Obviously Strategy-Proof Mechanisms∗

Shengwu Li†

Working Paper

First uploaded: 3 Feb 2015. This version: 17 July 2015.

1 Abstract

In mechanism design, strategy-proofness is often said to be desirable be-cause it makes it easy for agents to decide what to do. However, somestrategy-proof mechanisms are easier to understand than others. In this pa-per, I define what it means for a mechanism to be Obviously Strategy Proof(OSP). This is a refinement of strategy-proofness that applies to extensivegame forms. Using a formal model of cognitive limitations, I show that amechanism is OSP iff the optimality of truth-telling can be deduced withoutcontingent reasoning. I show that a choice rule is OSP-implementable iff itcan be carried out by a social planner under a particular regime of partialcommitment. Finally, I characterize the set of OSP mechanisms in a canon-ical setting, that encompasses private-value auctions with unit demand andbinary public good problems.

2 Introduction

Strategy-proof mechanisms are often said to be desirable. They reduce par-ticipation costs and cognitive costs, by making it easy for agents to decidewhat to do.1 They protect unsophisticated agents from strategic errors.

∗I thank especially my advisors, Paul Milgrom and Muriel Niederle. I thank NickArnosti, Douglas Bernheim, Gabriel Carroll, Matthew Jackson, Fuhito Kojima, MichaelOstrovsky, Alvin Roth, and Ilya Segal for their invaluable advice. All errors remain myown.†shengwu@stanford.edu1Vickrey (1961) writes that, in second-price auctions: “Each bidder can confine his

efforts and attention to an appraisal of the value the article would have in his own hands,at a considerable saving in mental strain and possibly in out-of-pocket expense.”

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They prevent waste from rent-seeking espionage. The resulting outcomedoes not depend sensitively on each agent’s beliefs about other agents’ pref-erences or information.

These benefits largely depend on agents recognizing that the mechanismis strategy-proof (SP). Only then can they conclude that they need notengage in detailed reasoning about their opponents’ strategies.

However, some strategy-proof mechanisms are simpler for real people tounderstand than others. For instance, ascending clock auctions and second-price sealed-bid auctions are isomorphic under current theories.2 Nonethe-less, laboratory subjects are substantially more likely to play the weaklydominant strategy under a clock auction than under sealed bids. (Kagelet al., 1987) Theorists have also expressed this intuition:

Some other possible advantages of dynamic auctions overstatic auctions are difficult to model explicitly within standardeconomics or game-theory frameworks. For example, . . . it isgenerally held that the English auction is simpler for real-worldbidders to understand than the sealed-bid second-price auction,leading the English auction to perform more closely to theory.(Ausubel, 2004)

In this paper, I model explicitly what it means for a mechanism to beobviously strategy-proof. This is a refinement of strategy-proofness, thatapplies to extensive game forms. This approach invokes no new primitives.It identifies a set of mechanisms as obviously strategy-proof, while remainingas parsimonious as the standard theory of extensive games.

A mechanism is obviously strategy-proof (OSP) if it has an equilibriumin obviously dominant strategies. A strategy Si is obviously dominant if, forany deviating strategy S′i, starting from any earliest information set whereS′i and Si disagree, the best possible outcome from S′i is no better than theworst possible outcome from Si.

Note that this requirement applies only to earliest information sets whereSi and S′i disagree. This requirement therefore depends on the extensivegame form.

Ascending clock auctions are OSP. Suppose Bidder 1 values the objectat $10. In an ascending clock auction, the price rises monotonically. If thecurrent price is below $10, then the best possible outcome from quitting now

2To be precise, if we restrict our attention to cut-off strategies in ascending clockauctions, then ascending clock auctions and second-price sealed-bid auctions have identicalreduced normal form representations.

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Figure 1: ‘Similar’ mechanisms from 1’s perspective.

is no better than the worst possible outcome from staying in the auction (andquitting at $10). If the price is above $10, then the best possible outcomefrom staying in the auction is no better than the worst possible outcomefrom quitting now.

Second-price sealed-bid auctions are SP, but not OSP. Consider thestrategies “bid $10” and “bid $11”. The earliest information set wherethese disagree is the point where Bidder 1 submits her bid. If Bidder 1 bids$11, she might win the object at some price strictly below $10. If Bidder1 bids $10, she might not win the object. The best possible outcome fromdeviating is better than the worst possible outcome from truth-telling. Thiscaptures an intuition expressed by experimental economists:

The idea that bidding modestly in excess of x only increasesthe chance of winning the auction when you don’t want to winis far from obvious from the sealed bid procedure. (Kagel et al.,1987)

I produce two characterization theorems, which suggest two interpreta-tions of OSP.

First, I propose a formal model of a cognitively limited agent. I showthat a strategy Si is obviously dominant if and only if such an agent canrecognize Si as weakly dominant.

Consider the mechanisms in Figure 1. Suppose Agent 1 has preferences:A � B � C � D. In (i), it is a weakly dominant strategy for 1 to play L.All three mechanisms are intuitively similar, but it is not a weakly dominantstrategy for Agent 1 to play L in (ii) and (iii).

