Diss. Stockholm : Handelshögsk., 2001.

It is not easy to predict what will happen tomorrow. It is perhaps even more difficult knowing what will occur next year or even two years from today. To put it short we are uncertain about the future. Swedish firms attempting to capture new export markets are also facing uncertainty. If a firm succeeds with predicting customer’s preferences they might be able to create a position for the firm´s products before any competition. It is however almost amusing to study successful marketing cases for export markets in a rear window. It is striking how firms have found winning positions which captured market after market with such apparent ease.  In this thesis the author attempts to present theoretical frameworks including internationalization theory and traditional marketing theory which can support exporting firms attempts to find new positions on export markets.  Previously in traditional marketing theory the solution has been to put trust in the marketing concept. Success in the marketplace was then a result of a careful segmentation process ending up in a positioning-decision supported by the marketing mix. The Nordic school of marketing among others introduced a new track in marketing with the relationship marketing concept focusing on the development of existing customer relationships. Internationalization theory (the process theory of internationalization) suggested that that firms increase commitment on export markets after a gradual learning process. Effectuation on the other hand has offered new perspectives. With its roots in the paradigm of bounded rationality a differing stance is offered for predicting the unpredictable. By following a model of bounded rationality the presented firms manage to craft positions for new and as a matter of fact as presented in the empirical part, also for existing markets. The paper highlights the importance of positioning for Swedish firms success on export markets.  It is a case study of how Swedish firms craft positions on new markets by taking action instead of making formal positioning decisions.  A model of bounded rationality is tested during interviews with five Swedish top-exporting firms. The paper finally presents a revised developed model for how successful firms act on export markets in order to craft positions. The model includes traditional marketing theory based upon a paradigm of bounded rationality. The paper contains however some question marks for the application of the marketing concept and traditional marketing theory. Rather a stance towards bounded rationality is emphasized.The final point of the thesis is the urge for emergence of alternatives in marketing to the marketing concept founded upon bounded rationality. These new models need to capture the actual actions taken by successful exporting firms in a more proficient and graspable way than existing traditional marketing models.

<p>It is not easy to predict what will happen tomorrow. It is perhaps even more difficult knowing what will occur next year or even two years from today. To put it short we are uncertain about the future. Swedish firms attempting to capture new export markets are also facing uncertainty. If a firm succeeds with predicting customer’s preferences they might be able to create a position for the firm´s products before any competition. It is however almost amusing to study successful marketing cases for export markets in a rear window. It is striking how firms have found winning positions which captured market after market with such apparent ease.  In this thesis the author attempts to present theoretical frameworks including internationalization theory and traditional marketing theory which can support exporting firms attempts to find new positions on export markets.  Previously in traditional marketing theory the solution has been to put trust in the marketing concept. Success in the marketplace was then a result of a careful segmentation process ending up in a positioning-decision supported by the marketing mix. The Nordic school of marketing among others introduced a new track in marketing with the relationship marketing concept focusing on the development of existing customer relationships. Internationalization theory (the process theory of internationalization) suggested that that firms increase commitment on export markets after a gradual learning process. Effectuation on the other hand has offered new perspectives. With its roots in the paradigm of bounded rationality a differing stance is offered for predicting the unpredictable. By following a model of bounded rationality the presented firms manage to craft positions for new and as a matter of fact as presented in the empirical part, also for existing markets.</p><p>The paper highlights the importance of positioning for Swedish firms success on export markets.  It is a case study of how Swedish firms craft positions on new markets by taking action instead of making formal positioning decisions.  A model of bounded rationality is tested during interviews with five Swedish top-exporting firms. The paper finally presents a revised developed model for how successful firms act on export markets in order to craft positions. The model includes traditional marketing theory based upon a paradigm of bounded rationality. The paper contains however some question marks for the application of the marketing concept and traditional marketing theory. Rather a stance towards bounded rationality is emphasized.The final point of the thesis is the urge for emergence of alternatives in marketing to the marketing concept founded upon bounded rationality. These new models need to capture the actual actions taken by successful exporting firms in a more proficient and graspable way than existing traditional marketing models.</p>

