Probabilistic Serial Mechanism Research Articles

Bounded incentives in manipulating the probabilistic serial rule

The Probabilistic Serial mechanism is valued for its fairness and efficiency in addressing the random assignment problem. However, it lacks truthfulness, meaning it works well only when agents' stated preferences match their true ones. Significant utility gains from strategic actions may lead self-interested agents to manipulate the mechanism, undermining its practical adoption. To gauge the potential for manipulation, we explore an extreme scenario where a manipulator has complete knowledge of other agents' reports and unlimited computational resources to find their best strategy. We establish tight incentive ratio bounds of the mechanism. Furthermore, we complement these worst-case guarantees by conducting experiments to assess an agent's average utility gain through manipulation. The findings reveal that the incentive for manipulation is very small. These results offer insights into the mechanism's resilience against strategic manipulation, moving beyond the recognition of its lack of incentive compatibility.

Simultaneous eating algorithm and greedy algorithm in assignment problems

The simultaneous eating algorithm (SEA) and probabilistic serial (PS) mechanism are well known for allocating a set of divisible or indivisible goods to agents with ordinal preferences. The PS mechanism is SEA at the same eating speed. The prominent property of SEA is ordinal efficiency. Recently, we extended the PS mechanism (EPS) from a fixed quota of each good to a variable varying in a polytope constrained by submodular functions. In this article, we further generalized some results on SEA. After formalizing the extended ESA (ESEA), we show that it can be characterized by ordinal efficiency. We established a stronger summation optimization than the Pareto type of ordinal efficiency by an introduced weight vector. The weights in the summation optimization coincide with agents’ preferences at the acyclic positive values of an allocation. Hence, the order of goods selected to eat in ESEA is exactly the one chosen in the execution of the well-known greedy algorithm.

Axiomatic characterizations of the constrained probabilistic serial mechanism

Afacan (Games and Economic Behavior 110: 71-89, 2018) introduces an object allocation with random priorities problem. He proposes the constrained probabilistic serial (CPS) mechanism. This study, for the first time in the literature, provides axiomatic characterizations of CPS. The first result characterizes it via non-wastefulness, claimwise stability, constrained ordinal fairness, and surplus invariance to truncations. The other axiomatizes CPS via constrained stochastic efficiency, claimwise stability, and constrained ordinal fairness. The independence of the axioms is provided.

A Simple Characterization of Assignment Mechanisms on Set Constraints

We consider the problem of allocating divisible/indivisible goods to agents according to agents’ ordinal preferences. Hashimoto et al. [15] provided a nonalgorithmic and axiomatic characterization of well-studied probabilistic serial (PS) mechanism. Recently, Fujishige et al. [12] generalized the PS mechanism where goods are enlarged from a fixed set to a family of sets which is a polytope defined by a system of linear inequalities associated with submodular functions. The above extended PS (EPS) greatly improved the flexibility of allocations. Based on these two results, in this paper, we investigate the nonalgorithmic and axiomatic characterization of EPS. We show that the EPS rule is the only mechanism satisfying the ordinal fairness and a newly defined non-wastefulness. The submodularity plays a crucial role in our arguments.

Tight social welfare approximation of probabilistic serial

We study the problem of approximate social welfare maximization (without money) in assignment problems for n agents who have cardinal preferences over a finite set of items. Probabilistic Serial (PS) mechanism is known to be ordinally efficient and envy-free. We prove that its social welfare approximation ratio is Θ(n−12) while no envy-free mechanism can achieve an approximation ratio better than O(n−12). In addition, our proof also implies a Θ(n−12) rank approximation of PS, answering an open question posted in [8].

Constrained random matching

This paper generalizes the Probabilistic Serial (PS) mechanism of Bogomolnaia and Moulin (2001) to matching markets with arbitrary constraints. The constraints are modeled as a set of permissible ex post allocations. A result of independent interest gives a simple geometric characterization of all lotteries over the set of permissible allocations: they are the ones that satisfy a collection of simple and tractable linear inequalities determined by the constraints. The inequalities correspond to the hyperplanes defining a convex polytope that is intuitively constructed from the given set. When a general version of the PS algorithm is executed under these inequalities, the outcome is an efficient and fair lottery over the set of permissible allocations. The method is general, can be applied to both one-sided and two-sided matching markets, and allows for multi-unit demand.

