What is EEPPASA and Why is it So Important to Harrison’s Applied Game Theory?
In Harrison’s Applied Game Theory (HAGT), we pay a lot of attention to something we call EEPPASA. This anagram represents effectiveness, efficiency, precision, productivity, accuracy, and self-awareness.
EEPPASA is a reflection of a way of thinking that merges many related thoughts from a wide range of thinkers; Including Lewis Carroll, Henry Kissinger, and Maya Angelo.
My merging of their thoughts has given birth to the following idea;
“If you do not know where you have been, where you are, and where you are going, any road is likely to lead you to the wrong place, at the wrong time, and for the wrong reason.”
Concerning HAGT, the quote above proposes that In order to maximize your potential in all aspects of life, you need to master EEPPASA. Any one of the elements that are ignored or applied improperly can be the cause of failure, unnecessary struggle, and even suffering. Let’s explore each element of EEPPASA.
Efficiency – the ratio of the useful work performed by a person, machine, or in a process to the total energy or other resources expended or benefits received.
Effectiveness – the degree to which something is successful in producing the desired result; success.
Precision – Specifically in HAGT this is an agreed-upon refinement in measurement, calculation, or specification, especially as represented by definitions accepted within an interactive or interdependent group in a specific game-based scenario. Just to avoid confusion, my use of the word precision in this context is a different definition than what might be applied to the precision tools that an electrician, carpenter, or surgeon might refer to. The distinction between precision and accuracy here is that Precision within any system represents the capacity to deliver an outcome within a specifically-defined range of possibility/acceptability. Whereas accuracy within a system is defined by a singular possibility of outcome to satisfy the definition.
Productive – The quantity and quality of the end product produced through some individual or group activity,
Accuracy – Specifically in HAGT this is an agreed-upon refinement in measurement, calculation, or specification, especially as represented by definitions accepted within an interactive or interdependent group in a specific game-based scenario.
The distinction between accuracy and precision and accuracy here is that Accuracy within a system is defined by a singular possibility of outcome to satisfy the definition, whereas Precision within any system represents the capacity to deliver an outcome within a specifically-defined range of possibility/acceptability.
Self-Awareness – Self-awareness is connected to consciousness. While consciousness is being aware of one’s environment and body and lifestyle, self-awareness is the recognition of that awareness. Self-awareness is how an individual consciously knows and understands their own character, feelings, motives, and desires. In EEPPASA this includes the awareness of love, kindness, compassion, empathy, wisdom, goodness, and the appreciation of beauty.
On the surface, these distinctions seem fairly obvious. However, in HAGT we are often presented with complex and extreme problems that cannot be solved through EEPPASA unless these elements and how they relate to and influence each other are explored. Let’s take precision versus accuracy. On the surface, they appear the same but they are quite different. What they have in common is that each relates to making experimental measurements.
As a futurist, game theorist, problem-solver, or strategists becomes more skilled they naturally begin to tune in to the important differences between precision and accuracy—and especially how they relate to quality in the decision-making process.
Assessing Variation In Measurement Systems
The distinction between precision and accuracy has to do with how they vary as measurement systems. If an individual or team is starting a quality improvement project that involves collecting data to manage quality or to monitor changes in various group processes, it’s important that the systems for collecting measurements are free of flaws, especially flaws caused by cognitive biases, and or logical fallacies.
After all, if you can’t trust your measurement system, then you can’t trust the data that it produces, and if can’t trust the data that it produces, you cannot predict what is likely to happen or solve a problem.
The distinctions between accuracy and precision come into play, especially when isolating constraints in a system when you need to isolate measurement system errors that may be taking place?
Here’s where accuracy and precision come into play. Accuracy refers to how close measurements are to the “true” value, while precision refers to how close measurements are to each other. In other words, accuracy describes the difference between the measurement and the part’s actual value, while precision describes the variation you see when you measure the same part repeatedly with the same device. Any team of strategists needs to understand these details and any new consequential piece of data can change all predictions, estimates, and measurements.
Another way of describing true value is as a psychometric concept that refers to the measure that would have been observed on a construct were there not any error involved in its measurement. … The concept of a true value relates to the concepts of reliability and validity.
