Randomized Wagering Mechanisms
Yiling Chen, Yang Liu, Juntao Wang

TL;DR
This paper introduces randomized wagering mechanisms that achieve all desirable properties, including Pareto optimality, overcoming previous impossibility results for deterministic mechanisms.
Contribution
It expands the design space of wagering mechanisms by proposing two classes of randomized mechanisms that satisfy all key theoretical properties.
Findings
Randomized mechanisms can achieve Pareto optimality and incentive compatibility.
Two classes of mechanisms: lottery-type and surrogate wagering mechanisms.
Mechanisms are robust to noisy ground truth.
Abstract
Wagering mechanisms are one-shot betting mechanisms that elicit agents' predictions of an event. For deterministic wagering mechanisms, an existing impossibility result has shown incompatibility of some desirable theoretical properties. In particular, Pareto optimality (no profitable side bet before allocation) can not be achieved together with weak incentive compatibility, weak budget balance and individual rationality. In this paper, we expand the design space of wagering mechanisms to allow randomization and ask whether there are randomized wagering mechanisms that can achieve all previously considered desirable properties, including Pareto optimality. We answer this question positively with two classes of randomized wagering mechanisms: i) one simple randomized lottery-type implementation of existing deterministic wagering mechanisms, and ii) another family of simple and randomized…
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Taxonomy
TopicsMachine Learning and Data Classification · Sports Analytics and Performance · Imbalanced Data Classification Techniques