Ordinal Bayesian incentive compatibility in random assignment model

We explore the consequences of weakening the notion of incentive compatibility from strategy-proofness to ordinal Bayesian incentive compatibility (OBIC) in the random assignment model. If the common prior of the agents is the uniform prior, then a large class of random mechanisms are OBIC with respect to this prior—this includes the probabilistic serial mechanism. We then introduce a robust version of OBIC: a mechanism is locally robust OBIC if it is OBIC with respect all independent and identical priors in some neighborhood of a given independent and identical prior. We show that every locally robust OBIC mechanism satisfying a mild property called elementary monotonicity is strategy-proof. This leads to a strengthening of the impossibility result in Bogomolnaia and Moulin (J Econ Theory 100:295–328, 2001): if there are at least four agents, there is no locally robust OBIC and ordinally efficient mechanism satisfying equal treatment of equals.

Probabilistic serial mechanism for multi-type resource allocation

In multi-type resource allocation (MTRA) problems, there are $$d\ge 2$$ types of items, and n agents who each demand one unit of items of each type and have strict linear preferences over bundles consisting of one item of each type. For MTRAs with indivisible items, our first result is an impossibility theorem that is in direct contrast to the single type ( $$d=1$$ ) setting: no mechanism, the output of which is always decomposable into a probability distribution over discrete assignments (where no item is split between agents), can satisfy both sd-efficiency and sd-envy-freeness. We show that this impossibility result is circumvented under the natural assumption of lexicographic preferences. We provide lexicographic probabilistic serial (LexiPS) as an extension of the probabilistic serial (PS) mechanism for MTRAs with lexicographic preferences, and prove that LexiPS satisfies sd-efficiency and sd-envy-freeness, retaining the desirable properties of PS. Moreover, LexiPS satisfies sd-weak-strategyproofness when agents are not allowed to misreport their importance orders. For MTRAs with divisible items, we show that the existing multi-type probabilistic serial (MPS) mechanism satisfies the stronger efficiency notion of lexi-efficiency, and is sd-envy-free under strict linear preferences and sd-weak-strategyproof under lexicographic preferences. We also prove that MPS can be characterized both by leximin-optimality and by item-wise ordinal fairness, and the family of eating algorithms which MPS belongs to can be characterized by lexi-efficiency.

Partial strategyproofness: Relaxing strategyproofness for the random assignment problem

We present partial strategyproofness, a new, relaxed notion of strategyproofness for studying the incentive properties of non-strategyproof assignment mechanisms. Informally, a mechanism is partially strategyproof if it makes truthful reporting a dominant strategy for those agents whose preference intensities differ sufficiently between any two objects. We demonstrate that partial strategyproofness is axiomatically motivated and yields a parametric measure for “how strategyproof” an assignment mechanism is. We apply this new concept to derive novel insights about the incentive properties of the probabilistic serial mechanism and different variants of the Boston mechanism.

Bounded Incentives in Manipulating the Probabilistic Serial Rule

The Probabilistic Serial mechanism is well-known for its desirable fairness and efficiency properties. It is one of the most prominent protocols for the random assignment problem. However, Probabilistic Serial is not incentive-compatible, thereby these desirable properties only hold for the agents' declared preferences, rather than their genuine preferences. A substantial utility gain through strategic behaviors would trigger self-interested agents to manipulate the mechanism and would subvert the very foundation of adopting the mechanism in practice. In this paper, we characterize the extent to which an individual agent can increase its utility by strategic manipulation. We show that the incentive ratio of the mechanism is 3/2. That is, no agent can misreport its preferences such that its utility becomes more than 1.5 times of what it is when reports truthfully. This ratio is a worst-case guarantee by allowing an agent to have complete information about other agents' reports and to figure out the best response strategy even if it is computationally intractable in general. To complement this worst-case study, we further evaluate an agent's utility gain on average by experiments. The experiments show that an agent' incentive in manipulating the rule is very limited. These results shed some light on the robustness of Probabilistic Serial against strategic manipulation, which is one step further than knowing that it is not incentive-compatible.

