The Evolution of Expertise in Decision Support Technologies: A Challenge for Organizations

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Transcript of The Evolution of Expertise in Decision Support Technologies: A Challenge for Organizations

Slide 1WORKSHOP: Inteligencia Computacional: Aplicaciones en Marketing y Finanzas
The Evolution of Expertise in Decision Support Technologies: A Challenge for
Organizations
GREC Research Group
* This research has been partially supported by the AURA research project (TIN2005-08873-C02), funded by the Spanish Ministry of Science and Information Technology and the Commission for Universities and Research of the Ministry of Innovation, Universities, and Enterprises of the Government of Catalonia.
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Road Map
Expert Systems (ESs)
Conclusions
The following is the road map we will take.
First we will discuss the reason for the study and why it is important and the motivation behind it, next we will move to the terms and concepts, then discuss on how expertise is leveraged within the three support technologies.
Then we will address how existing methodologies can enhance decision-making and collective intelligence tools.
Finally, we will look at conclusions from the study.
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Introduction and Motivation
Importance of decision-making
Computer Supported Cooperative Work (CSCW) provides a center point
An evolutionary view of three decision support technologies that support the use of expertise.
Expert Systems
Collective Intelligence Tools
Decision-making has been an important area of study, where many areas of researchers have contributed to its understanding.
Faced with new opportunities from emerging technologies researchers must also evolve to understand new methods organizations use in decision-making.
The research area of CSCW provides a center point for this study.
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Terms and Concepts
What is Expertise?
Multi-dimensional (Sternberg, 1997) with expert knowledge as the essential part (Tynjala, 1999)
Short supply and difficult to represent
Highly specialized or domain specific (Chi, Glaser, & Farr, 1988)
Skills honed through practice (Jackson, 1999)
Perform consistently more accurate in relation to others (Hartely, 1985)
Practical Knowledge
Self-regulative Knowledge
Formal Knowledge
Expert Knowledge Dimensions
First, we must discuss important terms and concepts such as What is Expertise?
Though no agreed upon definition exist in the literature, researchers would agree it is multi-dimensional with expert knowledge as the essential part, made up of formal knowledge, practical knowledge, and self-regulative knowledge.
As elusive as a definition is for expertise, its short supply and difficulty to represent makes it valuable for decision-making.
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Expertise in Law
Factual knowledge
Lawyer Expertise
For example, if we were to look at the expertise of a lawyer, formal knowledge consists of factual information where learning is explicit. A lawyer would know this information from schooling or case history.
Practical Knowledge develops in the skill of “knowing how” and is tacit where intuition plays a role. A lawyer has practical knowledge through their experiences from being in a legal setting.
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Expertise by Means of Technology
Expertise not limited to humans
Technology built to capture knowledge or represent expertise (Barton, 1987; Liou & Nunamaker, 1990; Smith, 1994)
Level of expertise can be augmented by increasing the amount of participants in the decision-making process
Expertise in Design
Number of People
Considering expertise is not limited to humans, rather technology has the capacity to capture and represent expertise, it is understandable that organizations have allocated significant resources to leverage expertise using technology.
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Leveraging Expertise
Objective:
To represent expertise to its users for decision-making when a human expert can not be found or is in short supply.
Playing a critical role for organizations and are a source for competitive advantage (Gill, 1995).
Contributing to decision-making through their representation of knowledge and reasoning of human experts (Weiss & Kulikowski, 1984).
By mimicking and replicating the cognitive process of a human expert, novice users can be supported to perform as well as experts (Cascante et al, 2002).
ES are a technology that facilitates learning through the transfer of tacit and explicit knowledge (Yoon et al., 1995; Gregor & Benasat, 1999).
Expert Systems
Attributes:
One method used by organizations to capture expertise is by employing expert systems. This is where we place our first person into the trading pit for decision-making.
Expert systems are used to represent expertise to its users for decision-making when a human expert can not be found or is in short supply.
Currently, expert systems are playing a critical role for organizations and are a source for competitive advantage by contributing to the representation of knowledge and reasoning of human experts.
Expert systems mimic the cognitive process of a human expert allowing novice users to perform as well as experts.
This is done through the transfer of tacit and explicit knowledge.
Though many organizations have successfully implemented expert systems changing external factors have forced organizations to approach critical decisions differently where fewer decisions are made by any single individual or expert system.
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Leveraging Expertise
Objective:
To capture the knowledge and contribution from the individual users to facilitate solutions to problems.
Occupies the center point for the aggregation of information and expertise from each participant.
