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.

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.

SOCIAL INFLUENCE IN RECOMMENDATION AGENTS: CREATING SYNERGIES BETWEEN MULTIPLE RECOMMENDATION SOURCES FOR ONLINE PURCHASES

With the increased popularity of online social networks, friends become an available recommendation source for decisions that are made on the Internet, such as online purchases. There is substantial benefit in integrating different recommendation sources into one recommendation system so that more information and indeed more relevant information can be provided to the user. However, there is also the burden on the user of having to cope with the broader scope of and sometimes differing advice provided. This paper focuses on the issue of potential cognitive dissonance between the user?s own preferences, social influencer?s (e.g., friend?s) recommendations, and advice from a recommendation agent (RA). It provides a model of how different recommendation system designs can lead to different magnitudes of dissonance and when. It also discusses the role of the user?s product knowledge on influencing the extent of and reaction with dissonance. This paper contributes to the designing of recommendation systems which can create synergies between different recommendation sources to best assist the user

Gameful Learning for a More Sustainable World

Municipal waste sorting is an important but neglected topic within sustainability-oriented Information Systems research. Most waste management systems depend on the quality of their citizens pre-sorting but lack teaching resources. Thus, it is important to raise awareness and knowledge on correct waste sorting to strengthen current efforts. Having shown promising results in raising learning outcomes and motivation in domains like health and economics, gamification is an auspicious approach to address this problem. The paper explores the effectiveness of gameful design on learning outcomes of waste sorting knowledge with a mobile game app that implements two different learning strategies: repetition and elaboration. In a laboratory experiment, the overall learning outcome of participants who trained with the game was compared to that of participants who trained with standard analogue non-game materials. Furthermore, the effects of two additional, learning-enhancing design elements – repetition and look-up – were analyzed. Learning outcome in terms of long-term retention and knowledge transfer were evaluated through three different testing measures two weeks after the training: in-game, through a multiple-choice test and real-life sorting. The results show that the game significantly enhanced the learning outcome of waste sorting knowledge for all measures, which is particularly remarkable for the real-life measure, as similar studies were not successful with regard to knowledge transfer to real life. Furthermore, look-up is found to be a promising game design element that is not yet established in IS literature and therefore should be considered more thoroughly in future research and practical implementations alike

A Low-Effort Recommendation System with High Accuracy - A New Approach with Ranked Pareto-Fronts

In recent studies on recommendation systems, the choice-based conjoint analysis has been suggested as a method for measuring consumer preferences. This approach achieves high recommendation accuracy and does not suffer from the start-up problem because it is also applicable for recommendations for new consumers or of new products. However, this method requires massive consumer input, which causes consumer reluctance. In a simulation study, we demonstrate the high accuracy, but also the high user’s effort for using a utility-based recommendation system using a choice-based conjoint analysiswith hierarchical Bayes estimation. In order to reduce the conflict between consumer effort and recommendation accuracy, we develop a novel approach that only shows Paretoefficient alternatives and ranks them according to the number of dominated attributes. We demonstrate that, in terms of the decision accuracy of the recommended products, the ranked Pareto-front approach performs better than a recommendation system that employs choice-based conjoint analysis. Furthermore, the consumer’s effort is kept low and comparable to that of simple systems that require little consumer input

Gameful Learning for a More Sustainable World – Measuring the Effect of Design Elements on Long-Term Learning Outcomes in Correct Waste Sorting

Municipal waste sorting is an important but neglected topic within sustainability-oriented Information Systems research. Most waste management systems depend on the quality of their citizens pre-sorting but lack teaching resources. Thus, it is important to raise awareness and knowledge on correct waste sorting to strengthen current efforts. Having shown promising results in raising learning outcomes and motivation in domains like health and economics, gamification is an auspicious approach to address this problem. The paper explores the effectiveness of gameful design on learning outcomes of waste sorting knowledge with a mobile game app that implements two different learning strategies: repetition and elaboration. In a laboratory experiment, the overall learning outcome of participants who trained with the game was compared to that of participants who trained with standard analogue non-game materials. Furthermore, the effects of two additional, learning-enhancing design elements – repetition and look-up – were analyzed. Learning outcome in terms of long-term retention and knowledge transfer were evaluated through three different testing measures two weeks after the training: in-game, through a multiple-choice test and real-life sorting. The results show that the game significantly enhanced the learning outcome of waste sorting knowledge for all measures, which is particularly remarkable for the real-life measure, as similar studies were not successful with regard to knowledge transfer to real life. Furthermore, look-up is found to be a promising game design element that is not yet established in IS literature and therefore should be considered more thoroughly in future research and practical implementations alike