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Group Decision Support System for System Design

Nagib Callaos,*** Richard Evans,** William Lesso,* and Belkis Callaos***

* Professor Emeritus and Former Associate Dean of the College of Engineering of the University of Texas at Austin ** Department of Computer Science, George Mason University *** Department of Processes and Systems, University Simon Bolivar and The International Institute of Informatics and Systemics (IIIS)

Presented as two Plenary Keynote Addresses at the

5th

World Multiconference on Systemics, Cybernetics and Informatics, 2001

Abstract

Our purpose in this article is to combine three previously published papers in order to apply a general Methodological frame to a specific problem. Because system design is a special case of problem solving, we will try to apply A Generalized Group Decision Support System (GDSS) for Group Problem Solving: (Callaos, et.al. 1999; Callaos and Lesso, 1999) to the specific area of System Design. This will be done by means of: 1) describing a GDSS for Group Decision Making, based on the Mathematical Solution to the Voter Paradox, 2) analyzing the concept of design, and making explicit its relations to: 2.1) intention, action, decision (Callaos and Callaos, 1995), and 2.2) to discovery (Evans, 2000), and group creativity (synectics). The GDSS for Group Problem Solving proposed here has two basic sub-systems: one would support the group creativity process, required for the generation of alternative designs, and the other would support: a) the group reasoning process, required to establish the pros and cons of each design alternative and b) the group decision process, based on the Ordinal Scales Delphi Method made possible by the Generalized Absolute Majority Rule, and/or the optimal hamiltonian path, both included in the Solution of the Voter Paradox. The ideas presented in the third part of this paper emerged while Richard Evans, one of the co-authors of this paper, worked with IBM and NASA over the past four years. The other two parts are suggested as complement to the third part, and as a way to make operative the basic ideas through the software developed according the first part of this paper. Keywords: GDSS, Group Problem Solving, Voter Paradox, Design, Intension, Action, Options, Invitation, Nomination, Confirmation, Self-Assessment, Meeting Purity, Customer, Builder, Associate, Idea-writing, Requirements-as-design-decisions, Ideas, Great Question, Introductions, Risk-based Design, Uncertainty, Headwaters, Insights, Issues, Initiatives, Greenhouses, Systems, Discovery System, Delivery System, Delivered System, 3-D Thinking, Dimensions, Independence-is-not-Unilateralness

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Introduction The main objectives of this paper are:

1. To relate three previously elaborated papers in order to achieve an integrated framework aimed at the application of a Collective Decision Support System (GDSS) to systems design, especially in the area of complex systems, where design decision should not be made by a person, due to the multiple effects of such kind of decisions. So, the content of this paper is based on the principle that people affected by the effects of design decisions should participate in such decisions.

2. To generate a working paper, where each one of the co-authors could elaborate with more details some issues of it, add other related issues, and where other scholars, researchers, or consultants could participate via feedback, or by means of elaborating further related aspects.

3. To entice other researchers, scholars and consultants to contribute with related papers to the track of this topic included in the 5th World Multi-Conference on Systemics, Cybernetics and Informatics (SCI 2001)

The content of the paper will have, basically, three sections, associated to the three papers to be related. In the first section we will try to describe a conceptual design and a basic architecture of a Generalized Group Decision Support System (GDSS) aimed to sustain processes of Group Problem Solving. This will be done by means of integrating: 1) problem definition and typification, 2) macro-phases of synectic methods, 3) GDSS types, 4) Collective Decision Theory with its related mathematical solution to the Voter (or Condorcet) Paradox and 5) application of the Operations Research Approach to this specific kind of problems. The Generalized GDSS to be described in the first section has been applied to several specific cases as, for example, Strategic Planning, Reengineering, Management Control, Participatory Management, etc. In this paper we will try to apply it to participatory systems design. This is a special kind of group problem solving. So a Generalized Group Problem solving methodology, and a Generalized GDSS, should be applicable to the specific case of Systems Design. In the second section of this paper we will try to work a systemic notion of “design”, which would, consequently, be an integrated and an integrative one. To be integrated, the notion of design should include its contextual relations, and to be integrative should be a comprehensibly unifying one. This would give us the conceptual framework for the principles this papers is based on. Consequently, we will attempt to identify an initial semantic comprehensive structure of the notion of “design”, which will lead us to a hypothetical conceptual infrastructure, which, in turn, will give us the pointers to the contextual relations and the other notions strongly related to the notion of design. After examining these contextual notions, we will check the validity of the hypothesis made about the conceptual infrastructure proposed for the notion of “design”. Banathy, et. al. (1979) made a very wide exploration on the different meanings/definitions given to the notion of “design” and extracted the commonalties they identified among the high diversity they found. This is a synthetic-inductive approach to the problem. We will try a synthetic-inductive/deductive approach i.e. a very brief semantic synthetic-induction first,

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concluding in a hypothesis formulation, followed by an analytic/deductive process oriented to the conceptual context. Furthermore, we will try, in the second section of this paper, to provide the reasoning about the principle, given above, which will be one of the basic support of this paper i.e. the people affected by systems design decisions should participate in the decision making process of these design decisions.

In the third section we will try to show that design involves discovery and since discovery is based on options generation, the foundation of design is option generation and assessment. Option generation might be supported be one of the GDSS’ sub-systems presented in the first section (the electronic brainstorming one), and since the assessment should be a collective one, it might be supported by another GDSS’ sub-system (the collective decision support one). The fostering of the emergence of design ideas, and the nurturing of their growth, is an essential part of system design, i.e. discovery. An adequate decision related to the discovered options is the other essential part or any designing process. The identification of an adequate set of ideas, and their comprehensive assessment, is effected by inviting, never imposing; by persuading, never compelling; and by nominating and confirming, never by directing or "requiring". The so-called requirements are design decisions, they are never separate stand-alone sentences. And, as design decisions, they should be participative ones, where people affected by these decisions are who should participate in the respective decision making process. The GDSS proposed in the first section would give the required support for this participatory process and for the required collective decision. As we will show in the third section, design as discovery relies in a major way on the united operation of the three roles of Customer, Builder, and Associate, as well as their respective relationships. It is also suggested (in the third section) that there are only these three roles, whether for person-to-person or organization-to-organization relationships. In organization-to-organization relationships, a GDSS like the one described in the first section would be highly useful. Adequate design requires a combination of creative intuitions, and structured thinking along with conversations, with others and/or with oneself. The GDSS described below have the potential of effectively supporting this kind of thinking and conversational processes and, in general, group creative processes as those described in the third section of this paper. 1. A Generalized GDSS for Group Problem Solving

1.1 Basic Definitions

The word “problem” derives from the Latin term “problema”, and the Greek’s “próblema”, formed on “probállein” which means “put forth”, “to put further on in the direction in question”. As long as the direction (in question) is related to explicit or implicit objectives, the term “problem” would mean, “to put further or in the direction of achieving explicit or implicit objectives, purposes or ends. Since ends are achieved through means, there will be two types of problems and, hence, two non-exclusive types of problem solving processes:

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P.1. Given a set of ends, inquire about the means to achieve them. P.2. Given a set of ends and a set of possible means to achieve them, find the “best” set of

means, where “best” is related to the given ends (effectiveness) and to the resources required (efficiency), as well as to possibly other implicit objectives.