Support the changing organizational structure, project based
teams, dispersed workforce, and greater emphasis on collaboration.
Aided groups to deal with to the changing dynamics characterized by greater knowledge, complexity, and turbulence (Huber, 1982; 1984).
Shown to reduce time, costs (Gallup, 1985), foster collaboration, communication, deliberation, and negotiations (Kull, 1982).
Group Decision Support Systems
Consequently, organizations have turned to group decision support systems to capture the knowledge and contributions from several decision-makers.
This is also where we add a few more people into the trading pit.
Group decision support systems occupy the center point for aggregation of information from the participants and support the changing organizational structure.
GDSSs aid in dealing with complexity and have shown to have several benefits.
Though GDSSs have supported effective decision-making, decision-makers are still constrained by the information they receive.
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What is Collective Intelligence?
The collective judgment of group can predict or forecast better than experts or groups of experts (Surowiecki, 2004)
Diverging from traditional thought- high levels of expertise are the best source for decision-making
Including many people in decision-making by harnessing lower levels of expertise for peak solutions (Page, 2007)
First, lets define what collective intelligence is? This is also where we add a large group of people into the trading pit for decision-making.
CI is based on the premise that the collective judgment of a large group is better at predicting or forecasting then individual experts or groups of experts.
This is divergent from traditional thought where high levels of expertise are the best source for decision-making
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Leveraging Expertise
information, and resources of its users.
Democratize decision-making by including many people in and outside the organization into the information gathering and decision-making process.
Prediction markets, incubates information scattered around the organization or network that allows non-experts to produce expert like results.
Challenges traditional roles of experts, may change answer givers to inquiry mediators in effort to harness the knowledge of the masses in decision-making.
Offer an additional tool in decision-making.
Collective Intelligence Tools
Organizations are leveraging the power of collective intelligence by implementing CI tools, that facilitate the summative body of knowledge, information, and resources of its users.
This is done by democratizing decision-making by including many people in and outside the organizations into the decision-making process by employing resources far greater then a company can employ internally.
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Enhancing Decision-Making
and CI Tools
Past attempts have made steps (Aiken et al. 1991; Turban & Watkins, 1986).
Opportunities for system integration to solve a wider spectrum of problems.
AI techniques to CI Tools
Transforming from passive to active agents
Intelligent components to increase participation
Managing interaction and collaboration between users
Decision Support Technologies *
Ill- Structured
Problem Structure
DSS
Now that we have looked at the different technologies that harness expertise, how can these technologies be further enhanced?
Past research has taken steps to enhancing decision-making by integrating support technologies however have not included emerging technologies.
Opportunities exist for system integration to solve a wider spectrum of problems which is highlighted by the graph on the right.
Artificial intelligence techniques offer real possibilities to enhance CI tools much like it has supported other decision-making technologies.
Design benefits may include transforming CI tools from passive agents to active agents.
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Conclusions
Shown
Indicated
Explored
Highlighted
An evolutionary perspective of expertise supported by decision support technologies.
how organizational use of expertise is changing which reflects the new roles of experts and non-experts in decision-making
how organizational expertise in short supply can be augmented
issues of design for integration with existing methodologies
To conclude this study has shown an evolutionary perspective of expertise supported by decision support technologies.
Then we highlighted how organizational expertise in short supply can be augmented
Next, we explored issues of design for integration with existing methodologies.
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Thank You!
References
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[4] J. Berg and T. Rietz, “Prediction Markets as Decision Support Systems”, Information Systems Frontiers, 5(1), 2003, 79-93.
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[8] L. Cascante, M. Plaisent, P. Bernard and L. Maguiraga, “The impact of expert decision support systems on the performance of new employee”, Information Resource Management Journal; Oct-Dec 15(4), 2002, 67-78.
[9] M. T. H. Chi, R. Glaser, and M. J. Farr, eds., The Nature of Expertise, 1988. Hillsdale, NJ: Erlbaum.
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[13] S. Diasio, “Collaborative Fusion: Expert, Group, and Collective Intelligence in Decision-making Support”, unpublished paper, 2008.
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References
[15] E.A Feigenbaum, “The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering’, International Joint Conference on Artificial Intelligence, 1977, 1014-1029.
[16] B. Gallup, “The impact of task difficulty on the use of a group decision support system”. Ph.D. dissertation, Dept. of Information and Decision Sciences, Univ. of Minnesota, 1985.
[17] M. J. Gannon, Organizational Behavior: A Managerial and Organizational Perspective, Boston: Little, Brown, 1979.
[18] T. Gill, “Early Expert Systems: Where are they now?”, MIS Quarterly. March, 1995.
