Interaction Value Interaction Value Analysis (I.V.A.) models a network of rational actors who generate value by interacting with each other. This model can be used to understand human organizations. Since people form organizations to facilitate interactions between productive individuals, the value added by interaction is the contribution of the organization. This paper examines the result of varying the queuing discipline used in selecting among back-logged interaction requests. Previously developed I.V.A. models assumed a First-infirst-out (FIFO) discipline, but using other disciplines can better represent the "Climate" of an organization.I.V.A. identifies circumstances under which organizations that control members' interaction choices outperform organizations where individuals choose their own interaction partners. Management can be said to "matter" when individual choices converge to a point where interactions generate a lower than optimal value. In previous I.V.A. models, relinquishing central control of interaction choices reduced the aggregate value by anything from 0% to 12%, depending on circumstances. This paper finds the difference between the two modes of organization to go as high as 47% if actors display preferences between interaction partners instead of treating all equally. A politically divided, dog-eat-dog, "Capitalist" climate follows one queuing discipline, which is found to generally increase the value that a strong control structure can add. A chummy, in-bred "Fraternal" climate gains from control in some circumstances (low interdependence or low differentiation), but not in others (high or medium interdependence and differentiation under low diversity, for example). These are compared to the previous version of I.V.A., in which the queuing discipline was FIFO and the climate deemed "Disciplined". Previously published findings on Organizational Climate are duplicated and extended with a higher level of detail. Priority queuing in an I.V.A. model is thus a useful proxy for Organizational Climate, open to future validation because its detailed predictions can be confirmed or falsified by observation.