Bo Strangert (RD5)

Remarks on the planning  for dynamic goal analysis 

Goal analysis should be an ongoing process at all project stages. This has been a significant theme in several papers on R&D on this website (e.g. RD1, RD3, RD4, A2). It's a process with many facets and is often difficult to conduct in a complex practical context. I limit my comments on the subject to some strategic and methodological issues that sometimes lead to confusion about terminology or conceptual meaning.

Planning conditions for innovative goal analyses

Figure 1 suggests a structure of general planning conditions. One main part is the formal architecture of planning and its associated formal coordination of the planning process. These are both influenced by the internal institutional context, particularly at an initial project stage. Their mutual relations were briefly described in the preceding paper RD4 in arborg.se.

The corresponding live planning activities are represented in a real-life arena or process space. It includes a psychological subspace of thinking and decision-making. The environmental context also influences the planning activities, apart from the internal institutional impact.

Figure 1. A schematic structure of general planning conditions.

Basically, the correspondence between the formal and the real processing spaces is that of a relation between a formal model and its empirical counterpart. However, the design model does not just mirror the planning activities, it may in essential ways direct thinking, decision-making, and development actions. The opposite direction of influence is especially important in complex endeavors; the insights and experiences during planning should have a dynamic impact on the progressing goal formation and the formal modeling structure. 

Incidentally, the dynamic, mutual relation just described is a characteristic of complex project endeavors. It's similar to the interactive process of concept development and experimentation (CD&E) about the development object per se.

A further step is to reflect on the design principles for structured planning and development, and how they can determine the outcome of modeling and experimentation.

Combined principles of development and research design

What is the difference between project development design and empirical research design, and which are the consequences for goal analysis? I dealt with this issue rather extensively in my paper on applied methods for R&D (A2 in arborg.se), but will reiterate some points as a background to a succeeding issue.

In Figure 1 of a preceding paper RD4, I illustrated different stages of goal analysis in terms of a process of generating specific means-end relations which started with a preliminary goal structure during the initial project planning. The preliminary goal structure is thought to be created from a "visionary" application in a predicted future context.

Obviously, this preliminary goal vision may more or less mismatch the resulting ultimate goal structure in real life. However, the trick is to reduce the mismatch through a cleverly designed process of CD&E, where the teleological designing of means-end structures is supported by validation through experimentation.

As one extreme case, you may have a clean-cut initial goal or program structure of tactical, organizational, and economic requirement specifications (e.g. TOEM) which can be suitably translated into verifiable means-end operations. It's desirable to use technological principles of fundamental causal laws or mechanisms for translation. For example, this is the case when the task is to construct a complicated physical object where the technological design principles are well known and applicable. Consequently, development results can be verified continuously when there is a closed hierarchical system of requirement specifications. An eventual scientific evaluation in a practical context may be deferred.

As a contrasting case, imagine a project idea about handling a demanding complex task or risk in real life. By definition, no integrated causal system can be identified, instead, there are sets of many interacting factors and relations, characterized by uncertainty, dynamics, and non-transparency.

The planning process must then itself be a complex endeavor. The process of goal analysis must be progressive, and the goal conception should gradually approximate the ultimate goal structure in the practical context. Generating the necessary means-end operations is tantamount to constructing tentative causal substructures to achieve the end-states. It's here the principles of research design are indispensable – the development steps should recurrently be validated through a process of CD&E. Hence, scientific validation through appropriate experimentation is a prime necessary condition. Verification is limited to secondary issues of quality assurance, certification, et cetera, of products and services.

In summary, handling complex tasks deserves a nonlinear design strategy, continuously validated by scientific experimentation (in a broad sense). Methodically, a combined design strategy of formal planning architecture and online process coordination would be useful (cf. RD4 in arborg.se).

The need for design support for goal-directed thinking

The preceding section emphasized that the teleological stance of development processes needs to be complemented by empirical validation attempts – preferably according to causal principles. Figure 2 (an elaborated version of Figure 1 in RD4) is an attempt to roughly illustrate the means-end philosophy of the formal architecture of planning (above the time axis). It suggests that planning is directed towards an ultimate goal structure, and evolves through the elaboration of an ordered sequence of means-end structures. As noted before, there are few technological guidelines to use, and the project work becomes itself a complex endeavor.

Thus, there are no causal maps to follow from vague program structures. Therefore, such tasks demand inventive problem solving, and this directs focus on the process coordination in real-time. It means a shift in the formal control of planning. Compared to the case with a well-established program structure and architecture of planning, the process coordination takes the lead to support innovative thinking. Thus, the formal structure will be a gradually evolving product of inventive planning instead of a directive conceptual framework or production system.

Process coordination denotes both a formal and a psychological side. The formal aspects are connected with task characteristics, such as selection, prioritizing, and timing activities and resources, or providing follow-up and support to manage the development process. One essential aspect concerns the coordination of participant activities. The underlying strategy is predominantly of the means-end type.

