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This dissertation consists of three essays that investigate decisions under limited attention. Chapter $1$ provides an introduction for this dissertation. In Chapter 2, I study additive representations under idempotent attention. Additive representations suggest that the utility of a menu equals the sum of the utility of alternatives in it, which implicitly assumes that a decision maker (DM) has full attention. However, many theoretical and empirical studies have argued that the DM does not always pay full attention. The purpose of this paper is to extend our understanding of additive representations to the context of limited attention. Specifically, we focus on additive representations when attention rules are idempotent, which are generalizations of attention filters. We propose the characterization of additive representations under idempotent attention as well as special cases of it: additive representations under attention filter. This model can also be applied to simple cardinality-based ordering and Borda scores when the DM has limited attention.In Chapter 3, I focus on satisficing under idempotent attention. Satisficing choice pattern has been accused of a lack of cognitive ability in perceiving and analyzing alternatives. In an effort to disentangle the limitations of noticing alternatives from other factors, this paper proposes a model of satisficing under limited attention. Our focus centers on the idempotent attention rules, leading to Satisficing under Idempotent Attention (SIA). Our study provides a characterization of SIA as well as a discussion of revealed attention and preference practices. Notably, these outcomes stem from choices made on a subset of menus. The revealed attention and preference, however, remain inherently non-unique. Additionally, considering the idempotent nature of attention filters and competition filters, we also present distinctive characterizations of satisficing under these two attention rules.Chapter 4 investigates choices u

Satisficing response behavior can be a threat to the quality of survey responses. Past research has provided broad empirical evidence on the existence of satisficing and its consequences on data quality, however, relatively little is known about the extent of satisficing over the course of a panel study and its impact on response quality in later waves. Drawing on panel conditioning research, we use question design experiments to investigate whether learning effects across waves of a panel study cause changes in the extent of satisficing and if so, whether general survey experience (process learning) or familiarity with specific question content (content learning) accounts for those changes. We use data from a longitudinal survey experiment comprising six panel waves administered within a German non-probability online access panel. To investigate the underlying mechanism of possible learning effects, the experimental study randomly assigned respondents to different frequencies of receiving identical question content over the six panel waves. Our results show the existence of satisficing in every panel wave, which is in its magnitude similar to the extent of satisficing in the probability-based GESIS Panel that we use as a benchmark study. However, we did not find changes in the extent of satisficing across panel waves, nor did we find moderation effects of the interval between the waves, respondents’ cognitive ability, or motivation. Additional validity analyses showed that satisficing does not only affect the distribution of individual estimates by 15 percent or more but also can have an effect on associations between variables.

Some respondents make careless responses due to the “satisficing,” which is an attempt to complete a questionnaire as quickly and easily as possible. To obtain results that reflect a fact, detecting satisficing and excluding the responses with satisficing from the analysis targets are required. One of the devised methods detects satisficing by adding questions that check violations of instructions and inconsistencies. However, this approach may cause respondents to lose their motivation and prompt them to satisficing. Additionally, a deep learning model that automatically answers these questions was reported. This threatens the reliability of the conventional method. To detect careless responses without inserting such screening questions, machine learning (ML) detection using data obtained from answer results was attempted in a previous study, with a detection rate of 55.6%, which is not sufficient from the viewpoint of practicality. Therefore, we hypothesized that a supervised ML model with a higher detection rate could be constructed by using on-screen answering behavior as features. However, (1) no existing questionnaire system can record on-screen answering behavior and (2) even if the answering behavior can be recorded, it is unclear which answering behavior features are associated with satisficing. We developed an answering behavior recording plug-in for LimeSurvey, an online questionnaire system used all over the world, and collected a large amount of data (from 5,692 people) in Japan. Then, a variety of features were examined and generated from answering behavior, and we constructed ML models to detect careless responses. We call this detection method the ML-ABS (ML-based answering behavior scale). Evaluation by cross-validation demonstrated that the detection rate for careless responses was 85.9%, which is much higher than the previous ML method. Among the various features we proposed, we found that reselecting the Likert scale and scrolling particularly co

This study aims to assess student motivation to provide valid responses to the PISA student questionnaire. This was done by modeling response times using a three-component finite mixture model, comprising two satisficing response styles (rapid and idle) and one optimizing response style. Each participant’s motivation was operationalized as their probability of providing an optimizing response to questionnaire items. Overall, the model offered a good fit to the data. Results indicate that most responders were motivated to optimize, with a slight decline toward the end. Further, results showed a positive effect of questionnaire-taking motivation on PISA performance, suggesting a positive relationship to test-taking motivation. In conclusion, response times can be valuable indicators for assessing survey response quality and may serve as a proxy for test-taking motivation.

This study aims to assess student motivation to provide valid responses to the PISA student questionnaire. This was done by modeling response times using a three-component finite mixture model, comprising two satisficing response styles (rapid and idle) and one optimizing response style. Each participant’s motivation was operationalized as their probability of providing an optimizing response to questionnaire items. Overall, the model offered a good fit to the data. Results indicate that most responders were motivated to optimize, with a slight decline toward the end. Further, results showed a positive effect of questionnaire-taking motivation on PISA performance, suggesting a positive relationship to test-taking motivation. In conclusion, response times can be valuable indicators for assessing survey response quality and may serve as a proxy for test-taking motivation.