We will see later that design is strongly related to intention, purpose and objectives, and designing processes are a special kind of problem solving processes. Consequently there will be two non-exclusive types of designing processes, analogous to the types of problem processes briefly described above. Accordingly, all what we can conclude, find, or propose in the domain of general problem solving might also be applied to design, designing processes, system design and system designing processes. Now let us go back to the problem-solving domain. A situation where ends or objectives are not given but should be identified and established is a meta-problematic situation. (In the design domain we will be dealing with a meta-design problem) In such a case, we are faced with a meta-problem, i.e. “the problem of defining the problem”, i.e. our end or purpose is to identify the objectives related to the situation, and to do so, we have to determine adequate means, or to select the “best” subset from a given set of means. This recursive characteristic allows us to focus on the problematic level in order to make a conceptual design of a GDSS oriented to support problem solving processes, because a similar conceptual GDSS will also be able to support a meta-problematic situation solving process. Likewise, this recursive formulation will allow us to focus in the design process, since meta-design level might be treated in a similar way. The definition of “problem” is by itself a problem, i.e. a meta-problem. Different authors define “problem” according to implicit or explicit objectives. Consequently, diverse definitions have emerged, as related to different objectives. Our definition of “problem” does not replace or exclude other definitions; on the contrary it includes them in a coherent systemic whole. According the objectives of the definer, a definition might be more or less adequate. This is coherent with the pragmatic-teleological truth of the Systems Approach (Churchman, 1971). Similarly, the definition of “design” is also a problem because different authors provide different definitions based on implicit or explicit design objectives. So, a systemic definition of design could be made using the GDSS we will describe in this section. Problem solving is the process of finding and/or selecting the best means to achieve some objectives. Different problem solving methods has been suggested. These methods are basically heuristic procedures, because they include phases related to the stimulation of individual creativity, i.e. creatics and/or group creativity, i.e. synectics. Both kinds of methods are important for the notion of design as discovery, as it will be described below, especially in the third section of this paper. Creativity heuristic methods have usually two macro-phases: a non-rational, intuitive, non-structured one, and a rational, non-intuitive, structured one. Different kinds of Collaborative Information Systems have been developed to support each one of these two phases. Electronic

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Brainstorming and Electronic Idea Generation Systems aim at the support of the first phase (Laplante, 1998; Aiken, et.al., 1996, Aiken, et.al., 1994; Dennis, et.al., 1998; Easton, et.al., 1990; Gallupe, et.al., 1992; Herniter and Gargeya, 1995; Nunamaker, 1991). Model-based, Multicriteria and Expert Systems-based Collaborative Information Systems have been developed to support the second phase (Belton, 1999; Fjermestad, 1998; Podinouski, 1999; Siskos and Spyridakos, 1999). According to our objectives in this paper we can distinguish between GDSSes for non-structured and structured problems. These two kinds of GDSSes will be used in this paper for the two macro-phases of synectics, respectively. A more detailed and comprehensive typification of GDSS could be found in Mirchandani and Pakath, 1999. 1.2. Collective Decision Theory

One important issue, not frequently treated in GDSS literature is Collective (or Group) Decision Theory. Group Decision Making based on ordinal individual preferences, and its respective Voter (or Condorcet) Paradox, is, to our knowledge, missing in the GDSS literature. This is a paradox in itself: How could Group or Collective Decision Theory (CDT) be absent from Group Decision Support Systems? Consequently, we will try to insert CDT into the GDSS architecture we are suggesting in this paper. We did this insertion in special GDSS we developed for different specific applications, obtaining encouraging results and positive feedback from our clients, and from the respective GDSS users. Elsewhere we briefly described the Collective Decision Problem and its related Voter (or Condorcet) Paradox (Callaos, 1976a; 1976b; 1980; and Callaos et.al, 1999): Transitive (rational) individual preferences may generate intransitive (irrational) collective preference, if we apply the quasi-universally accepted Absolute Majority Rule. We also presented our Mathematical Solution to the Voter Paradox, based on what we called The Generalized Absolute Majority Rule (GAMR). We also contrasted our solution to Arrow’s Impossibility Theorem. Arrow (1951) made a mathematical demonstration about the impossibility of solving the Voter Paradox. But, we did solve it and, consequently, showed Arrow’s axioms inconsistencies and methodological weaknesses. In the Appendix we present examples of the Voter Paradox and a visualization of its mathematical solution. 1.2.1 The Voter Paradox

Collective Decision Theory is at the heart of the GDSS proposed here. Consequently, we will briefly describe the problem of aggregating individual preferences into a collective preference, along with the Voter Paradox, and Arrow’s Impossibility Theorem. Ordinal data aggregation and ordinal social decision-making will be analyzed and solution to the Voter Paradox will be provided. We will present our critiques with regards to Arrow’s Axioms logical inconsistencies and his methodological weakness. This will be done as a product of our approach to the problem. The proposed solution has been and is being used in several research projects and in various real life projects. Software, based on the solution provided here, is being developed now a Beta test of an initial version is in progress. A first working prototype is being applied at present for several group judgment problems for re-engineering processes (a special kind of

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system design) and group opinion formation, generation and elicitation in an electoral situation for University authorities. The problem of aggregating individual preferences into a collective preference could be stated as follows: Given a set of alternatives A = {a1, ..., am} and the individual preferences of n persons, find an accepted rule for determining the collective preference of these n individuals, i.e. find a function SPF (social preference function) such that:

SPF: Pn(A) →P(A)

where P(A) = Am and Pn(A) = (P(A))n.

The most universally accepted rule is the Absolute Majority. This rule is for a set of two alternatives, and its application for three or more alternatives does not necessarily produce a collective preference. This characteristic of the Absolute Majority is better known as the Voter Paradox, which was first formulated by Condorcet (1743-94). This is why it is also known as Condorcet Paradox. Since it was formulated, several authors tried to find a solution. Borda (1781), Laplace (1812), Dodgson (Lewis Carroll) (1876), Nanson (1907), Galton (1907), Hoag and Hallett (1926), etc. tried to find an intuitively acceptable rule as substitute for the Absolute Majority one. Their efforts were oriented toward a consensual truth1. Arrow (1951) was the first who tried an axiomatic approach directing his effort toward an analytical truth. Our research was oriented toward a pragmatic-teleological truth as a goal and we used the consensual and the analytical truths as means to achieve such a goal. The efforts of the authors that looked for a consensual truth were in vain. Arrow’s efforts conveyed him toward his known Impossibility Theorem, which states that for a given set of universally accepted conditions, there is no collective decision rule, i.e. there is no transitive SPF. 1.2.2. Proposed Solution to The Voter Paradox

The problem of aggregating individual preferences into a collective preference could be restated as follows: Given a set of alternatives A = (a1, ... , am) and n individuals, each one with a preference function over the set A, find the “optimal” SPF, where “optimal” would mean the collective preference associated the minimum of total un-satisfactions2, or minimum variance between un-satisfactions3, or a tradeoff between both minimums. This problem’s restatement allowed us to find a solution for the Voter Paradox that contains as a special case the Majority rule for both cases: 1) for two alternatives, and 2) for cases of more than two alternatives where no Voter Paradox is presented, i.e. where we can find transitive collective preference by means of the traditional Absolute Majority. In this sense we can say the proposed solution is a generalization of the “Majority Rule”. To do so, we went to the reasons that make the Absolute Majority Rule so intuitively attractive, just and fair. The answer, in our opinion, is that the

1 For a detailed description of the meaning of “consensual truth”, “analytical truth” and “pragmatic teleological truth” see Churchman (1971) and Callaos (1980). 2 Capitalist flavor 3 Socialist flavor

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Absolute Majority Rule is the statistical media and as such is an “optimal” point in the sense of minimizing un-satisfaction (or maximizing satisfactions). In other words: if we have two alternatives A = {0,1} and n0 voters preferring 0 to 1, n1 voters preferring 1 to 0, and we want to decide between 0 and 1 in such a way as to maximize satisfactions (or minimize un-satisfactions), where an individual is satisfied when the collective decision coincides with his or hers, otherwise he is unsatisfied. Mathematically the problem could be described as follows: Maximize Z = n0(1-x) + n1x (1)

subject to

x = 0,1

So the answer is immediate, i.e.

x=0 if n0 > n1

x=1 if n0 < n1

Therefore, to solve the Paradox, we should generalize the optimization problem, given above, to more than two alternatives. There are two kinds of collective ordinal preference, according to whether the alternatives to be ranked are mutually exclusive (as in a presidential election or selecting a constitutional clause among several alternatives) or not (as in election of a board, or a budget ranking). The mutually exclusive case is the one who received much attention from different authors. The non-exclusive case has been less frequently treated. We will focus here on the mutually exclusive kind. Elsewhere (Callaos, 1971) we treated the case for non-exclusive alternatives. For the case of mutually exclusive alternatives we will propose a formulation for what we might call the “Generalized Absolute Majority Rule” (GAMR). Immediately we will prove that GAMR (which is an SPF for m alternatives), implies the Absolute Majority Rule (which is an SPF for 2 alternatives). In other words: we will show that the Absolute Majority Rule is a special case of GAMR, i.e. the case where m=2. Therefore, the same reasons that make the Absolute Majority Rule so universally acceptable should make the GAMR universally acceptable as well. In the Appendix we present examples of the Voter Paradox and a visualization of its mathematical solution. To formulate the GAMR let us represent the preferences aggregation problem by a graph. To generalize the Absolute Majority Rule is to find the hamiltonian path of alternatives aσ1, aσ2, … , aσm (where σi ≠ σj ∀i≠j and σi = 1, 2, … , m) in such a way as to maximize: Nσ1,σ2 + Nσ1,σ3 + … + Nσ1,σm + Nσ2,σ3 + Nσ2,σ4 + … + Nσ2,σm + Nσm-1,σm where Nσi,σj is the number of individuals preferring alternative aσi to aσj. Therefore the mathematical formulation of the problem will be as follows:

