Weak monotonicity and Bayes-Nash incentive compatibility

An allocation rule is called Bayes-Nash incentive compatible, if there exists a payment rule, such that truthful reports of agents’ types form a Bayes-Nash equilibrium in the directrevelation mechanism consisting of the allocation rule and the payment rule. This paperprovides characterizations of Bayes-Nash incentive compatible allocation rules in socialchoice settings where agents have one-dimensional or multi-dimensional types, quasi-linearutility functions and interdependent valuations. The characterizations are derived byconstructing complete directed graphs on agents’ type spaces with cost of manipulationas lengths of edges. Weak monotonicity of the allocation rule corresponds to the conditionthat all 2-cycles in these graphs have non-negative length.For one-dimensional types and agents’ valuation functions satisfying non-decreasingexpected differences, we show that weak monotonicity of the allocation rule is a necessaryand sufficient condition for the rule to be Bayes-Nash incentive compatibile. In the casewhere types are multi-dimensional and the valuation for each outcome is a linear functionin the agent’s type, we show that weak monotonicity of the allocation rule together withan integrability condition is a necessary and sufficient condition for Bayes-Nash incentivecompatibility.

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