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The View from Here: Degrees of sophistication

Mike Gangwer Published on 06 May 2014

In any organization expecting to deliver an output, the degree or level of sophistication is an important consideration. The appropriate level is a challenge for managers, the mid-level supervisors charged with implementing what the leaders decide to place in policy.

Operationally, working towards an output can be described as standard operating procedure, or SOP. These take the implementer through the step-by-step process of completing a task as a course of action necessary at reaching the deliverable of a complete output.



So much of what we do is now software-driven. Computers and laptops and tablets and smartphones are everywhere and inexpensive. I use an Apple smartphone and an iPad, both extraordinary devices that help me be productive, in touch and able to find answers to almost anything via an Internet connection.

Yet in the middle of this discussion is the dilemma of complexity. All organizations deal with this topic. And all of them are constantly trying to shift the degree of complexity up or down.

Organizations that strive to deliver an output strictly by SOP tend to have a greater degree of complexity because all the elements in the SOP must be carefully defined and structured. Organizations delivering outputs with less definition and structure can tend toward less complexity.

An additional consideration is the dichotomy of determinism and stochasticity. Determinism is generally a pathway determined by deduction and reason. Basing an output on what we already know can be a complex SOP; the output is in essence a prescription built upon cognitive reasoning. We view the world empirically and draw conclusions in a rational method. Much of science is built on this model.

The stochastic model is different. When the inputs cannot be bounded or completely constrained, the combination of open-ended inputs yields an output that is different every time the inputs are combined. Climate-change modeling is a good example of stochastic output.


So combining these ideas into an answer to the question, “What does this have to do with organizational outputs?” is challenging. This topic is clearly on my mind as I sit here and think about the work we are doing in conservation.

I am amazed at the level of complexity in this topic. We have software programs for nearly everything we do. At a recent training event, the topic was irrigation water management. And the handout included an eight-page document describing the SOP for completing a worksheet. As I reviewed it, I was struck by the fact that it is procedural, it is a step-by-step process by which the user places numbers into it and the output is delivered to the landowner.

My angst over it is this: At no point does the eight-page document provide the user with the rationale for why these matter. Or may I write this: The user does not have the fundamental understanding of the three variables and how they are used in a systems approach. We jumped right into filling out a software program without first determining at what level of complexity we must define our procedure.

We know that for any learning environment, we must begin with the basic levels of understanding. These are generally the laws of physics that describe our physical world; the laws of chemistry that define changes in molecular behavior as a function of time, temperature and domain; and the laws of numbers that govern functions of rate and rate per unit of time.

Our goal is to have a fundamental understanding of how these laws influence rate, volume and time all in the context of irrigation water management.

If these are understood, then the user can complete the task of delivering an irrigation water management plan to the landowner with a pencil, paper and calculator. The use of software simply increases the efficiency of the process. If the laws that govern these three variables are not understood, the user has little or no insight into the numbers used in populating the software program. This is an error that must be avoided.


The full comprehension of these laws was learned in college, one class at a time. Then during my senior year and graduate years, they were combined into a deterministic model with concurrent degrees of complexity. Yet the user can know and understand the greater complexity simply because the individual laws have been learned before they were combined into a systems model.

My angst is, again, that we skip over a required approach at confirming an understanding of the laws that govern our output. This may be described as owning and using software to produce output without knowing and appreciating the underlying laws that govern the entry data. I see this so often.

If the software in use must account for the lack of the user knowing the underlying laws, then we have greater complexity … if greater complexity, the learning requirement is increased and at some point most users reach saturation. When they do, the output is suspect because the user simply does not have the necessary training to know what is correct.

If the software output is supposed to be deterministic but in fact is stochastic in the real world, then we add to the dilemma the challenge of interpretation.

Irrigation water management attempts to be deterministic but in fact is stochastic. That is, for users without knowing the laws of what inputs are dynamic and unbound (examples are solar radiation, surface soil heat flux, deep percolation and root mass), the output is built upon rigid input parameterization.

The irrigation plan, therefore, has some degree of error given the input dynamics. The importance of knowing there is error and allowing for adjustment is important. The user and the landowner must know the scale of adjustment in a time period, such as a day or an irrigation cycle.

The point is this: If a technician can use the simple tools of a pencil and paper and calculator to define the variables of rate, volume and time, with the adjunct scale of change per unit of time, then parsimony is maintained.

The law of parsimony is simply built upon knowing what the appropriate levels of complexity required are and not adding any more. Maintaining simplicity is a desirable objective because what is simple is more easily implemented and managed.

Sophistication and complexity are necessary if the output demands them at a certain level based upon what we are trying to accomplish. Building an airplane requires a very sophisticated set of SOPs, but at a level of complexity no greater than necessary. The latter portion of the previous sentence is important: We do not add complexity if it is not needed.

For a lot of the work that we do, I suggest additional complexity has been added … largely because we are using software that accounts for the world in a deterministic manner rather than training our staff to recognize the fundamental laws of physics, chemistry and mathematics. My posit is: Teach the fundamentals first, and do so not on the computer screen but on a paper pad using a pencil and calculator.

To be clear, this is not going back in time or taking a Luddite approach. Rather it is to properly use complexity, especially in the real world of stochasticity, and build not just the output but a change in human behavior.

This is the outcome we seek. For as the viewer of any artwork, it is not about the view but what we do with the view to help us compose our lives. The art of our work is not about filling out the eight-page irrigation water management form but what we do with it to help the landowner change his or her behavior.

The only way we can do this, in my opinion, is base what we do on a thorough understanding of fundamental laws so we can know to what level or degree of sophistication is required. The goal is not to develop the output, but reach the true objective, the outcome of changing human behavior. PD

mike gangwer

Mike Gangwer
Agricultural Scientist