Dataset Information

The Lazarillo's game: Sharing resources with asymmetric conditions.

ABSTRACT: The Lazarillo of Tormes' picaresque novel introduces a story where two subjects sequentially extract (one, two or three) tokens from a common pool in an asymmetric information framework (the first player cannot observe her partners' actions). By introducing a reward for both subjects in case that in every period at least one subject had taken one single token, we define an interesting coordination game. We conduct an experiment with 120 undergraduate students to study their behavior in this framework. We find that if the second player is allowed to take more tokens than her partner, then the frequency of cooperators does not seem to be affected by the informational asymmetry. Nevertheless, this asymmetry (i) incentives the second player to use her 'power of extraction' while the social externality is still available, (ii) yields to more asymmetric profit distributions when subjects win the social externality and (iii) delays the breach period in case of coordination failure. Furthermore, the first choice of the first player is determinant for getting the reward.

SUBMITTER: Lacomba JA

PROVIDER: S-EPMC5509147 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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The Lazarillo's game: Sharing resources with asymmetric conditions.

Lacomba Juan A JA Lagos Francisco F Perote Javier J

PloS one 20170713 7

The Lazarillo of Tormes' picaresque novel introduces a story where two subjects sequentially extract (one, two or three) tokens from a common pool in an asymmetric information framework (the first player cannot observe her partners' actions). By introducing a reward for both subjects in case that in every period at least one subject had taken one single token, we define an interesting coordination game. We conduct an experiment with 120 undergraduate students to study their behavior in this fram ...[more]

PMID: 28704408

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Project description:Electronic medical records (EMRs) are increasingly repurposed for activities beyond clinical care, such as to support translational research and public policy analysis. To mitigate privacy risks, healthcare organizations (HCOs) aim to remove potentially identifying patient information. A substantial quantity of EMR data is in natural language form and there are concerns that automated tools for detecting identifiers are imperfect and leak information that can be exploited by ill-intentioned data recipients. Thus, HCOs have been encouraged to invest as much effort as possible to find and detect potential identifiers, but such a strategy assumes the recipients are sufficiently incentivized and capable of exploiting leaked identifiers. In practice, such an assumption may not hold true and HCOs may overinvest in de-identification technology. The goal of this study is to design a natural language de-identification framework, rooted in game theory, which enables an HCO to optimize their investments given the expected capabilities of an adversarial recipient.We introduce a Stackelberg game to balance risk and utility in natural language de-identification. This game represents a cost-benefit model that enables an HCO with a fixed budget to minimize their investment in the de-identification process. We evaluate this model by assessing the overall payoff to the HCO and the adversary using 2100 clinical notes from Vanderbilt University Medical Center. We simulate several policy alternatives using a range of parameters, including the cost of training a de-identification model and the loss in data utility due to the removal of terms that are not identifiers. In addition, we compare policy options where, when an attacker is fined for misuse, a monetary penalty is paid to the publishing HCO as opposed to a third party (e.g., a federal regulator).Our results show that when an HCO is forced to exhaust a limited budget (set to $2000 in the study), the precision and recall of the de-identification of the HCO are 0.86 and 0.8, respectively. A game-based approach enables a more refined cost-benefit tradeoff, improving both privacy and utility for the HCO. For example, our investigation shows that it is possible for an HCO to release the data without spending all their budget on de-identification and still deter the attacker, with a precision of 0.77 and a recall of 0.61 for the de-identification. There also exist scenarios in which the model indicates an HCO should not release any data because the risk is too great. In addition, we find that the practice of paying fines back to a HCO (an artifact of suing for breach of contract), as opposed to a third party such as a federal regulator, can induce an elevated level of data sharing risk, where the HCO is incentivized to bait the attacker to elicit compensation.A game theoretic framework can be applied in leading HCO's to optimized decision making in natural language de-identification investments before sharing EMR data.

Dataset Information

The Lazarillo's game: Sharing resources with asymmetric conditions.

Publications

The Lazarillo's game: Sharing resources with asymmetric conditions.

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