In order for Agent 1 to recognize that it is weakly dominant to play L inmechanism (i), he must think through hypothetical scenarios case-by-case:

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“If Agent 2 plays l, then I should play L, since I prefer A to B. If Agent 2plays r, then I should play L, since I prefer C to D. Therefore, I should playL, no matter what agent 2 plays.” This mental process is called contingentreasoning. Notice that the quoted inferences are valid in (i), but not validin (ii) and (iii).

Suppose Agent 1 is unable to engage in contingent reasoning. That is,he knows that playing L might lead to A or C, and playing R might lead toB or D. However, he does not understand how, case-by-case, the outcomesafter playing L are related to the outcomes after playing R. Then it is asthough he cannot distinguish (i), (ii), and (iii).

This idea can be made formal and general. I define an equivalence re-lation on the space of mechanisms: The experience of agent i at history hrecords the information sets where i was called to play, and the actions thati took, in chronological order.3 Two mechanisms G and G′ generate thesame experiences for i if there is a bijection from i’s information sets andactions in G, onto i’s information sets and actions in G′, such that:

1. G can produce for i some experience if and only if G′ can produce fori its bijected partner experience.

2. An experience might result in some outcome in G if and only if itsbijected partner might result in that same outcome in G′.

With this relation, we can partition the set of all mechanisms into equiv-alence classes. For instance, all three mechanisms in Figure 1 generate thesame experiences for Agent 1.

Within a mechanism, a partition of histories into information sets rep-resents imperfect information about past actions. Similarly, a partitionof mechanisms into equivalence classes represents imperfect understandingabout the properties of each mechanism. Any such partition is, implicitly,a model of a cognitively limited agent.

The partition defined by the relation “G and G′ generate the same expe-riences for i” rules out contingent reasoning. Suppose an agent is unable todistinguish games that generate the same experiences. He retains substan-tial knowledge about the structure of the game. He knows the informationsets at which he may be called to play, and the actions available at eachinformation set. He knows, for any experience, what outcomes may result.However, he is unable to reason case-by-case about hypothetical scenarios.

3An experience is a standard concept in the theory of extensive games; experiences areused to define perfect recall.

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The first characterization theorem states: A strategy Si is obviouslydominant in G if and only if it is weakly dominant in every G′ that generatesthe same experiences for i as G.

This shows that obviously dominant strategies are those that can berecognized as weakly dominant without contingent reasoning. An obviouslydominant strategy is weakly dominant in any game that generates the sameexperiences. In that sense, such a strategy is robustly dominant.

The Prisoner’s Dilemma is a special case of game (i) in Figure 1; playingdefect is not obviously dominant. On the other hand, if Agent 1 is informedof Agent 2’s action before making his decision, then playing defect is ob-viously dominant. Shafir and Tversky (1992) find that laboratory subjectsin a Prisoner’s Dilemma are more likely to play the weakly dominant strat-egy when they are informed beforehand that their opponent has cooperated(84%) or when they are informed beforehand that their opponent has de-fected (97%), compared to when they are not informed of their opponent’sstrategy (63%).4 This violates the Sure Thing Principle (Savage, 1954), butis predicted by the cognitive model that I have just exposited.

The second characterization theorem for OSP relates to the problemof mechanism design under partial commitment. In mechanism design, weusually assume that the Planner can commit to every detail of a mechanism,including the events that an individual agent does not directly observe. Forinstance, in a sealed-bid auction, it is assumed that the Planner can committo the function from all sealed bids to allocations and payments, even thougheach agent only directly observes his own bid. In some settings, this isunrealistic. If agents cannot individually verify the details of a mechanism,the Planner may be unable to commit to it.

Mechanism design under partial commitment is a pressing problem. Auc-tions run by central brokers over the Internet account for billions of dollarsof economic activity. (Edelman et al., 2007) In such settings, bidders maybe unable to verify that the other bidders exist, let alone what actions theyhave taken. As another example, some wireless spectrum auctions use com-putationally demanding techniques to solve complex assignment problems.

482% of subjects defect both when informed that their opponent has cooperated, andwhen informed that their opponent has defected. Note that random choosers would playthe weakly dominant strategy 50% of the time. In Shafir and Tversky’s data, switchingfrom coin flips to human beings with weakly dominant strategies yields a 13% increase inthe rate that the dominant strategy is played. Switching from weakly dominant strategiesto obviously dominant strategies yields a further increase of 19%. This suggests that, inaddition to social preferences, cognitive limitations have a role to play in explaining thedata.

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In these settings, individual bidders may find it difficult and costly to verifythe output of the auctioneer’s algorithm. (Milgrom and Segal, 2014)

For the second characterization theorem, I consider a ‘metagame’ wherethe Planner privately communicates with agents, and eventually decides onan outcome. The Planner chooses one agent, and sends a private message,along with a set of acceptable replies. That agent chooses a reply, which thePlanner observes. The Planner can then either repeat this process (possiblywith a different agent) or announce an outcome and end the game.

