Fair Division with a Secretive Agent

We study classic fair-division problems in a partial information setting. This paper respectively addresses fair division of rent, cake, and indivisible goods among agents with cardinal preferences. We will show that, for all of these settings and under appropriate valuations, a fair (or an approximately fair) division among n agents can be efficiently computed using only the valuations of n-1 agents. The nth (secretive) agent can make an arbitrary selection after the division has been proposed and, irrespective of her choice, the computed division will admit an overall fair allocation. For the rent-division setting we prove that the (well-behaved) utilities of n-1 agents suffice to find a rent division among n rooms such that, for every possible room selection of the secretive agent, there exists an allocation (of the remaining n-1 rooms among the n-1 agents) which ensures overall envy freeness (fairness). We complement this existential result by developing a polynomial-time algorithm that finds such a fair rent division under quasilinear utilities. In this partial information setting, we also develop efficient algorithms to compute allocations that are envy-free up to one good (EF1) and epsilon-approximate envy free. These two notions of fairness are applicable in the context of indivisible goods and divisible goods (cake cutting), respectively. This work also addresses fairness in terms of proportionality and maximin shares. Our key result here is an efficient algorithm that, even with a secretive agent, finds a 1/19-approximate maximin fair allocation (of indivisible goods) under submodular valuations of the non-secretive agents. One of the main technical contributions of this paper is the development of novel connections between different fair-division paradigms, e.g., we use our existential results for envy-free rent-division to develop an efficient EF1 algorithm.Comment: 27 page

Housing Vouchers and the Price of Rental Housing

Abstract. We estimate the effect of increasing the supply of housing vouchers on rents using a panel of units in the American Housing Survey. We do not find that an increase in vouchers affected the overall price of rental housing, but do estimate differences in effects based on an individual unit’s rent before the voucher expansion. Our results are consistent with voucher recipients renting more expensive units after receiving the subsidy. We also find the largest positive price increases for units in relatively supply inelastic cities, suggesting policy makers should take local attributes into account with targeting future housing subsidies

The Division Problem under Constraints

The work of G. Bergantiños is partially supported by research grants ECO2008-03484-C02-01 and ECO2011-23460 from the Spanish Ministry of Science and Innovation and FEDER. J. Massó acknowledges financial support from the Spanish Ministry of Economy and Competitiveness, through the Severo Ochoa Programme for Centres of Excellence in R&D (SEV-2011-0075) and through grant ECO2008-0475-FEDER (Grupo Consolidado-C), and from the Generalitat de Catalunya, through the prize "ICREA Academia" for excellence in research and grant SGR2009-419. The work of A. Neme is partially supported by the Universidad Nacional de San Luis, through grant 319502, and by the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), through grant PIP 112-200801-00655.The division problem under constraints consists of allocating a given amount of an homogeneous and perfectly divisible good among a subset of agents with single-peaked preferences on an exogenously given interval of feasible allotments. We characterize axiomatically the family of extended uniform rules proposed to solve the division problem under constraints. Rules in this family extend the uniform rule used to solve the classical division problem without constraints. We show that the family of all extended uniform rules coincides with the set of rules satisfying efficiency, strategy-proofness, equal treatment of equals, bound monotonicity, consistency, and independence of irrelevant coalitions

Analyzing the effects of insuring health risks : on the trade-off between short run insurance benefits vs. long run incentive costs

This paper constructs a dynamic model of health insurance to evaluate the short- and long run effects of policies that prevent firms from conditioning wages on health conditions of their workers, and that prevent health insurance companies from charging individuals with adverse health conditions higher insurance premia. Our study is motivated by recent US legislation that has tightened regulations on wage discrimination against workers with poorer health status (Americans with Disability Act of 2009, ADA, and ADA Amendments Act of 2008, ADAAA) and that will prohibit health insurance companies from charging different premiums for workers of different health status starting in 2014 (Patient Protection and Affordable Care Act, PPACA). In the model, a trade-off arises between the static gains from better insurance against poor health induced by these policies and their adverse dynamic incentive effects on household efforts to lead a healthy life. Using household panel data from the PSID we estimate and calibrate the model and then use it to evaluate the static and dynamic consequences of no-wage discrimination and no-prior conditions laws for the evolution of the cross-sectional health and consumption distribution of a cohort of households, as well as ex-ante lifetime utility of a typical member of this cohort. In our quantitative analysis we find that although a combination of both policies is effective in providing full consumption insurance period by period, it is suboptimal to introduce both policies jointly since such policy innovation induces a more rapid deterioration of the cohort health distribution over time. This is due to the fact that combination of both laws severely undermines the incentives to lead healthier lives. The resulting negative effects on health outcomes in society more than offset the static gains from better consumption insurance so that expected discounted lifetime utility is lower under both policies, relative to only implementing wage nondiscrimination legislation

