The attraction effect reveals that people do not compare alternatives independently of one another. Instead, they make comparisons, such that preferences between two alternatives can be reversed by adding or removing otherwise irrelevant alternatives. This behaviour is particularly difficult for rational models of decision making to explain since such models require the independent evaluation of alternatives. As such these models describe preference reversal behaviour as irrational. This thesis examines what rational decision making should look like once a person's cognitive bounds have been taken into account. The key finding is that contextual preference reversals like the attraction effect, far from being irrational, actually result from people making better decisions than they would if they assessed alternatives independently of one another. The research was grouped into three objectives concerning the attraction effect and the rationality of human cognition. The first of these was to identify under what conditions people exhibit the attraction effect, and what consequences the behaviour has for the outcomes they experience. Two experiments revealed that the effect is only exhibited in choice sets where alternatives are approximately equal in value and therefore hard to tell apart. This finding also means that the potential negative consequences of exhibiting the attraction effect are very small, because it only occurs when alternatives are similar in value. The second objective was to develop a computationally rational model of the attraction effect. Computational rationality is an approach that identifies what the optimal behaviour is given the constraints imposed by cognition, and the environment. Our model reveals why people exhibit the attraction effect. With the assumption that people cannot calculate expected value perfectly accurately, the model shows that in choices between prospects, the attraction effect actually results in decisions with a higher expected value. This is because noisy expected value estimates can be improved by taking into account the contextual information provided by the other alternatives in a choice set. The final objective was to provide evidence for our model, and the computational rationality approach, by making a novel prediction. We conducted an experiment to test the model's prediction that the attraction effect should be much reduced in the loss domain. We replicated existing attraction effect studies and extended them to the loss domain. The results replicated previous results in the gain domain and simultaneously revealed the novel finding that people did not exhibit the effect in the loss domain. People exhibit the attraction effect as a result of making the best decision possible given the cognitive resources they have. Understanding decision making as computationally rational can provide deep insights into existing phenomena. The method allows us to ascertain the causal link between cognitive mechanisms, a person's goal, and their decision making.

Studiens syfte var att undersöka fängelsereformsattityder i Sverige med syfte att utreda om dessa korrelerade med kön, politisk tillhörighet, ålder och urvalsgrupp. Detta utförs genom att replikera en tidigare amerikansk studie inom samma ämne. Resultaten från den svenska studien jämfördes även med den amerikanska. Urvalsgrupperna utgjordes av allmänheten(N=105), före-detta kriminella (N=48) och högskolestudenter vid Högskolan i Skövde (N=252). Respondenterna nåddes via en mailenkät.Resultaten visade att de svenska respondenterna hade mer positiva attityder gentemot fängelsereformer än deras amerikanska motparter.Före-detta kriminella var mer positivt inställda till fängelsereformer än studenter och allmänheten. Vad gäller politisk tillhörighet, hade vänsterblocket mer positiva attityder mot fängelsereformer än högerblocket. Ålder hade ett positivt samband med fängelsereformsattityder. Det framkom även att i Sverige har kvinnor till en högre grad än män, höll attityden att våldsbrottsförövare borde få en hårdare bestraffning än andra brottslingar. Det skulle kunna vara av intresse att en studie utförs med syfte att undersöka dessa attityder relaterat till kön, för att se ifall det rör sig om interkulturella skillnader. / The purpose of this study was to investigate prison reform attitudes to see if they correlated with gender, political affiliation, age and sample group. The sample groups were the general population (N=105), ex-criminals (N=48) and students at the University of Skövde (N=252). The respondents were reached by an email survey. This was done by replicating a previously done American survey study. The results from the Swedish study were reviewed in comparison with the American study. The results showed that Swedish respondents held more positive prison reform attitudes than did their American counterparts. Ex-criminals were more positive towards prison reforms than students and the general population. Regarding political affiliation, the Swedish left-wing had more positive attitudes towards prison reforms than did the right-wing. The study also found a positive correlation between age and prison reform attitudes. It was found that Swedish women had more punitive attitudes towards violent criminals than other criminals, than did the men. It would be interesting to research these attitudes and how they relate to gender, to see whether they are a product of intercultural differences.

