Classification of Human Decision Behavior: Finding Modular Decision Rules with Genetic Algorithms
- Publikationstyp:
- Konferenzbeitrag
- Metadaten:
-
- Autoren
- Frank Rothlauf
- Daniel Schunk
- Jella Pfeiffer
- Sammlungen
- metadata
- Editoren
- Hans-Georg Beyer
- Schlüsselwörter
- 330 Wirtschaft
- 330 Economics
- Paginierung
- Seiten: 2021 - 2028
- Ort der Veröffentlichung
- New York
- Datum der Veröffentlichung
- 2005
- Herausgeber
- ACM Press
- Herausgeber URL
- http://dx.doi.org/10.1145/1068009.1068346
- Datum der Datenerfassung
- 2020
- Datum, an dem der Datensatz öffentlich gemacht wurde
- 2020
- Zugang
- Public
- Titel
- Classification of human dynamic decision behavior : finding modular decision rules with genetic algorithms
Datenquelle: METADATA.UB
- Andere Metadatenquellen:
-
- Autoren
- Franz Rothlauf
- Daniel Schunk
- Jella Pfeiffer
- DOI
- 10.1145/1068009.1068346
- Zeitschrift
- Proceedings of the 7th annual conference on Genetic and evolutionary computation
- Name of conference
- GECCO05: Genetic and Evolutionary Computation Conference
- Online publication date
- 2005
- Datum der Veröffentlichung
- 2005
- Status
- Published
- Herausgeber
- ACM
- Herausgeber URL
- http://dx.doi.org/10.1145/1068009.1068346
- Datum der Datenerfassung
- 2023
- Titel
- Classification of human decision behavior
Datenquelle: Crossref
- Autoren
- Franz Rothlauf
- Daniel Schunk
- Jella Pfeiffer
- Editoren
- Hans-Georg Beyer
- Una-May O'Reilly
- ISBN-10
- 1-59593-010-8
- Zeitschrift
- GECCO
- Paginierung
- 2021 - 2028
- Datum der Veröffentlichung
- 2005
- Herausgeber
- ACM
- Herausgeber URL
- http://www.informatik.uni-trier.de/~ley/db/conf/gecco/gecco2005.html
- Titel
- Classification of human decision behavior: finding modular decision rules with genetic algorithms.
Datenquelle: DBLP
- Abstract
- 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.
- Autoren
- Franz Rothlauf
- Daniel Schunk
- Jella Pfeiffer
- Datum der Veröffentlichung
- 2005
- Conference start date
- 2005
- Titel
- Classification of human decision behavior : finding modular decision rules with genetic algorithms
Datenquelle: RePEc
- Abstract
- 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.
- Addresses
- Munich Center for the Economics of Aging (MEA)
- Autoren
- Franz Rothlauf
- Daniel Schunk
- Jella Pfeiffer
- Datum der Veröffentlichung
- 2005
- Conference start date
- 2005
- Titel
- Classification of Human Decision Behavior: Finding Modular Decision Rules with Genetic Algorithms
Datenquelle: RePEc
- Abstract
- 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.
- Addresses
- Dept. of Business Administration and Information Systems
- University of Zürich Institute for Empirical Research in Economics
- Autoren
- Franz Rothlauf
- Daniel Schunk
- Jella Pfeiffer
- Notes
- Financial support from the Deutsche Forschungsgemeinschaft, SFB 504, at the University of Mannheim, is gratefully acknowledged.
- Datum der Veröffentlichung
- 2005
- Conference start date
- 2005
- Titel
- Classification of Human Decision Behavior: Finding
Datenquelle: RePEc
- Autoren
- Franz Rothlauf
- Daniel Schunk
- Jella Pfeiffer
- DOI
- 10.1145/1068009.1068346
- Editoren
- H-G Beyer
- ISBN-10
- 1595930108
- Name of conference
- Genetic and Evolutionary Computation Conference
- Paginierung
- 2021 - 2028
- Datum der Veröffentlichung
- 2005
- Status
- Published
- Herausgeber
- ACM Press
- Datum der Datenerfassung
- 2020
- Titel
- Classification of Human Decision Behavior: Finding Modular Decision Rules with Genetic Algorithms
Datenquelle: Manual
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