In this example in Fig. 1, from Hannon & Ruth, presents a depiction of the scenario where an individual, at some level of intoxication, attempts to walk from an initial position toward a destination which is 100 feet away.
- cumulative net - The cumulative net result of flipping the coin
- Initial Value = 0
- net - represents heads inflow and tails outflow from cumulative net
- Flow Rate = [head rate] - [tail rate])
- head rate
- Value/Equation = IfThenElse([flipper] < .5, 1, 0)
- tail rate
- Value/Equation = IfThenElse([flipper] > .5, 1, 0)
- Show Slider Value = No
- flipper - generates a random number between 0 and 1 normally distributed
- Value/Equation = Rand(0,1)
- Time Settings
- Simulation Start = 0
- Simulation Length = 1000
- Simulation Time Step = 1
- Time Units = Seconds
Every time you run this model you will get a cumulative representation of the sum of the flips, +1 for heads, -1 for tails. Every run will be different, as evident in Fig. 2 and Fig. 3, even though the cumulative value for a sufficiently large number of flips should be 0.
Run this model several times to convince yourself that you will never get the same result though the result is most likely to vary across the zero value. You might also try running the model for 10,000 or 100,000 flips to examine the difference in results.
- ↑ Hannon, Bruce & Ruth, Matthias (1994) Dynamic Modeling. Springer-Verlag
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Systems Thinking World Q&A * Gene Bellinger