Organizations are decision-making systems. In many ways, organizations can be viewed as products of their decisional history. In a logical world (preferred by economists) the rational model of decision-making is quite sufficient: define the problem, generate and evaluate solutions, choose and implement solution, and monitor…While the approach makes sense, organizations face a reality where cause and effect are separated in time (something humans don’t always incorporate into planning, implementation, or evaluation) and the immediacy of organizational life often fails to consider the defining characteristic of our world: complexity.
It’s logical to think about decisions as intentions for maximizing gains in relation to goals, and for simple and even complicated scenarios, decisional rationality works. However, increased levels of complexity, environmental dynamism — e.g., the erratic, unpredictable nature of markets — and technological innovation, provide us with a reality where rational models of decision-making do not accurately reflect what’s going on. Nonlinearity has never been more (un)popular! This world does not provide us with omnipresent decision-makers that have timely access to all relevant data points. This world, instead, provides us with imperfect and incomplete information, embedded in a system with conflicting preferences — your objectives, your organization, its environment, the wider world — where time scores high on the scarcity index. These are the conditions that create bounded rationality (check out work by Simon, March, Cyert). To top it all off, humans are responsible for processing this information and expected to address a level of complexity using simpler mental models than the challenges themselves (which violates some pretty serious principles, e.g., requisite variety: a system must be as varied and complex as the environment it is hoping to control).
What do we do?!
First, we can acknowledge some things. 1) The world is too complex for you to reliably predict. 2) The assumptions of rational decision making do not accurately map onto the process of real-life decision making, so when things don’t work out, despite best efforts at strategic planning, it should not be a surprise. We cling to the rational model of decision-making: it is how organizations appear to be run; it is how students are taught to address problems from an early age. Moving beyond the illusion of decisional rationality is possible. We can even borrow from rational models, starting with two distinct, yet related, dimensions along which decisions are made: 1) the goals, or definition of a particular problem and 2) the methods by which to address the goal or problem.
Organizational goals, desired outcomes, or the way we define particular problems are extremely important on their own. They become doubly important when they inform how we go about achieving them. Often times, even after a problem is stated, the group tackling the challenge, does not always clarify the definition of the challenge. When this happens we introduce ambiguity into the decision-making process. Ambiguity can lead decision makers to hold multiple interpretations of the same problem. It is really important to emphasize that we often fail to acknowledge the lack of sharedness when defining a goal or problem. Ambiguity will be high when there is little or no agreement on the definition. This in turn creates disagreement, confusion, and and tension about what direction to take.
The second dimension of understanding — the methods — involves uncertainty. Uncertainty is a product of complexity and change; it penetrates the core of decision-making: the very knowledge that group members choose to consider as relevant in their quest to accomplishing stated goals. Uncertainty arises from disruptions in the environment, technical cores of organizations, lack of experience, changing contexts, and misunderstanding all of the above. Goals and their corresponding methods are obviously related but they are not the same thing. Creating this level of awareness can put a name to some of the tensions experienced during the decision-making process. Knowing the difference between ambiguity and uncertainty will allow groups to tease apart aspects of their process and identify sources that might be leading to low-quality decisions. Around this awareness, meaningful dialogue can emerge that will more adeptly consider the organization’s multiple stakeholders and environments.
Naming ambiguity and uncertainty is important for other reasons, as well. Modernity is characterized by technological innovation, increased workforce heterogeneity, virtuality, multidisciplinarity, and other trends of globalization. These factors increase the likelihood of ambiguity and uncertainty during decision-making for various reasons including challenges associated with diverse workforces (e.g., cultural differences, language barriers, professional background chasms) and lack of social capital in non-traditional structures (e.g., distributed teams). No one anticipates the world to become less complex. Thus, we can expect new sources of ambiguity and uncertainty to arise throughout our nonlinear systems. By creating a level of awareness and establishing a shared language around decision-making processes, organizations can be more responsive to tension, changes, and waste fewer resources in the pursuit of creating, responding, and surviving.
- Information is incomplete and imperfect and humans have limited information processing capacity
- Ambiguity arises when groups disagree about goals or how to define a problem
- Uncertainty = complexity + change; arises when groups lack understanding about environment, technical core, changing contexts, and disagree on how to move forward
- Trends of technological innovation and globalization will increase potential sources of ambiguity and uncertainty
- Shared language can alleviate some of the tension created by different dimensions of organizational decision-making processes