This post is based on Chapter 3 of Gareth Morgan’s classic book Images of Organization (Sage 1986), which opens with the following question: “Is it possible to design organizations so that they have the capacity to be as flexible, resilient, and inventive as the functioning of a brain?”
To start with, Morgan makes two important distinctions. The first distinction is between two different notions of rationality, and the second involves two contrasting uses of the “brain” metaphor.
Mechanistic or bureaucratic organizations rely on what Morgan calls “instrumental rationality”, where people are valued for their ability to fit in and contribute to the efficient operation of a predetermined structure. Morgan contrasts this with “substantial rationality”, where elements of organization are able to question the appropriateness of what they are doing and to modify their action to take account of new situations. Morgan states that the human brain possesses higher degrees of substantial rationality than any man-made system. (pp78-79)
Morgan also observes a common trend to use the term “brain” metaphorically to refer to a centralized planning or management function within an organization, the brain “of” the firm. Instead, Morgan wants to talk about brain-like capabilities distributed throughout the organization, the brain “as” the firm. Using the brain metaphor in this way leads to two important ideas. Firstly, that organizations are information processing systems, potentially capable of learning to learn. And secondly, that organizations may be holographic systems, in the sense that any part represents and can stand in for the whole. (p 80)
The first of these two ideas, organizations as information processing systems, goes back to the work of James March and Herbert Simon in the 1940s and 1950s. Simon’s theory of decision-making leads us to understand organizations as kinds of institutionalized brains that fragment, routinize and bound the decision-making process in order to make it manageable. (p 81) According to this theory, the organization chart does not merely define a structure of work activity, it also creates a structure of attention, interpretation and decision-making. (p 81) Later organization design theorists such as Jay Galbraith showed how this kind of decision-making structure coped with uncertainty and information overload, either by reducing the need for information or by increasing the capacity to process information. (pp 82-83)
Nowadays, of course, much of this information processing capacity is provided by man-made systems. Writing in the mid 1980s, Morgan could already see the emergence of the virtual organization, embedded not in human activity but in computer networks. If it wasn’t already, the organization-as-brain is now indisputably a sociotechnical system. The really big question, Morgan asks, is whether such organizations will also become more intelligent. (p84)
The problem here is that man-made systems (bureaucratic as well as automatic) tend towards instrumental rationality rather than substantial rationality. Such systems can produce goal-directed behaviour under four conditions. (p87)
- The capacity to sense, monitor and scan significant aspects of their environment
- The ability to relate this information to the operating norms that guide system behaviour
- Ability to detect significant deviations from these norms
- Ability to initiate corrective action when discrepancies are detected.
But this is merely single-loop learning, whereas true learning-to-learn calls for double-loop learning. Morgan identifies three factors that inhibit double-loop learning. (pp89-90)
- Division of responsibilities cause a fragmentation of knowledge and attention.
- Bureaucratic accountability and asymmetric information produce ethical problems such as deception. (This is a form of the principal-agent problem.)
- Organizations also suffer from various forms of collective self-deception, resulting in a gap between “espoused theory” and “theory-in-use”.
and he goes on to identify four design principles that may facilitate double-loop learning. (pp 91-95)
- Encourage and value openness and reflectivity. Accept error and uncertainty.
- Recognize the importance of exploring different viewpoints.
- Avoid imposing structures of action. Allow intelligence and direction to emerge.
- Create organizational structures and principles that help implement these principles.
The flexible, self-organizing capacities of a brain depend on four further design principles, which help to instantiate the notion of the “holographic” organization. (pp 98-103)
- Redundancy of function – each individual or team has a broader range of knowledge and skills than is required for the immediate task-at-hand, thus building flexibility into the organization.
- Requisite variety – the internal diversity must match the challenges posed by the environment. All elements of an organization should embody critical dimensions of the environment.
- Minimal critical specification – allow each system to find its own form.
- Learning to learn – use autonomous intelligence and emergent connectivity to find novel and progressive solutions to complex problems.
In conclusion, innovative organizations must be designed as learning systems that place primary emphasis on being open to enquiry and self-criticism. The innovative attitudes and abilities of the whole must be enfolded in the parts. (p 105) Morgan identifies two major obstacles to implementing this ideal.
- The realities of power and control. (p 108)
- The inertia stemming from existing assumptions and beliefs. (p 109)
Morgan says he favours the brain metaphor because of the fundamental challenge it presents to the bureaucratic mode of organization. (pp 382-3) Writing in the mid 1980s, Morgan noted that computing facilities were often used to increase centralization, and to reinforce bureaucratic principles and top-down hierarchical control, and expressed a hope that this was a consequence of the limited vision of system designers rather than a necessary consequence of the new technologies. “The principles of cybernetics, organizational learning, and holographic self-organization provide valuable guidelines regarding the direction [technology] change might take.” (p 108) A quarter of a century later, let’s hope we’re finally starting to move in the right direction.