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Max Z = ∑=

m

i 1∑

=

m

k 1

xik (∑≠

=

m

rr

11

Nir - ∑≠

=

m

ss

11

Nis (∑

9

Z* ≥ ∑−

=

1

1

m

i∑

+=

m

ij 1

Nτi, τj (8)

Let us show that no alternative aq ≠ aσ1 could be obtained as a winning one by applying the AMR. Let us suppose that aq is the AMR winning alternative, then: Nq,σj > Nσj,q ∀ σj = 1, … , m; σj ≠q (9) and let t be the place assigned to aq in the ordering obtained by GAMR and given in (6), i.e. aσ1, aσ2, … , aσt-1, aσt, ... , aσm (aσt = aq). We have already shown that for any different ordering should satisfy (8), then for the ordering: aσ1, aσ2, … , aσt-1, aσt+1, ... , aσm the following un-equality is satisfied:

Z* ≥ ∑=

m

j 2

Nσ1,σj +∑=

m

j 3

Nσ2,σj + ... + ∑==

m

tj 1

Nσt,σj + Nσt,σt-1 +∑=

m

tj

Nσt-1,σj - Nσt-1,σt + ∑+=

m

tj 2

Nσt+1,σj

+ ... + ∑=

m

mj

Nσm-1,σj (10)

From (7) and (10): 0 ≥ Nσt,σt-1 - Nσt-1,σt and since aq is the AMR winning alternative, unequality (9) should be satisfied, then:

Nqσt-1 ≤ Nσt-1q (since aq = aσt)

Nqσt-1 > Nσt-1q

Then there is no aq ≠ aσ1 that could be the AMR winning alternative. Therefore if there is any AMR winning alternative, it will be the same one as the GAMR winning alternative. Therefore if there is any AMR winning alternative, it will be the same one as the GAMR winning alternative. 1.2.3. Arrow’s Impossibility Theorem

The Voter (or Condorcet) Paradox was still unsolved when Kenneth Arrow (1951) claimed to have roved mathematically the impossibility to solve it, Arrow proved that there is no voting rule or method, i.e. no transitive SPF that could have the following—apparently innocous—conditions, which where the initial axioms in his axiomatic approach:

1. Unlimited Domain: The domain of the SPF should be Pn(A), i.e. SPF should be defined for all possible sets of individual preferences.

2. Positive Association of social and Individual Values: If one alternative raises or remain still in the preference of every individual, then “ceteris paribus,” it must not fall in the SPF.

3. Independence of Irrelevant Alternatives: If the removal from, or insertion into, the set of alternatives of a certain alternative “a” results in no change in any individual preference

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of the remaining alternatives, then it must cause no change in the SPF of those alternatives.

4. Citizen Sovereignty: For each pair of alternatives “a” and “b” there is some set of individual preferences for which the collective preference rank “a” above “b”.

5. Non-Dictatorship: The social preference between any two alternatives must not coincide with any one individual regardless of the preferences of other individuals.

Arrow proved mathematically that no SPF can fulfill these conditions (axioms) while keeping its transitiveness. This diminished, in our opinion, further research with regards to finding a solution to the Voter Paradox.

1.2.4. Inconsistencies in Arrow’s Axioms and Methodological Weakness of his Approach

Elsewhere (Callaos, 1976a) we exposed at length our arguments against Arrow’s approach. Here we will present an adapted version of the summary we presented in other place (1976b): 1. Several critiques have been written to Arrow’s approach, most of them from the philosophical implications of his axioms, and others in reformulating his axioms in a way that the Voter Paradox could be solved. But, no criticism has been given yet, as far as we know, on the logical foundation of his methodology. In our opinion Arrow’s five axioms (or conditions) are explicitly contradictory among themselves, so it is not surprising that Arrow would prove their contradiction after a nice “logical juggling”.

2. In condition 4 (Citizen Sovereignty) Arrow means that the SPF should not be imposed from outside of the individual preferences set. On the other hand Arrow imposes other conditions—called reasonable by him—on the SPF. This is a SPF non-imposed and imposed from the outside and at the same time. This is a methodological contradiction.

3. Beside his five conditions, Arrow implicitly imposed another one, namely “Social Rationality” or transitivity in the SPF. “Rationality” and “society” belong to different logical categories (unless we adopt and organismic philosophical point of view). “Rationality” is an individual characteristic, not necessarily a social one. Furthermore, even if we can adjudge rationality or non-rationality to a society, why should we equate it with the individualistic one? Furthermore, if we were to define “social irrationality” as intransitivity in the SPF, it would automatically depend on the rule, or voting method, used. The same society in the same voting process could be “irrational” according to the Majority Rule and “rational” according to another rule and this is a contradiction.

4. Condition 3 (Independence of Irrelevant Alternatives) is untenable under the light of our approach. It is absurd to believe, and to impose, the same result we get by optimizing for two variables (two alternatives) than for more of them. Sub-problems optimization does not assure the global problem optimization. Optimization of subsystems in an individual and independent way does not assure the optimization of the whole system.

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5. Condition 3 imposes indirectly the Majority Rule, which is based on binary choice. So, what Arrow did was to have the Majority Rule as one of his satisfactory conditions; then he re-proves the Condorcet’s Paradox, i.e. Majority Rule could lead to intransitive SPF. Condorcet had already done it in a very simple and still rigorous way. This would leave Arrow’s work with great emptiness.

1.3. Advantages of Including the GAMR in GDSS

Existing GDSS might get great benefits inserting adequately our Solution of the Voter Paradox (Callaos, et. al., 1999) as a subsystem in their architecture. There are fundamentally two basic reasons to think so: 1. GDSS could help in getting a group/collective decision based in ordinal scales, avoiding

both: 1.1. The Voter Paradox, i.e. the possible generation of intransitive group preferences, or

“irrational” group decisions. Simulations programs showed that the Voter Paradox would emerge in more than 30% for three alternatives, and more than 90% for six alternatives (or more), if we apply the Majority Rule, but the Voter Paradox does not exist with our solution.

1.2. The uncertainty of working with cardinal scales, because possible inter-subjectivity

inconsistencies. Different subjects, from the group, could use different cardinal scales, and there is no way to know it, or to prevent its possible occurrence. Ordinal scales do not have this kind of uncertainty.

2. The Delphi Method could be applied through our solution to the Voter Paradox preserving

and maintaining intact the benefits of the method, which basically are two: 1) Consensus building (variance reduction among individual judgments), and 2) moving group decision closer to facts, veracity, or agreement with reality. As it is known the Delphi Method is based on group members who would: 1) provide their opinions or judgments in cardinal scales; 2) get feedback related to opinions/judgments average and variance; and 3) have the opportunity to change their opinions/judgments according to the feedback received. It is also known that the Delphi Method (in cardinal scales) build consensus (decreasing the variance) and move the average to the real value with each feedback loops, until getting stabilized after 3 to 5 of these loops.

Ordinal Delphi could not be conducted because the Voter Paradox, and because other group decision rules, different to the Absolute Majority Rule (relative majority rule, for example) do not preserve the Delphi characteristic benefits. In fact some voting rules (different to the Absolute Majority) proved to be detrimental, because they may move the group preference and, hence, their decision away from the real value or from the truth. Our Solution to the Voter Paradox, the Generalized Absolute Majority Rule (GAMR) preserves the Delphi benefits.

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Consequently, our GAMR might complement GDSS, by means of removing the uncertainty of group decision making in cardinal scales, making feasible the use of ordinal scales and Ordinal Delphi, and preserving the benefits of Cardinal Delphi in Ordinal Delphi (Figure 1).

Figure 1 1.4. Basic Architecture of a Generalized GDSS for Group Problem Solving

The two sections we presented above converge in this one. As we said above, there are two basic kinds of problems (means identification and/or selection for given objectives) and two macro-phases in synectics (unstructured and structured). Both typifications relate to each other: 1. Means identification (for a given set of objectives) requires synectics’ unstructured phase. 2. Means selection (for a given set of objectives and means) requires synectics’ structured phase. On the other hand, problem solving requires creativity and/or creative methods. Hence, synectics could support group problem solving, and GAMR-based GDSS could, in turn, support synectics processes (figure 2).