Figure 2 presents the action side of process coordination in an arena of live development processes because its most intriguing and important parts occur in a psychological space. It contains the essential core of inventive planning. In Figure 2, problem-solving in planning is illustrated as a set of control processes; however, there are no specific theoretical implications of that other than an emphasis on problem-solving as an iterative and creative process

Figure 2. Inventive thinking as a core process in planning and development work.

If we presuppose that intentional thinking is at the heart of development work, then formal planning structure and process coordination should be designed according to its implications. In very general terms, it means that goal-directed action should be supported as well as assessed continuously with appropriate measures.

There are two points that deserve some reflections regarding the formal design of planning and development. The first one is about which formal procedures to use for invention and corroboration when confronting ill-defined tasks. Nowadays, an inductive stance seems to be the common, preferred way of confronting the need for innovation. Such clichés as "thinking outside the box" and the use of lenient procedures for collecting and analyzing "fresh" data may not possibly deliver what they are intended to.

Although it may sound paradoxical, there are very good reasons for using a hypothetico-deductive approach, in particular when the task conditions are fuzzy and the choice of options is uncertain. The reasons are about the need of inferring a reliable line of the invention. As conceived here, the deductive approach means that you successively formulate stringent hypotheses about invention options and test them empirically. The inductive moment in the process will be subordinate to the phase of hypothesis formulation.

A second point touches on philosophy of science and is about the congruence between thinking – in terms of cognitive psychology – and how planning is formally structured in terms of conceptual modeling. Briefly, does it matter if the products of intentional thinking are modeled as teleological or causal structures? For example, is one or the other psychologically more suitable to reinforce inventive planning in practice? Or are there any special theoretical reasons for choosing one or both modeling alternatives for complex social systems?

General answers to questions like these have been proposed by philosophers of cognitive and social science. For example, Elster (2010) suggests that the main task of social science is to explain social phenomena by causal mechanisms. Causal mechanisms commonly explain events by stating another event as a cause. In Elster's view, causal mechanisms in social events are not of the same kind as general causal laws in physics. Elster's view of scientific social explanation may be mistaken for the psychological mechanism of subjective attribution, although it's an application of the explicit hypothetico-deductive method. As a matter of fact, the causal principle is applicable also to the framework of means-end: A person's intention (mental representation of a goal) causes the achievement towards an end (goal).

A contrasting view is proposed by Harré (2002, p.166), who argues that psychology is a hybrid science with different but congruent "grammars" of everyday life. Activities can be described in different perspectives: The molecular M grammar includes cause-effect relations in physiological and biological micro representations as the organizing principles of analyzing the stream of activity. The organism (O) grammar includes a basic teleology that may be associated with cognition – a perspective of higher animals as agents acting to achieve some end. The person P grammar includes the rules/meaning relation as the organizing principle of the P-type analysis of the stream of human activity.

This conclusion of Harré is noteworthy: "Human beings are present to the world and to each other in three forms: as persons, as organisms and as complex clusters of molecules. None of the grammars grounded in these ontologies can be dispensed with, and none can be extended to comprehend the others without incoherence."(p.167). He asserts that: "The hybrid nature of psychology as both a cultural and material science appears in its meta-discourses that make use of all three grammars." Furthermore: "Misled by a mythical version of the methodology of physical sciences, psychologists have carried out all sorts of research projects into important aspects of cognition, framed in the Causal Picture" (p.170).

Project design and management options

The examination so far sums up two different but interdependent design choices to be made by a project manager. One is about how to design a project regarding the ultimate goal of development. This choice should be based on theoretical and empirical judgments, derived from the requirements of the object of development.

The use of an empirical research methodology for validation ought to favor a causal modeling approach. This is customary for physical objects of development. It's not as certain with human components in social systems, where there are other types of representation and modeling techniques, too. (Cf. Harré, 2002; Silverman, 2001; see also M1, www.arborg.se in Swedish.)

The other choice is about process coordination, that is, managing the planning process to optimize inventive planning. There are surely many general psychological principles to be used in management, for example about promoting divergent productive thinking as well as shared knowledge. But it is also a demanding task to coordinate activities of team members with vastly different knowledge domains and methodological skills. The diversity may include a range of issues from philosophical assumptions to specific skills in modeling techniques and data analysis. Nota bene that a project manager sometimes will be challenged in meta-discourses by participants with divergent methodological backgrounds.

To be sure, the demands on project managers in complex development tasks are

wide-ranging!

References

Elster, J. (2007). Explaining Social behavior. More Nuts and Bolts for the

     Social Sciences. New York: Cambridge University Press.

Harré, R. (2002). Cognitive Science: A philosophical introduction.

     London: Sage.

Silverman, D. (2001). Interpreting Qualitative Data. Methods for

     Analyzing Talk, Text and Interaction. 2nd Ed. London: Sage.

Strangert, B. (2013). Relevant papers on this website arborg.se: A2, RD1, RD3, RD4.