Algorithmics, Social choice, Market Design, and Other Topics

On Wednesday  21 April  at  12pm AEST  (Sydney/Melbourne time), Haris Aziz ( University of New South Wales ) will present  Efficient, Fair, and Incentive-Compatible Healthcare Rationing .  Information about forthcoming seminars is available at  our website .  Haris Aziz  (University of New South Wales):  Efficient, Fair, and Incentive-Compatible Healthcare Rationing   (joint with Florian Brandl) . Abstract :  In this talk, I will report on ongoing work on healthcare rationing algorithms. Fair and efficient rationing of healthcare resources has emerged as an important issue that has been discussed by medical experts, policy-makers, and the general public. We consider a healthcare rationing problem where medical units are to be allocated to patients. Each unit is reserved for one of several categories and the categories may have different priorities for the patients. We present flexible allocation rules that respect the p...

Below, we share videos of our three papers accepted at AAAI-21.  354: Achieving Envy-Freeness and Equitability with Monetary Transfers Haris Aziz https://slideslive.com/3894777 6 2210: Proportionally Representative Participatory Budgeting with Ordinal Preferences Haris Aziz, Barton Lee https://slideslive.com/3894809 8 3857: Optimal Kidney Exchange with Immunosuppressants Haris Aziz, Agnes Cseh, John P Dickerson, Duncan C McElfresh     https://slideslive.com/3894838 4

 Our new working paper on "Efficient, Fair, and Incentive-Compatible Healthcare Rationing" is now on arXiv.  Title : Efficient, Fair, and Incentive-Compatible Healthcare Rationing Authors : Haris Aziz and Florian Brandl Abstract:   As the COVID-19 pandemic shows no clear signs of subsiding, fair and efficient rationing of healthcare resources has emerged as an important issue that has been discussed by medical experts, policy-makers, and the general public. We consider a healthcare rationing problem where medical units are to be allocated to patients. Each unit is reserved for one of several categories and the patients have different priorities for the categories. We present an allocation rule that respects the priorities, complies with the eligibility requirements, allocates the largest feasible number of units, and does not incentivize agents to hide that they qualify through a category. Moreover, the rule is polynomial-time computable. To the best of our knowledge...

Transparent, equitable, and effective prioritisation of healthcare resources is becoming a big issue in the backdrop of COVID-19. It is becoming clearer that transparent algorithms with fairness guarantees are needed. Here are some NYT articles on the topic.  https://www.nytimes.com/2021/02/07/technology/vaccine-algorithms.html https://www.nytimes.com/2021/01/13/world/covid-vaccine-eligibility.html https://www.nytimes.com/2020/07/23/health/coronavirus-vaccine-allocation.html?fbclid=IwAR0ge9xQG9BrWT4DIl6qxUXCvL_uFr2x7yH_DlbHDKPcmwiHt3eptw58lx4

I'm sharing our latest working paper co-written with Anton Baychkov and Peter Biro.  H. Aziz, A. Baychkov, and P. Biro. Cutoff stability under distributional constraints with an application to summer internship matching. Working Paper 2021. [ PDF Link ] The paper proposes a natural stability concept called cutoff stability that is especially applicable to matching under feasibility constraints.  We show that a cutoff stable matching exists under a wide variety of feasibility constraints. 

The 2020 AI 10 to Watch list has been announced. Special congrats to John, Nisarg, and Fei who all work at the intersection of game theory and AI!   This year's 10 to Watch: Tathagata Chakraborti  works on human-AI collaboration and explainable AI. His main invention was to formulate the explanation process of AI systems as one of reconciliation of the mental model of the human with that of the agent model. He is considered to be one of the leading experts in human-aware planning and interpretability of agent behavior. John Dickerson  is working where computer science, economics, and operations research meet. He has developed scalable batch clearing algorithms that were the basis for the kidney exchange plan in the U.S.: a perfect combination of theoretical AI with a life-saving real world application. Fei Fang  has combined machine learning with game theory to address the important problems faced by our society. As an example, in the problem of antipoaching, poacher...

  Pathways Between Social Science and Computational Social Science Theories, Methods, and Interpretations Editors: Rudas, Tamás, Péli, Gábor (Eds.) This volume shows that the emergence of computational social science (CSS) is an endogenous response to problems from within the social sciences and not exogeneous. The three parts of the volume address various pathways along which CSS has been developing from and interacting with existing research frameworks. The first part exemplifies how new theoretical models and approaches on which CSS research is based arise from theories of social science. The second part is about methodological advances facilitated by CSS-related techniques. The third part illustrates the contribution of CSS to traditional social science topics, further attesting to the embedded nature of CSS. The expected readership of the volume includes researchers with a traditional social science background who wish to approach CSS, experts in CSS looking for substantive li...