Introduction: expertise and modeling expert decision making

This special issue brings together an interdisciplinary perspective of cognitive psychology, artificial intelligence (AI), and managerial decision making in identifying human expertise and modeling expert decision making. Research in psychology discusses the cognitive strategies that separate novices from experts, and the mechanisms by which expertise is acquired. A majority of work in machine learning in AI aims at developing systems that independently acquire expertise. Management researchers attempt to bridge these two areas, examining the nature of management expertise and the role of information systems in automating and augmenting managerial decision-making processes. Such research takes many forms: the applicability of statistical and inductive learning methods to model expertise has been studied. Inductive machine learning endeavors to learn the decision rules or patterns underlying the given example cases in database. More importantly, inductive machine learning approaches have been employed to build models of human decision making with rules/patterns. Learning systems or the ways in which information systems augment managerial judgment have been discussed. Process oriented efforts to better understand managerial expertise and outcome oriented research which lead to paramorphic models that replicate the outcome of expert decisions have been presented. In this special issue, Reuber examines the aspects of management expertise developed from experience and identifies issues associated with the study of management expertise. Jeng, Jeng, and Liang develop a tool to automate knowledge acquisition by applying a fuzzy inductive learning method especially for marginal cases. Troutt, Rai, and Tadisina illustrate how the maximum decision efficiency principle for parameter estimation is employed to aggregate expert data in deriving a linear score function. Kim, Chung, and Paradice analyze experts' decision strategies and investigate their relationship with inductive model performance. Srinivas and Shekar apply qualitative probabilistic networks to represent decision making processes by transforming the cognitive maps. Along with solid literature review, Villenueve and Fedorowicz present an object oriented approach in acquiring expertise for information systems design. Barr and Sharda describe the effects of a decision support system on decision outcomes that develop over time by evaluating improvements in decision quality. In the future, studies which examine the interaction between the use of computer-based decision aids and managerial expertise are of further interest. For those involved in the design and construction of decision making/aid systems, the implications of various theories of expertise are of importance, In addition, issues of expert bias, approaches to integrate human and computer expertise, the effects of computer tools on expert judgment, and the quality of data need to be considered. In particular, ways to measure the quality of discovered rules/patterns and models require further systematic studies. Exogenous factors that affect model performance, influence of various cognitive factors on human decision making, and domain characteristics that affect the model performance are some of the questions that will motivate us. We would like to express our gratitude to EditorIn-Chief Andrew B. Whinston for his support to put together this special issue. Thanks are also due to all