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In addition to dynamically generated behaviors, agent-based models often have state structures that are a dynamic function of the events in the models.
The distribution function of the events could be described as a stochastic process, which has no or very minor influence on restrictions and/or evolution-driven connections [ 7- 9, 18, 25- 27].
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where denotes the indicator function of the event.
almost surely for all, where is the indicator function of the event and stands for the standard normal distribution function.
Let and be sequences of random variables defined on a fixed probability space and the indicator function of the event.
almost surely for all, here and in the sequel, is the indicator function of the event, and stands for the standard normal distribution function.
For events A and B, (I(A)) denotes the indicator function of the event A, and (I(A,B =I Acap B)).
where, here and in the sequel, I ( A ) is the indicator function of the event A and Φ ( x ) stands for the standard normal distribution function.
Throughout this paper, the symbol denotes a positive constant which is not necessarily the same one in each appearance, and denotes the indicator function of the event.
({mathbf {1}}_{A}) is the indicator function of the event A. For a real-valued number c, let (c^=max{0, c}) and (c^=-min{0,c}).
where 1 is the indicator function of the event enclosed in the brackets; A i and B i denote the event that PU i and SU access channel i at time τ, respectively.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com