Stock recommendations,frequently produced under time pressure, are susceptible to being the result of automatic and intuitive thinking. This is associated with using heuristics in decision-making which is studied by an entire school of research – the heuristics and bias approach. Heuristics of representativeness, availability and anchoring including associated biases as defined by Tversky and Kahneman provide the theoretical framework for the study. This study is aimed at extending the understanding of biases in general and cognitive biases with regard to stock recommendations. A total of thirty equity recommendations were analyzed. A t-test showed that more biases were present in the incorrect recommendations. Overconfidence, illusion of validity and anchoring were among the most frequently observed. The vast majority of recommendations were characterized by insensitivity to predictability indicating that forecasters are seemingly unaware of the difficulty of accurately predicting where the stock price is going to be within the next three to six months.

The purpose of this research is to present the design and development of a routing system, custom developed for a fence manufacturing company in the continental US. The objective of the routing module of the system is to generate least cost routes from the home-center of the company to a set of delivery locations. Routes are evolved for a set of customer locations based on the sales order information and are frequently modified to include rush orders. These routes are such that each delivery is made within a given time window. Further, total truckload of all delivery locations over any particular route is not allowed to exceed the weight and volume capacities of the truck. The basic system modules such as user interface functions and database are designed using MS Access 2000. An interface module to retrieve data from existing ERP system of the company is developed to import pick-ticket information. A customer inter-distance maintenance module is designed with the abilities of a learning tool to reduce information retrieval time between the routing system and the GIS server. The Graphical User Interface with various screen forms and printable reports is developed along with the routing module to achieve complete system functionality and to provide an efficient logistics solution. This problem, formulated as a mixed-integer program, is of particular interest due to its generality to model problem scenarios in the production shop such as job-shop scheduling, material handling, etc. This problem is coded and solved for instances with different input parameters using AMPL/CPLEX. Results of test runs for the company data show that the solution time increases exponentially with the number of customers. Hence, a heuristic approach is developed and implemented. Sample runs with small instances are solved for optimality using AMPL/CPLEX and are used to compare the performance of the heuristics. However, test runs solved using the heuristics for larger instances are compared with the manual routing costs. The comparison shows a considerable cost savings for heuristic solutions. Further, a what-if analysis module is implemented to aid the dispatcher in choosing input parameters based on sensitivity analysis. In conclusion, further improvement of the routing system and future research directions are proposed.

No description available.

No description available.

No description available.

This research project will explore American philosopher John Dewey’s theory of transaction, and Shannon Sullivan’s interpretation of Dewey’s theory, the ‘Transactional Body’, and their inherent potential for the making and reception of painting. Dewey stated (Dewey, cited in Sullivan: 1-2) that organisms live as much in processes across and 'through' skins as in processes 'within skins'. Sullivan’s ‘transactional body’ is always in a state of flux, a morphic body in perpetual motion. Within an artistic context this raises the possibility of exploring the theory of transaction as it applies to painting, using the concept of automatic intuitive art practice. Central to this investigation will be direct connection between senses, instincts, intuition, and the painting. Sullivan suggests that truth and wisdom can be pursued through somatic experience. Therefore the process will be explored by an extension of the corporeal body through the physicality of gesture, movement, rhythm, colour, and mark making. A recurring subtext throughout this investigation will be that of ‘duality’; specifically that defined as the struggle between the use of the conscious, critical mind, allowing for the transaction between artist, paint and canvas to occur naturally and intuitively. The conscious mind / intuition duality manifests at various stages during the manufacture and reception of a painting. Whilst the project relies upon automatic and intuitive praxis, conscious decisions are made regarding the size and shape of the canvas, the medium used, and through reflective analysis of the completed work.