This study aims to assess student motivation to provide valid responses to the PISA student questionnaire. This was done by modeling response times using a three-component finite mixture model, comprising two satisficing response styles (rapid and idle) and one optimizing response style. Each participant’s motivation was operationalized as their probability of providing an optimizing response to questionnaire items. Overall, the model offered a good fit to the data. Results indicate that most responders were motivated to optimize, with a slight decline toward the end. Further, results showed a positive effect of questionnaire-taking motivation on PISA performance, suggesting a positive relationship to test-taking motivation. In conclusion, response times can be valuable indicators for assessing survey response quality and may serve as a proxy for test-taking motivation.

This study aims to assess student motivation to provide valid responses to the PISA student questionnaire. This was done by modeling response times using a three-component finite mixture model, comprising two satisficing response styles (rapid and idle) and one optimizing response style. Each participant’s motivation was operationalized as their probability of providing an optimizing response to questionnaire items. Overall, the model offered a good fit to the data. Results indicate that most responders were motivated to optimize, with a slight decline toward the end. Further, results showed a positive effect of questionnaire-taking motivation on PISA performance, suggesting a positive relationship to test-taking motivation. In conclusion, response times can be valuable indicators for assessing survey response quality and may serve as a proxy for test-taking motivation.

This study aims to assess student motivation to provide valid responses to the PISA student questionnaire. This was done by modeling response times using a three-component finite mixture model, comprising two satisficing response styles (rapid and idle) and one optimizing response style. Each participant’s motivation was operationalized as their probability of providing an optimizing response to questionnaire items. Overall, the model offered a good fit to the data. Results indicate that most responders were motivated to optimize, with a slight decline toward the end. Further, results showed a positive effect of questionnaire-taking motivation on PISA performance, suggesting a positive relationship to test-taking motivation. In conclusion, response times can be valuable indicators for assessing survey response quality and may serve as a proxy for test-taking motivation.

In this study, we investigate to what extent the respondent characteristics age and educational level may be associated with undesirable answer behaviour (UAB) consistently across surveys. We use data from panel respondents who participated in ten general population surveys of CentERdata and Statistics Netherlands. A new method to visually present UAB and an inventive adaptation of a non-parametric effect size measure are used. The occurrence of UAB of respondents with specific characteristics is summarized in density distributions that we refer to as respondent profiles. An adaptation of the robust effect size Cliff’s Delta is used to compare respondent profiles on the potentially consistent occurrence of UAB across surveys. Taking all surveys together, the degree of UAB varies by age and education. The results do not show consistent UAB across individual surveys: Age and educational level are associated with a relatively higher occurrence of UAB for some surveys, but a relatively lower occurrence for other surveys. We conclude that the occurrence of UAB across surveys may be more dependent on the survey and its items than on respondent’s cognitive ability.

The risk of multitasking is high in online surveys. However, knowledge on the effects of multitasking on answer quality is sparse and based on suboptimal approaches. Research reports inconclusive results concerning the consequences of multitasking on task performance. However, studies suggest that especially sequential-multitasking activities are expected to be critical. Therefore, this study focusses on sequential-on-device-multitasking activities (SODM) and its consequences for data quality. Based on probability-based data, this study aims to reveal the prevalence of SODM based on the javascript function OnBlur, to reflect its determinants and to examine the consequences for data quality. Results show that SODM was detected for 25% of all respondents and that respondent attributes and the device used to answer the survey are related to SODM. Moreover, it becomes apparent that SODM is significantly correlated to data quality measures. Therefore, I propose SODM behavior as a new instrument for researching suboptimal response behavior.

The risk of multitasking is high in online surveys. However, knowledge on the effects of multitasking on answer quality is sparse and based on suboptimal approaches. Research reports inconclusive results concerning the consequences of multitasking on task performance. However, studies suggest that especially sequential-multitasking activities are expected to be critical. Therefore, this study focusses on sequential-on-device-multitasking activities (SODM) and its consequences for data quality. Based on probability-based data, this study aims to reveal the prevalence of SODM based on the javascript function OnBlur, to reflect its determinants and to examine the consequences for data quality. Results show that SODM was detected for 25% of all respondents and that respondent attributes and the device used to answer the survey are related to SODM. Moreover, it becomes apparent that SODM is significantly correlated to data quality measures. Therefore, I propose SODM behavior as a new instrument for researching suboptimal response behavior.

In this paper, we assume that the natural rate of interest is fundamentally uncertain. Based on a small-scale macroeconomic model, info-gap theory is used to rank different monetary policy strategies in terms of their robustness against this uncertainty. Applied to the euro area, we find that an inert or patient monetary strategy is more robust to natural rate uncertainty than a strategy that follows an estimated Taylor rule. An actively responsive monetary strategy is least robust. Our analysis presents a methodology that is applicable in a wide range of policy analyses under deep uncertainty.

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing, as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.