This dissertation is a study of non-standard economic behavior. The first chapter concerns two widely observed violations of Independence of Irrelevant Alternatives, the Compromise and Attraction effects. I construct a novel method of representing them by reducing the context of a menu to a frame, encompassing the worst option along each attribute in the menu, and observing a collection of preferences indexed by frames. The agent behaves as though a good’s attractiveness along each attribute is judged relative to the frame with declining marginal utility. This allows me to give a novel interpretation of the compromise and attraction effects: they are consistent with indifference curves rotating clockwise as the frame moves down, and counter- clockwise as it goes left. It also allows me to give a representation theorem showing the behavioral axioms associated with a utility representation taking a good and the frame as arguments. The second chapter applies the representation from Chapter One to electoral politics. It shows that incorporating these preferences generates equilibria where extremist candidates enter plurality elections in order to attractively frame their preferred moderate candidate, even if the extremists have probability zero of obtaining office themselves. While such candidates are frequently observed in elections, and there are papers generating equilibria with centrist sure losers (including Solow (2015)), this is the first paper generating equilibria with these extremist candidates without unusual assumptions on election rules, or non single-peaked preferences. This paper creates a four candidate equilibrium with two extremist sure loser candidates, each on the fringes of opinion. The third chapter concerns the effect of guilt on preferences in the circumstance of gift giving. A decision maker who experiences guilt may receive an increase in surplus from a gift card allowing guilt-free indulgence, potentially beyond even the surplus she’d receive from an equivalent cash gift. This paper isolates the behavior of guilt avoidance by exploiting a multi-period setting which incorporates a distinction between the decision maker’s preferences over what she’d receive, and what she would choose. A representation inspired by Kopylov (2009) is adapted to this setting, providing a representation theorem for these preferences.

During initial business-to-business encounters, salespeople try to enhance buyers’ future interaction intentions. A common belief is that increasing buyers’ similarity perceptions increases the chances of future interaction. This study assesses the impact of the similarity-attraction effect on future interaction. By synthesising social psychology and marketing literature, a conceptual framework is proposed, in which perceived similarity influences salesperson trust. This relationship is mediated by task-related and social assessments of buyers. Task-related assessments comprise willingness (benevolence and integrity) and competence (power and expertise). Social attraction is conceptualised as likeability. Salesperson trust drives anticipated future interaction, together with organisational trust and anticipated added value. The conceptual framework was empirically tested through a cross-sectional survey. Dutch professional buyers assessed recent initial sales encounters. A sample of 162 dyads was analysed, using PLS-SEM, including FIMIX segmentation. This study demonstrates support for a third willingness construct: willingness behaviour. This construct implies that buyers are more influenced by expectations regarding behaviour, than assessments of salespeople’s attitudes. A homogeneous analysis supports the influence of perceived similarity on salesperson trust, both directly and through willingness behaviour. However, model-based segmentation uncovers a segment of cost-oriented dyads and a segment of more profit-oriented dyads. In cost-oriented dyads, there is no significant direct effect between perceived similarity and salesperson trust, and willingness behaviour nearly fully mediates this relationship. In more profit-oriented dyads, the similarity-attraction effect is not present. Theoretical and methodological contributions and managerial implications of these findings are discussed.

During initial business-to-business encounters, salespeople try to enhance buyers’ future interaction intentions. A common belief is that increasing buyers’ similarity perceptions increases the chances of future interaction. This study assesses the impact of the similarity-attraction effect on future interaction. By synthesising social psychology and marketing literature, a conceptual framework is proposed, in which perceived similarity influences salesperson trust. This relationship is mediated by task-related and social assessments of buyers. Task-related assessments comprise willingness (benevolence and integrity) and competence (power and expertise). Social attraction is conceptualised as likeability. Salesperson trust drives anticipated future interaction, together with organisational trust and anticipated added value. The conceptual framework was empirically tested through a cross-sectional survey. Dutch professional buyers assessed recent initial sales encounters. A sample of 162 dyads was analysed, using PLS-SEM, including FIMIX segmentation. This study demonstrates support for a third willingness construct: willingness behaviour. This construct implies that buyers are more influenced by expectations regarding behaviour, than assessments of salespeople’s attitudes. A homogeneous analysis supports the influence of perceived similarity on salesperson trust, both directly and through willingness behaviour. However, model-based segmentation uncovers a segment of cost-oriented dyads and a segment of more profit-oriented dyads. In cost-oriented dyads, there is no significant direct effect between perceived similarity and salesperson trust, and willingness behaviour nearly fully mediates this relationship. In more profit-oriented dyads, the similarity-attraction effect is not present. Theoretical and methodological contributions and managerial implications of these findings are discussed.

