An engaging perspective concerning organizational decision making dynamics is that an organization can be seen as a type of decision making computational system. An organization is composed of an assemblage of agents held together, in tension, by incentives (some shared, some selfish), assessment systems, and decision rights (power) accompanied with access to knowledge, some of which is protected. From the Human Resources discipline, this is the basis of ‘organizational architecture’, bound by Management Control Systems.
The Organizational Brain
In a 2010 Harvard Business Review article, ‘The Decision-Driven Organization’ (http://hbr.org/2010/06/the-decision-driven-organization/ar/1), it is proposed that organizations focus too heavily on formal management top-down structures when they should be more concerned with robust decision processes in terms of the interacting network of stakeholder roles and decision rights (Blenko et al, 2010).
The claim is that the fashion for perpetual reorganizations and restructuring misses a fundamental point: it is not the ‘power’ of individuals which drives strategic effectiveness, but rather the network of interactions and role-based rights which drive decision efficacy and agility. If we accept this ‘story’, even as a simple useful allegory, we can ‘map’ the ways in which actors interact in order to assess the robustness associated with decision-focused processes.
The organization can be seen as a type of ‘decision making machine’, albeit one which is quite slow and at times also quite flawed (through the influence of both inherent decision biases and agency forces). To the degree we consciously attempt to map potential process breakdowns which occur at the organizational communication network level, one can attempt to introduce intervention to overcome decision process shortcomings (i.e. when a process is followed, but the participants are not interacting in a robust way, or when biases such as overconfidence or availability sway a decision too quickly).
In complex, multi-staged decisions, there may be several social networks involved (each grounded by a different ‘phase’ in a staged decision process). In this case, a series of social decision networks begin to resemble something quite familiar: a neural network. Staged social decision networks, in aggregate, resemble a composite decision making ‘brain’.
If we think of an organization as a type of at times flawed decision making computer, we can consider building in redundancy and robustness in the ‘organizational brain’ via formal techniques for architecting neural network-based decision methods. Not science fiction, merely a recasting of our perspective concerning the nature of an organization as a network of agents, rather than a fractious and flawed mob of argumentative and self-centered individuals. If we cast off the notion that ‘organizational politics are inherently perverse’, we get closer to the ability to engineer organizational politics for strategic decision making fortitude.
In the research tradition, this connects to the theory of organizational sensemaking. Related emerging interdisciplinary research strands are: collective or organizational sensemaking; organizational multi-agent simulation (MAS); computational organization theory; socio-structural organizational decision analysis; and cyber-physical systems (CPS).
Each of the emerging research strands cited are similar in hybridizing socio-structural analysis with organizational research programs. Common themes include:
- viewing organizations as holistic decision making mechanisms,
- seeing individuals as role-based agents,
- viewing organizational agents as interacting in relatively simple rule-based frameworks,
- espousing the notion that agents interact in interlocking structural patterns, and
- viewing the organization as being an agglomeration of shifting, multi-contextual social networks.
Embracing such an organic understanding of organizational decision making highlights the organization as a whole over individual and short-term interests. Such a perspective amounts to a paradigm shift in the way organizations are studied and managed.
SELECTED LIST OF REFERENCES
- Blenko, M. W., M. C. Mankins, et al. (2010). “The Decision-Driven Organization.” Harvard Business Review.
- Carrington, P. J., J. Scott, et al., Eds. (2005). Models and Methods in Social Network Analysis. New York, Cambridge University Press.
- Davenport, T. H. and J. G. Harris (2007). Competing on Analytics: The New Science of Winning. Boston, MA, USA, Harvard Business School Press.
- Davenport, T. H., J. G. Harris, et al. (2010). Analytics at Work: Smarter Decisions, Better Results. Boston, MA, USA, Harvard Business Review Press.
- Grant, R. M. (1997). “The Knowledge-based View of the Firm: Implications for Management Practice.” Long Range Planning 30(3): 4.
- Kaner, M. and R. Karni (2004). “A Capability Maturity Model for Knowledge-Based Decision making.” Information Knowledge Systems Management 4: 27.
- Kiron, D. and R. Shockley (2011). “Creating Business Value with Analytics.” MIT Sloan Management Review 53(1): 10.
- Kiron, D., R. Shockley, et al. (2011). “Analytics: The Widening Divide.” MIT Sloan Management Review(Special Report): 21.
- Knoke, D., Yang, S. (2008). Social Network Analysis. London, SAGE Publications, Inc.
- LaValle, S., M. S. Hopkins, et al. (2010). “Analytics: The New Path to Value.” MIT Sloan Management Review: 22.
- LaValle, S., E. Lesser, et al. (2011). “Big Data, Analytics and the Path From Insights to Value.” MIT Sloan Management Review 52(2): 13.
- Nutt, P. C. (2002). Why Decisions Fail: Avoiding the Blunders and Traps that Lead to Debacles. San Francisco, CA, USA, Berrett-Koehler.
- Tan, C.-S., Y.-W. Sim, et al. (2011). “A Maturity Model of Enterprise Business Intelligence.” Communications of the IBIMA 2011: 11.
