Human dynamics and sociophysics suggest statistical models that may explain and provide us with better insight into social phenomena. Here we tackle the problem of determining the distribution of t...

A problem in human dynamics: modelling the population density of a social space

Human dynamics and sociophysics suggest statistical models that may explain and provide us with better insight into social phenomena. Here we tackle the problem of determining the distribution of the population density of a social space over time by modelling the dynamics of agents entering and exiting the space as a birth-death process. We show that, for a simple agent-based model in which the probabilities of entering and exiting the space depends on the number of agents currently present in the space, the population density of the space follows a gamma distribution. We also provide empirical evidence supporting the validity of the model by applying it to a data set of occupancy traces of a common space in an office building.

A Problem in Human Dynamics: Modelling the Population Density of a Social Space

This paper discusses on linear birth and death with immigration and emigration (BIDE) process to stochastic differential equation (SDE) model. Forward Kolmogorov equation in continuous time Markov chain (CTMC) with a central-difference approximation was used to find Fokker-Planckequation corresponding to a diffusion process having the stochastic differential equation of BIDE process. The exact solution, mean and variance function of BIDE process was found.

Stochastic differential equation model for linear growth birth and death processes with immigration and emigration

The Prendiville process is another variation of the logistic model which assumes linearly decreasing population growth rate. It is a continuous time Markov chain (CTMC) taking integer values in the finite interval. The continuous time Markov chain can be approximated by stochastic differential equation (SDE). This paper discusses the stochastic differential equation of Prendiville process. The work started with the forward Kolmogorov equation in continuous time Markov chain of Prendiville process. Then it was formulated in the form of a central-difference approximation. The approximation was then used in Fokker-Planck equation in relation to the stochastic differential equation of the Prendiville process. The explicit solution of the Prendiville process was obtained from the stochastic differential equation. Therefore, the mean and variance function of the Prendiville process could be easily found from the explicit solution.

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Papers

Stochastic differential equation model for linear growth birth and death processes with immigration and emigration

Stochastic differential equation model to Prendiville processes