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.

Re-visions of rationality?

Empirical evidence suggests proponents of the ‘adaptive toolbox’ framework of human judgment need to rethink their vision of rationality

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.

Pilot interaction with automated airborne decision making systems

An investigation was made of interaction between a human pilot and automated on-board decision making systems. Research was initiated on the topic of pilot problem solving in automated and semi-automated flight management systems and attempts were made to develop a model of human decision making in a multi-task situation. A study was made of allocation of responsibility between human and computer, and discussed were various pilot performance parameters with varying degrees of automation. Optimal allocation of responsibility between human and computer was considered and some theoretical results found in the literature were presented. The pilot as a problem solver was discussed. Finally the design of displays, controls, procedures, and computer aids for problem solving tasks in automated and semi-automated systems was considered

Evaluating three frameworks for the value of information: adaptation to task characteristics and probabilistic structure

We identify, and provide an integration of, three frameworks for measuring the informativeness of cues in a multiple-cue judgment task. Cues can be ranked by information value according to expected information gain (Bayesian framework), cue-outcome correlation (Correlational framework), or ecological validity (Ecological framework). In three experiments, all frameworks significantly predicted information acquisition, with the Correlational (then the Bayesian) framework being most successful. Additionally, participants adapted successfully to task characteristics (cue cost, time pressure, and information limitations) – altering the gross amount of information acquired, but not responding to more subtle features of the cues’ information value that would have been beneficial. Rational analyses of our task environments indicate that participants' behavior can be considered successful from a boundedly rational standpoint

Towards a Descriptive Model of Agent Strategy Search

It is argued that due to the complexity of most economic phenomena, the chances of deriving correct models from a priori principles are small. Instead are more descriptive approach to modelling should be pursued. Agent-based modelling is characterised as a step in this direction. However many agent-based models use off-the-shelf algorithms from computer science without regard to their descriptive accuracy. This paper attempts an agent model that describes the behaviour of subjects reported by Joep Sonnemans as accurately as possible. It takes a structure that is compatible with current thinking cognitive science and explores the nature of the agent processes that then match the behaviour of the subjects. This suggests further modelling improvements and experiments

Building more accurate decision trees with the additive tree.

The expansion of machine learning to high-stakes application domains such as medicine, finance, and criminal justice, where making informed decisions requires clear understanding of the model, has increased the interest in interpretable machine learning. The widely used Classification and Regression Trees (CART) have played a major role in health sciences, due to their simple and intuitive explanation of predictions. Ensemble methods like gradient boosting can improve the accuracy of decision trees, but at the expense of the interpretability of the generated model. Additive models, such as those produced by gradient boosting, and full interaction models, such as CART, have been investigated largely in isolation. We show that these models exist along a spectrum, revealing previously unseen connections between these approaches. This paper introduces a rigorous formalization for the additive tree, an empirically validated learning technique for creating a single decision tree, and shows that this method can produce models equivalent to CART or gradient boosted stumps at the extremes by varying a single parameter. Although the additive tree is designed primarily to provide both the model interpretability and predictive performance needed for high-stakes applications like medicine, it also can produce decision trees represented by hybrid models between CART and boosted stumps that can outperform either of these approaches