System Dynamics as a methodology has traditionally been concerned with the study of processes that can be described by continuous variables. Discrete or integer events, such as the number of sales made in a day or the number of factory closings in a year have either been approximated as continuous variables or else not dealt with. This paper examines another way of dealing with discrete events through the realization that any discrete event has a certain probability of occurance. These probabilities are continuous and conserved quantities and can be modeled as system dynamic levels. Treating probabilities as levels in dynamic simulations is a standard technique in stochastic modeling, markov models being one example. System dynamics' advantage over these other methods is that it can represent the impact of the results of the probabilistic study of the social feedback systems. This paper focuses on examples demonstrating the use of system dynamics to model uncertain events. These examples deal with the simple case of a Poisson process with a time varying event arrival rate. Extensions incorporating conditional and independent probabilities are also considered.

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Date created

• 1983

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• Document

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• Processing documentation available at: https://wiki.albany.edu/display/SCA/Processing+Ingested+Digital+Files

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System Dynamic Society Records

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• This collection is part of the University Archives