The Planner has partial commitment power: For each agent, she cancommit to use only a subset of her available strategies. However, the subsetshe offers to agent i must be measurable with respect to i’s observations inthe game. We call this a bilateral commitment. Suppose, for robustness,that each agent could hold any beliefs about the Planner’s strategy, subjectto the constraints of his bilateral commitment. What choice rules can beimplemented in this metagame?

The second characterization theorem states: A choice rule can be sup-ported by bilateral commitments if and only if that choice rule is OSP-implementable.

Thus, in addition to formalizing a notion of cognitive simplicity, OSP alsocaptures the set of choice rules that can be carried out with only bilateralcommitments.

After defining and characterizing OSP, I apply this new concept to theenvironment of binary allocation problems. In this environment, there is aset of agents N with continuous single-dimensional types θi ∈ [θi, θi]. Anallocation y is a subset of N . An allocation rule fy is a function fromtype profiles to allocations. We augment this with a transfer rule ft, whichspecifies money transfers for each agent. Each agent has utility equal to histype if he is in the allocation, plus his net transfer.

ui(θi, y, t) = 1i∈yθi + ti (1)

Binary allocation problems encompass several canonical settings. Theyinclude private-value auctions with unit demand. They include procure-ment auctions with unit supply; not being in the allocation is ‘winning thecontract’, and the bidder’s type is his cost of provision. They also includebinary public good problems; the feasible allocations are N and the emptyset.

Mechanism design theory has extensively investigated SP-implementationin this environment. fy is SP-implementable if and only if fy is monotonein each agent’s type (i.e. 1i∈fy(θ) is weakly increasing in θi). (Spence, 1974;

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Mirrlees, 1971; Myerson, 1981) If fy is SP-implementable, then the accom-panying transfer rule ft is essentially unique. (Green and Laffont, 1977;Holmstrom, 1979)

ft,i(θi, θ−i) = −1fy(θ)∈Yi inf{θ′i : i ∈ fy(θ′i, θ−i)}+ ri(θ−i) (2)

where ri is some arbitrary deterministic function of the other agents’preferences.

What are analogues of these canonical results, if we require OSP-implementationrather than SP-implementation? Are ascending clock auctions special, or arethere other OSP mechanisms in this setting?

I develop an analogous essential uniqueness theorem for extensive gameforms: Every mechanism that OSP-implements a private-value allocationrule is ‘essentially’ a monotone price mechanism, which is a generalizationof ascending clock auctions.

This theorem implies that when we desire OSP-implementation in thisenvironment, we need not search the (very large) space of all extensive gameforms. We can focus our attention on the class of monotone price mecha-nisms.

Next, I characterize the set of OSP-implementable allocation rules. Forthis part, I assume that the lowest type of each agent is never in the allo-cation, and is required to have a zero transfer.5 Given an allocation rule, Ishow how to identify subsets of R|N | that contain viable price paths for amonotone price mechanism. I provide a necessary and sufficient conditionfor an allocation rule to be OSP-implementable.

As a second application, I consider a generalization of the Edelman et al.(2007) online advertising environment. In this setting, agents bid for adver-tising positions, each worth a certain number of clicks. Each agent’s type isa vector of per-click values, one for each position. I show that if preferencessatisfy a single-crossing condition, then we can OSP-implement the efficientallocation and the Vickrey payments.

As a third application, I produce an impossibility result for a classicmatching algorithm: With 3 or more agents, there does not exist a mecha-nism that OSP-implements Top Trading Cycles. (Shapley and Scarf, 1974)

The rest of the paper proceeds in the usual order. Proofs omitted fromthe main text are in Appendix C.

5This assumption is borrowed from the Revenue Equivalence Theorem. (Myerson,1981)

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3 Related Literature

It is generally acknowledged that ascending auctions are simpler for realbidders than sealed-bid auctions. (Ausubel, 2004) Laboratory experimentshave investigated and corroborated this claim (Kagel et al., 1987; Kageland Levin, 1993). More generally, Charness and Levin (2009) and Espondaand Vespa (2014) document that laboratory subjects find it difficult to rea-son case-by-case about hypothetical scenarios. This mental process is oftencalled “contingent reasoning”, but has received little formal treatment ineconomic theory.6

There is also a strand of literature, including Vickrey’s seminal paper,that observes that sealed-bid auctions raise problems of commitment. (Vick-rey, 1961; Rothkopf et al., 1990; Cramton, 1998) For instance, it may bedifficult to prevent shill bidding without third-party verification. Rothkopfet al. (1990) argue that “robustness in the face of cheating and of fear ofcheating is important in determining auction form”.

This paper formalizes and unifies both these strands of thought. It showsthat mechanisms that do not require contingent reasoning are identical tomechanisms that can be run under bilateral commitment.

This paper also relates to the planned US auction to repurchase televi-sion broadcast rights. In this setting, complex underlying constraints havethe result that Vickrey prices cannot be computed without large approxi-mation errors. Milgrom and Segal (2014) propose the use of a clock auctionto repurchase broadcast rights. They recommend this over an equivalentsealed-bid procedure, arguing that clock auctions “make strategy-proofnessself-evident even for bidders who misunderstand or mistrust the auction-eer’s calculations”. The Milgrom-Segal clock auction uses advanced compu-tational techniques to solve a challenging allocation problem. However, it isobviously strategy-proof.

