I hear and I forget. I see and I remember. I do and I understand. - Confucius
One of Steven Covey's Seven Habits is to 'Seek first to understand, and then to be understood.' Human relationships are certainly advanced when we careful and thoughtfully listen to the perspective of others. But achieving an understanding of a topic requires us to probe much deeper until we have considered and acknowledged the viewpoints of all parties for the presenting circumstances. We must intellectually engage, properly diagnose the situation, recognize the body of knowledge relevant to it, confront the constraints imposed on solutions, and be able to act on available information in ways that will result in better outcomes being achieved.
When such understanding is required across an a diverse community of practitioners, rather than just from a few selected individuals, it become more difficult to dictate that they adopt proper behaviors. Culture rules. Each member of a culture brings their own expectations, beliefs, ceremonies, and traditions which they have adopted as a result of their own experiences. No amount of explanatory power is likely to win them over from the experience.
Inevitably, in navigating this terrain, tensions arise from the forces of:
- scope-driven, coarse-grained investment decision cycles necessary to allocate resources among competing demands
- iterative, time-boxed activities necessary to produce viable work products
- cultural cycles of learning, which rely on the fitness of these work products to existing or anticipated anticipated situations
- the responsiveness and resiliency of the service provider in successfully incorporating feedback from an evolving set of environments and constraints
Painful oscillations of performance can result from these tensions (especially in periods of decline rather than growth) unless some means of adaptive control can be introduced that are tied to first principles. Yet even in decline, big rewards are still possible, though achieving them will generally require commensurately painful decisions, actions, and risks. This uncertainty forms a cone of uncertainty around target outcomes that waxes and wanes in two separate waves. The first results from individual engineering and construction projects of any meaningful size and complexity. There will inevitably be winners and losers that result from these decisions. Yet inevitably, the routes over which future value will can be accessed must be concentrated for growth to be economically viable. Arthur M. Wellington eloquently described the challenges of such decisions in his classic 1887 work, The Economic Theory of the Location of Railroads:
The correct solution of any problem depends primarily on a true understanding of what the problem really is, and wherein lies its difficulty. We may profitably pause upon the threshold of our subject to consider first, in a more general way, its real nature: the causes which impede sound practice; the conditions on which success or failure depends; the directions in which error is most to be feared. Thus we shall attain that great perspective for success in any work—a clear mental perspective, saving us from confusing the obvious with the important, and the obscure and remote with the unimportant.
Wellington's focus on outcomes, constraints, priorities, and risks require a cognitive understanding of the concepts of operations for a domain of interest. Immanuel Kant described the power of abstractions in laying a foundation for such understanding:
In order to make our mental images into concepts, one must thus be able to compare, reflect, and abstract, for these three logical operations of the understanding are essential and general conditions of generating any concept whatever. For example, I see a fir, a willow, and a linden. In firstly comparing these objects, I notice that they are different from one another in respect of trunk, branches, leaves, and the like; further, however, I reflect only on what they have in common, the trunk, the branches, the leaves themselves, and abstract from their size, shape, and so forth; thus I gain a concept of a tree.
Abstractions and pattern recognition are innately linked. Generalizations of properties help us systematically develop and verify hypotheses of the connections between causes and effects. Unfortunately, these generalizations may not serve us by recognizing points of light as distant galaxies, rather than as asterisms that fit well with our popular narratives. Distractions abound, ideas are cheap, hindsight is biased, and information needed for proper decision-making may often be inaccurate or just plain unavailable.
Visionary early adopters need more than platitudes and good attitudes, especially when skeptics resist advice that is contrary to their traditions. In cultures of high resistance, change agents are grossly outnumbered. Stakeholders who are under pressure are more likely to fall back on historical patterns, even after they are no longer acceptable. Those behaviors have built their reputation and world view within their community. To satisfy expectations of some new role, these actors must understand and accept the new actions they will be responsible for, and their necessary interfaces with others, so improved outcomes can be achieved. Unfortunately, when reality is viewed through their lenses, causes and effects are often disconnected in time from context.
Often leaders are infected by reality distortion fields or magical thinking. Instead, leadership must shape the environment in which the communities making up this culture operate, and provide them with the necessary ingredients for change. Change agents can then define a set of objective criteria by which success will be measured, and facilitates charting a course towards satisfying those criteria, and their realization, consistent with an elaboration likelihood model.Their collective beliefs may be logically inconsistent with rational analysis; nevertheless, those assigned to new roles must embrace their responsibilities, and agree to support each other in making the necessary transformations. Hopefully, these require steps, rather than leaps, of faith.
