By: Sue Ebbers, Ph.D.
Many practitioners of instructional design have a professional disdain for the unproven, or the fantastic. We seek to align with reality, striving to be learning scientists that build evidence which supports human and organizational improvement. Our efforts start with a needs assessment, that tells us where the gaps are in results and what should be addressed first. Then we complete our needs analysis, which tells us why those gaps exist. Finally, we take the lessons learned through those phases and design a solution to effectively fix whatever problem we want to train learners to overcome.
The process for creating training solutions for businesses usually unearths some seemingly straightforward assumptions. For example, if you are addressing a quality issue in the manufacturing process, your assumption might be to just narrow it down to a materials issue or a labor issue. Unfortunately, this streamlined path to a common-sense answer is deceptive, and possibly even delusion, depending on other available information. Before taking an action that may deliver a crippling loss to your organization, read on to learn how models and simulation are used to test assumptions, plus how you can further avoid the dangers of an echo chamber.
Models and simulations are one way to help prove or disprove a theory
On the tail-end of a graduate program at FSU, students learned a valuable lesson indirectly. They were in the process of finishing up doctoral dissertation experiments and writing up the results for their committees to review. One took the opportunity to sign up for the first part of a modeling and simulations course, because it seemed like an interesting new avenue for study in the field. Although they had encountered models and simulations previously, these were different.
A model in the context of this class was described as a scaled-down representation of something larger or more complex that exists in physical space. Things that could be modeled included a wooden chair, a house, a city, or even a country.
The instructor went on to describe a simulation as a complex set of intertwined models, with each model representing a part of the larger system. The parts were programmed to behave independently, as well as in concert with one another, over time. In these simulations, various scenarios could be tested by programming one or more parts of the system and then running the simulation for a day, a week, a month, a year, even longer. An underlying principle was that these simulations would be based on defined assumptions.
How false “common sense” assumptions play out in the real world
The student learned something surprising about these assumptions after the first simulation that the professor ran during class. The simulation examined low-income housing as a sustainable living solution for people who were living in poverty and who:
had various characteristics,
or were involved in specific situations,
or were part of target populations.
The class anticipated that building this type of housing would cause a downward trend for poverty in the city. It seemed like a slam dunk group of assumptions, but they were wrong. The simulation was set up and programmed with the entire system of
events,
potential realities, and
the impacts of each one.
Scenarios were run, and the students learned that this housing solution would actually accomplish the exact opposite of the goal, in the long term. As properties were cared for and home values went up, eventually prices would rise. Then the housing would be acquired by different populations and the poor would be pushed out, losing their homes in the communities they had invested. This demonstration was incredibly eye opening for the class.
It went a long way to demonstrate the danger of pursuing a solution without fully understanding the various impacts that a solution might have throughout the system. Applying a well-meaning or “common sense” solution can often end up causing the opposite of what we anticipate it will. That can happen in the near future or the distant future, at any period along the way.
Isolate and remove harmful assumptions to help steer performance to objectives
So how do we avoid unrealized disaster, by taking actions today that aren’t grounded in correct assumptions. Successful companies make key investments on the front end of strategic business interactions to clarify the realities their organization faces. This helps change agents prove how and why a specific performance improvement solution is needed, and advise leadership accordingly. By taking models and building a series of evidence-based simulations, various approaches can be tested to contextually reinforce pathways to a company’s desired results.
Funding and investment return are two primary drivers for every enterprise, setting a finite continuum that helps bracket the accuracy threshold met by a solution. After all, imperfect is always better than nothing. Which is why you should always update your assumptions throughout the life of a project and rerun the simulations frequently to be certain team efforts continue to remain true and correct. Here are three ways you can help identify, isolate and remove the wrong assumptions.
1. Use a change management playbook process along with a diverse advisory brain trust
Working with a Fortune 500 company ten years ago, we saw first-hand how leadership took seriously potential future realities using their strategic playbook. This change management playbook tool examined potential impacts from the top of the markets on down to sourced materials in the next 5 years, 10 years and 15 years. Coming fully prepared with their own research, an experienced and diverse senior leadership team met annually to challenge all of their assumptions and predictions.
They reviewed key internal and external events of the past year including widely-reaching global things reported in the news, as well as smaller, less newsworthy situations that had arisen within the company. This thorough perspective provided a strong basis for eliminating decision influences that were no longer valid, and raised new considerations at the same time.
2. Employ the law of averages by crowd-sourcing wisdom from a population of external experts
Consider how funded cancer clinical trials function. They start with one or more assumptions that are tested, usually against the status quo. This testing takes a predetermined amount of time to:
conduct the study,
evaluate the results,
write them up,
have a jury of peers evaluate the evidence
present conclusions to another review panel,
then release everything to the general research community in a peer-reviewed-journal.
After that is complete, the next funded clinical trial can then start, building on one or more of the conclusions and carrying them forward. This is a deliberate and incremental building of wisdom, based on the evidence witnessed. That applies across the board, from the efficacy of a particular medication, to the approach, process and the extension of other therapies. The process is methodical and slow, but solidly based on proof. So that if research protocols are carefully followed throughout, outcomes from treatments grow steadily more predictable and desirable.
3. Avoid overly simplistic messaging that obfuscates the complexity of reality
There are several variations that play to this theme. Like: if it was easy, everyone would do it. Or: there’s no such thing as a new idea. Ultimately, human beings are complicated. We create structures that serve our interests, from the micro level all the way up to the global. Institutions like culture and government seem one way in the past, and different today. They are constantly changing. We hear distilled political messaging or popular slogans designed to be as simple and focused as possible so they provoke narrow, specific action. Those actions frequently result in completely unforeseen circumstances that might lead to war and destruction, or cooperation and progression.
It happens in other arenas and practices beyond politics or medicine. Marketing champions the dogged pursuit of call-to-actions that compel purchase. Engineering streamlines and hones activities into value streams that quest for ever greater efficiency. No matter what industry, practice or type of business you are in, there is a human tendency to ignore the seemingly irrelevant or less relevant on a path to simplicity.
Question the desire to feel righteous. Reflect on why you stand so strongly for something and persevere in a struggle, despite potential blind spots. Avoid letting any of the assumptions that undergird your business strategies be influenced by anything other than evidence, and be cautious of how that data is interpreted. Because there is another quote that applies here and was popularized by Mark Twain: “There are Lies, Damn Lies, and Statistics.” Don’t try to make fact reflect a desired truth.
Related Articles For Further Reading
6 Easy Steps To Delivering Business Process Improvement Results For Your Organization
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Best Practices For How To Align Employees To Organizational Results
Ready to find those bad assumptions?
Now that you understand how incorporating the wrong assumptions in your strategy can damage your business, it’s time to ferret them out. Nothing is sacred, so don’t be afraid to face the hard truths. Your company, your employees and your customers will enjoy the benefits of better performance. And you will sleep better at night, knowing that it was all based on sound evidence and a well-tested approach.
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