time, the aim is to find a proper sequence - a schedule - of how to process the jobs in order to best fulfill a certain objective. The contribution of this thesis aims at giving a state of the art survey and detailed review of research effort considering the objectives "minimizing the number of tardy jobs" and "minimizing the weighted number of tardy jobs". Further, the objectives of "minimizing the total tardiness", "minimizing the total weighted tardiness" and "minimizing the maximum tardiness" are adumbrated but reduced to a rough overview of research effort made.