- ambiguity about the nature of the problem(s) that should be addressed
- risks about the implementation of candidate solutions
- unreconciled misunderstandings and flawed assumptions
- unknowns about what options will be available in the future, and what environment they should be evaluated within
Though this uncertainty is often masked, projects may be not much more than a bundle of ideas which have attracted the attention of decision authorities. They may choose a fork in the road after trying other options, and discovering they did not adequately understand the future they were getting themselves into. In such situations, information will always be incomplete, and decisions will always be temporary. The causal chain between desirable effects and underlying upstream decisions may not be immediately apparent, even when the facts are known. Projects are always having changes in leadership, structure, or available resources. The longer the projects are, the greater the number of events that must be successful, and the more likely it will be that gaps in performance will creep in.
Sponsors usually have to choose from many different potential investment opportunities, each promising predictability and clamoring for precious resources, and presuming that all resources are interchangeable and offer similar value creation over the long term. In order to minimize their exposure to Parkinson's law, leaders strive to bound these sources of uncertainty, striving to achieve as predictable a set of outcomes as possible. If project outcomes were indeed normally distributed in such portfolios, it would be possible to dial in historical data and the risk tolerance sponsors were willing to absorb. But individual project outcomes are more likely to follow heavy-tailed distributions, with many underlying causes, from sparse or unreliable historical data to unfamiliar execution environments.
A further complication arises when this uncertainty arises when a common set of root causes are threaded through multiple projects in a portfolio. These root causes form a common failure mode across these projects that can be self-reinforcing. As an example, the mental models of the stakeholder's inevitably are simplifications of the diverse realities in which projects must be executed. Ideally, the highs and lows of this uncertainty would be offsetting. Steve McConnell believes that teams are more likely to commit to more than they are capable of delivering, because people have an innate desire to please. The laws of physics and lessons of history are inevitably stronger than the enthusiasm of change agents and the ambitions of project advocates. Yet unless such underlying root causes for failures can be recognized and confronted, these failure modes are likely to amplify the underlying uncertainty that typically emerges individual projects themselves.
This over-simplification is an essential property of environments that are exposed to changing circumstances. Even though problems, concepts, or ideas have been embraced by stakeholders, their actual definition may not be actionable, despite commitments to action. In these situations, each stakeholder may have their own mental models of what these abstractions mean, and are likely to each adopt the interpretation most useful to them or their organizations. The systems implications of these models may not be apparent. For example Erhard Reichlin, author of the Art of System Architecting, describes the application of a systems approach as:
... one that focuses on the system as a whole, linking value judgments (what is desired) and design decisions (what is feasible). A true systems approach means that the design process includes the problem as well as the solution. If a system is to succeed, it must satisfy a useful purpose at an affordable cost for an acceptable period of time.
Now consider the meaning of each the italicized words in Reichlin's description. Such abstractions are sometimes utilized by advocates with roughly right people, sometimes for political purposes and to provide projects room to maneuver. Yet until such abstractions are sufficiently elaborated into clear, specific, and actionable requirements, any efforts to pursue these goals are likely to be less focused, and can fall considerably short of meeting the needs of all (or even the most important) stakeholders.
Phillip Tetock, author of the book Superforecasting, provides us with a disturbing example of the fuzziness of the lines we draw between which emerging conditions are significant and which can be overlooked:
In March 1951 National Intelligence Estimate (NIE) 29-51 was published. "Although it is impossible to determine which course of action the Kremlin is likely to adopt," the report concluded, "we believe that the extent of [Eastern European] military and propaganda preparations indicate that an attack on Yugoslavia in 1951 should be considered a serious possibility." ...But a few days later, [Sherman] Kent was chatting with a senior State Department official who casually asked, "By the way, what did you people mean by the expression 'serious possibility'? What kind of odds did you have in mind?" Kent said he was pessimistic. He felt the odds were about 65 to 35 in favor of an attack. The official was started. He and his colleagues had taken "serious possibility" to mean much lower odds. Disturbed, Kent went back to his team. They had all agreed to use "serious possibility" in the NIE so Kent asked each person, in turn, what he thought it meant. One analyst said it meant odds of about 80 to 20, or four times more likely than not that there would be an invasion. Another thought it meant odds of 20 to 80 - exactly the opposite. Other answers were scattered between these extremes. Kent was floored.
