You, as a human being, are a complex, biological system.
What exactly does that mean and why is it relevant to your ability to make health-conscious decisions?
The Problem
One common problem I see playing out in our modern world is that most individuals tend to view health challenges using a simple, reductionist framework.
This is useful when it comes to challenges involving simple systems:
A Simple System: A system involving a small number of variables that interact in a clear pathway such that it is easy to see how a change in one variable impacts a change in all others.
Image 1: A linear pathway through a simple system, in which a change in an input variable (A) causes a change in a second variable (B) which causes a change in an output variable (C). The key here is that there are a small number of variables (in this case we have 3 variables) that interact, mechanistically, in a clear pathway such that it is easy to determine how a change in one variable causes a change in another and leads to a specific outcome.
Addressing Challenges Involving Simple Systems
Often in life, we come across problems that involve simple systems.
For example, we may enter into a dark room and need to be able to see, so we flip on the light switch.
Flipping the light switch makes a change in a simple circuit, leading to the light turning on:
Image 2: An example of a linear pathway through a simple system - a switch is flipped, closing a circuit that results in a light turning on
Let's take a second, perhaps more relevant example.
Think about a time you had an interaction with another individual, only to wake up the next day with a fever or cough.
Image 3: An example of a linear pathway through a simple system - an interaction with a sick individual leads to an infection and symptoms of a second individual
In this example, an interaction with another individual (one who happened to have a cold) led to you developing cold symptoms yourself. The mechanism: a viral infection.
See how, when given a certain type of problem, we can clearly understand how a specific behavior leads to a specific outcome via one (or a small number of) specific mechanism(s)?
Given this understanding of simple pathways, let's generalize this type of pathway into a model that is relevant to our behavior:
Image 4: A general model of a simple pathway in which a change in an input variable causes a change in a second variable, ultimately causing a change in a third variable. Note that this will be used as the base of the general model we will be building, in which we model an input as a behavior and the outcome as a measurable aspect of one's health.
When Simple Frameworks Fail Us
As we can see above, it is often relevant and useful to be able to clearly understand how one specific action (e.g., the flipping of a switch) leads to a specific outcome.
However, what happens as we add in more variables to our system?
Image 5: Adding two more variables to our initial simple pathway.
And what happens if these variables no longer act in a strictly linear fashion? That is, what happens as extra connections between variables become involved?
Image 6: Adding more connections between variables, including feedback loops, in which a change in one variable leads to a change in a variable that appeared earlier on in the pathway.
And what happens if these variables now start feeding back directly on the variables that acted on them in the first place?
Image 7: Adding even more connections, such that multiple feedback and feedforward loops are present in the pathway
At this point, given the many variables with multiple mechanistic interactions occurring upon multiple other variables, how could one possibly understand a path from one specific input variable (A) to a specific output variable (C)?
Image 8: Given a complex system, it is impossible to determine a clear, mechanistic pathway from a change in a specific input variable to a measurable output variable.
See the problem?
When we are looking at a complex system, one involving multiple variables that interact as a network with numerous interactions (including both feedback and feedforward loops), it becomes impossible to use a simple, reductionist approach to understanding what is going on.
That is, it is impossible to determine a clear pathway through the system (linking a change in one input variable to a change in an outcome variable) by isolating individual variables and understanding their mechanistic interactions with other variables.
When we have these systems of higher complexity, we need a different approach.
In Part 2 we will begin to look at that approach. But first, let's head to Lesson 2 to learn more about complex systems, including examples of how they come to play in health-conscious decisions.