  Information-theoretic bounded rationality describes utility-optimizing
decision-makers whose limited information-processing capabilities are
formalized by information constraints. One of the consequences of bounded
rationality is that resource-limited decision-makers can join together to solve
decision-making problems that are beyond the capabilities of each individual.
Here, we study an information-theoretic principle that drives division of labor
and specialization when decision-makers with information constraints are joined
together. We devise an on-line learning rule of this principle that learns a
partitioning of the problem space such that it can be solved by specialized
linear policies. We demonstrate the approach for decision-making problems whose
complexity exceeds the capabilities of individual decision-makers, but can be
solved by combining the decision-makers optimally. The strength of the model is
that it is abstract and principled, yet has direct applications in
classification, regression, reinforcement learning and adaptive control.
