Expert systems for knowledge management: crossing the chasm between information processing and sense making


Expert systems for knowledge management: crossing the chasm between
information processing and sense making

Y. Malhotra*

Florida Atlantic University, 818 N.W. 89th Avenue, Fort Lauderdale, FL 33324, USA

Abstract

Based on insights from research in information systems, information science, business strategy and organization science, this paper

develops the bases for advancing the paradigm of AI and expert systems technologies to account for two related issues: (a) dynamic radical

discontinuous change impacting organizational performance; and (b) human sense-making processes that can complement the machine

learning capabilities for designing and implementing more effective knowledge management systems. q 2001 Elsevier Science Ltd. All
rights reserved.

Keywords: Expert systems; Arti®cial intelligence; Knowledge management; Information systems; Information science; Business strategy; Discontinuous

change; Sense making; Information processing

ªThere has been an over-concentration on Shannon's

de®nition of information in terms of uncertainty (a

very good de®nition for the original purposes) with

little attempt to understand how MEANING directs a

message in a network. This, combined with a concen-

tration on end-points (equilibria) rather than proper-

ties of the trajectory (move sequence) in games has

lead to a very unsatisfactory treatment of the

dynamics of organizations.º Ð John H. Holland

(personal communication, June 21, 1995)
1

1. Introduction

The narrative cited above as an observation by the noted

psychologist and computer scientist John Holland was in

response to my query to him regarding the possibility of

using intelligent information technologies for devising

self-adaptive organizations. As meaning seems to be a

crucial construct in understanding how humans convert

information into action [and consequently performance], it

is evident that information-processing based ®elds of arti®-

cial intelligence and expert systems could bene®t from

understanding how humans translate information into mean-

ings that guide their actions. In essence, this issue is relevant

to the design of both human- and machine-based knowledge

management systems. Most such systems had been tradi-

tionally based on consensus and convergence-oriented

information processing systems, often based on mathema-

tical and computation models. Increasing radical discontin-

uous change (cf. Huber & Glick, 1993; Nadler, Shaw, &

Walton, 1995) that characterizes business environments of

today and tomorrow, however, requires systems that are

capable of multiple Ð complementary and contradictory

Ð interpretations.

Despite observations made by Churchman (1971) and

Mason and Mitroff (1973), the paradigm of information

systems, arti®cial intelligence (AI) and expert systems

have yet to address the needs posed by wicked environ-

ments that defy the logic of pre-determination, pre-

diction and pre-speci®cation of information, control

and performance systems (cf. Malhotra, 1997). Wicked

Expert Systems with Applications 20 (2001) 7±16PERGAMON

Expert Systems
with Applications

0957-4174/01/$ - see front matter q 2001 Elsevier Science Ltd. All rights reserved.

PII: S 0 9 5 7 - 4 1 7 4 ( 0 0 ) 0 0 0 4 5 - 2

www.elsevier.com/locate/eswa

* Tel.: 11-954-916-1585.

E-mail address: yogesh.malhotra@brint.com (Y. Malhotra).
1

Considering organizational adaptation for survival and competence as

the key driver for most organizational information and knowledge

processes (cf. Malhotra, 2000a,b,c), it seemed logical to develop the

model of IT-enabled self-adaptive organizations based upon technologies

that are often considered as a benchmark for self-adaptive behavior. In this

context, genetic algorithms (also referred to as adaptive computation) offer

the closest archetype for devising technology-enabled organizations that

could possibly exhibit self-adaptive behavior given the dynamically chan-

ging environment. By offering the basis for evolution of solutions to parti-

cular problems, controlling the generation, variation, adaptation and

selection of possible solutions using genetically based processes, it seemed

probable that genetic algorithms could offer the basis for self-adaptive

evolution of organizations. As solutions alter and combine, the worst

ones are discarded and the better ones survive to go on and produce even

better solutions. Thus, genetic algorithms breed programs that solve

problems even when no person can fully understand their structure.



business environments Ð characterized by radical

discontinuous change Ð impose upon organizations

the need for capabilities for developing multiple mean-

ings or interpretations and continuously renewing those

meanings given the changing dynamics of the environ-

ment. Scholars in business strategy have advocated

human and social processes such as `creative abrasion'

and `creative con¯ict' (cf. Eisenhardt, Kahwajy, &

Bourgeois, 1997; Leonard, 1997) for enabling the inter-

pretive ¯exibility (Nonaka & Takeuchi, 1995) of the

organization.