In multi-unit combinatorial auction problems, finding the optimal solu-tion is NP-hard, so the Vickrey-Clarke-Groves mechanism may be computa-tionally infeasible. Bartal et al. (2003) propose an approximating mechanismthat runs in polynomial time. In this mechanism, bidders are processed se-quentially. Each bidder is presented with a price vector and chooses hismost-preferred bundle, and his choice is used to adjust the price vectors

6There is a theory literature that studies agents that do not fully account for otheragents’ private information. (Eyster and Rabin, 2005; Crawford and Iriberri, 2007;Esponda, 2008) This is a related notion, but conceptually distinct from mistakes in con-tingent reasoning. In particular, these models predict no deviations from classical play instrategy-proof mechanisms.

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for the remaining bidders. One previously unmodeled advantage of suchmechanisms is that they are obviously strategy-proof.

OSP requires equilibrium in obviously dominant strategies. This is dis-tinct from O-solvability, a solution concept used in the computer scienceliterature on decentralized learning. (Friedman, 2002, 2004) Strategy Si issaid to overwhelm S′i if the worst possible outcome from Si is strictly betterthan the best possible outcome from S′i. O-solvability calls for the iterateddeletion of overwhelmed strategies. One difference between the two conceptsis that O-solvability is for normal form games, whereas OSP invokes a no-tion of an ‘earliest point of departure’, which is only defined in the extensiveform.7

4 Definition and Characterization

The planner operates in an environment consisting of:

1. A set of agents, N ≡ {1, . . . , n}.

2. A set of outcomes, X.

3. A set of preference profiles over X, Θ ≡∏i∈N Θi.

4. A set of game forms with consequences in X, G.

An extensive game form with consequences in X is a tuple 〈N,H,P, δc, (Ii)i∈N , g〉,where:

1. N is a set of agents.

2. H is a set of histories (sequences of actions, including the null sequenceh∅).

(a) Let Z be the terminal histories. z ∈ Z iff there does not existh ∈ H such that z is a proper subhistory of h.8

(b) Let A(h) be the actions available at h.

3. P is a player function. P : H \ Z → N ∪ c7Ascending clock auctions and sequential random serial dictatorship are OSP, but not

O-solvable. O-solvability is too strong for our current purpose.8As usual, this implies that infinitely long histories are terminal histories.

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4. δc is the chance function. For any h where P (h) = c, δc specifies aprobability measure on A(h).9

5. Ii is a partition of {h : P (h) = i} with the property that A(h) = A(h′)whenever h and h′ are in the same member of the partition.

(a) For any Ii ∈ Ii, we denote: P (Ii) = P (h) for any h ∈ Ii. A(Ii) =A(h) for any h ∈ Ii.

(b) If (a, a′) are such that a ∈ A(Ii), a′ ∈ A(I ′j), Ii 6= I ′j then we

regard a and a′ as distinct.

6. g is an outcome function. It associates each terminal history with anoutcome. g : Z → X

A strategy Si for agent i in game G specifies what agent i does at everyone of her information sets. Si(Ii) ∈ A(Ii). A strategy profile S = (Si)i∈Nis a set of strategies, one for each agent. When we want to refer to thestrategies used by different types of i, we use Sθii to denote the strategyassigned to type θi.

Let zG(h, S, δc) be the lottery over terminal histories that results ingame form G when we start from h and play proceeds according to (S, δc).zG(h, S, dc) is the result of one realization of the chance moves under δc. Wesometimes write this as zG(h, Si, S−i, dc).

ui(x, θi) ∈ R is the utility to agent i from outcome x given preferencesθi. Let uGi (h, Si, S−i, dc, θi) ≡ ui(g(zG(h, Si, S−i, dc)), θi). This is the utilityto agent i in game G, when we start at history h, play proceeds according to(Si, S−i, dc), and the resulting outcome is evaluated according to preferencesθi.

We require that δc has full support on the available moves A(h) when itis called to play.10 This is without loss of generality, since if some action isnot in the support of the chance function, then we can simply delete thataction from the game.

Definition 1. ψi(h) is the experience of agent i along history h. ψi(h) isan alternating sequence of information sets and actions. It is constructed asfollows: Let h1, . . . , hK be the subhistories of h where P (h) = i, in order.

9We require the probability distributions to be independent across different histories.10This ensures that obvious dominance has a useful invariance property: Namely, that

the set of obviously dominant strategies does not change when we add new chance movesthat occur with zero probability. Appendix A defines full support in a way that accom-modates infinite and uncountably infinite action sets.

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1. The (2k − 1)th element in ψi(h) is Ii : hk ∈ Ii.

2. If hk is a proper subhistory of h, then the (2k)th element in ψi(h) isthe action that immediately follows hk in h.

We use Ψi to denote the set {ψi(h) : h ∈ H and ψi(h) 6= {∅}}.

An extensive game form has perfect recall if for any information set Ii,for any two histories h and h′ in Ii, ψi(h) = ψi(h

′). We use ψi(Ii) to denoteψi(h) : h ∈ Ii.

