Classification of Human Decision Behavior: Finding

The understanding of human behavior in sequential decision tasks is important for economics and socio-psychological sciences. In search tasks, for example when individuals search for the best price of a product, they are confronted in sequential steps with different situations and they have to decide whether to continue or stop searching. The decision behavior of individuals in such search tasks is described by a search strategy. This paper presents a new approach of finding high-quality search strategies by using genetic algorithms (GAs). Only the structure of the search strategies and the basic building blocks (price thresholds and price patterns) that can be used for the search strategies are pre- specified. It is the purpose of the GA to construct search strategies that well describe human search behavior. The search strategies found by the GA are able to predict human behavior in search tasks better than traditional search strategies from the literature which are usually based on theoretical assumptions about human behavior in search tasks. Furthermore, the found search strategies are reasonable in the sense that they can be well interpreted, and generally that means they describe the search behavior of a larger group of individuals and allow some kind of categorization and classification. The results of this study open a new perspective for future research in developing behavioral strategies. Instead of deriving search strategies from theoretical assumptions about human behavior, researchers can directly analyze human behavior in search tasks and find appropriate and high-quality search strategies. These can be used for gaining new insights into the motivation behind human search and for developing new theoretical models about human search behavior.

Classification of Human Decision Behavior: Finding Modular Decision Rules with Genetic Algorithms

The understanding of human behavior in sequential decision tasks is important for economics and socio-psychological sciences. In search tasks, for example when individuals search for the best price of a product, they are confronted in sequential steps with different situations and they have to decide whether to continue or stop searching. The decision behavior of individuals in such search tasks is described by a search strategy. This paper presents a new approach of finding high-quality search strategies by using genetic algorithms (GAs). Only the structure of the search strategies and the basic building blocks (price thresholds and price patterns) that can be used for the search strategies are pre-specified. It is the purpose of the GA to construct search strategies that well describe human search behavior. The search strategies found by the GA are able to predict human behavior in search tasks better than traditional search strategies from the literature which are usually based on theoretical assumptions about human behavior in search tasks. Furthermore, the found search strategies are reasonable in the sense that they can be well interpreted, and generally that means they describe the search behavior of a larger group of individuals and allow some kind of categorization and classification. The results of this study open a new perspective for future research in developing behavioral strategies. Instead of deriving search strategies from theoretical assumptions about human behavior, researchers can directly analyze human behavior in search tasks and find appropriate and high- quality search strategies. These can be used for gaining new insights into the motivation behind human search and for developing new theoretical models about human search behavior.

Evaluation of Conceptual Models - A Structuralist Approach

The quality and thus the validation of conceptual models are of high economic importance. However, only little empirical work has focused on their evaluation so far. This raises the question whether a holistic approach to determining the quality of conceptual models is available yet. In order to describe the current state of research and to expose the so far neglected research fields we develop a two dimensional framework. With the help of this framework we can identify a notable shortcoming on conceptual model evaluation. Contrary to models on theories a lot of empirical work has been performed. Therefore we apply the structuralist approach from philosophy of science in order to develop an inner structure of conceptual models. Based on these findings we deduce the structural requirements that conceptual models shall meet. We explain the practical implications of our proposal and sketch an outlook to future scientific inquiries

An Integrated Method for Determination of the Oswald Factor in a Multi-Fidelity Design Environment

Aircraft conceptual design often focuses on unconventional configurations like for example forward swept wings. Assessing the characteristics of these configurations usually requires the use of physic based analysis modules. This is due to the fact that for unconventional configurations no sucient database for historic based analysis modules is available. Nevertheless, physic based models require a lot of input data and their computational cost can be high. Generating input values in a trade study manually is work-intensive and error-prone. Conceptual design modules can be used to generate sucient input data for physic based models and their results can be re-integrated into the conceptual design phase. In this study a direct link between a conceptual design module and an aerodynamic design module is presented. Geometric information is generated by the conceptual design module and the physic based results, in form of the Oswald factor, are then fed back. Apart from the direct link, an equation for determination of the Oswald factor is derived via a Symbolic Regression Approach

Playing and Reflecting Games: The Production of Gamified Learning Artefacts in Teacher Education

At the University, students played and reflected on different games within a digital course conducted by the teaching study program. In one session students chose and played different games. Guided by questions, they discussed and reflected in groups the potential of games for their own future teaching. Their new found experience was critical for the production of a gamified learning artefact in moodle. The self-evaluation showed that the participants were able to utilize their new set of skills and develop, implement and improve a learning artefact over a self-chosen topic. The following paper describes the didactical approach. We share the results of the students perspectives and learning outcomes towards game based dialogue. Students consider gamification and games as useful for different aspects of teaching

Classification of Human Decision Behavior : Finding Modular Decision Rules with Genetic Algorithms

The understanding of human behavior in sequential decision tasks is important for economics and socio-psychological sciences. In search tasks, for example when individuals search for the best price of a product, they are confronted in sequential steps with different situations and they have to decide whether to continue or stop searching. The decision behavior of individuals in such search tasks is described by a search strategy. This paper presents a new approach of finding high-quality search strategies by using genetic algorithms (GAs). Only the structure of the search strategies and the basic building blocks (price thresholds and price patterns) that can be used for the search strategies are pre-specified. It is the purpose of the GA to construct search strategies that well describe human search behavior. The search strategies found by the GA are able to predict human behavior in search tasks better than traditional search strategies from the literature which are usually based on theoretical assumptions about human behavior in search tasks. Furthermore, the found search strategies are reasonable in the sense that they can be well interpreted, and generally that means they describe the search behavior of a larger group of individuals and allow some kind of categorization and classification. The results of this study open a new perspective for future research in developing behavioral strategies. Instead of deriving search strategies from theoretical assumptions about human behavior, researchers can directly analyze human behavior in search tasks and find appropriate and high-quality search strategies. These can be used for gaining new insights into the motivation behind human search and for developing new theoretical models about human search behavior

Context-based Modeling – Conceptualization of a Novel Modeling Approach and Application for the Design of Business Documents

In this paper a novel reuse approach called context-based modeling is proposed and applied for the modeling of business documents. Business documents constitute mutual agreements, often legally binding between business partners. Already existing document standards reduce the efforts of implementing data exchange. However, the specific properties of an organization entail a need for adaptation. We show that available reuse approaches do not support this appropriately. Context-based modeling is proposed based on the reuse mechanisms aggregation, restriction, and specialization. Context-based modeling aims at both, minimal preparation of reuse combined with a high degree of guidance to create suitable models. The proposal is conceptually explored and practically applied to evaluate the feasibility and efficiency of the approach