ODDSCO's Knowledge Support Introductory Tutorial


An introductory tutorial about Knowledge Support Systems and Tools, from the practitioner's perspective.



Abstract. This tutorial provides an extensive overview, without a "naming of names" or detailed discussions, of the general situation within Knowledge Support for now and for the relatively near future.

Because the general trend is for increased integration of applications for improving computer-assisted decision making, when you know something about Executive Information Systems (EIS) and the Decision Support Systems (DSS) from which they came, Artificial Intelligence (AI), Expert Systems, Neural Networks, and DSS subsystems such as Operations Research (OR) tools, you will understand where the Decision Support Incorporating Documented Evaluations (DSIDE™) process overlaps other tools and systems carrying a knowledge support banner.

Mostly as a public service, based on the likely ratio of interested visitors to potential customers and/or clients, this free tutorial covers the subject sufficiently to partially enable your evaluation of the value of most available related products and services within your planned approach to goals fulfillment. (The word "partially" was emphasized to remind you that knowledge of material from the complete set of tutorials listed on the home page is necessary to properly evaluate the ODDSCO offerings.)

At the end of this tutorial is an opportunity to pose question(s) on this subject.


KNOWLEDGE SUPPORT AND TOOLS

TABLE OF CONTENTS (Return Links)

Return to Home Page [Use a Return Link after any of the listed subsections to quickly reach this option.]


Why Knowledge Support?

Fully melding the human mind with the computer is still in the future, but some amazing progress toward that goal has been made in the last few years. Automation increasingly applies toward enabling technology, the support of less structured, high creative content knowledge worker tasks.

Going beyond computerized networks for information handling, managers increasingly employ powerful personal workstations with statistics packages to support their company and corporate level decisions with information available interactively, on demand. Expanding productivity within today's reductions in management hierarchy requires that all knowledge workers involved in a given problem solution effort share their information.

The ancient problem of getting the critical information to the key people in the appropriate form at the right time still remains, although the methods of transfer have changed. Today, however, the information is less likely to be scarce as to be unobtainable by the persons who would apply it if they had it. Encroachments exist, such as externally networking various local area networks together for expanded individual interconnection with others of shared interests, but much remains to be done.

Return to Table of Contents


A Decision Support System--What is it?

A Decision Support System, as an entity, generally refers to a grouping of integrated and unified computer programs applied to provide nearly instantaneous, interactive, and managerial level mental processes support. It most likely includes strong relational data base management (RDBMS) capability for access to key company data kept on the mainframe computer. A DSS usually supports a form of alternatives development and selection.

The DSS label originally applied to Management Information System (MIS) extensions using some Information Technology applications that allowed for limited control of decision modeling where system complexity exceeds the normal human capabilities for comprehension. Now strongly associated with data processing, MIS is used to report ongoing performance, variance from expected conditions, and develop plans for fulfilling goals and implementing strategies. It also should flag problems and potential areas for concern if present operational trends continue.

Today, emphasis is more on planning for the future rather than management control of the existing situation and on bringing more logic and structure to the normally haphazard managerial decision-making processes. To fully utilize the indirect utility of computer power, a DSS requires controlled access to much of the current corporate MIS data base, along with a Management Science related means to fully analyze that information. (Later sections of this tutorial discuss the related tools.) Access to the company mainframe computer may be direct or via an attached PC (personal computer). The typical DSS offers the use of a fairly powerful internal modeling language to obtain full capability.

The primary purpose of the DSS, that of evaluating alternatives through formulation of Decision Models, distinguishes it from other computer-based tools. A key point is that the DSS user need not become a computer expert because the computer provides guidance equivalent to (or better than) that of a skilled manager in an on-line, interactive fashion with a graphical user interface (GUI).
A DSS should provide capability to model decision problems in terms of objectives, information acquisition, and meaningful model representation. It enables the development of routines specifically tailored to your individual requirements. It gives to you and to the more knowledgeable executives great power for determining the current health of your business and causes of ongoing trends for better or worse. Evaluations can be by product, profit center, project task force, time period, or other such useful grouping. You can insert hypothetical new causes in your models at selected simulated times and project possible effects, to verify hypotheses for almost any "What if ... ?" question you can imagine. You can relatively quickly evaluate many scenario projections and present only those causes resulting in the "best" outcomes as suggested actions to take.

The system tools act as a multiplier for your mental skills through an integration of tools previously requiring separate application and strong mathematical skills. While predictive models can outperform you through consistent rules application, the DSS cannot "replace" you because generation of complex potential solutions or alternatives requires human creativity.

The most probable application of a DSS, however, is in performing detailed financial analyses with extensive employment of statistical tests and projections. It also is valuable when you can apply equation-based models.

Eventually, a projected increase in capability (at moderate to great expense) of AI-enhanced programs with virtual reality presentations may finally fulfill the ultimate requirement of full analysis power without need for a corresponding understanding by managers of constraints bounding routines for statistical treatment. The program would at least warn you of the inappropriate application of a statistical measurement technique. It would show you the reasoning chain for its decision and persuade you with some logical explanation. With such extensive expertise built-in, the DSS would offer valid alternative tests and suggest use of related data base information.

Finally, because the advanced computer program could "learn" from interaction with users, expert human knowledge should gradually be imparted to the computer program's data base. The DSS then could propose a few suitable "What if ... ?" variations, based on the type of analysis you were performing.

