have to do with the evolution of some quantities with time. This evolution can happen easily with time or perhaps in discrete time steps. Here, we introduce dynamical systems in which the condition from the system evolves in discrete time steps, i.e., .

Whenever we model a method like a discrete dynamical system, we suppose we have a snapshot from the system in a sequence of occasions. The snapshots could occur annually, once every nanosecond, or perhaps irregularly, for example once whenever a new government is elected.

Whenever we take these snapshots, the concept is that we’re recording whatever variable determine the condition from the system: our selected that evolve with the condition space (see dynamical systems idea). To accomplish the outline from the dynamical system, we have to specify a guide that determines, given a preliminary snapshot, exactly what the resulting sequence of future snapshots should be.

Here, we introduce these fundamental concepts of the dynamical system with an example relating to the evolution of the population of moose.

#### Dynamics of moose population

##### Video 1

*Summary of discrete dynamical systems, part 1.*

More details about video.

##### Question about video 1

##### Video 2

*Summary of discrete dynamical systems, part 2.*

More details about video.

##### Question about video 2

##### Summary from moose videos

If your moose population begins with 1000 individuals and grows by 8% each year, we are able to model the populace having a discrete dynamical system

begin

m_-m_t &= .08 m_t

m_ &= 1000,

finish

in which the condition variable $m_t$ may be the number moose in year $t$. The *discrete* dynamical systems provides a rule for going from the snapshot from the moose population to a different snapshot from the moose population twelve months later. It’s possible to make use of the dynamical system to calculate the populace $m_1$ after twelve months, $m_2$ after 2 yrs, etc.

You will see more types of iterating discrete dynamical systems. You may also read how you can such as the moose model to be able to directly calculate the need for the moose population anytime, without getting to iterate the machine for every time point.

Resourse:https://mathinsight.org/discrete_dynamical_system_introduction

Key:*Introducing* discrete dynamical systems