It is also evident that there is an imperative need for

relating the static notion of information captured in data-

bases or processed through computing machinery to the

dynamic notion of human sense making. More importantly,

our current understanding of information as the [indirect]

enabler of performance can immensely bene®t from unra-

veling the intervening processes of human sense making

that are more directly related to action (or inaction) and

resulting performance outcomes (or lack thereof).

Based upon a review of the current state of AI and expert

systems research and practice in knowledge management,

this article develops the bases for AI and expert systems

researchers to develop knowledge management systems

for addressing the above needs. Section 2 provides an over-

view of the state-of-the-art expert systems research and

practice issues related to knowledge management highlight-

ing key relationships with the key theses of the article.

Section 3 offers a more current understanding of knowledge

management as it relates to organizational adaptability and

sustainability by drawing upon information systems and

business strategy research. Section 4 highlights the contrast

between the computational model of information processing

and human sense making while recognizing both as valid

meaning making processes. Finally, sense-making bases of

human action and performance are discussed in Section 6,

followed by conclusions and recommendations for future

research in Section 8.

2. State of related research and practice in AI and expert
systems

Faced with uncertain and unpredictable business environ-

ments, organizations have been turning to AI and expert

systems to develop knowledge management systems that

can provide the bases for future sustainability and compe-

tence. For instance, faced with competition and uncertainty

in the ®nance industry, banks are using neural networks to

make better sense of a plethora of data for functions such as

asset management, trading, credit card fraud detection and

portfolio management (Young, 1999). Similarly, insurance

and underwriting industries are relying upon knowledge

management and AI technologies to offer multiple channels

for rapid response to customers (Rabkin & Tingley, 1999).

Many such knowledge management implementations using

AI and expert systems rely upon the meaning making and

sense-making capabilities of AI and expert systems technol-

ogies and humans using them.

In recent years, there have been signi®cant advances in

endowing inanimate objects with limited sense-making

capabilities characteristic of self-adaptive behavior of

humans. For instance, some proponents of `perceptual intel-

ligence' (cf. Pentland, 2000) have suggested such capabil-

ities derived from a computers' ability to isolate variables of

interest by classifying any situation based on categorization

heuristics for taking appropriate action. Their suggestion is

that once a computer has the perceptual ability to know who,

what, when, where and why, then the probabilistic rules

derived by statistical learning methods are normally suf®-

cient for the computer to determine a course of action.

However, these models, though helpful for procedural deci-

sion making, need to advance beyond the static, pre-speci-

®ed and pre-determined logic to account for dynamically

changing environments that may require fundamental and

radical rede®nition of underlying rules as well as the beha-

vior of the actors.

Similarly, research on `perceptual interfaces' has been

trying to unravel how people experience information that

computers deliver (cf. Reeves & Nass, 2000). This stream of

research is based on the premise that human experience with

information is caused by stimulation of the senses. While

paying attention to the chemical senses (taste and olfaction),

the cutaneous senses (skin and its receptors), vision and

hearing, this research has yet to take into consideration

the interpretive, meaning making and sense-making

processes that occur at a more cerebral level. The personal

constructivist theory discussed in this article could help

better relate information to meaning and consequent beha-

vior (or actions) in above cases.

Simultaneously, the state-of-art research and practice in

data mining, often described as ªknowledge discovery from

databases,º ªadvanced data analysis,º and machine learn-

ing, has been trying to decipher how computers might auto-

matically learn from past experience to predict future

outcomes (Mitchell, 1999). However, as explained later,

current thinking in business strategy is imposing upon the

organization the need to move beyond prediction of future to

anticipation of surprise (Malhotra, 2000a,b). The most

advanced machine learning capabilities Ð such as those

of the most advanced chess-playing computer (cf. Camp-

bell, 1999) Ð are still limited by pre-speci®ed, pre-

determined de®nition of problems that are solved based

on the pre-speci®ed rules of the game.

Though interesting, such capabilities may have limited

use in the emerging game of strategy that is being rede®ned

as it is being played. In such game, all ªrules are up for

grabsº even though computational machinery has yet to

evolve to the stage of sensing changes that it has not been

pre-programmed to sense and to re-evaluate the rules

embedded in the logic devised by human programmers. In

contrast to machine learning, humans are endowed with

Y. Malhotra / Expert Systems with Applications 20 (2001) 7±168



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