Individual

opinions, judgements

and/or decisions

Group Opinions, judgements or decisions: 1. Without

cardinal scales uncertainties

2. With Delphi

methods benefits • Consensus

building • Group

decisions closer to facts and/or truth, and/or real values

Ideas

from individuals

Decision Alternatives

Group Preference or Decision

Initial

Group

Preference

Final Group Preference

GAMR-Based GDSS

EXISTING

GDSS

OUR VOTER

PARADOX

SOLUTION:

Generalized Absolute

Majority Rule:

GAMR

ORDINAL SCALES

DELPHI METHOD

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In addition, we said that, according to our aims in this paper, we might differentiate between GDSSs designed for unstructured problems and GDSSs conceived for structured problems. This division relates: 1) to our typification of problems; and 2) to the two phases of synectics. Then, figure 2 might be detailed as it is shown in figure 3.

Figure 2

Figure 3

GROUP PROBLEM SOLVING

SYNECTICS METHODS

GAMR-Based GDSS

MEANS

IDENTIFICATION

MEANS

SELECTION

SYNECTIC

INTUITIVE PHASE

SYNECTIC

RATIONAL PHASE

ELECTRONIC

BRAINSTORMING

OR

IDEA

GENERATION

UNSTRUCTURED

GAMR-Based GDSS

DATA AND

KNOWLEDGE BASES,

EXPERT SYSTEMS,

STATISTICAL,

OPERATIONS

RESEARCH AND/OR

DECISION THEORY

MODELS

STRUCTURED

GAMR-Based GDSS

Ends

Ends

Set of Means

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Based on figures 2 and 3, we can suggest a basic architecture of a Generalized GDSS for group problem solving, as it is shown in figure 4, where present types of GDSS has been extended and complemented by our Mathematical Solution to the Voter Paradox, as it has been describe above and summarized in figure 1.

Figure 4

Electronic

Brainstorming or

Idea Generation:

EBIG

Generation of a

Qualitative

Ends/Means

Matrix

Generation of a

Quantitative

Ends/Means

Matrix

Our Solution

to the

Voter Paradox:

GAMR

GAMR/Delphi

based

on

EBIG-GDSS

Ordinal

Scales Delphi

Operations Research

Modelling (Integer or

Mixed Programming)

for Selecting Best

Means Subset

GDSS based on

Utility Theory,

Policy Capturing or

Ordinal (or

Cardinal)

Preferences

Ordinal and/or Cardinal Delphi

via GAMR

1. Optimal Solution 2. 1st Sub-Optimal 3. 2nd Sub-Optimal • • • n. (n-1) Sub-Optimal

GAMR/Delphi

based GDSS

GAMR/DELPHI-based EBIG-GDSS GAMR/DELPHI-based STRUCTURED GDSS

FINAL GROUP

DECISION

PROVISIONAL

GROUP DECISION

Ends/Objectives

Means Generated

Means Pre-Selected

Individual Pre-Selection

Group Pre-Selection

Pre- Selected Means Set

Group Ideas

Group Ideas

Individual Opinion

Group Opinion

Qualitative Ends/ Means Matrix

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The basic architecture shown in figure 4 could be clarified by means of describing the fundamental sequence of tasks required for a typical group problem solving process. This sequence might be as follows: 1. Given a set of ends/objectives, we need to generate viable means to attain them. This could be

done supporting a GDSS for Electronic Brainstorming or Idea Generation (EBIG-GDSS) by our mathematical solution to the Voter Paradox, in the context of an Ordinal Delphi Method. This the GAMR/Delphi-based EBIG-GDSS could be implemented following the basinc main steps:

1.1. Group members interact with an EBIG kind of GDSS, generating ideas about potential

means to achieve the given objectives. The use of this kind of GDSS has been reported by several authors and diverse products have been described in the literature.

1.2. The potential means generated in 1.1, are:

1.2.1. ordered by each group member into individual preferences; and 1.2.2. ordered into a group preference by means of our Solution to the Voter Paradox,

i.e. GAMR.

1.3. Group ordinal preference among alternative means is feedbacked to each group member and he/she is allowed, then, to change his individual preference, in a Delphi loop until no more changes are required. Then the group preference given by GAMR will be the Provisional Group Decision.

2. The set of pre-selected means (through the provisional group decision) is input to

GAMR/Delphi-based structured GDSS, which output is the Final Group Decision. This might be achieved through the following steps:

2.1. An ends/means matrix is generated. 2.2. Each cell of this matrix is filled with group members opinions about how good or bad is

each pre-selected means in relation to each objective. A GAMR/Delphi-based EBIG-GDSS (as the one described in step 1) might be used to fill each cell with each group member opinions. In this way we get what it might be called qualitative ends/means matrix, where columns represent the ends, the “what” should be achieved; rows represent the means, the “how” they might be attained, and the cells the “why” each means is good (or bad) in attaining each end, according to group members opinions.

2.3. The qualitative matrix, is transformed to a quantitative ends/means matrix where:

2.3.1. Objectives are weighed by group members via cardinal and/or ordinal Delphi.

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2.3.2. Usefulness of each means in relation to each end is identified by Utility Theory, or Policy Capturing Technique, or any other adequate technique, at the individual level, and through Delphi Method at the group level. Ordinal Delphi will be supported by our Solution to the Voter Paradox (GAMR).

2.4. The quantitative ends/means matrix has all the parameters required to formulate the

problem at hand, as an integer (or mixed) programming one, or through other kind of Operation Research modeling.

2.5. Solving the problem, mathematically formulated in 2.4., gives us the optimal solution,

as well as 1st, 2nd, ..., nth sub-optimal ones. 2.6. The set of optimal and sub-optimal solutions (along with sub-optimality distance from

the optimal solution) are input to a final group decision process, where the set of solutions are ordered by each group member and group ordering is achieved via our solution to the Voter Paradox (GAMR), complemented by an Ordinal Delphi. In this way each member will have three kinds of information to make his ordering:

a) The rational results given by the Operational Research model; which parameters are

based on group judgments.

b) His/her intuitive judgment about such results, which might contain some important, but not quantified variables.

c) His/her group’s rational-intuitive results, given trough the application of GAMR to

individual intuitions with O.R. model rationality.

The result of 2.6 is the final group decision. 1.4. Conclusions

We tried to make, in this section, a general definition of “problem”, which allowed us to identify problems types and to relate them to Synectics macro-phases, to Systems Design and to GDSS types found in the literature. This, in turn, showed us the basic blocks of the system architecture we are looking for. We stressed the paradoxical fact that Group Decision Theory is not found integrated in the GDSS literature. Consequently we proposed our solution to the Voter Paradox as a way to make this integration and to extend conventional GDSS, as to include the Ordinal Delphi Method. Then we presented the general architecture we are proposing to make the mentioned integrations. In our consulting activities, we applied the architecture suggested here to a considerable diversity of GDSS, with different levels of automation. We applied our proposed GAMR/Delphi-based GDSS to Strategic Planning, Managerial Control, Curricula Design, Investment Projects Selection, Technological Innovation Projects Selection, National Constitution Creation, Marketing, Logo Selection, Reengineering Projects Methodology Selection, Software Selection,

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etc. In this paper, we are trying to apply our proposed GAMR/Delphi-based GDSS to Systems Design. Right now, general purpose software is being developed for the Generalized GDSS described here. The Venezuelan equivalent to the National Science Foundation (Comisión Nacional de Ciencias y Tecnología: CONICIT) is financing partially the project. Private corporations are co-financing it. An initial version, or a working prototype, is being beta tested now, through the use if the software to different problem solving processes. In order to show other potential applications of the GAMR/Delphi-based GDSS proposed here, and its respective software, to the domain of Systems Design, it is very desirable to analyze the concept of design. This will be done in the next section. 2. The concept of Design A systemic notion of “design” would be an integrated and an integrative one. To be integrated, the notion of design should include its contextual relations, and to be integrative it should be a comprehensibly unifying one. In this section, we will attempt to identify an initial semantic comprehensive structure, which will lead us to a hypothetical conceptual infrastructure, which, in turn will give us the pointers to the contextual relations and the other notions strongly related to the notion of design. After examining these contextual notions, we will check the validity of the hypothesis made about the conceptual infrastructure of the notion of “design“. Banathy, et. al. (1979) made a very wide exploration on the different meanings/definitions given to the notion of “design” and extracted the commonalties they found among the high diversity they found. This is a synthetic-inductive approach to the problem. We will try an inductive/deductive approach: a very small semantic synthetic/induction first, concluding in a hypothesis formulation, followed by an analytic/deductive process oriented to the conceptual context. We will also relate our conceptual finding to the main pragmatic purposes of this paper. 2.1. A Semantic Approach

From a thesaurus we observe that there are seven groups of synonyms of “design”. Three of these are verbs and four are nouns. So, we might make a preliminary conclusion that there are two macro-senses in the meaning of “design”: as a process and as a product, in a temporal existence and in an “atemporal” one, a chronological sense and a logical one.