Despite such uncertainty, organizations must institutionalize learning across projects so the impacts of these systemic risks can be reduced, and long-term behavioral improvements can be realized. Peter Senge describes the landscape in which this organizational learning must unfold:
Many scholars have considered the concept of organizational learning as a dichotomy. In its basic, primary form they have described it as action-oriented, routine, and incremental, occurring within existing (mental) frameworks, norms, policies and rules. In the face of profound change in organizational environments, these scholars argue that a qualitatively distinct, secondary form of learning is necessary. This aims to change the (mental) frameworks, norms, policies and routines underlying day-to-day actions and routines.
The simplest form of Senge's organizational learning is called single loop learning, and involves providing appropriate feedback to members of an organization within their established, generally accepted, prescriptive practices. A more powerful form of learning is called double loop learning, and requires teams to recognize and reflect on the patterns of behaviors which arise under particular circumstances. Double loop learning is particularly important in influencing teams to 'learn how to learn'. In their book Becoming a Learning Organization, Swieringa and Wierdsma introduced an even more advanced concept they called 'triple loop learning', which involves open inquiry into underlying "why's."…that permits insight into the nature of paradigm itself'. Research has subsequently highlighted that there are many different interpretations of this concept, though having a justification for believing the change is likely (rather than just possible) would seems essential.
We can acquire knowledge through training, experimentation, and direct experience, but should turn to the field of epistemology to discern the essential properties of propositions which our mental models and arguments are grounded upon:
- we must believe these proposition to be true
- we must be justified in our beliefs (have a legitimate rationale for them), and
- the propositions in fact must be true (rather than just figures of our imagination).
Narratives can be memorable, but can also be deceptive. Evidence-based change efforts provide a more rigorous way to translate the mental models of stakeholders into a more robust language foundation for meaningful evaluations and healthy debate. This robustness can at times be hard to pin down; much depends upon whether rational arguments will be considered, and whether deductive reasoning or inductive reasoning is appropriate in the situation. This is one of the reasons Oscar Wilde eloquently described the search for truth as “rarely pure and never simple.”
Facts and data can illuminate what is true, but people only believe what they can see and do. The combination necessitates examination of the proposition several times. They must then have an opportunity to critically examine their own conclusions confident of a threat free environment. Honest brokers can then properly consider the merits and hazards of each point of view, so the interactions of logical consequences can be understood. This new understanding can then form the basis of more effective play-calling, and stabilizing so that repeatable experiments that can be properly evaluated, and the ramifications of each decision-makers points of view can be objectively and safely considered.
To create such an environment, actors must have opportunities to learn in a variety of situations, and understand the behaviors they produce adequately so that causes can be connected to effects, and more informed decision-making can be orchestrated. As figure 3 shows, research in experiential learning has demonstrated that knowledge transfer in the form of oral presentations is effective less than 20% of the time. By creating situations in which students are more engaged in their learning experiences, the likelihood of successfully transferring knowledge can increase to an average of about 50%. But when students are able to learn by doing (and especially by deciding), and are presented with opportunities to try out ideas in a safe environment, the odds of knowledge transfer nearly triple from what is expected from a passive receiving experience.
As Ray Madachy, the author of the book Software Process Dynamics, observes:
For organizational processes, mental models must be made explicit to frame concerns and share knowledge among other people on a team. Everyone then has the same picture of the process and its issues. Senge and Roberts provide examples of team techniques to elicit and formulate explicit representations of mental models. Collective knowledge is put into the models as the team learns. Elaborated representations in the form of simulation models become the basis for process improvement... Models can be used to quantitatively evaluate the software process, implement engineering, and benchmark process improvement. Since calibrated models encapsulate organizational metrics. organizations can experiment with changed processes before committing project resources.
The following articles demonstrate the potential power of using 'management simulations' to provide just such a capability. These simulations can be helpful in aligning the unique perspectives of stakeholders and give them opportunities to try out alternative decisions in realistic project situations. This gives these stakeholders with opportunities to connect the dots across time and space. Each article walks through a scenario that is relevant to systems and software engineering practice, and provides a series of 'runs' which reveal the performance of the simulation over a project's timeframe. Team members can interact with these simulations at discrete time intervals that align with typical decision points, rather than trying to connect cause and effect across the timeframe of an entire project. Simulation participants can also be organized into small teams to introduce competition into the exercise, and give each of these teams the opportunity to collectively talk through what they believe is happening, based upon the simulation outputs within that timeframe.