Parole Board Decision-Making using Adversarial Risk Analysis

Joshi, Chaitanya; Nel, Charné; Cano, Javier; Polaschek, Devon L.L.

doi:10.1080/00031305.2024.2303416

The American Statistician

2024

DOI: 10.1080/00031305.2024.2303416

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Parole Board Decision-Making using Adversarial Risk Analysis

Chaitanya Joshi,

Charné Nel,

Javier Cano

et al.

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2024

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Cited by 2 publications

References 40 publications

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Personalized pricing decisions through adversarial risk analysis

Rasines,

Naveiro,

Insua

et al. 2024

Int Trans Operational Res

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Pricing decisions stand out as one of the most critical tasks a company faces, particularly in today's digital economy. As with other business decision‐making problems, pricing unfolds in a highly competitive and uncertain environment. Traditional analyses in this area have heavily relied on game theory and its variants. However, an important drawback of these approaches is their reliance on common knowledge assumptions, which are hardly tenable in competitive business domains. This paper introduces an innovative personalized pricing framework designed to assist decision‐makers in undertaking pricing decisions amidst competition, considering both buyer's and competitors' preferences. Our approach (i) establishes a coherent framework for modeling competition mitigating common knowledge assumptions; (ii) proposes a principled method to forecast competitors' pricing and customers' purchasing decisions, acknowledging major business uncertainties; and (iii) encourages structured thinking about the competitors' problems, thus enriching the solution process. To illustrate these properties, in addition to a general pricing template, we outline two specifications—one from the retail domain and a more intricate one from the pension fund domain.

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Personalized pricing decisions through adversarial risk analysis

Rasines,

Naveiro,

Insua

et al. 2024

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

On global robustness of an adversarial risk analysis solution

Yang,

Joshi,

Ruggeri

2024

Statistica Neerlandica

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Adversarial Risk Analysis (ARA) can be a more realistic and practical alternative to traditional game theoretic or decision theoretic approaches for modeling strategic decision‐making in the presence of an adversary. ARA relies on quantifying the decision‐maker's (DM's) uncertainties about the adversary's strategic thinking, choices and utilities via probability distributions to identify the optimal solution for the DM. ARA solution will be sensitive to the choices of prior distributions used for modelling the expert beliefs. Yet, to date, no mathematical results to characterize the robustness of the ARA solution to the misspecification of one or more prior distributions exist. Prior elicitation is known to be challenging. We present the very first mathematical results on the global robustness of the ARA solution. We use the distorted band class of priors and establish the conditions under which an ordering on the ARA solution can be established when modelling the first‐price sealed‐bid auctions using the nonstrategic play and level‐ thinking solution concepts. We illustrate these results using numerical examples and discuss further areas of research.

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Parole Board Decision-Making using Adversarial Risk Analysis

Cited by 2 publications

References 40 publications

Personalized pricing decisions through adversarial risk analysis

Personalized pricing decisions through adversarial risk analysis

On global robustness of an adversarial risk analysis solution

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