Descendants of indigenous people in the United States (Native Americans) are underrepresented in happiness studies. The social-cognitive mechanisms involved in happiness are also poorly understood. Here we test a social-cognitive model for theorizing the happiness of Native Americans. Self-identified Native Americans (120 women, 59 men, aged 18–79 years) in the Phoenix, Arizona metropolitan area answered a survey which measured interdependent and independent self-construals, decision-making style (satisficing and maximizing), and subjective happiness. Relationships among these variables were examined using path analysis. For the participants’ happiness, independent self-construal was relatively less important than interdependent self-construal, as social harmony and interdependence with others tend to be salient traditional Native American cultural values. However, in agreement with previous studies with Euro-American samples, subjective happiness was positively correlated with satisficing, and negatively correlated with maximizing, suggesting societal factors such as the availability of options and choices in the U.S. as possible influences. These findings contribute to the literature by revealing the basis of happiness in social-cognitive processes, and have important implications for understanding the happiness of an under-researched population.

Self-Evaluation of Educational Leadership Practices During COVID-19

Decision-making methods used in geospatial decision making are computationally complex prescriptive methods, the details of which are rarely transparent to the decision maker. However, having a deep understanding of the details and mechanisms of the applied method is a prerequisite for the efficient use thereof. In this paper, we present a novel decision-making framework that emanates from the need for intuitive and easy-to-use decision support systems for geospatial multi-criteria decision making. The framework consists of two parts: the decision-making model Even Swaps on Reduced Data Sets (ESRDS), and the interactive visualization framework. The decision-making model is based on the concept of satisficing, and as such, it is intuitive and easy to understand and apply. It integrates even swaps, a prescriptive decision-making method, with the findings of behavioural decision-making theories. Providing visual feedback and interaction opportunities throughout the decision-making process, the interactive visualization part of the framework helps the decision maker gain better insight into the decision space and attribute dependencies. Furthermore, it provides the means to analyse and compare the outcomes of different scenarios and decision paths.

Decision-making methods used in geospatial decision making are computationally complex prescriptive methods, the details of which are rarely transparent to the decision maker. However, having a deep understanding of the details and mechanisms of the applied method is a prerequisite for the efficient use thereof. In this paper, we present a novel decision-making framework that emanates from the need for intuitive and easy-to-use decision support systems for geospatial multi-criteria decision making. The framework consists of two parts: the decision-making model Even Swaps on Reduced Data Sets (ESRDS), and the interactive visualization framework. The decision-making model is based on the concept of satisficing, and as such, it is intuitive and easy to understand and apply. It integrates even swaps, a prescriptive decision-making method, with the findings of behavioural decision-making theories. Providing visual feedback and interaction opportunities throughout the decision-making process, the interactive visualization part of the framework helps the decision maker gain better insight into the decision space and attribute dependencies. Furthermore, it provides the means to analyse and compare the outcomes of different scenarios and decision paths.

Decision-making methods used in geospatial decision making are computationally complex prescriptive methods, the details of which are rarely transparent to the decision maker. However, having a deep understanding of the details and mechanisms of the applied method is a prerequisite for the efficient use thereof. In this paper, we present a novel decision-making framework that emanates from the need for intuitive and easy-to-use decision support systems for geospatial multi-criteria decision making. The framework consists of two parts: the decision-making model Even Swaps on Reduced Data Sets (ESRDS), and the interactive visualization framework. The decision-making model is based on the concept of satisficing, and as such, it is intuitive and easy to understand and apply. It integrates even swaps, a prescriptive decision-making method, with the findings of behavioural decision-making theories. Providing visual feedback and interaction opportunities throughout the decision-making process, the interactive visualization part of the framework helps the decision maker gain better insight into the decision space and attribute dependencies. Furthermore, it provides the means to analyse and compare the outcomes of different scenarios and decision paths.

Decision-making methods used in geospatial decision making are computationally complex prescriptive methods, the details of which are rarely transparent to the decision maker. However, having a deep understanding of the details and mechanisms of the applied method is a prerequisite for the efficient use thereof. In this paper, we present a novel decision-making framework that emanates from the need for intuitive and easy-to-use decision support systems for geospatial multi-criteria decision making. The framework consists of two parts: the decision-making model Even Swaps on Reduced Data Sets (ESRDS), and the interactive visualization framework. The decision-making model is based on the concept of satisficing, and as such, it is intuitive and easy to understand and apply. It integrates even swaps, a prescriptive decision-making method, with the findings of behavioural decision-making theories. Providing visual feedback and interaction opportunities throughout the decision-making process, the interactive visualization part of the framework helps the decision maker gain better insight into the decision space and attribute dependencies. Furthermore, it provides the means to analyse and compare the outcomes of different scenarios and decision paths.