Definition 2. G is the set of all extensive game forms with consequences inX and perfect recall, where δc has full support.

A choice rule is a function f : Θ → X. If we consider stochastic choicerules, then it is a function f : Θ→ ∆X.11

A solution concept S is a set-valued function with domain G × Θ. Ittakes values in the set of strategy profiles.

Definition 3. f is S-implementable if there exists

1. G ∈ G

2. (Sθ)θ∈Θ ≡ ((Sθii )i∈N )θ∈Θ

such that, for all θ ∈ Θ

1. Sθ ∈ S(G, θ).

2. f(θ) = g(zG(∅, Sθ, δc))

Note that our concern is with weak implementation: We require thatSθ ∈ S(G, θ), not {Sθ} = S(G, θ). This is to preserve the analogy withcanonical results for strategy-proofness, many of which assume weak imple-mentation. (Myerson, 1981; Saks and Yu, 2005)

We use “(G, (Sθ)θ∈Θ) S-implements f” to mean that (G, (Sθ)θ∈Θ) fulfilsthe requirements of Definition 3. We use “G S-implements f” to meanthat there exists (Sθ)θ∈Θ such that (G, (Sθ)θ∈Θ) fulfils the requirements ofDefinition 3.

11For readability, we generally suppress the latter notation, but the claims that followhold for both deterministic and stochastic choice rules. Additionally, the set X could itselfbe a set of lotteries. The interpretation of this is that the planner can carry out one-timepublic lotteries at the end of the mechanism, where the randomization is observable andverifiable.

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Definition 4 (Weakly Dominant). In G for agent i with preferences θi, Siis weakly dominant if:

∀S′i : ∀S′−i :

Eδc [uGi (h∅, S′i, S′−i, dc, θi)] ≤ Eδc [uGi (h∅, Si, S

′−i, dc, θi)]

(3)

Let α(Si, S′i) be the set of earliest points of departure for Si and S′i.

That is, α(Si, S′i) contains the information sets where Si and S′i have made

identical decisions at all prior information sets, but are making a differentdecision now. We define this recursively as follows:12

Definition 5 (Earliest Points of Departure). Ii ∈ α(Si, S′i) if and only if:

1. Si(Ii) 6= S′i(Ii)

2. There exists h ∈ Ii, S−i, dc such that h is a subhistory of zG(h∅, Si, S−i, dc).13

3. There does not exist (h, h′, I ′i) such that:

(a) h′ is a proper subhistory of h

(b) h ∈ Ii(c) h′ ∈ I ′i(d) I ′i ∈ α(Si, S

′i)

Definition 6 (Obviously Dominant). In G for agent i with preferences θi,Si is obviously dominant if:

∀S′i : ∀Ii ∈ α(Si, S′i) :

suph∈Ii,S′−i,dc

uGi (h, S′i, S′−i, dc, θi) ≤ inf

h∈Ii,S′−i,dcuGi (h, Si, S

′−i, dc, θi)

(4)

12The perfect recall assumption is necessary for this to work. Suppose a game ofimperfect recall, with one agent, with histories {∅, L,R,RL,RR}, with information setI1 = {∅, R}. Then the strategies S1(I1) = L and S1(I1) = R have no earliest point ofdeparture. This recalls Piccione and Rubinstein’s paradox of the absentminded driver.(Piccione and Rubinstein, 1997)

13This is just saying that Si does not itself rule out Ii from the path of play. Eventhough this requirement is not stated symmetrically, for all (Si, S

′i), α(Si, S

′i) = α(S′i, Si).

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Compare Definition 4 and Definition 6. Weak dominance is defined us-ing h∅, the history that begins the game. Consequently, if two extensivegames have the same normal form, then they have the same weakly dom-inant strategies. Obvious dominance is defined with histories that are ininformation sets that are earliest points of departure. Thus two extensivegames with the same normal form may not have the same obviously domi-nant strategies. Switching to a direct revelation mechanism may not preserveobvious dominance, so the standard revelation principle does not apply.

In what sense is obvious dominance obvious? Definition 4 contains thequantifier “∀S−i”. To assess that Si weakly dominates S′i, one has to keeptrack of as many inequalities as there are combinations of opponent strate-gies. By contrast, Definition 6 does not contain “∀S−i”. To assess that Siobviously dominates S′i requires only one inequality at each earliest pointof departure. Thus, for agents who find case-by-case reasoning difficult,obvious dominance may be easier to assess than weak dominance.

Definition 7 (Strategy-Proof). S ∈ SP(G, θ) if for all i, Si is weakly dom-inant.

Definition 8 (Obviously Strategy-Proof). S ∈ OSP(G, θ) if for all i, Si isobviously dominant.

Consider the case where there is a set of |N | objects and each agent’stype is a vector of per-object values. Sequential Random Serial Dictatorshiprefers to the procedure where, in a random order, each agent takes one objectfrom the set that remains.

Proposition 1. Sequential Random Serial Dictatorship is obviously strategy-proof.

Proof. By inspection.

Proposition 2. If G is obviously strategy-proof, then G is weakly group-strategy-proof.