人類社會已邁入一個新的階段，而現階段的經濟特徵就是高度發達的服務產業。隨著網路科技進步，各式各樣的e化服務透過網路傳遞，在節省了時間與金錢成本，並提升了服務價值的同時，由於服務具有無形性、不可分離性、異質性及不可儲存性等特點，使得服務經營者也面臨空前激烈的競爭環境；然而在此同時，資訊科技的發展也進入Web2.0時代，強調互動、體驗以及共創價值。本研究在e化服務平台之上，以服務本體論為基礎，結合社會語意網路與認知科學，分析使用者群體基於相同服務需求所浮現出的使用者區隔，進行吸引效果與社會網路分析，建構一套提升服務對使用者的吸引力以及服務創新的轉型的方向，提供給服務經營者做為參考依據。本研究主要分為三個模組：Segmentation Module、Context Module、Analysis Module，在統一服務本體論描述的e化服務平台之上，基於相同服務需求所浮現出使用者區隔，基於吸引效果與社會關係改變服務組合，最後再分析服務使用者的選擇行為。本研究之預期貢獻為：(1)運用社會語意網路，以宏觀角度管理與挖掘使用者需求(2)運用認知科學之情境設計，突顯服務優勢，進而發展創新服務。 / There is a growing market for services and increasing dominance of services in economies worldwide. Nowadays there are many services delivered through Internet which can decrease both service providers and customers’ time and money and increase the service value during service consumption. Differing from physical goods, the four characteristics of service (Intangible, heterogeneous, simultaneous production and consumption and perishable) make the service providers face a competitive environment. In the meanwhile, the concept of web2.0 focuses on interaction, experience, and co-production between service providers and customers in the Internet which can be seen as a platform. Consequently, this research would like to propose a model to help service providers to innovate services. Based on a service ontology and combination of semantic social network and perception science, the model has three steps. First, through semantic social network, emerge the customer segments based on the same need. Second, in order to sustain the service attraction to the customers, for the specific customer segment, manipulate the service choice set by the attraction effect way. Third, for service innovation and transformation, engage new necessary components which refer to resources in this paper. So the social network analysis may provide the guidance for the service providers to build up new services. This study has two research objectives: (1)Find and manage customer needs through semantic social network.(2)Keep the service attraction to the customers and build new services based on perception science and social network analysis.

Certains problèmes ne peuvent être résolus ni par les ordinateurs seuls ni par les humains seuls. La visualisation d'information est une solution commune quand il est nécessaire de raisonner sur de grandes quantités de données. Plus une visualisation est efficace, plus il est possible de résoudre des problèmes complexes. Dans la recherche en visualisation d'information, une visualisation est généralement considérée comme efficace quand elle permet de comprendre les données. Les méthodes d'évaluation cherchent à déterminer si les utilisateurs comprennent les données affichées et sont capables d'effectuer des tâches analytiques comme, par exemple, identifier si deux variables sont corrélées. Cette thèse suggère d'aller au-delà de ce ``paradigme de l'analyse visuelle'' et élargir le champ de recherche à un autre type de tâche: la prise de décision. Les tâches de décision sont essentielles à tous, du directeur d'entreprise qui doit prendre des décisions importantes à l'individu ordinaire qui choisit un plan de carrière ou désire simplement acheter un appareil photo. Néanmoins, les décisions ne se résument pas à la simple compréhension de l'information et sont difficiles à étudier. Elles peuvent impliquer des préférences subjectives, n'ont pas toujours de vérité de terrain, et dépendent souvent de connaissances externes aux données visualisées. Pourtant, les tâches de décision ne font pas partie des taxonomies de tâches en visualisation et n'ont pas été bien définies. De plus, la recherche manque de métriques, de méthodes et de travaux empiriques pour valider l'efficacité des visualisations pour la prise de décision. Cette thèse offre une définition opérationnelle pour une classe particulière de tâches de décision, et présente une analyse systématique qui identifie les visualisations multidimensionnelles compatibles avec ces tâches. Elle présente en outre la première comparaison empirique de techniques de visualisation multidimensionnelle basée sur leur capacité à aider la décision, et esquisse une méthodologie et des métriques pour évaluer la qualité des décisions. Elle explore ensuite le rôle des instructions dans les tâches de décision et des tâches analytiques équivalentes, et identifie des différences de performance entre les deux tâches. De même que les sciences de la vision informent la visualisation d'information sur les limites de la vision humaine, aller au-delà du paradigme de l'analyse visuelle implique de prendre en compte les limites du raisonnement humain. Cette thèse passe en revue la théorie de la décision afin de mieux comprendre comment les humains prennent des décisions, et formule une nouvelle taxonomie de biais cognitifs basée sur la tâche utilisateur. En outre, elle