Some respondents make careless responses due to the “satisficing,” which is an attempt to complete a questionnaire as quickly and easily as possible. To obtain results that reflect a fact, detecting satisficing and excluding the responses with satisficing from the analysis targets are required. One of the devised methods detects satisficing by adding questions that check violations of instructions and inconsistencies. However, this approach may cause respondents to lose their motivation and prompt them to satisficing. Additionally, a deep learning model that automatically answers these questions was reported. This threatens the reliability of the conventional method. To detect careless responses without inserting such screening questions, machine learning (ML) detection using data obtained from answer results was attempted in a previous study, with a detection rate of 55.6%, which is not sufficient from the viewpoint of practicality. Therefore, we hypothesized that a supervised ML model with a higher detection rate could be constructed by using on-screen answering behavior as features. However, (1) no existing questionnaire system can record on-screen answering behavior and (2) even if the answering behavior can be recorded, it is unclear which answering behavior features are associated with satisficing. We developed an answering behavior recording plug-in for LimeSurvey, an online questionnaire system used all over the world, and collected a large amount of data (from 5,692 people) in Japan. Then, a variety of features were examined and generated from answering behavior, and we constructed ML models to detect careless responses. We call this detection method the ML-ABS (ML-based answering behavior scale). Evaluation by cross-validation demonstrated that the detection rate for careless responses was 85.9%, which is much higher than the previous ML method. Among the various features we proposed, we found that reselecting the Likert scale and scrolling particularly co

Some respondents make careless responses due to the “satisficing,” which is an attempt to complete a questionnaire as quickly and easily as possible. To obtain results that reflect a fact, detecting satisficing and excluding the responses with satisficing from the analysis targets are required. One of the devised methods detects satisficing by adding questions that check violations of instructions and inconsistencies. However, this approach may cause respondents to lose their motivation and prompt them to satisficing. Additionally, a deep learning model that automatically answers these questions was reported. This threatens the reliability of the conventional method. To detect careless responses without inserting such screening questions, machine learning (ML) detection using data obtained from answer results was attempted in a previous study, with a detection rate of 55.6%, which is not sufficient from the viewpoint of practicality. Therefore, we hypothesized that a supervised ML model with a higher detection rate could be constructed by using on-screen answering behavior as features. However, (1) no existing questionnaire system can record on-screen answering behavior and (2) even if the answering behavior can be recorded, it is unclear which answering behavior features are associated with satisficing. We developed an answering behavior recording plug-in for LimeSurvey, an online questionnaire system used all over the world, and collected a large amount of data (from 5,692 people) in Japan. Then, a variety of features were examined and generated from answering behavior, and we constructed ML models to detect careless responses. We call this detection method the ML-ABS (ML-based answering behavior scale). Evaluation by cross-validation demonstrated that the detection rate for careless responses was 85.9%, which is much higher than the previous ML method. Among the various features we proposed, we found that reselecting the Likert scale and scrolling particularly co, journal article

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing, as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.

Decision-making methods used in geospatial decision making are computationally complex prescriptive methods, the details of which are rarely transparent to the decision maker. However, having a deep understanding of the details and mechanisms of the applied method is a prerequisite for the efficient use thereof. In this paper, we present a novel decision-making framework that emanates from the need for intuitive and easy-to-use decision support systems for geospatial multi-criteria decision making. The framework consists of two parts: the decision-making model Even Swaps on Reduced Data Sets (ESRDS), and the interactive visualization framework. The decision-making model is based on the concept of satisficing, and as such, it is intuitive and easy to understand and apply. It integrates even swaps, a prescriptive decision-making method, with the findings of behavioural decision-making theories. Providing visual feedback and interaction opportunities throughout the decision-making process, the interactive visualization part of the framework helps the decision maker gain better insight into the decision space and attribute dependencies. Furthermore, it provides the means to analyse and compare the outcomes of different scenarios and decision paths.

Cette thèse tire son origine de l’appréhension des praticiens de la recherche commerciale envers la professionnalisation des répondants résultant de l’utilisation des panels Web. Plusieurs praticiens estiment que les répondants complétant un grand nombre de sondages le feraient par l’appât du gain résultant des incitatifs et seraient moins vigilants en répondant aux sondages. Les praticiens déploient à ces fins des techniques de détection d’erreurs de réponse pour identifier des répondants inattentifs. La thèse porte sur l’évaluation de trois techniques de détection d’erreurs de réponse. Les résultats révèlent que les techniques de détection d’erreurs de réponse reposant sur des manifestations de l’heuristique du seuil de la réponse satisfaisante comme les schèmes linéaires et un court temps d’achèvement souffrent d’erreurs de type 1 et de type 2. À terme, ces erreurs peuvent influencer les résultats des sondages. L’analyse révèle également que le nombre de sondages complétés par les panélistes n’est pas un bon indicateur d’erreurs de réponse en utilisant les tests de la logique. Ainsi, les panélistes complétant un grand nombre de sondages n’effectuent pas significativement plus d’erreurs de réponse. Cela invalide en quelque sorte les appréhensions envers la professionnalisation des panélistes., This thesis draws its inspiration from the apprehensions of research practitioners towards respondent professionalization resulting from the use of web panels. Many practitioners assume that panellists who complete a large number of surveys do so to maximise their gains resulting from the provided incentives. These respondents are then assumed to be less vigilant in answering the said surveys. As a result, research firms deploy response error detection techniques to identify inattentive respondents. The thesis assesses three commonly deployed response error detection techniques. The results show that techniques relying on satisficing manifestations such as straightlining and speeding tend to suffer from type 1 and type 2 errors. These errors can alter the results of the survey and thus affect de validity of the findings. The findings also reveal that the number of surveys completed by panellists is not a good predictor of response errors as measured by a logic test. As a case in point, panellists who completed many surveys are not more prone to show response errors. The latter somewhat invalidates the earlier mentioned apprehensions towards panellists.

