Two limitations of the traditional PRM are outlined and two compelling paradigm shifts are introduced. First, the traditional PRM assumes that risks are standalone, noninteracting events. This results in overlooking a substantial part of project risk exposure stemming from risk interactions, especially in complex projects. If project complexity is ignored, project schedule and cost contingencies yielded by the Monte Carlo methodology could be too low, resulting in project failures. The nonlinear Monte Carlo modeling technique, which counts risk interactions, is introduced in this chapter. This epitomizes shifting from linear (traditional) to nonlinear PRM in complex projects. Second, in the traditional PRM, the project alternative selection is based on subjective assumptions regarding the external project environment. This overlooks ongoing dynamics and deep uncertainties in the external environment. This assumes that the initially predicted demand for produced products will keep meeting stakeholders’ expectations until the project is decommissioned. What if these assumptions are wrong from the get-go? Or if the demand dwindles in operations due to dynamics in the external environment? The developed project might resemble a “bridge to nowhere” in both cases. Dynamic adaptive methodology (DAM) that handles deep uncertainties in the external environment is introduced and supported by a shift from the traditional predict-then-act to monitor-and-adapt decision-making. This represents the second paradigm shift. The power of systems dynamics supported by implications of game theory is called for to facilitate these two paradigm shifts. Two practical, although highly simplistic, modeling examples on nonlinear Monte Carlo and external environment modeling are provided to demonstrate the introduced paradigm shifts.