John Hooker
Professor | Carnegie Mellon University
Pittsburgh, PA, UNITED STATES
John Hooker is a pioneer in the integration of optimization and constraint programming technologies.
Biography
John Hooker is University Professor of Operations Research and T. Jerome Holleran Professor of Business Ethics and Social Responsibility at Carnegie Mellon University. He has also held several visiting posts, most recently...
Areas of Expertise (5)
Operations Research
Constraint Programming
Ethics
Cross-Cultural Management
Music Theory
Media Appearances (3)
Mass. bill allows inmates to swap organs for less prison time. Ethics experts say it's exploitative.
Yahoo! News online
2023-02-03
“I don’t see an ethical justification for the proposed Massachusetts law,” John Hooker, an ethics professor at Carnegie Mellon University, told Yahoo News. “If it is OK to release prisoners early due to organ donation, they should be released early without the donation.”
Hebert, Hooker and Kraut Named University Professors
Carnegie Mellon University News online
2022-04-11
Three Carnegie Mellon University faculty members have been elevated to the rank of University Professor(opens in new window), the highest distinction a faculty member can achieve at CMU. The newly appointed University Professors are Martial Hebert, John Hooker and Robert E. Kraut.
It Is Perfectly Moral To Torture A Robot — But We Should Never Do It
Business Insider online
2014-08-07
As Carnegie Mellon ethicist John Hooker once told our Robots Ethics class, while in theory there is not a moral negative to hurting a robot, if we regard that robot as a social entity, causing it damage reflects poorly on us. This is not dissimilar from discouraging young children from hurting ants, as we do not want such play behaviors to develop into biting other children at school.
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Industry Expertise (2)
Business Services
Management Consulting
Accomplishments (5)
Computers and Chemical Engineering Journal - Best Paper Award, Computers and Chemical Engineering (professional)
2015
Tepper School - Sustained Teaching Excellence Award (professional)
2009
Association for Constraint Programming - Program Chair, Constraint Programming Conference (professional)
2017
Tepper School of Business - Gerald L. Thompson Excellence in the Classroom Award (professional)
2016
Association for Constraint Programming - Best Paper Award, Constraint Programming Conference (professional)
2015
Education (2)
Vanderbilt University: Ph.D., Philosophy
University of Tennessee: Ph.D., Operations Research
Affiliations (1)
- Institute for Operations Research and the Management Sciences (INFORMS) : Fellow
Links (5)
Articles (5)
A guide to formulating fairness in an optimization model
Annals of Operations Research2023 Optimization models typically seek to maximize overall benefit or minimize total cost. Yet fairness is an important element of many practical decisions, and it is much less obvious how to express it mathematically. We provide a critical survey of various schemes that have been proposed for formulating ethics-related criteria, including those that integrate efficiency and fairness concerns. The survey covers inequality measures, Rawlsian maximin and leximax criteria, convex combinations of fairness and efficiency, alpha fairness and proportional fairness (also known as the Nash bargaining solution), Kalai–Smorodinsky bargaining, and recently proposed utility-threshold and fairness-threshold schemes for combining utilitarian with maximin or leximax criteria. The paper also examines group parity metrics that are popular in machine learning.
Achieving consistency with cutting planes
Mathematical Programming2023 The primary role of cutting planes is to separate fractional solutions of the linear programming relaxation, which results in tighter bounds for pruning the search tree and reducing its size. Bounding, however, has an indirect impact on the size of the search tree. Cutting planes can also reduce backtracking by excluding inconsistent partial assignments that occur in the course of branching, which directly reduces the tree size. A partial assignment is inconsistent with a constraint set when it cannot be extended to a full feasible assignment. The constraint programming community has studied consistency extensively and used it as an effective tool for the reduction of backtracking. We extend this approach to integer programming by defining concepts of consistency that are useful in a branch-and-bound context.
Combining leximax fairness and efficiency in a mathematical programming model
European Journal of Operational Research2022 A trade-off between fairness and efficiency is an important element of many practical decisions. We propose a principled and practical method for balancing these two criteria in an optimization model. Following an assessment of existing schemes, we define a set of social welfare functions (SWFs) that combine Rawlsian leximax fairness and utilitarianism and overcome some of the weaknesses of previous approaches. In particular, we regulate the equity/efficiency trade-off with a single parameter that has a meaningful interpretation in practical contexts. We formulate the SWFs using mixed integer constraints and sequentially maximize them subject to constraints that define the problem at hand.
Stochastic planning and scheduling with logic-based Benders decomposition
INFORMS Journal on Computing2022 We apply logic-based Benders decomposition (LBBD) to two-stage stochastic planning and scheduling problems in which the second stage is a scheduling task. We solve the master problem with mixed integer/linear programming and the subproblem with constraint programming. As Benders cuts, we use simple no-good cuts as well as analytic logic-based cuts we develop for this application. We find that LBBD is computationally superior to the integer L-shaped method. In particular, a branch-and-check variant of LBBD can be faster by several orders of magnitude, allowing significantly larger instances to be solved. This is due primarily to computational overhead incurred by the integer L-shaped method while generating classic Benders cuts from a continuous relaxation of an integer programming subproblem.
Operations Research Perspectives
Operations Research2022 Geographical considerations such as contiguity and compactness are necessary elements of political districting in practice. Yet an analysis of the problem without such constraints yields mathematical insights that can inform real-world model construction. In particular, it clarifies the sharp contrast between proportionality and competitiveness and how it might be overcome in a properly formulated objective function. It also reveals serious weaknesses of the much-discussed efficiency gap as a criterion for gerrymandering.
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