This thesis is a philosophical submission aiming towards the cultivation of a pluralistic jurisprudence. It is animated by the felt need for an equitable accommodation of social disagreement in legal reasoning. It attends this by focussing specifically on developing an account of value pluralism which can be judicially understood, not as a comprehensive ontology, but as a pragmatic structuring of normativity with its own distinctive ethos and conception of rationality. This distinctiveness necessitates a careful explanation, defence, and demonstration of viability within the literature of jurisprudence, as well as, critically, the construction of terms on which a normative conviction in pluralism could successfully be judicially and legally vindicated.To this end, the thesis is divided into two Parts. Part I works towards a normative account of pluralism, looking to capture what is politically/legally appreciable about the concept, as illustrated through the value pluralism of Isaiah Berlin (Chapter 1). But, recognizing critiques of that philosophy –specifically, the critique by Ronald Dworkin – Part I will also be concerned to refurbish the Berlinian account of plurality for greater legal appreciation by drawing on support from pragmatic epistemology, Elizabeth Anderson's expressivist value theory, and recent scholarship in 'satisficing' decision theory. These conjoin in the construction of a defensible instantiation of pluralism (Chapter 2). The inquiry of Part II, then, seeks grounds of both solidarity and criticality for Part I's pluralism within contemporary analytical jurisprudence. In particular, Part II looks to the motivational complexes and valuative patterns underlying Joseph Raz's exclusive positivism (Chapter 3), Ronald Dworkin's interpretivism (Chapter 4), and Richard Posner's legal pragmatism (Chapter 5). Part II's inquiry seeks jurisprudential tools suitable to mount a rational resistance against the monistic orthodoxy of legal thought, and in doing

This article investigates the data quality of ego-centered social network modules in web surveys. It specifically examines whether these modules are subject to the effects of the repeated measurement of the same questions known as panel conditioning effects. Ego-centered social network modules are especially at risk of panel conditioning effects because many of the components in these modules are repetitive. Based on the theories of motivated underreporting and survey satisficing, we hypothesized that respondents reduce the length of the module by underreporting their network size and/or network density. To systematically test for panel conditioning effects, we experimentally varied the treatment frequency in a longitudinal study design, which included three panel waves. The results of our study showed that we generally obtained high quality data with relatively large reported network sizes and densities, low rates of item non-response, and low non-differentiation. In contrast to our expectations, the reported average network sizes were not smaller, and the network densities were not lower when respondents were asked to answer the same social network module multiple times. We found, however, patterns of individual change in network sizes that might be due to panel conditioning. Respondents with large network sizes in a panel wave reported smaller network sizes in the subsequent wave, while respondents with small network sizes reported larger network sizes in the subsequent wave. Respondents’ ability and motivation did not affect these results. Thus, we would like to encourage researchers to further explore the opportunity of implementing ego-centered social network modules in cross-sectional as well as longitudinal self-administered surveys, while being cautious that in longitudinal surveys the chance of panel conditioning effects may increase with the average network size and the response burden of the network module.

In contrast to the relative abundance of conceptualisations of information literacy, the earlier research has placed considerably less attention on its alternatives. The findings show that there are shades of being less and non-literate beyond a mere lack of necessary skills or engagement in inappropriate practices. Information illiteracy can be experienced as a problem but it can also represent a conscious choice for delimiting and organising information practices. From a theoretical and practical perspective, this study suggests that both information literacies and information illiteracies should be taken into account in information literacy research and education, and when developing and deploying information systems and services to compensate for the lack of literacies.

In contrast to the relative abundance of conceptualisations of information literacy, the earlier research has placed considerably less attention on its alternatives. The findings show that there are shades of being less and non-literate beyond a mere lack of necessary skills or engagement in inappropriate practices. Information illiteracy can be experienced as a problem but it can also represent a conscious choice for delimiting and organising information practices. From a theoretical and practical perspective, this study suggests that both information literacies and information illiteracies should be taken into account in information literacy research and education, and when developing and deploying information systems and services to compensate for the lack of literacies.

In contrast to the relative abundance of conceptualisations of information literacy, the earlier research has placed considerably less attention on its alternatives. The findings show that there are shades of being less and non-literate beyond a mere lack of necessary skills or engagement in inappropriate practices. Information illiteracy can be experienced as a problem but it can also represent a conscious choice for delimiting and organising information practices. From a theoretical and practical perspective, this study suggests that both information literacies and information illiteracies should be taken into account in information literacy research and education, and when developing and deploying information systems and services to compensate for the lack of literacies.

In contrast to the relative abundance of conceptualisations of information literacy, the earlier research has placed considerably less attention on its alternatives. The findings show that there are shades of being less and non-literate beyond a mere lack of necessary skills or engagement in inappropriate practices. Information illiteracy can be experienced as a problem but it can also represent a conscious choice for delimiting and organising information practices. From a theoretical and practical perspective, this study suggests that both information literacies and information illiteracies should be taken into account in information literacy research and education, and when developing and deploying information systems and services to compensate for the lack of literacies.

