In this paper we study an experiment with human agents strategically interacting in a game characterized by continuous time and continuous strategy space. The research is focused in studying the agents interaction dynamic under different experimental settings. The agents play a two person game that is an extension of the classic Cournot duopoly. Having agents making decision continuously allows us to track the temporal structure of strategy evolution very precisely. We can follow the agents continuous behavior evolution avoiding the data under-sampling. To our knowledge this is the first attempt to approach experimentally the continuous time decision making. We also emphasize that the focus of our work is not the Cournot model but rather the more general problem of studying the agents strategic interaction dynamic in continuous space time. Flaming the problem as the well studied Cournot Duopoly would be a good starting point. In economics dynamics studies the oligopoly model literature in both discrete and continuous time is one of the richest. There is also a vast literature in experimental economics about repeated games in general and more specifically in duopoly/oligopoly models. Cox and Walker studied whether subjects can learn to play the Cournot Duopoly strategies comparing the experimental results with the theoretical prediction of learning models. The Cox Walker experiment differs from our settings because it is in discrete time and is an evolutionary dynamics framework through a random matching mechanism of the experimental subjects. From the theoretical perspective many works have been focused in studying the Cournot model in a dynamical settings. Okuguchi and Szidarovsky formulated a continuous time version of the Cournot Oligopoly with multiproduct firms. They analyzed the stability of the equilibrium and proved that it is stable, under certain conditions, independently from the value of the adjustments. Chiarella and Khomin extended this analysis to unstable dynamics in Cournot duopoly. They used analytical and numerical tools to study the relevance of time lags and nonlinearities in relation with the convergence of quantities and prices to some stable attractors. Lately, Chiarella and Szidarovsky analyzed the case with continuously distributed time lags and without full information: firms experiences time lag in obtaining and implementing information in the price and the outputs. They showed that without time-lag a steady state is always asymptotically stable. Vice versa in presence of time lags local instability can occur. Other studies, like in Kopel, discussed the effect on non-monotonic reaction curves on the system dynamical properties. We can have non monotonic reaction function if, for example, quantity demanded is reciprocal to price and firms are facing constant unit costs or vice versa with linear demand function and cubic marginal costs function. The non-monotonic reaction function assumption causes chaotic dynamic in pricing.