Cooperation, allocation and strategy in interactive decision-making

Game theory is the mathematical theory to analyze the behavior of rational decisionmakers in both cooperative and strategic interactive situations. It aims to resolve these situations by developing mathematical models and applying mathematical tools to provide insights in the interactive decision-making process. This dissertation studies the theoretical model of a transferable utility game with limited cooperation possibilities as well as altruistic equilibrium concepts for the model of a strategic game. Furthermore, this dissertation deals with several interactive allocation and operations research problems related to claims, sequencing and purchasing situations in which both cooperative and strategic approaches play a role

Fair allocation of indivisible goods under conflict constraints

We consider the fair allocation of indivisible items to several agents and add a graph theoretical perspective to this classical problem. Thereby we introduce an incompatibility relation between pairs of items described in terms of a conflict graph. Every subset of items assigned to one agent has to form an independent set in this graph. Thus, the allocation of items to the agents corresponds to a partial coloring of the conflict graph. Every agent has its own profit valuation for every item. Aiming at a fair allocation, our goal is the maximization of the lowest total profit of items allocated to any one of the agents. The resulting optimization problem contains, as special cases, both {\sc Partition} and {\sc Independent Set}. In our contribution we derive complexity and algorithmic results depending on the properties of the given graph. We can show that the problem is strongly NP-hard for bipartite graphs and their line graphs, and solvable in pseudo-polynomial time for the classes of chordal graphs, cocomparability graphs, biconvex bipartite graphs, and graphs of bounded treewidth. Each of the pseudo-polynomial algorithms can also be turned into a fully polynomial approximation scheme (FPTAS).Comment: A preliminary version containing some of the results presented here appeared in the proceedings of IWOCA 2020. Version 3 contains an appendix with a remark about biconvex bipartite graph

Truthful and Fair Resource Allocation

How should we divide a good or set of goods among a set of agents? There are various constraints that we can consider. We consider two particular constraints. The first is fairness - how can we find fair allocations? The second is truthfulness - what if we do not know agents valuations for the goods being allocated? What if these valuations need to be elicited, and agents will misreport their valuations if it is beneficial? Can we design procedures that elicit agents' true valuations while preserving the quality of the allocation? We consider truthful and fair resource allocation procedures through a computational lens. We first study fair division of a heterogeneous, divisible good, colloquially known as the cake cutting problem. We depart from the existing literature and assume that agents have restricted valuations that can be succinctly communicated. We consider the problems of welfare-maximization, expressiveness, and truthfulness in cake cutting under this model. In the second part of this dissertation we consider truthfulness in settings where payments can be used to incentivize agents to truthfully reveal their private information. A mechanism asks agents to report their private preference information and computes an allocation and payments based on these reports. The mechanism design problem is to find incentive compatible mechanisms which incentivize agents to truthfully reveal their private information and simultaneously compute allocations with desirable properties. The traditional approach to mechanism design specifies mechanisms by hand and proves that certain desirable properties are satisfied. This limits the design space to mechanisms that can be written down and analyzed. We take a computational approach, giving computational procedures that produce mechanisms with desirable properties. Our first contribution designs a procedure that modifies heuristic branch and bound search and makes it usable as the allocation algorithm in an incentive compatible mechanism. Our second contribution draws a novel connection between incentive compatible mechanisms and machine learning. We use this connection to learn payment rules to pair with provided allocation rules. Our payment rules are not exactly incentive compatibility, but they minimize a measure of how much agents can gain by misreporting.Engineering and Applied Science