The original concept of DSS was for fairly extensive executive mental process and strategic decision making support, but the recent tendency has been toward its use by middle management and staff for their day-to-day tactical decisions. So many heavily advertised decision aiding programs with extremely limited capability have been called DSS that the term has become worse than imprecise. In fact, anything providing Decision Analysis support to anyone can be considered a DSS. Its meaning has been diluted to the point where another label is required to encompass the original concept:

Return to Table of Contents


Executive Information Systems (EIS)

The more senior the executive, the greater the tendency toward reliance on a subordinate's development of DSS information and toward personal reliance on the "soft" data of a more informal nature. Hierarchical authority is becoming less workable than knowledge for making realistic decisions, because ignorance effectively increases with responsibility. Agenda development, communications, monitoring of status, interpretation of discovered trends, and evaluating impact of significant events for dissemination of a timely coordinated corrective action plan within a strategic perspective have become more important at the executive levels. The top executives want to understand the context of their domain of authority and responsibility, to take the pulse of critical items, to obtain the same answer from any staff member asked the same historical or current data question, and to be warned of important events as input to their decisions for minimization of surprise. They want all this support for what they do without wasting their own or their staff's time and without necessity for extensive training of any who must access the data base.

Information technology is the key, even when the information is received second-hand from staff knowledge workers. The data may be qualitative, but it must fulfill individual requirements for content, format, frequency, and timeliness.

A substantial amount of inference and intuition developed from broad experience and knowledge facilitates quick recognition of opportunities for agenda changes to guide subordinate action. An EIS that would thoroughly support such unstructured thinking is a rather difficult system to construct. Cost limits much information access to the desired information. Typically, an EIS will let you pick a subtopic within some major topic you are studying, search out other files containing the key words, the retrieve and present them graphically for inspection.

This is something like hypertext (a hierarchical data access approach first popularized on the Apple Macintosh™ desktop computers as the Hypercard™ program). The stronger versions of EIS can retrieve data via an interconnected network from multiple computer types with varying operating systems and data base formats.

The glut of information is nearly as bad as actual scarcity, when you wish to add to your stock of knowledge, because power is derived from the intelligence contained therein. The job of the EIS is to distill data to its essence, to its meaning for your specific situation with respect to an applicable standard or event.

To that end, the EIS will help you to create ad hoc reports and apply custom tools that perform "data mining," the specifically directed intelligent processing of massive or frequently updated data for detection of subtle but significant statistical relationships, as well as to accomplish repetitive and routine work. You can look at details of choice, when you wish to see behind the common set of summaries, because you can't always simply explain exactly what you need. Because such tailoring of access is made easy, redirection of middle management attention is quicker.

The net effect of a well-designed EIS is support for accomplishment of the organizational mission. It is always there, taking the pulse, so the executive can be elsewhere working on strategic goals. To that end, it must perform analyses well and provide high quality output presentations. An immediate benefit of central maintenance of the EIS data base is reduction in staff work in user organizations.

Return to Table of Contents


Artificial Intelligence

The field of AI is occupied with finding ways to make machines perform tasks in ways which would be characterized as intelligent if done by humans. Some AI researchers attempt to model human thought processes for solving a class of problems which give computers trouble but which humans regularly solve. Humans learn, plan, diagnose faults, play games, and create new concepts without using a formally defined mathematical procedure until a demand for "proof" arises.

AI therefore emphasizes processing of symbols over numbers, combines plausibility with standard logic while making inferences and deductions, tolerates some errors while working with "fuzzy" concepts, employs heuristics as well as mathematical algorithms for problem solving, and searches associated knowledge data bases for applicable structured facts to process while working with always ambiguous natural language. Knowledge processing is machine reasoning or rule selection that results in apparently purposeful, intelligent appearing behavior for problem solution.

With extensive AI, the EIS could characterize itself for the mental processes or decision-making style of a given user and act as a higher level mentor or consultant with helpful critiques of your work. Even if you do not fear the concept of an intelligent machine as do many, complete understanding of natural intelligence required before AI can properly substitute for a knowledgeable human is not imminent. Speech understanding is a goal of many researchers, but that is a much tougher job than natural language processing which provides today's intelligent seeming interface with humans. (Even humans have difficulty with accents, homonyms, and individual idiosyncracies in speaking.)

Another major problem is having computers recall as humans do, when it works well, because that is another poorly understood area of natural intelligence. Neural networks (described later) are as close as it gets for now. The successful applications are mostly in the Expert System category (described in the next section). The best you can hope for is machines that are quite clever, but with serious limitations in many areas.

That is because knowledge principally concerns when or how to appropriately use the what of the exponentially increasing volume of data, information, and intelligence obtainable in unstructured form from information sources that are not yet machine-readable (humans).

Computers attempting to process knowledge, even where the required data is available in electronic form, must have a well-bounded domain within which to work. Humans are required for handling the dynamic context and uncertain reality in which today's businesses exist. Computers can find some of the patterns, but humans define their search requirements, determine their meaning, make the final decisions, and must provide at least part of the interface with other humans.

The Japanese Fifth Generation Software computer language projects (since essentially abandoned for large scale support) were attempting to find ways to convert knowledge into wisdom as informed judgment through inferences based on analogous situations. That particular nut appears to be too tough to crack with brute force in a short time. True machine intelligence requires creativity or production of better "ideas" and solutions to problems which exceed responses and context originally programmed.

Since it may well be impossible to make computers truly wise, you probably should avoid setting them to tasks requiring wisdom in the human sense. Whenever you do, you abdicate the power to choose and become subservient to a machine with inferior reasoning power. (Just as most knowledge workers in their daily toil, eh?) You may just be replacing the old dangers of human limitations with a new, improved mediocrity. In safety critical applications, this is undesirable. All that wonderful technology still requires human "vision" with experienced judgment for accomplishment of strategic objectives with its application.

The geometrically (some would say exponentially) increasing volume of data must be prefiltered in accordance with some human's judgement before its placement in the model. The AI or knowledge-based Expert Systems (discussed next) can't solve those problems. Even that information is not sufficient, although the temptation to build your models solely with (and to rely on the answers derived from) a too-handy data base is substantial. This is most true when you are under typically great schedule pressure to produce a report. Model results would appear to be logical and consistent, concealing the poverty of information on which you may be basing critical conclusions.

