Integrating Decision Support Systems: Expert, Group, and Collective Intelligence Steve Diasio* & NĂşria Agell ESADE Business School- Barcelona GREC Research Group
IC’ AI 09 Las Vegas, 2009
* 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.
Road Map • • • •
Introduction and Motivation Framework for Integration Terms and Concepts Leveraging Expertise in Decision Support Technology – Expert Systems (ESs) – Group Decision Support Systems (GDSSs) – Collective Intelligence Tools (CI Tools) • Enhancing Decision-Making and CI Tools • Conclusions and Future Work
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Introduction and Motivation •
Organizations today face a changing environment; – external conditions change rapidly (Ilinitch et al, 1996). – organizational structures flat and dispersed (Malone, 2006). – traditional roles of experts have been “squeezed” or of decreased importance (Mauboussin, 2008).
•
Today’s new environment places a premium on collaboration creating renewed interest in decision support technology to survive and remain competitive (Hamel & Breen, 2008). • Information technology is playing an increasing role in facilitating a firm’s success and is woven thread in the fabric of the organization (Zammuto et al, 2007). • The paper aims to understand how integration of expert systems (ESs), group decision support systems (GDSSs), and collective intelligence tools (CI tools) can enhance decision-making.
Framework for Integration • Abundance of decision support tools at their disposal. • Tools have been independently built (Turban & Watkins, 1986) for individual problems but be flexible to adapt to the changing conditions and needs. • Individually shown advantages of using such systems, however have not extended or offered in theory or practice an integrated system that supports organizational needs in expertise for decision-making. Integrated System Expert Systems
GDSSs
Proposed integrated support system
CI Tools
Framework for Integration • Abundance of decision support tools at their disposal. •
•
Tools have been independently built (Turban & Watkins, 1986) for individual problems but be flexible to adapt to the changing conditions and needs. Individually shown advantages of using such systems, however have not extended or offered in theory or practice an integrated system that supports organizational needs in expertise for decision-making. Integrated System
Expert System
GDSSs
Proposed integrated support system
CI Tools
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)
Formal Knowledge
•
Expert Knowledge Dimensions
Practical Knowledge
e it v la e u g edg e f-r owl l Se Kn
Expertise in Law Lawyer Expertise Practical Knowledge
Formal Knowledge •Factual knowledge •Learning of explicit information •In school or cases
Self-regulative Knowledge
•Intuition •Experience in legal setting •Tacit and difficult to express
•Reflective skill •Evaluation of action •Monitor argument and presentation to jury
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 Expert Systems GDSSs Collective Intelligence Tools
Number of People Level of Expertise in Systems Design
Leveraging Expertise Expert Systems Objective:
To represent expertise to its users for decision-making when a human expert can not be found or is in short supply.
Attributes:
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).
Leveraging Expertise Group Decision Support Systems Objective:
Attributes:
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).
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)
Leveraging Expertise Collective Intelligence Tools Objective:
Attributes:
To facilitate the summative body of knowledge, 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 nonexperts 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.
Enhancing Decision-Making and CI Tools • Past attempts have made steps (Aiken et al. 1991; Turban & Watkins,
WellStructured
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
*Figure adapted from Aiken et al. 1991
ES
Problem Structure
CI Tools GDSS
DSS
IllStructured Few
Many
Group Size Supported
Decision Support Technologies *
Differences Between ES, GDSSs, CI Tools Attributes
ES
GDSS
CI TOOLS
Objective
Replicate or mimic human experts
Facilitate solutions for a group of people
To sum the knowledge and information of many people
Who makes the recommendation (decision)?
The system or heavily weighted if human is involved
The group and/ or systems through ranking
The System/ Tool
Major orientation (characteristic)
Transfer of expertise (humanmachine-human)
Build group consensus
Transfer of hard to find information or qualitative to quantitative data
Nature of support
Individual or group
Group
Individual or group
Problem area characteristic
Narrow domain
Semi/ Unstructured, broad
Limited variability
Type of problem treated
Repetitive
Unique/ not often / important
Forecasting/ dispersed collaborators/ Probabilistic
Reasoning capability
Yes (deduction)
No
Yes (depending on the tool (induction)
Assumptions
Closed-world
Limited to users boundaries
Changing
Expertise Level or In-depth knowledge of problem
Specific/ Expert Level
Dependent on task or problem
All levels including learning capacity with use
Figure 4 Differences between ES, GDSS, CI Tools Adapted from Aiken et al, 1991]
Conclusions Shown an evolutionary perspective of expertise supported by decision support technologies.
Highlighted how organizational expertise in short supply can be augmented
Indicated how organizational use of expertise is changing which reflects the new roles of experts and non-experts in decision-making
Explored issues of design for integration with existing decision support technology
Thank You! Steve Diasio & NĂşria Agell {stephen.diasio; nuria.agell} @esade.edu ESADE Business School- Barcelona GREC Research Group
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