Division of Information Technology, Engineering and the Environment
ISST - Transport Systems
ISST - Transport Systems
The terms impact and growth are somewhat interchangeable, with impact tending towards the immediate sense of a single period, and growth being more of a cumulative value, e.g. over several periods of a simulation or whatever. Also growth doesn’t necessarily imply increase, it can be negative.
The term Cross Impact is usually used in the sense of the factor in question as being dependent upon another factor, and the cross impact being the effect of the latter upon the former.
A model is a model, a graph is a graph, and a population is the set of genes, and their associated parameters being employed in a Genetic Search, which is what the whole exercise is for. Structure of a Model What is a model? The foundation of the model is the factors represented by the Model Window (every thing not evident here will be explained below).
A factor is a name, some values, some connections and a set of attributes. The primary value of a factor is its self impact, the rate at which the factor will grow each simulation period, if unrestrained/encouraged. Another value is its start value. This is the initial value assigned in the first period of the simulation. A more important value, which only a few factors will use, is the target value. This is the desired result for the factor at the end of the simulation. This forms the objective function for the search procedure. A factor has the following set of attributes:
Controllable: Is the factor part of the solution for the search procedure? Delayed Impact: If the factor is active only for a subset of the simulation time. Bounds: Limits on the total growth of a factor. Thresholds: The impact values which can influence a factor. Impacts less than the lower threshold have no effect, and impacts greater than the upper threshold are limited. Mainly used for cross impacts System Shocks: Exceptional growths for specific periods.
Graphs: If the factors growth during the simulation is graphed. Cross Impacts: The proportion of another factors growth in a period which caused a growth in the specified factor. Delayed Cross Impacts: When the growth of a factor for a period is carried over to the next period. This is mainly useful if a factor has a zero self impact, but benefits from cross impacts form other factors. This enables other dependent factors to in turn benefit from the cross growths in this factor… understand?
The model, apart from being the sum of the factors, contains a few bits of other information, mostly to do with default values for new factors, but also some global flags. Specifically, bounds, thresholds and system shocks can be turned off, and also cross impacts can be forced to delayed or immediate if desired.
A Simulation: A simulation is the self impacts and cross impacts being exercised over a specified number of periods. Given the nature of cross impacts, recursive cross impacts and the discrete nature of simulation time in particular, it wouldn’t be a good idea to get too picky over whether a particular event occurs in, say, period 9 or period 10. Some things get delayed for a period, but over a number of periods, it all works out. The simulation save the cumulative growths for each factor for each period, which can later be graphed or saved for further analysis. Old simulation results which have been saved cannot be used again by Genie. Graphs Factors which have the graph attribute set are graphed at the end of each simulation run. The graphs are displayed in a reduced form in the Graph Browser window, and can be viewed at full size if desired. The graphs are useful for setting target values for searching, and also for comparing growths.
Populations: A population is a collection of genes, and information as to how to manipulate them. Some of this information is local to each gene, or even to the chromosomes within the genes, and some is universal to the population. The details are discussed below in ‘Population Windows’. Populations are attached to and saved with a model. A population cannot migrate across models. Many populations can be attached to one model.
Defaults And Limits Model Maximum Factors: 100 Factor Name Length: 20 Default Start Value: 1.0 Maximum System Shocks: 12 (per factor) Maximum VariGrowth Points: 12 (incl. ends) Maximum Simulation Periods: 100 Default Periods: 20 Maximum Graphs: 50
Populations Maximum Population Size: 100 Default Size: 100 Minimum Population Size: 10 Maximum Genes: 10 Maximum Resolution: 30 Default Resolution: 8 Self Impact Range: -1.0 .. 1.0 Maximum Targets: 10 Maximum Generations: 500 Default Generations: 50
