
# 4.2: Classiﬁcations of Model Equations

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There are some technical terminologies I need to introduce before moving on to further discussions:

Linear system A dynamical equation whose rules involve just a linear combination of state variables (a constant times a variable, a constant, or their sum).

Nonlinear system Anything else (e.g., equation involving squares, cubes, radicals, trigonometric functions, etc., of state variables).

First-order system A difference equation whose rules involve state variables of the immediate past (at time $$t−1$$) onlya

Higher-order system Anything else.

aNote that the meaning of “order” in this context is different from the order of terms in polynomials.

Autonomous system A dynamical equation whose rules don’t explicitly include time $$t$$ or any other external variables.

Non-autonomous system A dynamical equation whose rules do include time $$t$$ or other external variables explicitly.

Exercise 4.3

Decide whether each of the following examples is (1) linear or nonlinear, (2) ﬁrst-order or higher-order, and (3) autonomous or non-autonomous

1. $$x_{t} = ax_{t−1} + b$$

2.$$x_{t} = ax_{t−1} + bx_{t−2} + cx_{t−3}$$

3. $$x_{t} = ax_{t−1}(1−x_{t−1})$$

4. $$x_{t} = ax_{t−1} + bxt−2^{2} + \sqrt[c]{x_{t−1}x_{t−3}}$$

5. $$x_{t} = ax_{t−1}x_{t−2} + bx_{t−3} + sin(t)$$

6. $$x_{t} = ax_{t−1} + by_{t−1}, y_{t} = cx_{t−1} + dy_{t−1}$$

Also, there are some useful things that you should know about these classiﬁcations:

Non-autonomous, higher-order difference equations can always be converted into autonomous, ﬁrst-order forms, by introducing additional state variables.

For example, the second-order difference equation

$x_{t}=x_{t-1}+x_{t-2} \label{(4.5)}$

(which is called the $$Fibonacci \ sequence$$) can be converted into a ﬁrst-order form by introducing a “memory” variable $$y$$ as follows:

$y_{t} = x_{t-1}\label{(4.6)}$

Using this, $$x_{t−2}$$ can be rewritten as $$y_{t−1}$$. Therefore the equation can be rewritten as follows:

$x_{t} = x_{t-1}+y_{t-1}\label{(4.7)}$

$y_{t} = x_{t-1}\label{(4.8)}$

This is now ﬁrst-order. This conversion technique works for third-order or any higher-order equations as well, as long as the historical dependency is ﬁnite. Similarly, a non-autonomous equation

$x_{t} = x_{t-1} +t\label{(4.9)}$

can be converted into an autonomous form by introducing a “clock” variable z as follows:

$z_{t}= z{t-1} +1, z_{0} =1\label{(4.10)}$

This deﬁnition guarantees $$z_{t−1} = t\]. Using this, the equation can be rewritten as $x_{t} = x_{t-1}+ z_{t-1},\label{(4.11)}$ which is now autonomous. These mathematical tricks might look like some kind of cheating, but they really aren’t. The take-home message on this is that autonomous ﬁrst-order equations can cover all the dynamics of any non-autonomous, higher-order equations. This gives us conﬁdence that we can safely focus on autonomous ﬁrst-order equations without missing anything fundamental. This is probably why autonomous ﬁrst-order difference equations are called by a particular name: \(iterative \ maps$$.

Exercise 4.4

Convert the following difference equations into an autonomous, ﬁrst-order form.

1. $$x_{t} = x_{t-1}(1-x_{t-1})sint$$

2. $$x_{t} = x_{t-1} +x_{t-2}-x_{t-3}$$

Another important thing about dynamical equations is the following distinction between linear and nonlinear systems:

Linear equations are always analytically solvable, while nonlinear equations don’t have analytical solutions in general.

Here, an analytical solution means a solution written in the form of $$x_{t} = f(t)$$ without using state variables on the right hand side. This kind of solution is also called a closed form solution because the right hand side is “closed,” i.e., it only needs $$t$$ and doesn’t need $$x$$. Obtaining a closed-form solution is helpful because it gives you a way to calculate (i.e., predict) the system’s state directly from $$t$$ at any point in time in the future, without actually simulating the whole history of its behavior. Unfortunately this is not possible for nonlinear systems in most cases.