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The game-theoretic risk management framework put forth in the precursor work"Towards a Theory of Games with Payoffs that are Probability-Distributions"(arXiv:1506.07368 [q-fin.EC]) is herein extended by algorithmic details on howto compute equilibria in games where the payoffs are probability distributions.Our approach is "data driven" in the sense that we assume empirical data(measurements, simulation, etc.) to be available that can be compiled intodistribution models, which are suitable for efficient decisions aboutpreferences, and setting up and solving games using these as payoffs. Whilepreferences among distributions turn out to be quite simple if nonparametricmethods (kernel density estimates) are used, computing Nash-equilibria in gamesusing such models is discovered as inefficient (if not impossible). In fact, wegive a counterexample in which fictitious play fails to converge for the(specifically unfortunate) choice of payoff distributions in the game, andintroduce a suitable tail approximation of the payoff densities to tackle theissue. The overall procedure is essentially a modified version of fictitiousplay, and is herein described for standard and multicriteria games, toiteratively deliver an (approximate) Nash-equilibrium. An exact method usinglinear programming is also given.

On Game-Theoretic Risk Management (Part Two) -- Algorithms to Compute Nash-Equilibria in Games with Distributions as Payoffs