Precision is not concerned with true value. For instance, something can be precise yet not exactly accurate. Let’s explore this as we break down precision further into two components:
- Repeatability: The variation observed when the same operator measures the same part repeatedly with the same device.
- Reproducibility: The variation observed when different operators measure the same part using the same device.
It’s important to note that measurement systems can suffer from both precision and accuracy constraints. Thus there are times where measurement will be any of the following:
- Accurate and precise
- Accurate yet imprecise
- Inaccurate yet precise
- Neither accurate nor precise.
If you still don’t get this distinction lets, try this simple example.
Have you ever seen those scales in the supermarket that are used to weigh produce?
You place an apple on the scale and there are little lines on the scale to indicate ounces. Here you may be testing the weight of the apple using a scale that measures the bags precisely (in other words, there is little variation in the measurements), but not accurately – measuring 2.3 oz., 2.35432 oz., and 2.31693 oz. for three samples. Here the measurement is precise but not accurate.
Or maybe your measurements are all over the place, with samples measuring at 11.64 oz., 12.35 oz., and 13.04 oz., in which case your scale may be neither accurate nor precise.
But how can you detect these problems in your measurement system?
Evaluating Accuracy & Precision
For the strategist or problem-solver who requires both accuracy and precision, there are specific measurement systems analysis tools in Minitab Statistical Software, including Gage Linearity and Bias Studies and Gage R&R Studies, which can help an expert reveal if a scale needs to be recalibrated or if a newly hired operator might be measuring ingredients consistently.
When putting EEPPASA into action the question arises of what should a strategist do if they detect accuracy and/or precision errors?
The next step is to focus improve the measurement system. Only when this has been done can you rely on the data and moving forward with continually improving EEPPASA.
If your measurement correction system is built for accuracy and precision, then you will likely have other systems in place to help you decide if recalibrating a scale or conducting more training for new team members might be just what you need to get your measurement systems to be both accurate and precise.
At the end of the day, the way to measure EEPPASA is by determining how productive you are.
As I mentioned earlier, productivity describes various measures of efficiency, effectiveness, and precision.
There are many different definitions of productivity (including those that are not defined as ratios of output to input) and the choice among them depends on the purpose of the productivity measurement and/or data availability. The key source of difference between various productivity measures is also usually related (directly or indirectly) to how the outputs and the inputs are aggregated into scalars (a combination of people, places, and things) to obtain such a ratio-type measure of productivity.
Productivity is a crucial factor in the production performance of small groups, firms, and nations. Increasing productivity in an organization where there are gamification incentives can raise living standards because more real income improves people’s ability to purchase goods and services, enjoy leisure, improve housing and education and contribute to social and environmental programs. Productivity growth also helps businesses to be more profitable.
When exploring EEPPASA to measure success you can use one class of inputs or many. When using one class of input, but not multiple factors, it is called partial productivities. In the “real world” measurement in production usually means measures of partial productivity, since there may be so many factors that it is hard to measure them.
Measured and interpreted correctly, these components are indicative of productivity development and approximate the efficiency with which inputs are used in your business to produce goods and services. However, as I mentioned, productivity is only measured partially — or approximately. In a way, these measurements are somewhat inaccurate because they do not measure everything, but it is possible to interpret correctly the results of partial productivity and to benefit from them in practical situations. At an organizational level, typical partial productivity measures are such things as worker hours, materials, or energy used per unit of production. EEPPASA is a synergetic approach to problem-solving and each of the elements is linked to the others, so any failure in one affects all of them.
Before the widespread use of computer networks, partial productivity was tracked in tabular form and with hand-drawn graphs. Tabulating machines for data processing began being widely used in the 1920s and 1930s and remained in use until mainframe computers became widespread in the late 1960s through the 1970s. By the late 1970s, inexpensive computers allowed larger organizations especially industrial operations to perform process control and track productivity. Today data collection is largely computerized and almost any variable can be viewed graphically in real time or retrieved for selected time-periods.
When using HAGT to address real-world problems, and for isolating hidden constraints, and subtle patterns within a system, it is EEPPASA that will enable you to achieve what seemed impossible before. This is so, even in a situation where only partial productivity is possible, as has been happening concerning the diagnosis of Covid 19, the changing procedures in dealing with the effects of the disease, and the rollout of the vaccine.
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