System: Collection of entities/components that interact with each other and the environment in an attempt to achieve some goal

Model: Representation of some of the components of the system and of some of their actions and inter-relationships which is useful for describing or predicting the behavior of the system

Model must be useful.

State Variables & Attributes: Mathematical variables used by symbolic models to describe the state of the system being modeled.

Dynamic Vs Static Models: Dynamic models explicitly represent passage of time whereas static models refer only to single instant of time.

Continuous Vs Discrete Model: In continuous model, state variables can change at any time. However, in discrete model, state variable values change at a countable number of instants in time.

Deterministic Vs Stochastic Model: For deterministic model, results are identical if inputs are same. Deterministic model contains no probability or random effects. However, stochastic models incorporate uncertain occurrences by using probability distributions over sample space of possible outcomes.

High Resolution (HR): An HR model includes detailed interactions of individual systems or components; interactions being resolved at 1-1 level

Verification: Whether simulation model implementation (say, computer program) performs as intended

Validation: Determine whether simulation model (and not computer program) is an accurate representation of real system under study.

Building a Blockchain

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