The accuracy and validity of the data must be verified, as well as its utility for the proposed analysis purpose, before you should have confidence in any reports.

Also, there is a large probability that relevant history will be unavailable to your analyses. Because of storage expense, which increases with the scope and volume (size) of the data base, there is always the problem of "selective deposit and retention" of data. For instance, descriptions of those alternatives not selected during previous decisions, along with assessments of their probability of success or failure, usually are not in the corporate data base or even archived in long-term storage.

In brief, the reality which you think you are calculating can be quite distant from the reality your models should encompass, so accuracy and completeness of the results always should be suspect. For example, the short term factors used as input may be inappropriate for predicting long term results, but the output numbers appear without warning of probable inaccuracy. The input data may be inconsistent for various reasons related to their earlier processing. This reality is popularly known as GIGO (garbage in, garbage out). At best, the typical data base is likely to support only the more obvious solutions.

Archival data consists of records produced by and for others, without forethought of your current analysis needs, so such records are of uncertain validity. Data that could help you evaluate past decisions often will have been misplaced, overlooked, or discarded. This is the omnipresent problem for all forms of decision support, better known as NINO (nothing in, nothing out).

The traditional hierarchical management organizations will try to retain the first (and sometimes only) look at critical information and to restrict its distribution. Opportunities to design and build inadequate analyses always lurk nearby.

Return to Table of Contents


Expert Systems

Expert Systems often are considered the most successful applications of Artificial Intelligence concepts to computer programs, as a fairly standard current feature is ability to explain their internal logic to users. (This feature is used more during their development to arrange knowledge and rules, as most people just want to know what to do next, but novices who wish to become expert will find it informative. Advances in this explanation facility will provide alternate explanations and analoguous related examples upon expression of user dissatisfaction with the first explanation.)

Expert Systems apply knowledge to transform input data into more immediately useful knowledge, which is a limited form of intelligence. They implement either rule-based or knowledge-based approaches to "reasoning" optimum solutions to difficult problems within their narrowly defined expertise.

In that sense, by installing a set of rules, the developer or knowledge engineer learns what the expert(s) know and how they reason, then teaches the Expert System what it must "know" to later guide the user. The knowledge base will then contain both factual and heuristic knowledge. The latter experiential knowledge is that which separates the experts from the novices. The system rules will have weights or confidence factors for dealing with uncertain information. For such cases, you want the Expert System to explain its reasoning.

Professional knowledge work can be accelerated by a factor of ten or more. This is white collar work, folks, where productivity gains are difficult and future company competitive edges are constructed! The improvement is accomplished by providing the information you need for the situation you are in immediately, without your involvement in the search. Planning and other complex jobs that formerly took an hour or more of active time (plus interperson transfer for divided areas of knowledge that can add to days as the item awaits attention) become a few minutes work. The improvement in quality and consistency alone provides substantial customer satisfaction. The cost savings, the return on investment, can be terrific.

The value of Expert Systems is greatest in preserving transient human knowledge beyond the lifetime or availability of the source, for offering information and suggestions in a consistent manner. Retired persons who have been called back to consult like the idea of "immortality" as their usefulness is captured for ongoing use in an apprentice for the people still working.

Knowledge from several to many experts can be combined into a "community intelligence", making the Expert System better than any one individual could be. This does not bode well for consultants who wish to compete with, rather than assist the development of, Expert Systems. The information and the suggestions are carefully installed as relevant data and decision rules into the application-specific knowledge base. Pieces of the "company culture" or how it really works sometimes show up in the expert's rules, which can lead to beneficial (or otherwise) organizational changes. Decision rules are processed during an Expert System's operation by the internal generic "inference engine" software program. Unlike the way standard computer programs work, Expert Systems select and adjust formulas to fit the situation.

Expert Systems are particularly successful where the knowledge base changes frequently, such as diagnosis, forecasting, monitored controlling, and scheduling.

Internally, the programs use an inference engine paradigm, a search and connection approach to machine reasoning. Metarules control internal priority of processing rules for resolving conflicts and advancing toward the solution. The most well-known methods are known as forward and backward chaining.

Forward chaining, data-driven reasoning, works from the outside in, trying every potential solution path against the facts and results of selected rules until some combination leads to the goal.

Backward chaining, or goal-driven reasoning, begins at a defined probable final objective as the conclusion and determines which of the facts and rules will support that end by searching through the premises and applying validity tests. The knowledge states and operators for transforming those states make up the problem space. Knowledge guided discovery of a connected path, for the sequence of operators which transforms the beginning state into the goal state, solves the problem.

Expert Systems commonly integrate forward and backward chaining, with the selection of when to use each made by the internal inference engine. An approach is to switch between forward chaining to each hypothesis and backward chaining to determine its validity. The search stops at a final conclusion.

Some packages in this category use examples from a set of successful approaches to given problems and develop their own rules. (They provide a translation from human to Expert System using their inner program rules.) Such domain-independent programs are called Expert System Generators or Shells.

Imagine including constructors for all of the DSIDE™ process decision bases in an Expert System! (Some Expert System Shells interface with popular spreadsheets, so the Weighting Template and a customized Decision Template, as described in the Decision Process tutorial, would integrate well.) Of course, that only applies when the expert being emulated uses those tools more skillfully than others to whom they are available. Installing it into a hybrid system with conventional programming capability to provide a seamless interface also is possible.

Translation from a human expression of a rule to the form usable by a software package can be difficult at best, virtually impossible at worst. As a knowledge engineer, you must understand both the language of a specific field and any peculiarities of the individual expert's verbal expressions, which may include a distinct aversion to elaboration. (Don't try this cold. Study the field enough to become humble from appreciation of your ignorance and earn a minimal respect for your novice knowledge with correct use of the jargon.) The field is much like System Engineering as discussed in another tutorial in this series.

