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We live and work in an increasingly complex and dynamic world. The demands of working in such environments require that negotiators understand situations of conflict and work with these situations in correspondingly complex and dynamic ways. Dynamical systems theory offers important insights and tools to enhance the understanding of difficult social conflicts, including the conceptualization of ongoing destructive conflicts as strong attractors: a particular form of self‐organization of multiple elements comprising the mental and social systems associated with conflict. This article describes the pedagogical use of a computer simulation of conflict attractors (the attractor software) that allows participants to visualize and work interactively with the dynamics of conflict as they unfold over time. It further describes a negotiation workshop that employs the simulation to enhance participants' understanding of complex long‐term dynamics in conflict and presents the findings of two outcome studiescomparing the effectiveness of a workshop that employed the simulation with one that employed a traditional integrative problem‐solving method. While not definitive, these studies suggest that an understanding of the dynamical approach to conflict, supported by use of the attractor software, can promote the generation of more sustainable solutions for long‐term conflicts.

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Traditional, static negotiation theories focus on descriptions of various external factors that influence the outcome of negotiations. They are useful in predicting the negotiation outcome in a limited way, because the result of the negotiation is ultimately determined not only by objective facts, but is worked out during the negotiation itself. We propose a Dynamical Negotiation Network (DNN) model that links the negotiation outcome with the process of attaining that outcome. This model represents the negotiation process in terms of a dynamically constructed network of interconnected nodes of meaning. The structure of the network and dynamics of its creation determine the outcome of a negotiation. In the presented study, we examined 58 participants who negotiated in dyads. There were many objective facts; those that were taken into consideration, the order they appeared in the negotiation, what structure of relationships they formed, helped determine a 'shared reality' that drove negotiation outcomes. We show that the DNN model explains the outcomes of their negotiations more precisely than do the static elements of the situation.