Ordinal Bayesian Incentive Compatibility in Random Assignment Model

We explore the consequences of weakening the notion of incentive compatibility from strategy-proofness to ordinal Bayesian incentive compatibility (OBIC) in the random assignment model. If the common prior of the agents is a uniform prior, then a large class of random mechanisms are OBIC with respect to this prior - this includes the probabilistic serial mechanism. We then introduce a robust version of OBIC: a mechanism is locally robust OBIC if it is OBIC with respect all independent priors in some neighborhood of a given independent prior. We show that every locally robust OBIC mechanism satisfying a mild property called elementary monotonicity is strategy-proof. This leads to a strengthening of the impossibility result in Bogomolnaia and Moulin (2001): if there are at least four agents, there is no locally robust OBIC and ordinally efficient mechanism satisfying equal treatment of equals.

Size versus truncation robustness in the assignment problem

We study the size of matchings (expected number of agents matched to some objects) generated by random mechanisms in the assignment problem. We show that no mechanism that satisfies two weak axioms, weak truncation robustness and weak regularity, achieves an approximation ratio better than 1−1∕e≈63.2%. This result indicates that it is impossible to achieve a matching size larger than 63.2% of the maximum feasible size in the worst case, as long as agents’ preferences over objects are private information. Our result indicates that the random serial dictatorship mechanism and probabilistic serial mechanism (which indeed has an approximation ratio of 1−1∕e) have the best approximation ratio among a broad class of mechanisms.

Short trading cycles: Paired kidney exchange with strict ordinal preferences

I study kidney exchange with strict ordinal preferences and with constraints on the lengths of the exchange cycles. Efficient deterministic mechanisms have poor fairness properties in this environment. Instead, I propose an individually rational, ordinally efficient and anonymous random mechanism for two-way kidney exchange based on Bogomolnaia and Moulin’s (2001) Probabilistic Serial mechanism. Individual rationality incentivizes patient–donor pairs who are compatible with each other to participate in the exchange, thus increasing the overall transplantation rate. Finally, individual rationality, ex-post efficiency and weak strategyproofness are incompatible for any mechanism.

Size versus Truncation Robustness in the Assignment Problem

We study the size of matchings (expected number of agents matched to some objects) generated by random mechanisms in the assignment problem. We show that no mechanism that satisfies two weak axioms, weak truncation robustness and weak regularity, achieves an approximation ratio better than 1 - 1/e = 63.2%. This result indicates that it is impossible to achieve a matching size larger than 63.2% of the maximum feasible size in the worst case, as long as agents' preferences over objects are private information. Our result indicates that the random serial dictatorship mechanism and probabilistic serial mechanism (which indeed has an approximation ratio of 1 - 1/e) have the best approximation ratio among a broad class of mechanisms.

Submodular optimization views on the random assignment problem

We present a non-pricing allocation scheme of divisible goods to agents with utility functions and submodular constraints on goods. The main contribution of the present paper is that through our non-pricing allocation scheme we reveal the close relation between (1) the recent results in the allocation schemes of the random assignment problem and its extensions with ordinal or lexicographic preferences on goods and (2) the monotone algorithms of fair (egalitarian) allocations with separable utility functions and submodular constraints investigated a few decades ago. The underlying submodularity structure plays a crucial role, so that the probabilistic serial mechanism of Bogomolnaia and Moulin and other related mechanisms can naturally be extended to problems with submodular constraints.

The object allocation problem with random priorities

The existing priority-based object allocation literature restricts objects' priorities to be deterministic. However, agents might be probabilistically prioritized ex-ante, for instance, through a non-uniform tie-breaking rule. This paper generalizes the deterministic setting by allowing priorities to be random. In this probabilistic environment, we first introduce a fairness notion called claimwise stability in the spirit of the usual stability of Gale and Shapley (1962). We show that the natural generalization of the deferred acceptance mechanism (Gale and Shapley, 1962), so called probabilistic deferred acceptance mechanism, is claimwise stable, but with the downside that it is stochastically dominated by another claimwise stable rule. We then introduce a new mechanism called the constrained probabilistic serial, which is built on the probabilistic serial mechanism of Bogomolnaia and Moulin (2001). It is both claimwise stable and constrained sd-efficient. The paper then systematically compares the probabilistic deferred acceptance and constrained probabilistic serial mechanisms in terms of their strategic and fairness properties.