Multiple ways of asking the same questions often leads to some inconsistent answers that lead to new questions which improve the result. The expert may invent a better way to do the task because of the frequent need to explain processes. You could ask if the expert is willing to wager on the accuracy of the answers as a level of probability. Remember, too, that experts are subject to the same vagaries of judgment as anyone else.

Human evaluation of actual feasibility is required when the input statements are known to be flawed, because provision of the best solution never is guaranteed. Perhaps this is why Expert Systems perform best when the user is expert in that field.

Most Expert Systems probably should be restricted to financial forecasting and other types of planning and analyses where the rules are fairly well-defined and effectiveness gains should be highly visible. Other fruitful areas are diagnostic systems where the problem cannot be well specified, where most input is qualitative natural language expressions, and where quality of categorical associations in raw data need to be discovered with application of rough set theory during data "mining." Don't start with a large, difficult domain, no matter how critical, such as a major process control system. Development of Expert Systems for complex problems requires thorough comprehension of the computer as well as knowledge of the problem area.

Walk before you run, using the rapid development or incremental delivery approach to provide increasingly added value while managing expectations. Your first efforts should be with company internal processes, particularly those which involve "bureaucratic rules" but are infrequently performed and require "relearning" every time, to take advantage of greater initial understanding. The customer service "help desk" system allows less expert people to provide the initial telephone contact. (Governments should provide Expert Systems for rental by anyone needing to fill out the forms applying for certain permits, etc. Research involved in preparing an international technical patent application comes to mind.)

The main technical difficulty with development of useful Expert Systems is extraction of expertise from the experts. Unfortunate, but nevertheless true, the expert may not actually understand and thus will be unable to coherently explain the strategy for how a particular problem is solved. What they do just "seems" right to them.

Further, the extracted knowledge is likely to contain assumptions and biases of the Expert System programmer (knowledge engineer) as well as those of the expert, further contributing to possible errors of implementation.

Some Expert Systems are based more on Simulation Modeling (described later in this tutorial) than upon a rule-based paradigm. This allows detection of problems not considered in the original design which a rule-based system would miss and overcomes deficiencies in reasoning by experts. Simulation Models of complex systems sometimes are very difficult to construct, however.

Nevertheless, the strictly logical, computational models mostly used for AI and Expert Systems have been tried and found wanting in attempts to mimic several critical areas of human intelligence. The paradigm has begun to shift toward connectionist modeling concepts.

A big part of the new concepts is the admission that people are not rational, no matter how nice and pure that would be. The rational models could not handle the common sense aspects of intelligence, the interwoven experiences that allow you to adapt to complexities of living as a human being.

Return to Table of Contents


Neural Networks

The truly revolutionary departure from traditional computing, neural networks are a development of attempts to model how the human brain actually works with software and specialized hardware which forms a hybrid digital/analog computer. Interest in neurobiology and the invention of electronic circuitry hardware and/or computer simulation models of interconnected neurons and synapses, neurocomputing, has emerged from a long dormancy and is accelerating. The greatest strength of neural networks, their application niche so far, is in pattern recognition problems.

Unlike standard computers, with data bits in specific locations that must be remembered for recollection and use, the information is spread throughout the neural network's memory system. As in the brains of creatures, many neurons are interconnected with many other neurons. Each neuron (processing element) contains a threshold activated summing input and a defined transfer function for determining its output.

While neural networks do not emulate the brain at more than the most rudimentary level, processing speeds can be several orders of magnitude faster than traditional computers for the appropriate problems. A digital computer processes everything in terms of binary states of 0 and 1. The neural network processing elements have an essentially continous range (like probability) from 0 to 1, inclusive. They automatically incorporate a form of fuzzy logic, which is useful for certain kinds of problems where being human-like is more effective.

Like the human mind, neural networks are a content-addressable memory that brings forth an association through pattern matching. Retrieval accuracy is proportional to the quantity of information contained in the input stimulus. After thirty years of research, the key to current major successes was an added layers approach with extra neurons hidden between the input and output layers.

Different overlapping sets of the hidden neurons become actively involved in decisions about different inputs. In fact, you can't teach a neural network one thing at a time. The whole set of facts must be used in the training, with substantive differences between each item for best recall. As a model of the brain's neural system, the current neuron simulations are a tiny fraction of the complexity involved in the real thing. Nevertheless, that minute percentage has accomplished amazing things in application areas involving patterns, such as shape recognition, classification, and completion.

For instance, neural networks successfully work with fuzzy, inaccurate data and can find patterns without specific instruction. Where AI requires that the rules for reasoning a specific problem be programmed ahead of time, neural networks adjust their interconnection weighting as part of their pattern recognition training, providing a form of learning law for discovering or inducing the "rules" from examples. High accuracy of results requires iterative training with a large set of examples, although prefiltering the examples can reduce required number of both training examples and hidden layers.

The limitations also are substantial. Neural networks may not discover the mathematically optimal solution to a problem and will instead produce a "close" answer. They cannot be given a rule outright, but must be repeatedly taught the rule through examples. The neuron processing elements "learn" in self-organizing systems by adjusting their input thresholds and weights in accordance with the goal of increasing their "success" rate. The substantial time for this training is reduced with supervised learning techniques such as back propogation, the adjustment of previous layer connection weighting as well as that of the current layer by presentation of the error produced during the earlier training.

Meanwhile, selecting the appropriate network type (many exist and the number is growing) and developing it for a given task takes trial and error, tweaking and tuning of number elements, number of layers, learning rules and transfer functions. Network size may be inadequate for solving real problems although sample problem and training data were handled. Further, as a network becomes more adaptive, its responses become less predictable. (A response that certainly is human-like!)