In contrast to the relative abundance of conceptualisations of information literacy, the earlier research has placed considerably less attention on its alternatives. The findings show that there are shades of being less and non-literate beyond a mere lack of necessary skills or engagement in inappropriate practices. Information illiteracy can be experienced as a problem but it can also represent a conscious choice for delimiting and organising information practices. From a theoretical and practical perspective, this study suggests that both information literacies and information illiteracies should be taken into account in information literacy research and education, and when developing and deploying information systems and services to compensate for the lack of literacies.

This paper attempts to identify behavioral patterns and compare their average success considering several criteria of bounded rationality. Experimentally observed choice behavior in various decision tasks is used to assess heterogeneity in how individual participants respond to 15 randomly ordered portfolio choices, each of which is experienced twice. Treatments differ in (not) granting probability information and in (not) eliciting aspirations. Since in our setting neither other regarding concerns nor risk attitude matter and probability of the binary chance move is (optimal) choice irrelevant, categorizing decision types relies on parameter dependence and choice adaptations. We find that most participants reduce systematically sub-optimality when following the identified criteria.

Intrigued by the possibilities of improving the data quality of web surveys by incorporating human-like features, we developed a video-web survey for this study. This paper describes an experiment that compares response behavior in the video-web mode to traditional web and interviewer-administered surveys. The disclosure of sensitive information and respondents’ engagement were examined. Overall, despite the visual and auditory representation of a human interviewer in the video-web mode, video-web seems to have been experienced by respondents much like a traditional web survey. Based on these results, we argue that for human-like features to fully increase the level of engagement it would require the inclusion of responsiveness. However, researchers should be aware of possible social presence effects that may arise when creating web surveys with responsive human-like features.

Learning and evolution are adaptive or “backward-looking” models of social and biological systems. Learning changes the probability distribution of traits within an individual through direct and vicarious reinforcement, while evolution changes the probability distribution of traits within a population through reproduction and selection. Compared to forward-looking models of rational calculation that identify equilibrium outcomes, adaptive models pose fewer cognitive requirements and reveal both equilibrium and out-of-equilibrium dynamics. However, they are also less general than analytical models and require relatively stable environments. In this chapter, we review the conceptual and practical foundations of several approaches to models of learning that offer powerful tools for modeling social processes. These include the Bush-Mosteller stochastic learning model, the Roth-Erev matching model, feed-forward and attractor neural networks, and belief learning. Evolutionary approaches include replicator dynamics and genetic algorithms. A unifying theme is showing how complex patterns can arise from relatively simple adaptive rules.

The aim of this thesis is to study how students perceive and handle internet’s continuous flow of information in everyday life, through news report, social media and targeted advertising. By examining the students attitudes and thoughts about the extent to which they find the information they meet relevant this study seeks to understand how they find what is relevant to them and how they limit the intake of information by different combinations of selection processes. For this purpose interviews were carried out with seven university students from different fields and levels. Three categories of theories about selection processes serves as a theoretical framework for the analysis and results. The categories are referred to as external, personalized and personal factors. The external factors include gatekeeping theory, news valuation and agenda theory and the personalized factors include theories about filter bubbles and echo chambers. The personal factors include strategies to avoid information overload such as filtering and withdrawing, it also includes satisficing as a stop rule for information seeking. The study shows that the three categories of selection processes are affected by emotional factors referred to as simplicity, interest and sensation. These drives the student to get more deeply informed. The students attitudes towards personalized information shows to be divided between if it should be more firmly adapted to each individual, or if that would increase the feeling of being monitored. In the students reflections regarding saved information emerges three feelings of negativity described as reluctance, lack of control and unease. Despite these feelings of negativity the phenomenon is accepted. Seeing that the alternative is disconnecting from modern society and life, acceptance shows to be the most reasonable alternative.

Learning and evolution are adaptive or “backward-looking” models of social and biological systems. Learning changes the probability distribution of traits within an individual through direct and vicarious reinforcement, while evolution changes the probability distribution of traits within a population through reproduction and selection. Compared to forward-looking models of rational calculation that identify equilibrium outcomes, adaptive models pose fewer cognitive requirements and reveal both equilibrium and out-of-equilibrium dynamics. However, they are also less general than analytical models and require relatively stable environments. In this chapter, we review the conceptual and practical foundations of several approaches to models of learning that offer powerful tools for modeling social processes. These include the Bush-Mosteller stochastic learning model, the Roth-Erev matching model, feed-forward and attractor neural networks, and belief learning. Evolutionary approaches include replicator dynamics and genetic algorithms. A unifying theme is showing how complex patterns can arise from relatively simple adaptive rules.

The aim of this thesis is to study how students perceive and handle internet’s continuous flow of information in everyday life, through news report, social media and targeted advertising. By examining the students attitudes and thoughts about the extent to which they find the information they meet relevant this study seeks to understand how they find what is relevant to them and how they limit the intake of information by different combinations of selection processes. For this purpose interviews were carried out with seven university students from different fields and levels. Three categories of theories about selection processes serves as a theoretical framework for the analysis and results. The categories are referred to as external, personalized and personal factors. The external factors include gatekeeping theory, news valuation and agenda theory and the personalized factors include theories about filter bubbles and echo chambers. The personal factors include strategies to avoid information overload such as filtering and withdrawing, it also includes satisficing as a stop rule for information seeking. The study shows that the three categories of selection processes are affected by emotional factors referred to as simplicity, interest and sensation. These drives the student to get more deeply informed. The students attitudes towards personalized information shows to be divided between if it should be more firmly adapted to each individual, or if that would increase the feeling of being monitored. In the students reflections regarding saved information emerges three feelings of negativity described as reluctance, lack of control and unease. Despite these feelings of negativity the phenomenon is accepted. Seeing that the alternative is disconnecting from modern society and life, acceptance shows to be the most reasonable alternative.