Etymologically, “design” derives from the latin term designare (to mark out), and this word, in turn, derives from signum (sign). Peirce—the founder of semiotics: the science of signs—gives many definitions of “sign”, the most referenced one is “a sign ... is something that stand somebody for something in some respect or capacity“(Collected Papers, vol.II, Par. 228; emphasis added) The notion of “sign“ as “something standing for something“ has been very used through history, and it could be associated, by analogical thinking, to the notion of “re-presentation”. Hence, the notion of “design”, as a process, could be thought as “marking out”, “generating a sign”, “producing a representation”; and as a product could be thought as the “representation” produced, the “sign” generated. But, in the sense associated with “system design,” it is not any kind of sign or representation; it is not a fantasy for example. It is a special

kind of representation. Representation is the genus of design.representation is not necessarily a designdifferentiate design from other species belonging also to the genus of representation. Up topresent we only need to know that designprincipal kinds: mental and physical “design”: as a mental and as a physical representation. Some grthe sense of mental representation, such as, for example, “intend“, “aim“, “contemplate“, “purpose“. Other groups are related to the sense of physical representation such as, for example, “blueprint“, “chart“, “lay out“, “ma A first semantic/conceptual framework could be derived by crossing the two semantic dichotomies found: “design” as process and as product, and “designrepresentation. In this way we will get four senses (or the term “design”, as it is shown in table 1the 2x2 matrix.

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ntation is the genus of design. Design is a representation, but a necessarily a design. We will try to find the specific characteristics that

differentiate design from other species belonging also to the genus of representation. Up toent we only need to know that design is a representation and, as such, there could be two

physical representations. So, we have another two macro: as a mental and as a physical representation. Some groups of synonyms are related to

the sense of mental representation, such as, for example, “intend“, “aim“, “contemplate“, “purpose“. Other groups are related to the sense of physical representation such as, for example, “blueprint“, “chart“, “lay out“, “map out“, “set out“.

A first semantic/conceptual framework could be derived by crossing the two semantic as process and as product, and “design” as mental and as physical

representation. In this way we will get four senses (or sub-notions, or sub-concepts) associated”, as it is shown in table 1, where synonyms are distributed in the four cells of

Table 1

Design is a representation, but a We will try to find the specific characteristics that

differentiate design from other species belonging also to the genus of representation. Up to the is a representation and, as such, there could be two

representations. So, we have another two macro-senses of oups of synonyms are related to

the sense of mental representation, such as, for example, “intend“, “aim“, “contemplate“, “purpose“. Other groups are related to the sense of physical representation such as, for example,

A first semantic/conceptual framework could be derived by crossing the two semantic as mental and as physical

concepts) associated , where synonyms are distributed in the four cells of

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The term “plan” appears in each one of the four cells. Hence, the notion of “plan” is completely included in the notion of “design” and has the same four senses given in table 1. Consequently, it is adequate to differentiate, in the notion of “design”, between “plan“ and the “object” planned, i.e. the purpose sought, the aim quested, the intention wrought, the intention to be achieved by mental effort and/or physical labor. Thereupon, we will have a semantic/conceptual framework of 2x2x2 matrix, based on three dichotomies, i.e. process/product, mental/physical representation, and object sought/plan to achieve the object. Therefore, eight sub-notions or sub-concepts form the conceptual infrastructure that supports the notion or the concept of design. In a future paper we will try to organize the high diversity found in the literature about the definition of design, according to the conceptual infrastructure found here. Our pre-hypothesis is that most authors emphasize into some of the eight sub-notions or sub-concepts found here, stressing on one characteristic or the other, as it is more adequate to the case they are sealing with. For the present purpose, let us assume this pre-hypothesis and continue analyzing the sub-concepts found, especially those that provide the contextual relationships we are looking for. Let us suppose we have an integrative notion of “design” and let us now direct the search toward making such a notion an integral part of its conceptual context. In order to do so, we will briefly analyze the concepts of representation, intention and plan. The first two concepts have been largely treated in the philosophical literature. Hence, we will provide here a much resumed treatment as a first step in this direction. Notice that a collective definition of “design” might be achieved applying the structured GAMR/Delphi-based GDSS to the 2x2x2 matrix. In this way, each cell in the 2x2x2 matrix will be weighted through an Ordinal Delphi process (which is feasible only by applying GAMR, our solution to the Voter Paradox). In this way we will have a fuzzy notion, or concept, which defining fuzzy set is given by the 2x2x2 matrix in which each cell is weighted by a collective decision making process.

2.2. Design as Representation

In terms of traditional logic, we identified, so far, the genus of the notion of “design” and the sub-species of this notion. “Representation” is the genus of “design”, thus, to define “design” we should analyze its genus’ comprehension (Port-Royalist) or connotation (Mill), and its differentia as specie. We have already identified the eight sub-species of “design” and their respective differentia as such. Thus, the next step is to identify the predicates of “representation”, since what is predicable from the genus (representation), is also predicable from the specie (design). After this step we will try to identify the differentia of the specie “design.” As we said before, the notion of “representation” has been largely treated along the history of philosophy: so, all what we will do here is to present a very brief summary of the features that we think are relevant to our inquiry. It will be a very first step that could be followed, in the future by a more explanatory study.

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The etymological meaning of “represent“ is to bring into presence, hence to make clear, demonstrate, symbolize, stand in place of (Weekley, 1967). From a psychological perspective, we can distinguish among several kinds of “representation”, as follows:

1. Herbert Spencer (1855) distinguished different kind of cognitions: Presentative, Presentative-Representative, Representative, and Re-representative, but “when simplified, marks two general classes—presentative and representative.” (Thomson, 1878) (Presentative and Representative Cognitions (pp. 270-276). The Presentative cognition, which is presented immediately, is of two kinds: 1) that which is directly delivered by our sense-perception, and 2) that which presented in self-consciousness. Presentative cognition is the apprehension of an object effectively present, as in perception; or in the “presentative” knowledge where the related terms correspond to present and existing objects (Spencer, 1855). Consequently, design requires the apprehension of an object effectively present. Needs and design requirements apprehension should be part of the designing process. Design includes requirements identification, which are not a given initial condition, as several authors explicitly say or implicitly stand for. This fact will be retaken in the last section of this paper. Since requirements identification imply decisions about what are requirements and what are not, and for differentiating between necessary and desirable requirements, as well as for setting the priorities among desirable requirements, and since these decisions are not always individual decisions, but are collective, or group ones (as it is the case of complex systems design serving a variety od users), then requirements identification, classification and priorities setting need GDSS to be generated, especially our proposed GAMR-based GDSS

2. The representation in the mind of past perceptions, i.e. memory representations, remembrances. When design depends on group perceptions or collective memory, a GAMR-based GDSS is again a very desirable feature.

3. The anticipation of future happenings by means of a combination of past perceptions; be it reproductive or productive. In this sense, representation is frequently equated to imagination. Anticipation of undesirable effects of different design options, as well as its desirable ones, are better identified by a group than by an each individual of the group, because the techniques of Judgmental Forecasting. Hence, GAMR-based GDSS is almost a necessity for this aspect of design.

4. The mental union of several perceptions (not present, nor past, nor anticipated). In this sense representation is paralleled with imagination, fantasy, or hallucination. Here, design is a representation paralleled with imagination, but not fantasy or hallucination. This characteristic differentiates design from other representation species. Design is a representation of desirable and feasible state of affairs, product, process, or system. Non-feasible or non-possible representations are not designs. Both desirability and feasibility (possibility) are better assessed by a group than by any of its individual members, especially in systems design where the users and those affected by the design are multiple persons, a collectivity or a community. In such a case a GDSS is probably a must for an adequate design, and a GAMR-based GDSS would be highly desirable.