A new ex-ante efficiency criterion and implications for the probabilistic serial mechanism

We introduce and analyze an efficiency criterion for probabilistic assignment of objects, when only ordinal preference information is available. This efficiency criterion is based on the following domination relation: a probabilistic assignment dominates another assignment if it is ex-ante efficient for a strictly larger set of utility profiles consistent with the ordinal preferences. We provide a simple characterization of this domination relation. We revisit an extensively studied assignment mechanism, the Probabilistic Serial mechanism (Bogomolnaia and Moulin, 2001), which always chooses a “fair” assignment. We show that the Probabilistic Serial assignment may be dominated by another fair assignment. We provide conditions under which the serial assignment is undominated among fair assignments.

The Random Assignment Problem with Submodular Constraints on Goods

Problems of allocating indivisible goods to agents in an efficient and fair manner without money have long been investigated in literature. The random assignment problem is one of them, where we are given a fixed feasible (available) set of indivisible goods and a profile of ordinal preferences over the goods, one for each agent. Then, using lotteries, we determine an assignment of goods to agents in a randomized way. A seminal paper of Bogomolnaia and Moulin (2001) shows a probabilistic serial (PS) mechanism to give an ordinally efficient and envy-free solution to the assignment problem. In this article, we consider an extension of the random assignment problem to submodular constraints on goods. We show that the approach of the PS mechanism by Bogomolnaia and Moulin is powerful enough to solve the random assignment problem with submodular (matroidal and polymatroidal) constraints. Under the agents’ ordinal preferences over goods we show the following: (1) The obtained PS solution for the problem with unit demands and matroidal constraints is ordinally efficient, envy-free, and weakly strategy-proof with respect to the associated stochastic dominance relation. (2)For the multiunit demand and polymatroidal constraint problem, the PS solution is ordinally efficient and envy-free but is not strategy-proof in general. However, we show that under a mild condition (that is likely to be satisfied in practice) the PS solution is a weak Nash equilibrium.

Incompatibility of efficiency and strategyproofness in the random assignment setting with indifferences

A fundamental resource allocation setting is the random assignment problem in which agents express preferences over objects that are then randomly allocated to the agents. In 2001, Bogomolnaia and Moulin presented the probabilistic serial (PS) mechanism that is an anonymous, Pareto optimal, and weak strategyproof mechanism when the preferences are considered with respect to stochastic dominance. The result holds when agents have strict preferences over individual objects. It has been an open problem whether there exists a mechanism that satisfies the same properties when agents may have indifference among the objects. We show that for this more general domain, there exists no extension of PS that is ex post efficient and weak strategyproof. The result is surprising because it does not even require additional symmetry or fairness conditions such as anonymity, neutrality, or equal treatment of equals. Our result further demonstrates that the lack of weak SD-strategyproofness of the extended PS mechanism of Katta and Sethuraman (2006) is not a design flaw but is due to an inherent incompatibility of efficiency and strategyproofness of PS in the full preference domain.

Ex-Ante Fair Random Allocation

When allocating indivisible objects, agents might have equal priority rights for some objects. A common practice is to break the ties using a lottery and randomize over deterministic mechanisms. Such randomizations usually lead to unfairness and inefficiency ex-ante. We propose a concept of ex-ante fairness and show the existence of an agent-optimal ex-ante fair solution. Ex- ante fair random allocations are generated using allocation by division, a new method of generating random allocations from deterministic mechanisms. Insights from the two-sided matching theory and the recent random assignment literature are unified and extended. The set of ex-ante fair random allocations forms a complete lattice under first-order stochastic dominance relations. The agent-optimal ex-ante fair mechanism includes both the deferred acceptance algorithm and the probabilistic serial mechanism as special cases.