We implement a risky choice experiment based on one-dimensional choice variables and risk neutrality induced via binary lottery incentives. Each participant confronts many parameter constellations with varying optimal payoffs. We assess (sub)optimality, as well as (non) optimal satisficing by eliciting aspirations in addition to choices. Treatments differ in the probability that a binary random event, which are payoff-but not optimal choice-relevant is experimentally induced and whether participants choose portfolios directly or via satisficing, i.e., by forming aspirations and checking for satisficing before making their choice. By incentivizing aspiration formation, we can test satisficing, and in cases of satisficing, determine whether it is optimal.

The aim of this thesis is to study how students cope with crisis-related information, such as news reports and public discussions on the Syrian refugee crisis. With this in view, the study seeks to identify the strategies chosen to manage this sort of information, as well as the motivations underlying those choices. For this purpose inter-views are carried out with nine students of library and information science. Herbert Simon’s theory of bounded rationality serves as a theoretical framework for the analysis of the results, aided by more recent theories of the role of affect in decision making. The study shows that the students used various strategies for coping with crisis-related information. They used filtering and withdrawal as strategies for information avoidance, limiting the amount of information encoun-tered, and they used satisficing as a strategy for negotiating information needs, thus limiting their information seeking. The motivations for information avoidance were mainly emotion-based, while the motivations for satis-ficing were cognition-based. However, assuming all motivated, goal-oriented behaviour to be rational behaviour, the strategies used by the students were rational, or rather based on bounded rationality. All strategies also turned out to be flawed in some way, which is why a full repertoire of strategies – both emotion-based and cognition-based, with the ability to complement each other – is desirable. This is a two years master’s thesis in library and information science.

A problem for evaluators is that what people ‘say’ is often, but not always, different from what they ‘do.’ Most thinking, knowledge, behaviour, decisions are automatic, quick and have the characteristics of intuition, little or no effort and no voluntary control (Kahneman 2011b). Our everyday behaviours and thinking are largely based on implicit knowledge consisting of mental representations, or ‘schema’; cognitive knowledge about whatever we are thinking about or acting towards. The schemas are triggered by association and are further elaborated by association. As a result active schemas are internally coherent and semantically consistent (Graesser, Gernsbacher, and Goldman 1997). Schemas allow us to act without consciously thinking about what we will do or how: we simply ‘do,’ ‘know’ and ‘understand.’ A consequence of our reliance on implicit knowledge is that what people ‘say about’ phenomena does not always reflect what they ‘do.’ Elicitation techniques are increasingly being used in research and evaluation to get beyond what people say, apparently by tapping into the implicit cognitive understandings. Elicitation techniques are being used in social research, educational research, market research and evaluation. However, users of elicitation techniques need to understand the limits on the validity of such techniques and how to improve their effectiveness. Understanding the cognitive processes that mediate responses such cognitive processes can help improve the way evaluators obtain data. This thesis aims to improve our understanding of how to use projective or elicitation techniques validly in evaluation and research interviews. The majority of the thesis reviews the literature across several domains and disciplines to explore the cognitive processes that underlie people’s responses to elicitation techniques. The domains and disciplines include cognitive psychology, neurophysiology, phenomenological sociology, evaluation and social research (quantitative and qu

The aim of this thesis is to study how students cope with crisis-related information, such as news reports and public discussions on the Syrian refugee crisis. With this in view, the study seeks to identify the strategies chosen to manage this sort of information, as well as the motivations underlying those choices. For this purpose inter-views are carried out with nine students of library and information science. Herbert Simon’s theory of bounded rationality serves as a theoretical framework for the analysis of the results, aided by more recent theories of the role of affect in decision making. The study shows that the students used various strategies for coping with crisis-related information. They used filtering and withdrawal as strategies for information avoidance, limiting the amount of information encoun-tered, and they used satisficing as a strategy for negotiating information needs, thus limiting their information seeking. The motivations for information avoidance were mainly emotion-based, while the motivations for satis-ficing were cognition-based. However, assuming all motivated, goal-oriented behaviour to be rational behaviour, the strategies used by the students were rational, or rather based on bounded rationality. All strategies also turned out to be flawed in some way, which is why a full repertoire of strategies – both emotion-based and cognition-based, with the ability to complement each other – is desirable. This is a two years master’s thesis in library and information science.