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Although all four kinds of representations, briefly described above, characterize design as well, the third and fourth are the most related to it. Design is the combination of reproductive and productive perceptions oriented to a future existence, and directed by an intention. It is the imagination of what it is possible and desirable according to an intention. It is to imagine what it could exist by means of existing objects. It is a “poietic” (productive) imagination, intentionally directed, and action oriented. From an epistemological perspective, “representation” has been conceived by the scholastics as “similitude”. “To represent something—according to Aquinas—is to contain the similitude of the thing“(On the Truth of the Catholic Faith; q.7, a.5.) To know is to represent an existing object (of knowing) by a simile; it is to simulate an existing object by a resembling idea. In this sense we could think of “design” as the knowledge of a pre-existence, a pre-figuring; it is a “pre-knowledge”, a pre-cognition, of something that it is to come. It is a “pre-knowing” by means of what is known. When “what is known” is distributed among different persons, the pre-knowing is also distributed among them. So, no individual person should take design decision in such a case, if an adequate, feasible, right and just design is minded. Designers should care, not just for doing the design right, but also for conceiving the right design. Experience and knowledge in the respective domain is a must for doing the design right, but group, or collective judgment and decisions are what it is required for conceiving the right design. In the late scholastics, the senses of “images” and “meaning” were added to the significance of “representation”. Descartes emphasized on its sense of image and Kant generalized its meaning as to signify: (1) any cognitive act or content no matter if they are similitude of a knowing object or not; and (2) any non-private, public structure, frames, models or scheme that is cause or effect of such cognitive acts or contents. In this sense, the notion of “design” would refer to cognitive acts or contents, and/or their public cause or effect, all of which are future-oriented, representing non-existing physical objects. The epistemological value of these private acts/contents and its public cause/effect depend on the feasibility and desirability of their physical existence and on the accompanied intention to make them come true. It is evident here the epistemological value (and not just the utilitarian one) of group, or collective, judgments and decisions. Hence, it is clear the importance and the instrumental value of GDSS (and GAMR-based GDSS) for the generation of epistemological value in design processes. Kant differentiated between reproductive and productive imagination (Critique of Pure Reason; A79/B104, A123.) The role of the “productive imagination” is not limited to the pure reason; Kant extended its role to the practical reason and to judgment (Critique of Judgment, 17). Since design is based on judgment and practical reason, Kant’s differentiation is an adequate one to use here. An analogous differentiation had been made by Christian Wolff (Psychologia Empirica, Par. 92.) Accordingly, “design” could be conceived as a turnout of a productive imagination, oriented to the future existence of an object and, hence, accompanied by the intention that makes the object’s existence come true. It is a kind of “a priori synthesis” with the consorted intention to

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bring it to physical existence. The productive imagination is a process/product of image dividing and combining. Because what we concluded above from Kant, this process/product dividing and combining should be done by the group, or the collectivity, who is the cause the cognitive design representation or who are receiving the effects of such a representation or design. Again, it is evident the importance and the instrumental value of GDSS, and of GAMR-based GDSS, in designs affecting more persons than just the designer(s). In the case of designing, our productive imagination generates a mental “a priori synthesis”, a mental image or representation of a “non-existing-yet” physical object. This mental representation might, in turn, be physically represented though a drawing, a diagram, a visual schema, a material model, etc. This physical representation is done in order to communicate the mental image to other person(s), i.e. to cause a mental representation in other(s). The physical representation is an effect of the original mental image, and a cause of other mental images. Private mental designs could be made public by means of their physical representation. This would create mental representation in the other person, according to which the design might be modified in a process of participative design. In such a case, group judgments and design decisions might be needed, which require GDSS, especially GAMR-based GDSS. The starting point and the essence of the design process is a mental one and, as such, it is necessarily intentional. According to Brentano, mental (psychic) phenomena possess—unlike the physical ones—intentionality, i.e. they refer to an object. A perception is always a “perception of “something”, a conscience—as Husserl (1900) emphasized—is always a “conscience of “something. Mental phenomena, unlike physical ones, exist always in the mind. This is why the scholastics called them “inexistence” which should not be confused with “non-existence” or absence of existence. In its scholastic sense, “inexistence” means “existent-in” other thing (Boudry, 1980). Brentano emphasized this scholastic sense: “this intentional existence—he wrote —is exclusively characteristic of mental phenomena. No physical phenomenon manifests anything similar. Consequently, we can define mental phenomena by saying that they are such phenomena as to include an object intentionally within themselves” (Bruce, 1967). In this sense, design could be conceived as a pre-existent intentional inexistence. 2.3. Design as Intention

The term “intention” refers to the act and the effect of tending toward something. In this sense, with an etymological flavor, the term intentio was defined by Aquinas (Summa Theologica; Ia-IIa, qXII, a1). The notion of “intention” relates the knower with the known, the perceiver with the perceived, i.e. the subject with the object. This is why “intention” is a central notion in phenomenology, which opposes strongly any kind of reductionism, and any way of isolating the subject from the object. The perceiver/knower is always perceiving/knowing something. To be a perceiver/knower is to be related to what it is perceived/known. To be a subject is to be necessarily related to an object; and to be an object is to be related to a subject. There is neither an isolated subject, as such, nor an isolated object as such.

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The characteristic of “intention”, relating subject and object, generates ambiguities in the meaning of the term, which sometimes is used to refer to the subject’s mental potential, act or content; and other times is used to refer to the object or to the circumstances or conditions. This equivocalness of “intention” has been recognized since the scholastics (as, for example, in St. Thomas Aquinas and St. Bonaventure), up to the present (Aune, 1967). Being the notions of design and intention so conjoined, it is no surprise that the senses of the notion of design, identified at the beginning of this paper, are analogous to the senses, which seem to have been identified for the notion of intention. The senses of process and product, we found for the notion of design, seem to correlate with the notion of act and content; and mental and physical design seem to correlate with subject and object. Collaborative Design (frequently used in complex systems design) is produced by Collective Intentionality which might be supported and made explicit by Group Communication Support systems, in general, and by GDSS, or more specifically, by GAMR-Delphi based GDSS. Individual intentions, or purposes, form parts of a group or collective intentionally. These individual intentions usually differ from each other and, consequently, a consensus should be identified with regards to the collective intention. The consensus identification process requires sharing information about the respective individual intentions, making individual decisions about what should be the collective intention, identifying the collective decision as input to the Delhi process required to increase the consensus level, and to make the final collective, or group, decision; which is a necessary step in the designing process. 2.4. Design and Action

Important essentialities of the notion of intention are constructive of the notion of design. The first of these essentialities is the inherent disposition to action (Aune, 1967, p. 198) of intention and, hence, of design. When there is no disposition to action there could not be any intention, or design. A desire might conflict with another desire and not be followed by action, but intention and, hence, design generates action. Intentions and desires are both pro-attitudes, but—as Bratman emphasized—just intentions are conduct-controlling pro-attitudes. Desires are potential influencers of action, while intentions are actual influences of actions. (Bratman, 1987, p.16) Consequently, a design is also a conduct-controlling pro-attitude. If a design generates no action it is not a real design, it is a virtual one, it is a desire which, when contrasted by other desires, refrained from action, is an option not an intention; hence not a design. To have an epistemological and practical value, design should generate action that would produce the object designed; it should bring to physical existence the pre-existent, the “non-existent-yet” physical object of the design. Else, what is the use of design? What is its reason of being? 2.5. Design and Practical Reasoning

Not any action would bring a pre-existent intentional inexistence (the design of a non-existent-yet physical object) into existence. Just an adequate and an effective action would do it. To do so, we need a practical reasoning. This is the second essentiality of the notion of intention and, hence, of design. Apart from having an intention in the “disposition to action“ sense, “it is also possible to intend in an occurrent, nondispositional sense -that is, to engage in ‘acts’ of

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intending. This is possible because resolving is an ‘act’ that counts as a special case of intending- namely, intending as an immediate consequence of deliberation or choice“ (Aune (1967), p. 200). Consequently, design is also an immediate consequence of deliberation or choice. Group design would be requires group deliberation and choice. A GDSS, as the one described above, would be a very desirable – if not necessary – support to such a group deliberation and choice. As a mere disposition to action “intentions may form themselves as effortlessly and as unconsciously as beliefs, which they resemble; but sometimes, as in deliberation or choice, one forms an intention explicitly, consciously, and occurrently -in which case one’s intending may have a character of a resolve... Here one’s intending, as act, is a ’practical’ thought, serving as the conclusion of a line of practical reasoning“ (Aune, 1967, p. 200). Consequently, Group design requires Group Practical Thought, which, in turn, requires a GDSS, or a GAMR-based GDSS, like the one described above. If our actions were influenced by deliberation only at the time of action, the influence of such deliberation would be rather minimal, since deliberation requires time, effort and other limited resources, and there is an obvious limits to the extent to which one could successfully deliberate at the very time of action. Consequently, we need some ways by which deliberation and rational reflection will be allowed to influence action and to take place before the action’s time. Consequently, plans are a must for an opportune deliberating and rational reflection, and a Generalized GDSS for Group Problem solving, would be of a great help, for the generation of these plans. 2.6. Design and Planning