Return to Table of Contents


Expert Networks

As the hardware and software simulations of neural networks continue to develop and improve, the next wave of advances in AI are likely to be from further expansion of their combination with Expert Systems. When the embedded neural network performance is satisfactory for the purpose, this results in the best of both worlds. The neural network determines the likeliest situation and the Expert System selects the action to take and performs it.

The neural network can discover the rules in example situations. Therefore, one approach to answer justification is to use a neural network to generate the knowledge base, then to use an inference engine for interpretation of the knowledge. Each system seems to have what the other lacks, so a combination that minimized the problems of each was the next natural goal. A neural network applied to process performance monitoring can be taught to defer to an expert system when variances become too large, as a form of Management by Exception.

Building both types is not double effort because neural networks may be developed in much less time than separate expert systems and the experts are used only to revise and tune the rules.

With all the progress, its only a start. How the brain "knows" something to be true is not reproducible in an algorithm. Inspiration, the sudden production of an ingenious or original idea, will continue to elude the machine approach for the foreseeable future. A standard System Engineering problem, establishing performance metrics for the people, applies here as well. How do you measure progress of a knowledge engineer along the way to developing an acceptable Expert System?

Return to Table of Contents


Adaptive Fuzzy Systems

Hybrids made from combinations of neural networks and AI/Expert Systems which apply fuzzy logic as well as logic based on crisp sets. As stated earlier, the positive strength of neural networks is pattern recognition. Fuzzy logic, working with the output of neural networks, assists development of structured rules so previously intractable problems may be solved with incomplete rules specification. Expert rules setup the neural net, to jumpstart its learning process. The requirement for an intermediate mathematical model is skipped and rules are derived from the behavior exhibited by the neural network. The rules change as the neural network learns and are immediately accessible, speeding up research as well as applications of the systems.

Return to Table of Contents


Group DSS (GDSS)

The concept of an executive thought support tool implies either support for managerial communications or personal contact with other key executives before a DSS can be more than one of many tools that are available to assist decision- making. The benefits are most obvious in environments where decision solution convergence to some group consensus is essential. Experiments in automated decision conferencing have led to development of the Group Decision Support Systems installed in special meeting rooms with a physical arrangement that is heavily dependent on recent developments in information technology for electronic networking of the computer workstations. The basic idea is that better decisions may be made if you can remove any supervisor/subordinate pressure and/or any natural leader influences.

Group member behavior during a negotiation problem can be substantially different when the face-to-face element of the meeting is not available to anyone. This is accomplished by placing each group member into a separate plush workstation booth where input at the keyboard is anonymous. A common large screen display, whose content is controlled by an assigned group leader, is in view of all participants. The group leader coordinates all common screen presentations and places jointly agreed constructions either on that screen or into a common data base. A public group data base is the source for a participant's development of individual problem models. The group leader is sent ideas, results, or actual models used by participants, as appropriate.

A single meeting room is not essential to fulfill the concept, making global long distance, international GDSS possible. New, high resolution screens could display the common screen information and results in a "window" while participant problems are in the remainder of the screen. The GDSS approach could greatly assist EIS utility, if key supporting executives are participants.

Another desirable feature is automatic generation of Decision Models, based on historical modeling requirements, with an easy adaptation to the new requirements. The Fourth-Generation Languages are an attempt to come close to that capability, through generation of database or modeling programs in response to either menu selections from a control program or from applying specific natural language statements. The resulting program will be simplistic or else the user must possess some programming capability, although this problem is alleviated a bit with an Expert System Shell front end to lead the user through the difficulties.

Return to Table of Contents


Decision Support Subsystems

The difficulty of integrating Operations Research tools into a single DSS package leaves many useful methods for employment as separate general models. Still, for those equipped with adequate knowledge to properly use them, the math-based subsystems have key advantages related to the DSIDE™ process. Specifically, they possess theoretical justification for their application to specific portions of the problem and provide Decision Research support for improvement in your future decision-making processes.

The subsystem category is where you should list most of the Decision Support related software currently offered for use with personal computers (PCs). After you peel the marketing hyperbole away and examine skeletons of various Decision Support packages, you will find only a somewhat reduced subset of the requirements for an ultimate DSS.

Such offerings are classifiable as either decision aid or decision modeling packages. Whatever they are called, their objectives are identical. Most programs in the first category have you assign weights or values to each decision factor, without much in the way of provision for removing subjectivity in the weighting process. (Even graphical assistance to a weighting assignment is insufficient because it still is much too easy to confirm a preestablished decision.)

Also, the typical use of a simplistic "musts" and "wants" approach without any revelation of the full range of alternatives for an important factor can seriously oversimplify the problem. Admittedly, that is much better than modeling without any structure to your decision and could be sufficient to arrive at a best decision for many non-complex performance analyses. In business problems, however, complexity is a standard attribute.

Return to Table of Contents


DSS Tools Observations

Integration of the mathematical tools within a DSS shell allows the useful techniques to be made useable. Arrangements of math models, like Expert Systems, should be kept flexible for application to specific, suitable problems.

Many of the tools offered to you for making decisions would suffer somewhat under price/performance comparison with the process outlined in this book, although some clerical work is automated in them. Few commercially available decision aid systems, if any, reveal to the user the algorithms employed within. A mystique surrounds the program's capability as simple mathematics become jealously guarded "trade secrets."

Widespread public release of program source code is not a sought objective, as an "intellectual property right" in the program should exist. However, application of requisite numerical methods is not easy, and user knowledge that proper safeguards are in place when using tricky computer algorithms can prevent program tools misuse and costly errors resulting from user ignorance.

You may be told that the reason some program's price is so high is that it includes technical support when you require it. That support may extend only to your being told how to solve a specific problem, however. Of course, you may choose to ignore implications of standard disclaimers about a computer program's possible fitness for any advertised use. (That is only the lawyer's attempt to forestall a lawsuit which otherwise might arise from a user inconsiderate enough to argue that the product should work as claimed.)