Straightlining, an indicator of satisficing, refers to giving the same answer in a series of questions arranged on a grid. We investigated whether straightlining changes with respondents’ panel experience in the LISS panel in the Netherlands. Specifically, we considered straightlining on 10 grid questions in LISS core modules (7 waves) and on a grid of evaluation questions in the LISS panel (150+ waves). For both core modules and evaluation questions we found that straightlining increases with respondents’ panel experience for at least three years. Straightlining is also associated with younger age. Where straightlining corresponded to a plausible set of answers, prevalence of straightlining was much larger (15-40%) than where straightlining was implausible (

Trustworthiness is an important emerging requirement for software systems deployed by the U.S. Air Force. Trustworthiness, briefly stated, is the ability of a software system to be safe, secure, and reliable within a normal operating environment. However, most software systems have not been developed with trustworthiness in mind. Therefore, how do we systematically identify deficits in trustworthiness in existing systems so that they may be re-engineered with trustworthiness as a priority? The Non-Functional Requirements (NFR) Approach provides a framework for identifying gaps in trustworthiness in existing systems, and recommending mechanisms to overcome this shortfall in trustworthiness in re-engineered systems. In this project, we applied the NFR Approach to the Air Force's Phoenix middleware system and determined that it has an 89% shortfall in trustworthiness. The advantages of identifying this deficit include determination of trustworthiness in current systems, exploring environments in which current systems may be used or reused, and prioritizing trustworthiness requirements when these legacy systems are re-engineered., Published in CrossTalk: The Journal of Defense Software Engineering, v27 n1 p4-11, January/February 2014. Presented at the Software Technology Conference held in Salt Lake City, Utah, in April 2013.

To United Minds, large-scale surveys are an important offering to clients, not least the public opinion poll Väljarbarometern. A risk associated with surveys is satisficing – sub-optimal response behaviour impairing the possibility of correctly describing the sampled population through its results. The purpose of this study is to – through the use of multivariate outlier detection methods - identify those observations assumed to be non-representative of the population. The possibility of categorizing responses generated through satisficing as outliers is investigated. With regards to the character of the Väljarbarometern dataset, three existing algorithms are adapted to detect these outliers. Also, a number of randomly generated observations are added to the data, by all algorithms correctly labelled as outliers. The resulting anomaly scores generated by each algorithm are compared, concluding the Otey algorithm as the most effective for the purpose, above all since it takes into account correlation between variables. A plausible cut-off value for outliers and separation between non-representative and representative outliers are discussed. The resulting recommendation is to handle observations labelled as outliers through respondent follow-up or if not possible, through downweighting, inversely proportional to the anomaly scores.

To United Minds, large-scale surveys are an important offering to clients, not least the public opinion poll Väljarbarometern. A risk associated with surveys is satisficing – sub-optimal response behaviour impairing the possibility of correctly describing the sampled population through its results. The purpose of this study is to – through the use of multivariate outlier detection methods - identify those observations assumed to be non-representative of the population. The possibility of categorizing responses generated through satisficing as outliers is investigated. With regards to the character of the Väljarbarometern dataset, three existing algorithms are adapted to detect these outliers. Also, a number of randomly generated observations are added to the data, by all algorithms correctly labelled as outliers. The resulting anomaly scores generated by each algorithm are compared, concluding the Otey algorithm as the most effective for the purpose, above all since it takes into account correlation between variables. A plausible cut-off value for outliers and separation between non-representative and representative outliers are discussed. The resulting recommendation is to handle observations labelled as outliers through respondent follow-up or if not possible, through downweighting, inversely proportional to the anomaly scores.

In resource allocation problems for systems with moving planning horizons and significant uncertainty, typical of supervisory control environments, it is critical that some balance of human-automation collaboration be achieved. These systems typically require leveraging the computational power of automation, as well as the experience and judgment of human decision makers. Human-automation collaboration can occur through degrees of collaboration from automation-centric to human-centric, and such collaboration is inherently distinct from previously-discussed levels of automation. In the context of a command and control mission planning task, we show that across a number of metrics, there is no clear dominant human-automation collaboration scheme for resource allocation problems using three distinct instantiations of human-automation collaboration. Rather, the ultimate selection for the best resource allocation decision support system will depend on a cost-benefit approach that could include mitigation of workload, conformance to intended design characteristics, as well as the need to maximize overall mission performance.

In resource allocation problems for systems with moving planning horizons and significant uncertainty, typical of supervisory control environments, it is critical that some balance of human-automation collaboration be achieved. These systems typically require leveraging the computational power of automation, as well as the experience and judgment of human decision makers. Human-automation collaboration can occur through degrees of collaboration from automation-centric to human-centric, and such collaboration is inherently distinct from previously-discussed levels of automation. In the context of a command and control mission planning task, we show that across a number of metrics, there is no clear dominant human-automation collaboration scheme for resource allocation problems using three distinct instantiations of human-automation collaboration. Rather, the ultimate selection for the best resource allocation decision support system will depend on a cost-benefit approach that could include mitigation of workload, conformance to intended design characteristics, as well as the need to maximize overall mission performance.

We present the Maximization Inventory, which consists of three separate scales: decision difficulty, alternative search, and satisficing. We show that the items of the Maximization Inventory have much better psychometric properties when compared to the original Maximization Scale (Schwartz et al., 2002). The satisficing scale is a new addition to the study of maximization behavior, and we demonstrate that this scale is positively correlated with positive adaptation, whereas the decision difficulty and alternative search scales are positively correlated with nonproductive decisional behavior. The Maximization Inventory was then compared to previous maximization scales and, while the decision difficulty and alternative search scales are positively correlated with similar previous constructs, the satisficing scale offers a dimension entirely different from maximization