The objective of this research is to develop the framework of empirical-evidence equilibria (EEEs) in stochastic games. This framework was developed while attempting to design decentralized controllers using learning in stochastic games. The overarching goal is to enable a set of agents to control a dynamical system in a decentralized fashion. To do so, the agents play a stochastic game crafted such that its equilibria are decentralized controllers for the dynamical system. Unfortunately, there exists no algorithm to compute equilibria in stochastic games. One explanation for this lack of results is the full-rationality requirement of game theory. In the case of stochastic games, full rationality imposes that two requirements be met at equilibrium. First, each agent has a perfect model of the game and of its opponents strategies. Second, each agent plays an optimal strategy for the POMDP induced by its opponents strategies. Both requirements are unrealistic. An agent cannot know the strategies of its opponents; it can only observe the combined effect of its own strategy interacting with its opponents. Furthermore, POMDPs are intractable; an agent cannot compute an optimal strategy in a reasonable time. In addition to these two requirements, engineered agents cannot carry perfect analytical reasoning and have limited memory; they naturally exhibit bounded rationality. In this research, bounded rationality is not seen as a limitation and is instead used to relax the two requirements. In the EEE framework, agents formulate low-order empirical models of observed quantities called mockups. Mockups have unmodeled states and dynamic effects, but they are statistically consistent; the empirical evidence observed by an agent does not contradict its mockup. Each agent uses its mockup to derive an optimal strategy. 1Since agents are interconnected through the system, these mockups are sensitive to the specific strategies employed by other agents. In an EEE, the two requirements are weakened. First, each agent has a consistent mockup of the game and the strategies of its opponents. Second, each agent plays an optimal strategy for the MDP induced by its mockup. The main contribution of this dissertation is the use of modeling to study stochastic games. This approach, while common in engineering, had not been applied to stochastic games.

This dissertation consists of three research papers on cheap talk game and satisficing behaviour. The first chapter examines the potential for communication via cheap talk between an expert and a decision maker whose type (preferences) is uncertain. The expert privately observes states for each type of the decision maker and wants to persuade the decision maker to choose an action in his favour by informing her of the states. The decision maker privately observes her type and chooses an action. An optimal action for the decision maker depends upon both her type and type-specific states. In equilibrium the expert can always inform the decision maker in the form of comparative statements and the decision maker also can partially reveal her type to the expert or public. The second and third chapters build a dynamic model of satisficing behaviour in which an agent’s “expected” payoff is explicitly introduced, where this expectation is adaptively formed. If the agent receives a payoff above her satisficing level she continues with the current action, updating her valuation of the action. If she receives a payoff below her satisficing level and her valuation falls below her satisficing level she updates both her action and satisficing level. In the second chapter, we find that in the long run, all players satisfice. In individual decision problems, satisficing behaviour results in cautious, maximin choice and in normal form games like the Prisoner’s Dilemma and Stag Hunt, they in the long run play either cooperative or defective outcomes conditional on past plays. In coordination games like the Battle of the Sexes, Choosing Sides and Common Interest, they in the long run coordinate on Pareto optimal outcomes. In the third chapter, we find that satisficing players in the long run play subgame dominant paths, which is a refinement of subgame perfection, and identify conditions with which they ‘always cooperate’ or ‘fairly coordinate’ in repeated Prisoner’s Dilemma and Battle of the Sexes games, respectively, and truthfully communicate in sender-receiver games. Proofs and simulations are provided in appendices.

Since equity premium puzzle had long been a problem, many economists tried to give reasonable interpretations to the puzzle. I focus on the type of theories using bounded rationality as the answer to the problem. I am willing to find out that whether the puzzle still exists in recent decades. If it does exist, are the theories of bounded rationality still able to explain the puzzle? In the beginning, I introduce two theories referring to bounded rationality. Afterwards, I empirically analyze the data of the U.S., Japan and Euro-area by using a simpler model based on rationality. Interestingly, circumstances vary a lot from country to country. One theory may be suitable for one country but not for the others. Even so, the “suitable” theory fails to completely explain the whole tendency of variation during the observed period in the country. In the future, we still need to explore in depth of the puzzle.