Nevertheless, when you do not know precisely what program action takes place under specific circumstances, you cannot be certain it will be appropriate for your problem. That is true, both with and without program support by the manufacturer. (Trust a program developer who reveals program processes, for what is not known can indeed hurt you.)

You must know what factors are not accounted for by the model design, what conditions can change the model precision or destroy model assumptions. When you do know how a program works, you can put bounds on the validation problem and have a chance of success. You won't have immunity to effects of GIGO, of course, but the decision models you construct could possibly be valid. Not concerning yourself with this issue can lead you to uncritically accept the computer aid's assistance and to perform your decision-making less well than you would have without a computerized decision aid. You could well be misled into working up short-term results which lead to less than optimum long-term effects.

Return to Table of Contents


Related Tools

The value of computer utilities goes beyond their immediate or apparent purposes, in that they can lend insight to managers who are struggling to grasp meaning hidden in assembled facts. Insight is not the same as infallibility, of course, plus the tools work less well in health, education, welfare, and other socially-oriented areas where objectives and measures are less quantitative. Computer-based data processing tools greatly assist decision-making but are not fully equipped for the necessary work. The following paragraphs describe tools which are known to be useful in that limited sense.

Return to Table of Contents


Decision Tree Modeling

Other useful tree forms than inverted exist. A Decision Tree works from the starting node at the left to graphically depict the evaluation and choice branches of decisions with regard to events and their potential results from action choices and divides into two or more identified results from the choices, leading to two or more final outcomes. Each branching route shows a separate alternative or course of action within the multi-stage decision. The convention is squares for action-fork nodes and circles for event-fork nodes. Results are assigned a numeric outcome or probability for each of two or more routes to their next decision nodes. At that point, conversion into a payoff table is possible and each choice has an earned numeric total for ranking the options. (Does that seem familiar?) The decision tree expands by treating each decision node as the starting node for a new expansion. The expansion process continues until you assign a final set of decision nodes and their expected values or profits. The best route (or selection of decisions) is that which maximizes total profit.

The Decision Tree chronology is shown left to right. Computation of expected profit proceeds from right to left by summing the values multiplied by probabilities into the next leftward result node. The next left decision node selects the highest profit just computed for the result nodes. Computation using the resulting payoff or profit proceeds leftward in that fashion until you assign the maximum profit to the final, leftmost decision node. The Decision Tree theoretically shows the optimal action to take upon arrival at the future decision points. It uses a fairly straightforward process, combining action choices with the results of those actions or events (and their probabilities of occurrence), but has two distinct, important disadvantages.

For complex problems, particularly those involving information valuation, the decision branches proliferate at a terrific rate and make manual methods virtually impossible. Removal of dominated action choice branches is simplifying or pruning the tree. The decision trees for such complex problems are more likely to confuse the decision maker than contribute to greater understanding of the problem until extensive pruning is accomplished.

It is easy to descend to levels so deep that the requirement for detail obscures critical decision issues.

Worse, however, is that decision tree modeling requires adequate foreknowledge of the profit and probability to assign to each route and node for obtaining the outcomes. The insurance industry has developed actuarial tables, but such knowledge usually is very difficult to obtain. Probability knowledge usually is unobtainable if you are an independent consultant whose expertise is in other fields or if you work for a smaller company that could not afford to develop the necessary data. Most people have problems treating new or unfamiliar data properly for making changes to their Decision Model.

The principal advantage of the tree diagrams is depiction of sequences for multi-stage decisions over an extended period, because few decisions can be made in isolation from other decisions. Knowledge of the decision structure can help even when detail information is uncertain. You then at least understand all the options and various consequences of their selection. Options you might previously have rejected out of hand become possibilities.

Return to Table of Contents


Simulation Modeling

Simulation Modeling often has a close but indirect relationship to Technical Performance Measurement (TPM). Computer programs historically have been a powerful means to construct mathematical models. Simulation capability has grown to include complex digital and analog electronic systems, such that proposed designs may be evaluated well before they are built for trial. Increasing complexity will make prototypes too expensive, adding impetus to simulations. Increased computing power has brought simulation models more into employment as valid representations of actual systems operating in their predicted environment. An extension is their manipulation in ways that would be expensive, impractical, or even dangerously impossible with the real thing. Performance analysis with simulation models can provide predictions of throughput, response times, and utilization of resources.

For business, that makes Simulation Modeling effective for scheduling processes, estimating demand and capacity needs, some forecasting, and discovering and addressing potential problems for Project Risk Assessment (see the tutorial on that subject).

Expected behavior of actual systems under virtually any scenario can be inferred, providing good parametric estimates for input to your Decision Models. It is an engineering rather than a Management Science or Operations Research approach, but is just as usable by businesspersons. The results usually are numeric and consistent with decision criteria.

A theoretical system, which has critical functional parameters and attributes that are virtually impossible to measure, should have accurate estimations. An example is a queuing or waiting line model for validating required capacity of a proposed service facility. Where attributes are best characterized by a high dependency on other system attributes, on probability distributions, or are difficult to analyze mathematically, discrete-event simulations can provide usable predictions of complex candidate system performance under worst-case scenarios.

In discrete-event simulations, event arrivals are obtained in two ways. First, they can be predetermined and provided as starting input from a list. Second, each arrival of a specific type computes the arrival time of its successor with a function. The function can range from a continuous probability distribution to an arbitrarily fixed step series. The choice may be determined by a pseudo- random number generating function, so repeated runs can evaluate single changes with the same input, or randomly selected starting values can evaluate multiple runs of unchanged models.

Continuous simulations depend on sets of numerical equations as the principle system model, for simulating non-stop processes that change continuously over time such as petroleum refining or steel-making. Instead of updating the simulation model's world time on occurrence of the next discrete event, time is changed in small increments and all time-based formulas are recomputed. This approach costs more computer time than event-driven simulation.

