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This dissertation explores some of the implications of a richer conceptualization of human cognitive processes on three strategically important phenomena. Each essay explores its phenomenon of interest using findings on cognition from different fields including cognitive neuroscience, cognitive psychology, and social psychology, among others, and uses this cognitive perspective to generate novel predictions. The first essay explores the process by which decision makers make subjective forecasts, that is, forecasts that are based on subjective estimates and "gut feeling" as opposed to a rational decision calculus. It develops a parsimonious model of this process that is fundamentally based on the associative nature of information processing in the mind. The model has some counter-intuitive properties and is able to account for some well-documented cognitive "biases". Finally, when applied to strategic forecasting settings of significant complexity that entail deliberation, it generates some novel predictions. The second essay explores the phenomenon of creative strategic leaps, that is, the phenomenon whereby the leaders of some firms are able to identify radically different strategic opportunities from the status-quo of the industry. The essay models these mental leaps as re-categorizations of strategic situations by these leaders. It develops a formal, micro-founded model of categorization that is based on findings from multiple disciplines including cognitive psychology, social psychology, cognitive neuroscience and marketing. The model is then used to propose two techniques that could increase the chances of making such novel mental leaps. The final essay explores the emotional impact of the prior performance of a firm on its strategic decision making process, specifically on the distance of its strategic search and choices. It identifies a few robust mechanisms from cognitive psychology, cognitive neuroscience and behavioral economics relating to the impact of emotions on cognitive functioning, and then integrates and applies them to generate two hypotheses. The hypotheses make empirically testable predictions on the M&A choices of firms, which are then tested using a large panel dataset. Strong support is found for the predictions in the data.
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