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In policy networks actors use access relations to influence preferences of other actors. Establishment and shifts of access relations and their consequences for outcomes of decisions are the main focal points In this paper. Unlike most policy network studies, we therefore do not take the network and its relations as given and constant. Instead we device computer simulation models to account for the dynamics in policy networks. We compare different models and investigate the resulting network structures and predicted outcomes of decisions. The choice among the alternative models is made by their correspondence with empirical network structures and actual outcomes of decisions. In our models, we assume that all relevant actors aim at policy outcomes as close as possible to their own preferences. Policy outcomes are determined by the preferences of the final decision makers at the moment of the vote. In general, only a small fraction of the actors takes part in the final vote. Most actors have therefore to rely on access relations for directly or indirectly shaping the preferences of the final decision makers. For this purpose actors make access requests to other actors. An access relation is assumed to be established if such a request is accepted by the other actor. Access relations require time and resources. Actors are therefore assumed to be restricted in the number of access requests they can make and the number of requests they can accept. Moreover, due to incomplete information and simultaneous actions by other actors, actors have to make simplifying assumptions in the selection of their "best" requests and learn by experience. We device two base models that correspond to two basic views on the nature of political processes. In the first view politics is seen as power driven. Corresponding to this view, actors aim at access relations with the most powerful actors in the field. They estimate their likelihood of success by comparing their own resources with those of the target actors. Power also determines the order in which actors accept requests. In the second view, policy matters and actors roughly estimate the effects access relations might have on the outcome of decisions. Actors select requests to "bolster" their own preference as much as possible. We will show that these base models and some intermediate ones result in fundamentally different network structures and predicted outcomes. Moreover, we will show that the policy driven models do fundamentally better than the power driven models. [ABSTRACT FROM AUTHOR]

In political systems and large organizations, ultimate decision makers are usually just a small subset of all actors in the social system, To arrive at acceptable decisions, decision makers have to take into account the preferences of other actors in the system. Typically preferences of more interested and more powerful actors are weighted heavier than those of less interested and powerful actors. This implies that the total average of an actor on the decision is determined by the combination of his power (his potential) and his interest (his willingness to mobilize his power). As the exact level of an actor's leverage is difficult to estimate for the other actors in the system, an actor is able to optimize his effects on outcomes of decisions by providing strategic information. In this papers, first an analytic solution is presented for the optimization of strategic leverage in collective decision making by one single actor. In this solution, the actor makes assumptions about the leverage other actors will show in decision making. Subsequently, the actor optimizes the outcomes of decisions by manipulating the distribution of his leverage over a set of issues. The analytic solution can be theoretically interpreted by decomposing the solution into three terms, the expected external leverage of the other actors on the issue, the evaluation of the deviance of the expected from the preferred outcome of the issue, and the restrictions on the distribution of leverage over the issues. The higher the expectation of the leverages the other actors will allocate to the issue, the less an actor is inclined to allocate leverage to the issue. The higher the evaluation of the deviance, the more an actor ts inclined to allocate leverage to the issue. This is restricted, however. by the required distribution of leverages over the issues. The researcher is able to manipulate these restrictions to investigate its consequences for the outcomes. In the next step, we investigate whether we can Find a Nash equilibrium if all actors optimize their leverage simultaneously. Under certain conditions, a Nash equilibrium can be found by an iterative process in which actors update their estimates on each other's leverages on the basis of what the other actors have shown in previous iterations. Application of the model to artificial data shows chat actors with strong preferences in the center have more possibilities to realize good outcomes than other actors. On the basis of an empirical application it is shown that a Nash equilibrium does not always arise after a large number of iterations unless actors have learning capabilities or arc severely restricted in their strategic behavior. [ABSTRACT FROM AUTHOR]