Some complex models will require a combined approach, where continuous time updating occurs until a specific condition defines an event that shifts the model into discrete-event updating until the continuous updates are required. All simulations share three properties:

Simulations are much easier to construct than formal math models duplicating complex systems, particularly with use of new object-oriented programming system (OOPS) constructs. When expert opinion provides input hypotheses, the derived predictions are useful for Decision Model attribute performance measures. One analyst can explore a multitude of concepts in a brief period, when compared with the time required for building even a simple physical prototype. You can learn from mistakes without harm to anything, gain insight and really understand the problem.

It is an accelerated version of the process employed by scientists conducting basic research. A tentative formulation is explored, which gives rise to new questions, which lead to theoretical insights and thence to an improved formulation, which ...

Clearly, a valid, verifiable simulation of selected or all portions of the candidate system, under proposed operational use, may be the only possible means for accurately establishing what capability it should provide after incarnation as a physical realization. The model responds to external stimuli, internal conditions, and performs operational functions in simulated real time. Simulation Modeling then is the best approach to estimating performance values for input to a decision model, to providing a check on the anticipated results and to conducting a Sensitivity Analysis for determining which data are required at what accuracy. The object is to gain confidence in model validity before making decisions with the results, providing an "insurance" when working with paper concepts. Unanticipated results can arise from unsuspected interactions of system parameters, which gives more insight and knowledge to the users. The final result should be a sharable and fully understandable concept.

General models are easy to build but of limited value, however, so you must anticipate the uses and expect to iteratively refine the model. Its structure should be modular, to permit timely modifications for support of decisions. Simplification then means that only those system features important to the project are modeled because you need not duplicate reality. For decision making, you only need enough realism to evaluate the proposed changes and simulate potential risks. Add detail only if absolutely necessary.

Surprisingly, simplistic seeming structures deliver complex, lifelike behavior and can relieve more ignorance than detailed special case models. It is akin to discovering a general behavioral theory for formulating the underlying assumptions in a class of system problems as you find that many of the simulation constructs are appropriate to problems in apparently unrelated disciplines. Thus, as in many things, clearly defining the problem with a System Engineering approach gets you well along toward the solution.

Nevertheless, the simulation model resembles other decision aids with ability to assist analyst communication with the decision maker. Model output must be understandable and appear valid to the simulation customer, whether presented interactively with graphics while the simulation is running or as results following each run. Interactive output allows observation as waiting lines change length or as the system undergoes other such dynamic system behavior during the simulated time.

Generic models may be tailored by differing users to solve similar problems. Models may be interconnected with other models for simulation of subsystems within a system, helping to prevent any single model from becoming overdetailed and cumbersome. Hierarchical, structured models may have selectively enabled modules to reduce execution times or to focus analyses when the area of interest is contained.

Another important point is that simulations can use "raw" or actual historical input data, statistical distributions, or some combination of the two. Therefore, statistical analysis frequently is incorporated in simulation modeling languages, providing illusion of inferential validity. Typically, however, available data are unlikely to be all useful and appropriate to your problem.

Simulation models generally do not optimize, so they will not automatically select the best choice from alternatives, but a model of each alternative may be applied to the problem and the best result indicates the best system. As in the DSIDE™ process, the various modeled figures of merit may be combined to provide a single output value.

An extremely valuable aspect of computer program simulation modeling is the forced understanding of a problem required for development of even a semi- realistic model. Your expertise rises as you build the model. Confirming infeasibility of alternatives may lead to a new, feasible approach. Making a large series of runs can build the sample required for confirming probability distributions and associated parameters for arrival times associated with the distance travelled and other control variables for Just-In-Time manufacturing facility service by a supplier.

Simulators train people to do many things, so training someone to build simulation models is an appropriate task for a simulator.

Return to Table of Contents


Optimization Modeling

Optimization modeling is knowledgable application of selected mathematical programming techniques from Management Science or Operations Research to determine best courses of action to take in highly structured, well-defined and well-understood areas, such as engineering design or resource allocations to maximize benefits or minimize cost.

Linear, mixed integer, non-linear mixed integer, dynamic programming models, stochastic process and network optimization models represent this class of tools. Linear programming and other optimization models successfully solve scheduling, transportation route, investment mix, and other similar resources distribution problems where options are many.

Linear programming is computerized solution of multiple linear algebraic formulas with multiple variables. (Linear means that no variable is raised to square or higher power.) Some of the formulas are solution constraint expressions such as quantity limits. An objective function is either minimized or maximized during simultaneous solution of the equations. A form of linear programming that seeks compromise solutions accomplishes goal programming.

Return to Table of Contents


Prediction Modeling

Evaluating results of various alternative selections often will require projection of each environment into the future. For that, as for other forms of modeling, you want reality represented simply enough to understand and use for corporate strategic or shorter term planning.

Examples of prediction modeling are trend line forecasting models which use various smoothing techniques, budget models, various extrapolation formulas, and curve-fitting models. People perform poorly at consistently integration data from a wide variety of sources, so statistical models help. Such models often are useful for determining that opting for doing nothing (which projects the current situation) will lead to a future problem, as well.

Extrapolation from the present is always uncertain and it gets worse the farther in the future you seek answers. When you have lots of historical data you can check the predictive value of a model by checking how well it projects actual trends. Even a model having high correlation of predictions with historical data values, which is all one could ask for, can be wrong about the future when some new external occurrence changes the rules. Therefore, never extrapolate from past data without massive disclaimers. The longer term you propose to predict, the larger the disclaimers should be.