Plans are also required for intra-personal and/or inter-personal coordination. By constructing plans for the future, we facilitate coordination in both our activities over time, and our activities with the activities of others. By setting plans, we enable our present deliberation and practical reasoning to influence our later conduct, extending the influence of our deliberation beyond the present moment and beside ourselves. As a design gets complex requires us to go beyond the present and beside ourselves, consequently, it will require plans. A plan (or various plans) is (are) usually required for achieving the “pre-existent intentional inexistence”, and a plan (or various plans) is (are) required for bringing to existence the mental and/or the physical representation of such “pre-existent intentional existence”. But—as Bratman (1987) asserted—“we do not, of course, promote coordination and extend the influence of deliberation by means of plans that specify, once and for all, everything we are to do in the future. Rather, we typically settle on plans that are partial and then fill them in as need be and as time goes by. This characteristic incompleteness of our plans is of the first importance. It creates the need for a kind of reasoning characteristic of planning agents: reasoning that takes initial, partial plans as given and aims at filling them in with specifications of appropriate means, preliminary steps, or just relatively more specific courses of action” (emphasis added). This continuous planning and re-planning requires a planning GDSS, when it is related to complex system design and, hence, to group decision.

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There are several whys supporting Bratman’s assertion about the inherent partiality and incompleteness of plans, especially those related to complex designs. Let’s enumerate briefly some fundamental whys: 1. Experiments had shown that people have perception processes which can handle between

5 and 9 (7+-2) things at once (Miller, 1956). Consequently, complex situations should be handled by means of different levels of abstraction, where details are not shown in the highest level of abstraction. Consequently, the most abstract, or general plan would not contain the details that will be filled in at lower levels of abstraction. The general plan will not have the specificities of the special plans forming parts of the general one. Then, the general plan will be partial and incomplete, considered in comparison with the specific ones.

2. Plans need time to be executed. The larger and the more complex the plan is, the larger

the time required for its execution. And, the larger the execution time, the larger the probability of modifications in the initial conditions, and the larger the amount of new relevant information that will emerge. Consequently, the larger and the more complex the plan is, the larger the probability that such a plan will be inadequate at some time in its execution process. Thereupon, as the plan reaches further in the future, the probability of change and new information will increase (exponentially), and, hence, the details will be less relevant. Consequently, the plan will be more partial and more incomplete, as it protracts in the future.

3. In an empirical research, Braybrooke and Lindblom (1970) found that executives and

policy makers, when facing complex problems, try to clarify and plan with details just the next step, the next planning increment, leaving the succeeding steps, or increments not so clear and so detailed, i.e. leaving the following planning increments partial, incomplete and even obscure.

4. Our experience in designing and implementing complex systems (educational,

organizational and informational) evidenced the verisimilitude and the applicability of Braybrooke and Lindblom’s conclusion, as well as the appropriateness and the relevance of Bratman’s arguments. In fact, we have been developing a Methodology for Systems Analysis and Synthesis, using Braybrooke and Lindblom’s conclusions and Bratman’s philosophical perspective among the foundational bases of our methodological theory construction. We have already done a general description of such a methodology (Callaos, 1992a; Callaos and Callaos, 1991), different applications in the area of educational systems design and implementations, (see, for example, the design of the Latin-American School of Statesmen and Executives: LSSE, Callaos, 1992), few applications in organizational design and implementation, more than 130 applications in information systems analysis, synthesis and implementation (see, for example, Callaos and Callaos 1992a), and an application to the design and experimental implementation of Total Quality Designing System (Callaos and Callaos 1992b).

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2.6. Action-Design

We can conclude without any hesitation that when the designing process is not simple, plain and facile, (1) it should be done with successive partial and incomplete plans to be filled in along with the design activities, as the process progress toward an accepted pre-existent intentional inexistence, which could physically be represented as a verbal model and/or a visual diagrammatic maquette; and (2) the design should be an evolutionary one, and the designing process should be accompanied from the earliest possible stage with implementation actions, which will be conducted, in turn, with successive partial and incomplete plans. In this way, the design process and the implementing action will be interwoven, interacting with each other,

with reciprocal loops of feedback and feedforward (figure 6). The design of the Latin-American School of Statesmen and Executives (Callaos, 1992b) is an example where details could be found with regards to the application of the diagram of figure 6 to a very specific case.

Figure 6

Design is always intentional and action-oriented. The essence of design is to generate action in some direction and/or for some creation/production. It should not be isolated from action since it is strongly related to it. Both are parts of the same whole, both are members of the same organically dynamic system. Design gives direction and action gives propulsion to the whole. They are polar opposites, and as such, they complement and require each other. So, there is no way in separating them without deteriorating their essence. Usually, design comes before and is input to material action. But when we are dealing with a complex system, design and action should be conducted concurrently, even though design will initially start alone up till an initial design of the first prototype, or archetype, of the wanted system is available. From there on,

INCREMENTAL PLANNING OF THE IMPLEMENTATION

PROCESS

EVALUATION FOR

FEEDBACK AND FEEDFORWARD

PHYSICAL ACTION: IMPLEMENTATION

OF SUCCESSIVE DESIGNS

INCREMENTAL PLANNING OF THE

DESIGNING PROCESS

META-DESIGN

LEVEL

MENTAL ACTION (WITH PHYSICAL REPRESENTATION OF INTERMEDIATE

AND FINAL PRODUCTS)

INTERMEDIATE AND FINAL

DESIGNS (PHYSICAL

PHISYCAL SYSTEMS

IMPLEMENTATION

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design and action should be interwoven, interacting with each other, by means of reciprocal loops of feedback and feedforward, in an evolutionary process that could be called action-design, and which is to be nurtured by the ingredients of action-research and action-learning. In the case of collaborative design, decisions with regards to which actions need to be taken should be group, or collective, decisions. Consequently, GDSS, or more specifically, GAMR-Delphi GDSS, would effectively support the collective decision making process.

3. Design as Discovery4

In this section we will try to present a brief synopsis of some key principles, concepts, and models of system design, presented in the context title of "design as discovery". The emergence of the ideas into the present form has occurred while Richard Evan, one of the co-authors of this paper, being privileged to work with IBM and NASA over the past four years. (Evans, 2001) But, while there is an enormous debt to every individual in those organizations for their generous and insightful participation, it must be stressed that the thoughts here expressed do not in any way whatever carry any affiliation, concurrence, or endorsement, etc. of either those institutions nor any other person. The opportunity to work with others is only mentioned here to express more focused appreciation. For similar reasons, while over five hundred texts have contributed to this work [section 3 of this paper], there are no references cited in the initial paper where are integrating in this one. The aim is to minimize the risk, prior to very careful reviews, of implying any concurrence in these principles, concepts, and models by any author(s). Subsequent papers will both cite the many applicable references as well as treat each topic in more appropriate detail. The work began as sessions in "System Requirements", and that led to the initial perception that the very verb "require" and all the attendant implications and applications of the noun "requirements [as well as requirements engineering, etc.] was worth reconsideration. Thus the proposal that the very notion of a whole set of statements, sentences, etc. being forever separate in nature, purpose, substance etc. as "requirements", from another set called "design" decisions, is not valid, and thus a very seriously false premise as that concept is so ubiquitous. To generate a requirement set require decision to be taken, and these are part of the design decision, and as such, if they should be taken be a group, a GDSS, as the one described in the first part of this paper, would be a very good support for the design process and its inherent decision making process. Concomitant with the review of "requirements" per se came the perception that the typical means for developing design decisions [including design reviews, independent reviews, Red Teams, etc.] might also be seriously flawed.

4 The content of this section is the same paper that Professor Evans presented at the 4th World Multi-conference on Systemics, cybernetics, and Informatics, which was included in the respective proceedings (Evans, 2001). Some text were added in order to link the content of this section to previous two sections of this paper.

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Design as discovery has also been found to rely in a major way on the united operation of the three roles [relationships] of Customer, Builder, and Associate. Likewise, it is suggested that there are only these three roles, whether for person-to-person or organization-to-organization relationships. It has further been seen that design is thinking, and that structured thinking, specifically three dimensions or 3-D thinking is essential in enabling the critical expansion and awareness of all dimensions of design. An example is the three dimensions of systems themselves, namely the Discovery and Delivery systems that are over and above the typical one-dimensional idea of just the design of a Delivered system. Several other design-as-discovery topics are also presented in summary form in the remainder of the paper. 3.1. Principles, Concepts, Models

The structure of system design Principles, Concepts, and Models address, as depicted in the figure, an expansion on the early “task or project-centered” work of leaders such as Taylor and Galbraith, and then W. Edwards Deming’s contribution to recognize the need for more than just optimum projects or tasks--namely for processes. The recognition is that, even more than processes, the need is to address the principles, concepts, and models for the application of processes. Their effect and essentiality might be seen to be equivalent to the impact of a presumption of innocence versus guilty on the same judicial processes.

