Dirk Vriens

Introducing Organizations as Social Systems Conducting Experiments

In this book, it is our aim to describe organizations as social systems conducting experiments with their survival. More in particular, we want to explain what we mean by this description, and based on this explanation, we want to formulate principles for the design of organizations, enabling them to survive, i.e., enabling them to continue to conduct these experiments. Organizations as social systems conducting experiments: “What kind of description is that?” “Can it deepen our understanding of organizations?” “Can it help to improve the conditions for their survival by providing principles underpinning organizational design?” and if so, “What are these design principles?” These are all relevant and “natural” questions that might come up when reading the aim of this book. We do think it deepens our understanding of organizations and allows for finding principles improving their design. However, it may take the rest of the book to argue why. In this introduction, we cannot exhaustively answer these questions, so we have to content ourselves with a tentative and hopefully sufficiently persuasive description of the main topic of the book: organizations as social systems conducting experiments and finding principles to improve their design.

Causal loop diagrams as a de-escalation technique

Escalation of commitment, the tendency of decision makers to keep on investing in losing courses of action, has been shown to be a costly decision bias that affects many areas of decision making. Even though escalation is a widely studied phenomenon, there has been comparatively little research on how to avoid this bias. The present study focuses on de-escalation of commitment and proposes that causal loop diagrams (CLDs) can help to decrease escalating commitment to a failing course of action. By means of an experiment, this study shows that using a CLD decreases escalation tendencies.

Problem-Setting Support

Abstract Recent literature on decision-support shows a tendency to shift attention away from the choice-phase towards the definition (the setting) of the decision-problem. Moreover, the need for this shift is more strongly felt when the decision-problem gets less structured. When focusing on the choice-phase, the goal of the decision-process is to lay down several comparable alternatives and to choose one that is satisfactory. A different setting of the problem may, however, yield different alternatives which may have been unnoticed in the original setting. The important question is therefore how different problem-settings may emerge. Recent attempts to answer this question include “idea-generation” and the search for “generative metaphors”. This paper will address an alternative answer, in which the goals of the decision-maker are taken as a means to seek alternatives beyond the original problem-setting. It is assumed that a problem-definition is connected to one or more goals a decision-maker seeks to realize. A thorough analysis of these goals may yield connected goals of a higher order for which realizations might be found that also (dis-)solve the problem. Looking at different levels may result in alternatives that were not considered at a lower level of (goal-) abstraction. If, moreover, several (hierarchically) independent goals are considered, one has to find a combination of realizations for the different goals that can be performed together. This may be done by means of a goal-matrix, which is the result of considering several goal-hierarchies at the same time. A central theme is, that when more alternatives with respect to one goal are found, the chance increases that a suitable combination for all the independent goals can be found. The goal-matrix is the vehicle to arrive at different problem-settings in which alternatives may be stipulated. In this approach, the traditional distinction between fixing goals, generating alternatives and choosing among them blurs and the whole process will be termed “problem-setting”. Supporting this “setting of the problem” (and hence also the search for alternatives within these different settings) by means of the proposed method, is different from traditional decision support. Therefore, it must be outlined what this support amounts to. In the first part of this paper the need for the possibility of setting a problem in several distinct ways is given. Then, it is discussed how problems might be set differently by means of goal-matrices, and how alternatives courses of action are related to different problem-settings. Finally, attention will be paid to supporting problem-setting.

The Experimental Arche: Ashby’s Cybernetics

In this chapter we introduce Ashby’s cybernetics – a theory about the regulation of all kinds of systems. Ashby’s cybernetic theory is fundamental to our perspective on organizations as “social systems conducting experiments” because it provides us with the conceptual tools to describe the “experimental arche” of organizations (see Chap. 1). In particular, Ashby’s theory on regulation enables us to arrive at a first description of organizations conducting experiments, making apparent (1) that the objects organizations experiment with – goals, transformation processes, infrastructural parts or operational regulatory activities – are related to three types of (organizational) regulation, and (2) how conducting such experiments should be regulated itself. Moreover, because Ashby’s notion of regulation is intimately tied to the survival of systems, his theory can be used to make explicit how conducting organizational experiments is linked to the survival of organizations.

Beer: Functional Design Principles for Viable Infrastructures

In Part I of the book, we explored the two “archai” of organizations indicating that they are social systems conducting experiments. In the present part, we will give a systematic exposition of ways of organizing this experiment. Given the “logic” of the experiment, this means that we have to look for principles enabling the design of infrastructural conditions allowing organizations to experiment. These infrastructural conditions are so important because an organization’s potential to select and reselect goals, infrastructures, operational regulation, and transformation processes (and all other “objects” related to these “focal” objects), crucially depends on the design of its infrastructure.

Epilogue to Part I: The Two “Archai” Combined

In Chap. 1 we advanced the position that organizations have an experimental and a social “arche.” These “archai” are features of organizations that cannot be negated without negating organizations altogether. They are unavoidable characteristics of the “phenomena” we call organizations. Following Aristotle’s “method” of starting with the phenomena as we experience them, we introduced the “archai” referring to everyday experiences with organizations.

Poor Survival: Disciplining Organizational Behavior

In this chapter, we want to discuss poor survival. Because there are many possible instances of poor survival, we selected an especially vivid example, i.e., an example that illustrates everything that is possibly worrying about it. We take this example from Foucault’s book Surveiller et Punir (1975, 1977).

Epilogue to Part II: functional and specific design principles

In the previous two chapters, we unfolded Beer’s functional and de Sitter’s specific design principles. As argued, these principles can be used to diagnose and design organizational (infra)structures supporting experiments with meaningful survival. This means that we have realized the objective set for Part II of the book. In this epilogue, we summarize these principles (8.2) and reflect on their status (8.3). In this reflection, we argue that the design principles are not contingent and risky, like the selections figuring in the experiment, but necessary and certain. Section 8.4 marks the transition to Part III of the book.

The Second “arche”, Organizations as Social Systems: Luhmann

In the previous chapters, we explored the first organizational “arche,” i.e., we discussed organizations as conducting risky experiments with meaningful survival. We based this discussion on insights taken from first- and second-order cybernetics.

Specific Design Principles: de Sitter’s Organizational Structures

In the previous chapter, we discussed Beer’s Viable System Model; a functional model specifying desired effects required for viability. These effects can be used as criteria for diagnostic or design purposes. We also pointed at limitations of the Viable System Model. As a functional model it does not address the question of the embodiment of functions. Although it specifies desired effects, it does not positively address the question of how to design their realization. Simply put, the strength of the Viable System Model is stating what effects should be realized, not how they should be realized. For instance, functions three and four should engage in a relatively complex and balanced dialogue about plans for innovation, but what is needed to realize this dialogue? How should one distribute tasks and responsibilities among organizational members, so that this dialogue can be carried out properly? How should one select, allocate, and train the people involved in these dialogues, and how does one design the technological infrastructure supporting the complex communication processes required for innovation? In short: how does one design the infrastructure realizing the desired effects for viability?