Action-outcome probabilities for rational choice can be grounded in causal models both when the causal structure is known and when it is unknown, with an extension to causal Nash Equilibrium.
Causal Structure Learning: a Bayesian approach based on random graphs
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abstract
A Random Graph is a random object which take its values in the space of graphs. We take advantage of the expressibility of graphs in order to model the uncertainty about the existence of causal relationships within a given set of variables. We adopt a Bayesian point of view in order to capture a causal structure via interaction and learning with a causal environment. We test our method over two different scenarios, and the experiments mainly confirm that our technique can learn a causal structure. Furthermore, the experiments and results presented for the first test scenario demonstrate the usefulness of our method to learn a causal structure as well as the optimal action. On the other hand the second experiment, shows that our proposal manages to learn the underlying causal structure of several tasks with different sizes and different causal structures.
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cs.AI 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models
Action-outcome probabilities for rational choice can be grounded in causal models both when the causal structure is known and when it is unknown, with an extension to causal Nash Equilibrium.