Proof. Take any type profile θ. Take S ∈ OSP(G, θ). Suppose there was acoalition N ⊆ N that could jointly deviate to strategies (Si)i∈N and all bestrictly better off. Fix (Si)i∈(N\N) and dc such that all agents in the coalitionare strictly better off. Along the resulting terminal history, there must bea first agent i in the coalition to deviate from Si to Si. That first deviationhappens at some information set Ii ∈ α(Si, Si). Since S ∈ OSP(G, θ), agenti cannot strictly gain from deviating to Si; a contradiction.

Corollary 1. If G is obviously strategy-proof, then G is strategy-proof.

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4.1 Cognitive limitations

We now define an equivalence relation between mechanisms. In words, Gand G′ generate the same experiences for i if there exists a bijection fromi’s information sets and actions in G onto i’s information sets and actionsin G′, such that:

1. ψi is an experience in G iff ψi’s bijected partner is an experience inG′.

2. Outcome x could follow experience ψi in G iff x could follow ψi’sbijected partner in G′

Definition 9. Take any G,G′ ∈ G, with information partitions Ii, I ′i andexperience sets Ψi,Ψ

′i. G and G′ generate the same experiences for i

if there exists a bijection λ from Ii ∪A(Ii) onto14 I ′i ∪A′(I ′i) such that:

1. ψi ∈ Ψi iff λ(ψi) ∈ Ψ′i

2. ∃z ∈ Z : g(z) = x, ψi(z) = ψi iff ∃z′ ∈ Z ′ : g′(z′) = x, ψ′i(z′) = λ(ψi)

where we use λ(ψi) to denote {λ(ψki )}Tk=1, where T ∈ N ∪∞.

For G and G′ that generate the same experiences, we define λ(Si) to bethe strategy that, given information set I ′i in G′, plays λ(Si(λ

−1(I ′i))).

Theorem 1. For any i, θi: Consider the equivalence classes on G defined bythe relation “G and G′ generate the same experiences for i”. Si is obviouslydominant in G if and only if for every G′ in the equivalence class of G, λ(Si)is weakly dominant in G′.

The “if” direction permits a constructive proof. Suppose Si is not obvi-ously dominant in G. Then we can take a sequence of ‘experience-preserving’transformations of G, to produce some G′ where λ(Si) is not weakly domi-nant. The “only if” direction proceeds as follows: Suppose there exists someG′ in the equivalence class of G, where λ(Si) is not weakly dominant. Thenwe can use λ−1 to locate an information set in G, and a deviation S′i, thatdo not satisfy the obvious dominance inequality. Appendix C provides thedetails.

14This definition entails that λ maps Ii onto I′i and A(Ii) onto A′(I′i). If an infor-mation set in G was mapped onto an action in G′, then any experience involving thatinformation set would, when passed through the bijection, result in a sequence that wasnot an experience, and ipso facto not an experience of G′.

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4.2 Supported by bilateral commitments

Suppose the following game: As before we have a set of agents N , outcomesX, and preference profiles

∏i∈N Θi. However, there is one player in addition

to N: Player 0, the Planner.The Planner has an uncountably infinite message space M . At the start

of the game, each agent i ∈ N privately observes θi. Play proceeds as follows:

1. The Planner chooses one agent i ∈ N and sends a query m ∈M , alongwith a set of acceptable replies R ⊂M

2. i observes (m,R), and chooses a reply r ∈ R.

3. The Planner observes r.

4. The Planner either selects an outcome x ∈ X, or chooses to sendanother query.

(a) If the Planner selects an outcome, the game ends.

(b) If the Planner chooses to send another query, go to Step 1.

We allow the Planner’s strategy S0 to assign outcomes to infinitely longsequences of queries and replies.

For i ∈ N , i’s strategy specifies what reply to give, as a function of herpreferences, the past sequence of queries and replies between her and thePlanner, and the current (m,R). That is

Si(θi, (mk, Rk, rk)t−1k=1,mt, Rt) ∈ Rt (5)

We use Sθii to denote the strategy played by type θi of agent i.Let Ψ0 be the set of all Planner strategies. The standard full commitment

paradigm is equivalent to allowing the Planner to commit to a unique S0 ∈Ψ0. Instead, we assume that for each agent, the Planner can commit to asubset Ψi

0 ⊆ Ψ0 that is measurable with respect to that agent’s observationsin the game.

γi is some full sequence of messages and responses seen by player i,(mk, Rk, rk)

Tk=1 for T ∈ N∪∞, along with the resulting outcome. Γi denotes

a communication event, which is some set of such objects. Let µ(Γi, S0, SN )be the probability that some γi ∈ Γi occurs when the strategies are (S0, SN ).

Definition 10. Ψi0 is a bilateral commitment if there exists Γi such that:

S0 ∈ Ψi0 if and only if

∀SN : µ(Γi, S0, SN ) = 0 (6)

15

A bilateral commitment is (equivalently) specified by a ‘prohibited’ setof observations Γi for player i. Bilateral commitments are those such that,if the planner reneges, then agent i might detect this reneging (i.e. observesome γi ∈ Γi) with positive probability.