Esta tese investiga heurísticas de criação de oportunidades empreendedoras em startups. Pesquisas sobre ação empreendedora com base em heurísticas são desenvolvidas tendo como premissa que o mercado oferece várias oportunidades, prontas para serem selecionadas. Tais pesquisas não investigam como se dá a ação empreendedora quando há a necessidade de criação de uma nova oportunidade. Pesquisas sobre ação empreendedora com base em heurísticas também não exploram conceitualmente e empiricamente a criação de oportunidades empreendedoras por startups. Para preencher essas lacunas, esta tese se propõe a analisar a aderência da tipologia proposta por Bingham e Eisenhardt (2011) na criação de oportunidades empreendedoras por startups. A proposição que se faz é a seguinte: a tipologia proposta por Bingham e Eisenhardt não prevê heurísticas de gestão para a criação de novas oportunidades, já que tem como premissa que o mercado dispõe de várias oportunidades prontas para serem capturadas. Com base nesta análise, a seguinte pergunta dirige esta tese: Quais as heurísticas presentes na criação de oportunidades empreendedoras? Para responder a esta pergunta, esta tese constrói um quadro conceitual a partir da literatura e emprega a abordagem de pesquisa empírica, através da análise de ações empreendedoras unindo percepções, decisões e ações através de estudos de múltiplos casos realizados em oito startups. A contribuição central desta tese é a proposição de uma tipologia de heurísticas relacionadas à criação de oportunidades empreendedoras, adaptando a tipologia proposta por Bingham e Eisenhardt. Enquanto a tipologia de Bingham e Eisenhardt determina que empresas aprendem heurísticas dos tipos seleção, processual, prioridade e temporal, esta tese determina que empresas também aprendem heurísticas do tipo \'criação\' e propõe uma nova tipologia de heurísticas para a criação de oportunidades empreendedoras: \'startups aprendem heurísticas dos tipos criação, processual, prioridade e temporal\', uma vez que necessitam criar uma oportunidade que o mercado ainda não dispõe. / This thesis investigates heuristics creating entrepreneurial opportunities for startups. Research on entrepreneurial action based on heuristics are developed with the premise that the market offers several opportunities, ready to be selected. Such surveys do not investigate how is the entrepreneurial action when there is a need to create a new opportunity. Research on entrepreneurial action based on heuristics did not explore conceptually and empirically creating entrepreneurial opportunities for startups. To fill these gaps, this thesis aims to analyze the adherence of the typology proposed by Bingham and Eisenhardt (2011) in creating opportunities for entrepreneurial startups. The proposition that does is the following: the typology proposed by Bingham and Eisenhardt does not provide management of heuristics to create new opportunities, as it is premised that the market offers many opportunities ready to be captured. Based on this analysis, the following question directs this thesis: What heuristics present at the creation of entrepreneurial opportunities? To answer this question, this thesis builds a conceptual framework from the literature and employs empirical research approach through the analysis of entrepreneurial activities linking perceptions, decisions and actions through multiple cases performed in eight startups studies. The main contribution of this thesis is to propose a typology of heuristics related to creating entrepreneurial opportunities, adapting the typology proposed by Bingham and Eisenhardt. While the typology of Bingham and Eisenhardt requires that companies learn heuristics types selection, procedure, priority and time, this thesis requires that companies also learn heuristics like \'creation\' and proposes a new typology of heuristics to create opportunities entrepreneurial: \'startups learn heuristics types creation, procedure, priority and time\', since they need to create an opportunity that the market does not yet have.

A new minimum spanning tree (MST) based heuristic for clustering biological data is proposed. The heuristic uses MSTs to generate initial solutions and applies a local search to improve the solutions. Local search transfers the nodes to the clusters with which they have the most connections, if this transfer improves the objective function value. A new objective function is defined and used in the heuristic. The objective function considers both tightness and separation of the clusters. Tightness is obtained by minimizing the maximum diameter among all clusters. Separation is obtained by minimizing the maximum number of connections of a gene with other clusters. The objective function value calculation is realized on a binary graph generated using the threshold value and keeping the minimumpercentage of edges while the binary graph is connected. Shortest paths between nodes are used as distance values between gene pairs. The efficiency and the effectiveness of the proposed method are tested using fourteen different data sets externally and biologically. The method finds clusters which are similar to actual ones using 12 data sets for which actual clusters are known. The method also finds biologically meaningful clusters using 2 data sets for which real clusters are not known. A mixed integer programming model for clustering biological data is also proposed for future studies.

No description available.

Selecting an effective method for combining the votes of classifiers in an ensemble can have a significant impact on the overall classification accuracy an ensemble is able to achieve. With some methods, the ensemble cannot even achieve as high a classification accuracy as the most accurate individual classifying component. To address this issue, we present the strategy of Heuristic Weighted Voting, a technique that uses heuristics to determine the confidence that a classifier has in its predictions on an instance by instance basis. Using these heuristics to weight the votes in an ensemble results in an overall average increase in classification accuracy over when compared to the most accurate classifier in the ensemble. When considering performance over 18 data sets, Heuristic Weighted Voting compares favorably both in terms of average classification accuracy and algorithm-by-algorithm comparisons in accuracy when evaluated against three baseline ensemble creation strategies as well as the methods of stacking and arbitration.