No matter how well founded, if the prediction involves potential failure or extreme pessimism, the attentive audience will be small. Developments in other industries may have overwhelming effect on the acceptance of your product. Competitive technologies can render even new products obsolete at a stroke, as when electronic calculators supplanted slide rules and electromechanical adding machines. Failures outnumber successes by such a wide margin that you can learn little from studying only the successes. The lesson for you is to temper enthusiasm with pessimistic reality while others are caught up in the fad. Use multiple, simple methods or combinations of forecasts to see if all approaches point to the same result and challenge the underlying assumptions. Faulty assumptions automatically provide flawed forecasts. In other words, sometimes you must use some subjective judgment to see if all potential impacts on the forecast were sufficiently considered.

Regression analysis is a mathematical process that measures the apparent relationship of an item of interest with items thought to influence it. The degree of "fit" for predictors helps you decide on their usability in forecasting results. Avoid fancier mathematical methods to concentrate on finding out what makes better forecasts. One such item is plotting historical data on the same graph as the forecast. Another is forecasting under several of the most likely scenarios.

Return to Table of Contents


Scenario Analysis

Related to Prediction Modeling, but indirectly, scenario analysis is assembling a set of plausible alternative futures. Scenarios may be presented as flowing narratives or prose descriptions of what the future will be like with a particular adoption of the new technology. The trail from the present to the future may be explained. Multiple scenarios explore each plausible branch in the paths leading to each alternative future. The business plans that are adaptable to as many alternatives as possible reduce the risk that ensues when "all eggs are in one basket."

As with regular forecasting, attempting to assign probabilities to multiple scenarios leads to assuming that you should plan for the one considered most likely. That removes the benefit of developing a robust strategy to accommodate most alternatives.

Instead, work at making the scenarios equally probable and assign thematic labels indicating different paths of arrival, such as slow acceptance, heavy competition with similar technology, heavy competition with competing technology, rapid price decreases, and so forth. This forces consideration of the unpleasant, providing a reality check.

Return to Table of Contents


Statistical Modeling

An area of concern is the provision of statistical tools. Statistical procedures help you to separate distinct events with assignable causes from random occurrences. Except for some quite complex Simulation Models, forecasting from trends is difficult without employing statistical techniques. Models that will produce invalid output at terrific speed are easy to construct. To avoid errors, you must know what the result really means and what logical processes to employ when applying a particular statistical measurement.

The purpose of performing statistical tests on a population is to allow you to infer one or more valid conclusions regarding a group of observations or measurements and allow discarding irrelevant information. The basic concept of measurement assumes repeatable trials, but many events are unique. The sample must be random, of sufficient size and assume a proper underlying distribution.

Standard deviation is a measure of data tendency to gather about the average or arithmetic mean. Large standard deviation indicates a large dispersion and greater risk in assuming that the computed mean is reliable for making predictions. Dividing the standard deviation by the expected value gives the coefficient of variation as a measure of the data dispersion or relative risk.

Many books reveal how to compute those values and perform statistical tests. Detailed discussion would be straying from the subject of this book, so the mathematics will not be shown here.

A confusion between statistical significance and a difference important to your overall system can distract you from really critical issues. When you have options in your program to pick an improper analysis for the data required by the conceptual design of your model, you had better know what you are doing or work within a plan developed by someone with a strong background in statistical methods.

For typical DSIDE™ process problems, training and experience in the design of engineering experiments would be preferable to experience with sociology/psychology forms of statistical inference.

Measurement errors for tangible systems aspects (equipment performance) often have the laws of physics as validity bounds.

Measures of indirectly observed human behavior, however, easily can be based on what may be politely called "that which is not so." Beyond outright lies and other distortions of fact, respondents to surveys tend to offer idealized or popular answers rather than relevant information. It is extremely difficult to construct survey material that avoids biasing the results. Of course, if your wish is to support a particular conclusion, suitably emotion-laden or "politically correct" questions are quite easy to frame.

Finally, because no universally useful statistical test exists, it frequently is best to avoid using statistical tests to develop either Simulation Model or Decision Model parameters. Resist the strong temptation to summon those routine(s). Rely, instead, on as many directly measurable attributes as possible or you may find yourself claiming an unsupportable statistical inference.

Return to Table of Contents


Spreadsheet Modeling

You may be aware that virtually all popular personal computer systems offer nearly as many competing spreadsheet programs as word processors. Primarily, applications of spreadsheet programs involve financial modeling and investment planning, although an experienced, careful spreadsheet analyst could perform a decision sequence similar to the DSIDE™ process with one. Spreadsheet programs have become popular for modeling systems where the decision parameters are comparable as cost elements. (Deficiencies in that approach are some of the problems that led to development of the decision process described in another tutorial.)

A spreadsheet analysis is difficult to independently audit for propriety. That's because it can't assist construction of clearly understandable presentations of analysis conclusions without some tailored report generation. The "integrated" programs can help a lot with that, of course, but this tutorial assumes using a stand-alone program for greatest coverage.

Advanced spreadsheet users can and will do whatever they deem appropriate, of course, but going to that depth about peripheral aspects of a spreadsheet program's operation here would again overcomplicate a moderately complex subject. Spreadsheets do allow you to construct models where you can see the automatically calculated result of a single input, thus their power for economic or financial analysis. Certain forms of analysis are much more difficult to fit into a spreadsheet approach, however. An example is linear programming, which heavily uses matrix algebra. It has been accomplished, with add-in programs, but suffers from similar shortcomings to the use of statistics—the need to know when a given approach is suitable, as well as how to use it, for best results. The techniques say yes or no while the best answer often is maybe.

If you have any questions on this tutorial subject, please contact the author as listed below. A response will occur and a FAQs section may result.

Return to Table of Contents


This tutorial is presented by:

The ODDSCO Co. Logo<br> (A stylized duck).<br>

Optants Documented Decision Support Co.
(ODDSCO)
297 Casitas Bulevar
Los Gatos, CA 95032-1119
(408) 379-6448 FAX: (Same, by arrangement)



www.optants.com


E-mail:
Tutorial Author: jonesjh@optants.com
Other subjects: consult@optants.com