To obtain a pareto-optimal solution, the classical cooperative mpc implements a categorical altruism imposed by a fixed global cost shared by all the local controllers. instead, this thesis implements a situational altruism where a global cost, neither imposed nor fixed, emerges from convex local costs and local specifications. the satisficing controllers employ a distributed algorithm to find a solution that lies in a convex region that is satisfactory and sufficient for all controllers (satisficing = satisfy + suffice), while optimizing in the direction of the analytic center of such a region. the system is modeled as being a network of linear subsystems, coupled by their inputs, and the algorithm uses a distributed interior-point method to avoid fixed points when the constraints are also coupled. the optimal solution of the satisficing mpc, besides pareto-optimal, gives more importance to the controllers with a worst performance at the moment. situational altruism permits a more balanced division of resources, avoiding the exploitation of one controller by the others. the satisficing mpc is shown to be stabilizing even if suboptimal, provided that it is satisficing. to this end, stabilizing constraints are added to the basic formulation.