démontre empiriquement que des biais peuvent être présents même quand l'information est bien visualisée, et qu'une décision peut être ``correcte'' mais néanmoins irrationnelle, dans le sens où elle est influencée par des informations non pertinentes. Cette thèse examine finalement comment mitiger les biais. Les méthodes pour améliorer le raisonnement humain reposent souvent sur un entraînement intensif à des principes et à des procédures abstraites, qui se révèlent souvent peu efficaces. Les visualisations offrent une opportunité dans la mesure où ses concepteurs peuvent remodeler l'environnement pour changer la façon dont les utilisateurs assimilent les données. Cette thèse passe en revue la théorie de la décision pour identifier de possibles solutions de conception. De plus, elle démontre empiriquement que supplémenter une visualisation par des interactions qui facilitent des stratégies de décision alternatives peut mener à des décisions plus rationnelles. Via des études empiriques, cette thèse suggère que le paradigme de l'analyse visuelle n'est pas en mesure de relever tous les défis de la prise de décision aidée de la visualisation, mais qu'aller au-delà peut contribuer à faire de la visualisation un puissant outil de prise de décision. / There are problems neither humans nor computers can solve alone. Computer-supported visualizations are a well-known solution when humans need to reason based on a large amount of data. The more effective a visualization, the more complex the problems that can be solved. In information visualization research, to be considered effective, a visualization typically needs to support data comprehension. Evaluation methods focus on whether users indeed understand the displayed data, can gain insights and are able to perform a set of analytic tasks, e.g., to identify if two variables are correlated. This dissertation suggests moving beyond this "visual analysis paradigm" by extending research focus to another type of task: decision making. Decision tasks are essential to everybody, from the manager of a company who needs to routinely make risky decisions to an ordinary person who wants to choose a career life path or simply find a camera to buy. Yet decisions do not merely involve information understanding and are difficult to study. Decision tasks can involve subjective preferences, do not always have a clear ground truth, and they often depend on external knowledge which may not be part of the displayed dataset. Nevertheless, decision tasks are neither part of visualization task taxonomies nor formally defined. Moreover, visualization research lacks metrics, methodologies and empirical works that validate the effectiveness of visualizations in supporting a decision. This dissertation provides an operational definition for a particular class of decision tasks and reports a systematic analysis to investigate the extent to which existing multidimensional visualizations are compatible with such tasks. It further reports on the first empirical comparison of multidimensional visualizations for their ability to support decisions and outlines a methodology and metrics to assess decision accuracy. It further explores the role of instructions in both decision tasks and equivalent analytic tasks, and identifies differences in accuracy between those tasks. Similarly to vision science that informs visualization researchers and practitioners on the limitations of human vision, moving beyond the visual analysis paradigm would mean acknowledging the limitations of human reasoning. This dissertation reviews decision theory to understand how humans should, could and do make decisions and formulates a new taxonomy of cognitive biases based on the user task where such biases occur. It further empirically shows that cognitive biases can be present even when information is well-visualized, and that a decision can be ``correct'' yet irrational, in the sense that people's decisions are influenced by irrelevant information. This dissertation finally examines how biases can be alleviated. Current methods for improving human reasoning often involve extensive training on abstract principles and procedures that often appear ineffective. Yet visualizations have an ace up their sleeve: visualization designers can re-design the environment to alter the way people process the data. This dissertation revisits decision theory to identify possible design solutions. It further empirically demonstrates that enriching a visualization with interactions that facilitate alternative decision strategies can yield more rational decisions. Through empirical studies, this dissertation suggests that the visual analysis paradigm cannot fully address the challenges of visualization-supported decision making, but that moving beyond can contribute to making visualization a powerful decision support tool.