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The FlowLogo interface allows interactively changing parameters using 'tuneable' dials, which can adjust agent decisions and policy levers during simulations.
Agent based models (ABMs) consist of a set of abstracted entities referred to as agents, and a framework using simplified rules for simulating agent decisions and interactions.
Objective: To evaluate antiplatelet prescribing patterns by Indiana University Hospital (IU) neurologists, determine what drives antiplatelet agent decisions, and determine changes made with recurrent cerebrovascular events despite proven antiplatelet therapy.
Although our model reflects agent decisions that occur in an academic institution, we acknowledge that this may differ between hospitals.
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The meso-scale model describes the way individual agent decision functions interact to produce collective behavior within social network communities.
This allows an evaluation of policies that accounts for agent decision-making and social and technological change.
Taken together, the meso- and macro-scale components of the proposed modeling framework quantify the way agent decision functions interact to produce collective behavior at the population level.
We focus on the use of evaluators – configurable, composable modules for data analysis, modeling, and prediction that are chained together at runtime to support agent decision-making.
We call ri the agent decision function and si = oTwi agent i's social signal, and interpret the wij as defining a weighted social network for the group of N agents.
The role of the meso-scale model is to quantify and illuminate the manner in which agent decision functions interact within social network communities, while the macro-scale model characterizes the interactions of agents between communities.
Although having smaller intervals facilitates the learning agent decision making, an increase in number of intervals increases the state space size and slows down the learning process convergence rate.
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