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The Trend component represents forecasting in behavioral models and has been proposed by Sterman [3, pp. 634 - 638]. The basic idea is rather intuitive: Forecasters perceive a present condition of some input as a smoothed input (ppc), while they also perceive the historic or reference condition (rc) as a smooth of their current perception.
Dividing the diffence between the current condition and the reference condition by the reference condition will determine the fractional increase. A further division by the time horizon for the reference condition will then turn this into a fractional rate. The thus calculated rate of change (i.e., the indicated trend) may then again be smoothed, if smoothTrend = true.