Projects [Tasks]

Processes

Principles

Principles are considered to be accepted truths, judgments, policies, values, etc. Concepts are means or ways to apply the principles. Models are structures for the implementation of the concepts. An example principle is, as with presumed innocent versus guilty: invitation, nomination, and confirmation rather than imposition, compulsion, and unilateral investigation. An example of a concept or "way" is three-dimensional thinking [3-d Thinking] based on asking the Great Question of "What might be at least three dimensions of this." An example of model or structure is the Customer, Builder, Associate model [ABC Model] for the operating structure of basic three roles/relationships.

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3.2. Ideas, Options, Assessment

Design as discovery is the care and feeding of ideas: both as options and also their comprehensive assessment. Design is founded on options, and thus on the quality of the "source" or headwaters of ideas/options and their assessment is the critical dimension. System design has as a prerequisite the richest feasible set of options and their comprehensive assessment. And assessment is itself dependent on the options considered: optional perspectives, optional criteria, optional metrics, and etc. Further, as all design is decision under uncertainty, it is risk-based design and predicated on the effectiveness of the exploration, the search, the inquiry, in short: the discovery. Design as discovery is inaugurated and conceived in the nurturing of ideas: ideas about the situation; the diagnoses of the existing and desired situation [state] to identify contributing problems and the interrelationships of those problems; ideas about possible ways to address the problems; ideas about assessment of these activities and results, ideas about the implementation of the selected solutions; and even ideas about the assessment activity itself. Ideas [the headwaters of design] are initially as tiny and essentially hidden and inconspicuous as seeds: as in the phrase the "germ" of an idea. They are similarly perishable and in need of the most precise nurture and care to even germinate, let alone mature. Also, as with seeds, a particular challenge is [as with all exploration/discovery] to discern the desired from the inappropriate--the wheat from the tares. Often all seem equally promising and fitting. The nurture and maturation sufficient to enable the discrimination and selection of preferred ideas demands the greatest care: idea greenhouses, thought conservatories, inspiration nurseries. Design as discovery calls for the design itself of climate-controlled environments to enable options and possibilities and alternate views, etc. to be safely and securely conceived and then be strengthened as they germinate, emerge, and--when seen to be possibly more on the side of good--continue to grow. This is the essence of system design: The care and feeding of ideas. 3.3. Invitation, Nomination, Confirmation

Confirm, never compel or "require" A foundation principle for the achievement of design ideas is to invite never impose. It is to introduce and then enrich other's nominations by suggestions, explanations, persuasion--never by compelling. It is to apply so-called "independent" resources as ones to show, to teach, to illustrate, to encourage. It is to help identify optional features for consideration in self-nominated plans, procedures, design decision options, assessment plans, etc.--plans and decisions to then be confirmed. The one to whom delegated fully responsible to nominate, the delegate fully responsible in confirming. Thus both united--equally yoked in a common endeavor--one to nominate, the other to confirm.

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3.4. Teams vs. Teamwork

The term "team" has become applied to formal organizational entities--with team leaders etc. But this has confused the spirit of a team with the term team. The spirit of a team is the spirit of teamwork, that exists by virtue of a common goal, not because of a common boss. A basketball team on the floor has no single boss. That form of teamwork is crucial in the formative efforts of option generation, and thus needs formally organized [the coach controls who is on the court] but informally operated efforts. The suggested structure is tables of three. To achieve ideal option generation [formally organized but informally operated effort] there is never a “team leader”. A GDSS, and more specifically a GAMR-based GDSS as the one described in the first section of this paper is a very useful support for this non-leader system design process. 3.5. Design Decisions

Decisions are by a single designated individual. “Groups” are only for the generation of options and their assessment A single responsible individual is the one responsible, most of all, for the formal organization of the informally operated “team” efforts. A GDSS, as the one described in the first section of this paper, would support group options generation, via electronic brainstorming, ideas writing, Nominal Group Technique, etc., and group assessment via collective judgment (by way of individual judgments synthesis) If for some reason, some design decision should be taken by the group, as a group, then, the GAMR-based GDSS described above will be almost a must for this kind of decisions in a complex systems design situations. Never a designated “team lead” etc., thus all members feel equally responsible to each other and for each other—and for the options and assessments. Design decisions [that then serve as "requirements" on all subsequent decisions] address, as shown, the three dimensions of Ratables [Measurables], Relationships [with all other decisions, etc.] and the Rationale--to enable not only assurance of full compliance by all subsequent decisions, but to provide the essential framework for those successive decisions.

Decisions

Relationships

Ratables

Rationale

There is a hierarchy of decisions, but it is by timing sequence--a temporal hierarchy--not be so-called system levels, such as top-level decisions.

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3.6. Three Roles: Customer, Builder, Associate Design as discovery involves, as illustrated in the figure, only three roles [relationships], whether person-to-person or organization-to-organization: Customer, Builder, Associate. Design relies on the integrity of the formally organized yet informally operated headwaters of option generation and assessment, effected in the framework of a united operation of the three [and the only three] roles: Customer, Builder, and Associate--the ABC Model--applying Invitation, Nomination, and Confirmation, and ever asking the Great Question. This approach of formally organized yet informally operated headwaters of option generation and assessment, would be very facilitated with an information system support like the GDSS (or GAMR-based GDSS) described in the first section of this paper.

Customer

BuilderNomination

Formally organized-- but Informally Operated ]

Confirmation

Associate

Option Generation and Assessment efforts

Headwaters--Design Foundation

Design is dependent on the united accountability of those in Builder and Customer roles. The ideal is for them to be equally yoked--united in the pulling, as depicted, of their wagon:

Equally Wagon

Customer

Builder

Yoked

Those in Builder roles are accountable for design options nominated [with recommendations] for their system. The Customer is accountable for the confirmation of the design of their system. Those in Builder roles "Conduct", those in Customer roles "Preside". Both seek the wisdom of the other--the Customer seeks the nominations of the Builder, the Builder seeks the confirmation of the Customer, and both seek the insights of those in an Associate role. This approach is a very important one for systems design effectiveness. Several co-authors of this paper, experienced the pragmatic value of this approach while designing more than 100 information systems. Effectiveness of the design process as well as the effectiveness of the system designed (after its deployment) depends highly on the “builder-conduct-customer –preside” approach. In complex systems design it is frequent to find situations where the builder is not just one person, and the customer, or the user, is neither a person, but in both cases it is frequent to find a builder group and a customer or a user group of person. In such cases’ a GDSS would of a great help in the

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respective systems design processes. Those in Associate roles are altruistically dedicated to the success of the other two; having no authority in the relationship they have the greatest potential influence. Design is equally dependent on the integrity of the operation [as the design headwaters] of the option generation and assessment effort that is formally organized and informally-operated by those in Builder roles. 3.7. Delegation: [Contract for] Design Work, Not for "A" Design Design as discovery is contracted for as design work, not for "a" design. Customers select [delegate to] those to serve in their Builder roles based on their design abilities, not on their proposed design. Contract award criteria include past performance and current capability to provide design work, not an actual complete design--prepared in isolation--as the basis for the contract competition. 3.8. Self-Assessments

Design as discovery concentrates on self--especially on Builder "self-assessments" that are builder-nominated and customer-confirmed: every designer a designer of systems--every engineer an engineer of systems. Group self-assessment requires the kind of support that could be provided by the GDSS for Group Problem Solving described in the first part of this paper.

3.9. Meeting Purpose Purity

Effectiveness in design meetings is dependent on their purity of purpose. It is suggested that there are three orthogonal design "meeting" dimensions--each a dimension of purpose: Preparation, Presentation, Confirmation.

Meeting Purpose

Confirmation

Presentation

Preparation Preparation "meetings" are formally organized but informally operated [no boss--and ideally sets of only three participants. These activities are the most crucial: they are the headwaters, the birthplaces, the nurseries and greenhouses for the generation and assessment of design options. It is this activity that needs to be re-enthroned. Presentations are by information briefings, lectures, conferences, and etc.

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Confirmation meetings as the third an