Definition 11. A choice rule f is supported by bilateral commitments(Ψi

0)i∈N , if there exists (S0, SN ) such that:

1. For all θ: (S0, SN ) results in f(θ).

2. For all i ∈ N : S0 ∈ Ψi0

3. For all i ∈ N , for all θi, for all S′N\i, for all S′0 ∈ Ψi0: Sθii is a best

response to S′N\i and S′0

The first requirement states that the strategy profile must result in theoutcome prescribed by the choice rule. The second requirement is that thePlanner’s strategy is compatible with the commitment she offers to eachagent. The third requirement is that each agent i’s strategy Si is weaklydominant, when we consider the Planner as just another player whose strate-gies are confined to Ψi

0.“Supported by bilateral commitments” is just one of many partial com-

mitment regimes. This one requires that the commitment offered to eachagent is measurable with respect to events that she can observe. In reality,contracts are seldom enforceable unless each party can observe breaches.Thus, “supported by bilateral commitments” is a natural case to study.

Theorem 2. f is OSP-implementable if and only if there exist bilateralcommitments (Ψi

0)i∈N that support f .

The intuition behind the proof is as follows: A bilateral commitmentΨi

0 is almost equivalent to the Planner committing to ‘run’ only gamesin some equivalence class of G, for the equivalence classes in Theorem 1.To be precise, a bilateral commitment is equivalent to committing to runmechanisms in the closure of such an equivalence class, where the distancefunction is with respect to the total variation distance of chance moves. Indefining G, we assumed that δc has full support on the available actions whenit is called to play. A bilateral commitment permits the Planner to run amechanism where some chance actions are never played. Such a mechanism

16

does not generate the same experiences as the full-support versions, but wecan get arbitrarily close to it without leaving the equivalence class.15

Consequently, we can find a set of bilateral commitments that support fyiff we can find some (G, (Sθ)θ∈Θ) such that, for every i, for every θi, for everyG′ that generates the same experiences for i, λ(Sθii ) is weakly dominant inG′. By Theorem 1, this holds iff fy is OSP-implementable. Appendix Cprovides the details.

5 Applications

5.1 Binary Allocation Problems

We now consider a canonical environment, (N,X,Θ,G). N and G remaingeneral as before. Let Y ⊆ 2N be the set of feasible allocations, with repre-sentative element y ∈ Y . An outcome consists of an allocation y ∈ Y anda transfer for each agent, X = Y × Rn. t ≡ (ti)i∈N denotes a profile oftransfers.

Preferences are quasilinear. Θ =∏i∈N Θi, where Θi = [θi, θi], for 0 ≤

θi < θi <∞. For θi ∈ Θi

ui(θi, y, t) = 1i∈yθi + ti (7)

For instance, in a private value auction with unit demand, i ∈ y iff agenti receives at least one unit of the good under allocation y. In a procurementauction, i ∈ y iff i does not incur costs of provision under allocation y. θiis agent i’s cost of provision (equivalently, benefit of non-provision). In apublic goods game, Y = {∅, N}.

An allocation rule is fy : Θ → Y . A choice rule is thus a combinationof an allocation rule and a payment rule, f = (fy, ft), where ft : Θ → Rn.Similarly, for each game form G, we disaggregate the outcome function,g = (gy, gt). In this part, we concern ourselves only with deterministicallocation rules and payment rules, and thus suppress notation involving δcand dc.

Definition 12. An allocation rule fy is S-implementable if there exists ftsuch that (fy, ft) is S-implementable.

Definition 13. fy is monotone if for all i, for all θ−i, 1i∈fy(θ) is weaklyincreasing in θi.

15One tempting modification of the theory is to define equivalence classes to include theirclosures with respect to chance moves. This would result in a non-transitive equivalencerelation.

17

In this environment, fy is SP-implementable if and only if fy is monotone.This result is implicit in Spence (1974) and Mirrlees (1971), and is provedexplicitly in Myerson (1981).16

Moreover, if an allocation rule fy is SP-implementable, then the accom-panying payment rule ft is essentially unique.

ft,i(θi, θ−i) = −1fy(θ)∈Yi inf{θ′i : i ∈ fy(θ′i, θ−i)}+ ri(θ−i) (8)

where ri is some arbitrary deterministic function of the other agents’preferences. This follows easily by arguments similar to those in Green andLaffont (1977) and Holmstrom (1979).

We are interested in how these results change when we require OSP-implementation. In particular:

1. What condition on fy characterizes the set of OSP-implementable al-location rules?

2. For OSP-implementation, is there an analogous ‘essential uniqueness’result on the extensive game form G?

Definition 14 (Pruning). Take any G = 〈N,H,P, (Ii)i∈N , g〉, and (Sθ)θ∈Θ.P(G, (Sθ)θ∈Θ) ≡ 〈N, H, P , (Ii)i∈N , g〉 is the pruning of (G, (Sθ)θ∈Θ), con-structed as follows:

1. H = {h ∈ H : ∃θ : h is a subhistory of zG(∅, Sθ)}

2. For all i, if Ii ∈ Ii then (Ii ∩ H) ∈ Ii.17

3. (P , g) are (P, g) restricted to domain H.