The study aims to understand the underlying factor why McDonald's customers return despite previous failure customer experiences. Why do customers come back to the company that contributes to the former dissatisfaction? McDonald's attempt to maintain unceasing purposeful emergence fail at the local level, where the customer contact occurs. McDonald's offerings and customers' bounded rationality results in that customers are satisfied with an "ok" experience, which adds to their low expectations of McDonalds. This makes clear that McDonald's does not have to make an effort through constant adaptation at the local level to achieve a "great" level of satisfaction. Because the customer is satisfied with an "ok" experience, and not require more to return to McDonalds. If you can lower your customers' expectations so much that they do not care about the previous failure customer experiences, the company's competitive invincible, even without continuous adjustment.

The notion that corporations would voluntarily devote resources to serve non-shareholder interests seems to contradict the purpose of commerce. Yet, corporate social responsibility ranks among the most prominent aspects of contemporary capitalism, reaching – in the words of one author – a point of nearly universal adoption among businesses. Over four decades of empirical testing has provided no incontrovertible evidence to support the belief that businesses benefit, even in the long run, from responsible behaviour. Peculiarly, then, it appears that corporations are defying the logic of competitive markets by investing in CSR en masse without an established business case for doing so. Inspiring the work is a research question rooted in the observation of a counter-intuitive: if not profit, in every circumstance, what is turning the attention of nearly every major corporation away from their bottom line and towards social interests? The thesis explores what other factors may lay behind the business community’s curious adoption of CSR, including a new hypothesis that corporate leaders may be diverging from the normative ideal of rational choice and following boundedly-rational patterns of behaviour. It argues that CSR is a form of risk-averse corporate behaviour from a private sector that has seen tremendous growth and gain since the end of the Second World War.

The literature on bounded rationality and learning in macroeconomics has often used recursive algorithms to depict the evolution of agents' beliefs over time. In this thesis we assess this practice from an applied perspective, focusing on the use of such algorithms for the computation of forecasts of macroeconomic variables. Our analysis develops around three issues we find to have been previously neglected in the literature: (i) the initialization of the learning algorithms; (ii) the determination and calibration of the learning gains, which are key parameters of the algorithms' specifications; and, (iii) the choice of a representative learning mechanism. In order to approach these issues we establish an estimation framework under which we unify the two main algorithms considered in this literature, namely the least squares and the stochastic gradient algorithms. We then propose an evaluation framework that mimics the real-time process of expectation formation through learning-to-forecast exercises. To analyze the quality of the forecasts associated to the learning approach, we evaluate their forecasting accuracy and resemblance to surveys, these latter taken as proxy for agents' expectations. In spite of taking these two criteria as mutually desirable, it is not clear whether they are compatible with each other: whilst forecasting accuracy represents the goal of optimizing agents, resemblance to surveys is indicative of actual agents behavior. We carry out these exercises using real-time quarterly data on US inflation and output growth covering a broad post-WWII period of time. Our main contribution is to show that a proper assessment of the adaptive learning approach requires going beyond the previous views in the literature about these issues. For the initialization of the learning algorithms we argue that such initial estimates need to be coherent with the ongoing learning process that was already in place at the beginning of our sample of data. We find that the previous initialization methods in the literature are vulnerable to this requirement, and propose a new smoothing-based method that is not prone to this critic. Regarding the learning gains, we distinguish between two possible rationales to its determination: as a choice of agents; or, as a primitive parameter of agents learning-to-forecast behavior. Our results provide strong evidence in favor of the gain as a primitive approach, hence favoring the use of surveys data for their calibration. In the third issue, about the choice of a representative algorithm, we challenge the view that learning should be represented by only one of the above algorithms; on the basis of our two evaluation criteria, our results suggest that using a single algorithm represents a misspecification. That motivate us to propose the use of hybrid forms of the LS and SG algorithms, for which we find favorable evidence as representatives of how agents learn. Finally, our analysis concludes with an optimistic assessment on the plausibility of adaptive learning, though conditioned to an appropriate treatment of the above issues. We hope our results provide some guidance on that respect.

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