[19] R. Hanson, “Decision Markets”, IEEE Intelligent Systems, 14, 1999, 16-19.
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[22] J. Howe, Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business, New York, Crown Business, 2008.
[23] G. P. Huber, “Group decision support systems as aids in the use of structured group management techniques”, In DDS-82 Conference Proceedings, 1982, 96-108.
[24] G. P. Huber, “Issues in the Design of Group Decision Support Systems”, MIS Quarterly, Sept. 8(3), 1984, 195- 204.
[25] P. Jackson, Introduction to Expert Systems, (3rd) ed, Pearson Addison Wesley, 1999.
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[27] Y. Liou, and J. Nunamaker, “Using a Group Decision Support System Environment for Knowledge Acquisition: A Field Study”, Proceedings of the ACM SIGBDP conference on Trends and directions in expert systems. Orlando, Florida, United States, 1990, 212 – 236.
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[29] S. Page, The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies, Princeton University Press, 2007.
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References
[31] E. H. Shortliffe, “Computer-based medical consultation MYCIN”, American Elsevier, 1976.
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[34] R. J. Sternberg, “Cognitive conceptions of expertise”, In P.J. Feltovich. K. M. Ford and R. R. Hoffman. Expertise in context. Human and Machine, Menlo Park, CA: AAAI Press/ The MIT Press, 1997. 149-162.
[35] J. Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, Little, Brown, 2004.
[36] P. Tynjala, “Towards expert knowledge? A comparison between a constructivist and a traditional learning environment in the university”, International Journal of Education Research. 31, 1999, 357-442.
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[38] S. Weiss and C. A. Kulikowski, A Practical Guide to Designing Expert Systems, Chapman and Hall Ltd, 1984.
[39] S. E. White, J. E. Dittrich and J. R. Lang, “The Effects of Group Decision Making Process and Problem Situation Complexity on Implementation Attempts”, Administration Science Quarterly, 25(2), 1980, 428-440.
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Terms and Concepts
What is Expertise?
Multi-dimensional (Sternberg, 1997)
Skills honed through practice (Jackson, 1999)
Perform consistently more accurate in relation to others (Hartely, 1985)
Practical Knowledge
Self-regulative Knowledge
Formal Knowledge
Leveraging Expertise
Objective
To represent expertise to its users for decision-making when a human expert can not be found or is in short supply.
Playing a critical role for organizations and a source for competitive advantage (Gill, 1995).
Contributing to decision-making through their representation of knowledge and reasoning of human experts for its users (Weiss & Kulikowski, 1984).
By mimicking and replicating the cognitive process of a human expert novice users can be supported to perform as well as experts (Cascante et al, 2002).
Users of ESs learn faster for decision-making that would occur through development over time.
Expert Systems
Conclusions
Highlighted how organizational expertise in short supply can be augmented.
Explored issues of design for integration with existing methodologies.
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Leveraging Expertise
Replicating the knowledge and reasoning of human expert (Cascante et al, 2002).
To capture the knowledge and contributions from the individual users for group consensus and collaboration (DeSantis & Gallup, 1987).
To facilitate the summative body of knowledge, information, and resources of its users and the premise that the collective judgment of many are better then individual experts or groups at predicting or problem-solving (Surowiecki, 2004; Page, 2007).
Expert
Systems
GDSSs
Box-Office Forecasting: An Analysis of Expert Systems and Collective Intelligence Tools
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FILM INDUSTRY APPROACHES TO LEVERAGING EXPERTISE
New movies consisting of long production cycles, complicated logistics, and high production costs reinforce the need for correct and accurate forecasts for box office revenue.
Box office success prior to movie production and release are ridden with high levels of uncertainty for customer demand (Sawhney & Eliashberg 1996, Eliashberg et al. 2000), which has lead to a high percentage of films failing; further emphasizing that film forecasting is largely nonscientific.
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Espagogix Ltd. a UK-based company
Epagogix’s proprietary expert system, based on a neural network (algorithms) analysis, uses movie script attributes to correlate future success or failures on past movies produced.
The expert system uses variables such as genre, star actors, technical effects, and release time to find statistical patterns to forecast financial success.
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Hollywood Stock Exchange (HSX)
Hollywood Stock Exchange (HSX) (www.HSX.com) is an internet based stock exchange where participants trade shares of virtual stocks that bet on the future box office revenue of movies.
Participants buy and sell shares of movie stocks (like a real stock market).
Web, traders are able to make new predictions as they acquire new information, making trades reflect in real-time stock prices and predictions of the future market demand for the movie.