We now define a monotone price mechanism. Informally, a monotoneprice mechanism is such that, for every i,

1. Either

(a) We present i with a fixed transfer associated with not being inthe allocation.

16These monotonicity results for are for weak SP-implementation rather than full SP-implementation implementation. Weak SP-implementation requires Sθ ∈ SP(G, θ). FullSP-implementation requires {Sθ} = SP(G, θ). There are monotone allocation rules forwhich the latter requirement cannot be satisfied. For example, suppose two agents withunit demand. Agent 1 receives one unit iff v1 > .5. Agent 2 receives one unit iff v2 > v1.

17Note that the empty history h∅ is distinct from the empty set. That is to say, (Ii∩H) =∅ does not entail that {h∅} ∈ Ii.

18

(b) And a going transfer associated with being in the allocation.

2. Or

(a) We present i with a fixed transfer associated with being in theallocation.

(b) And a going transfer associated with not being in the allocation.

3. The going transfer falls monotonically. (Equivalently, the going pricerises.)

4. Whenever the going transfer strictly falls, i has the option to quit,taking the fixed transfer.

5. If the going transfer could fall in future, i has a unique non-quittingaction.

The “either” clause contains ascending clock auctions as a special case.The “or” clause contains descending price procurement auctions; agents thatdo not win the contract receive a fixed zero transfer. There is a positive pay-ment associated with winning the contract (i.e. not being in the allocation),which starts at a high level and counts downwards.

Definition 15 (Monotone Price Mechanism). A game G is a monotoneprice mechanism if, for every i ∈ N , at every earliest information set I∗isuch that A(I∗i ) > 1:

1. Either: There exists a real number t0i , a function t1i : {Ii : I∗i ∈ψi(Ii)} → R, and a set of actions A0 such that:

(a) For all a ∈ A0, for all z such that a ∈ ψi(z): i /∈ gy(z) andgt,i(z) = t0i .

(b) A0 ∩A(I∗i ) 6= ∅.(c) For all I ′i, I

′′i ∈ {Ii : I∗i ∈ ψi(Ii)}.

i. If I ′i ∈ ψi(I ′′i ), then t1i (I′i) ≥ t1i (I ′′i ).

ii. If I ′i is the penultimate information set in ψi(I′′i ) and t1i (I

′i) >

t1i (I′′i ), then A0 ∩A(I ′′i ) 6= ∅.

iii. If I ′i ∈ ψi(I ′′i ) and t1i (I′i) > t1i (I

′′i ), then |A(I ′i) \A0| = 1.

(d) For all z where I∗i ∈ ψi(z):i. Either: i /∈ gy(z) and gt,i(z) = t0i .

19

ii. Or: i ∈ gy(z) and gt,i(z) = t1i (I′i), where I ′i is the last infor-

mation set in ψi(z).

2. Or: There exists a real number t1i , a function t0i : {Ii : I∗i ∈ ψi(Ii)} →R, and a set of actions A1 such that:

(a) For all a ∈ A1, for all z such that a ∈ ψi(z): i ∈ gy(z) andgt,i(z) = t1i .

(b) A1 ∩A(I∗i ) 6= ∅.(c) For all I ′i, I

′′i ∈ {Ii : I∗i ∈ ψi(Ii)}.

i. If I ′i ∈ ψi(I ′′i ), then t1i (I′i) ≥ t1i (I ′′i ).

ii. If I ′i is the penultimate information set in ψi(I′′i ) and t1i (I

′i) >

t1i (I′′i ), then A1 ∩A(I ′′i ) 6= ∅.

iii. If I ′i ∈ ψi(I ′′i ) and t0i (I′i) > t0i (I

′′i ), then |A(I ′i) \A1| = 1.

(d) For all z where I∗i ∈ ψi(z):i. Either: i ∈ gy(z) and gt,i(z) = t1i .

ii. Or: i /∈ gy(z) and gt,i(z) = t0i (I′i), where I ′i is the last infor-

mation set in ψi(z).

Notice what this definition does not require. The going transfer need notbe equal across agents. Whether and how much one agent’s going transferchanges could depend on other agents’ actions. Some agents could face aprocedure consistent with the ‘either’ clause, and other agents could face aprocedure consistent with the ‘or’ clause. Indeed, which procedure an agentfaces could depend on other agents’ actions.

Theorem 3. If (G, (Sθ)θ∈Θ) OSP-implements (fy, ft), then G ≡ P(G, (Sθ)θ∈Θ)is a monotone price mechanism, and (G, (Sθ)θ∈Θ) OSP-implements (fy, ft),where (Sθ)θ∈Θ is (Sθ)θ∈Θ restricted to G.

The next theorem characterizes the set of OSP-implementable allocationrules. It invokes two additional assumptions.

First, we assume that fy admits a finite partition, which means that wecan partition the type space into a finite set of |N |-dimensional intervals,with the allocation rule constant within each interval. This assumption islargely technical. It is required because OSP is defined for discrete-timeextensive game forms. OSP is not defined for continuous-time auctions,although we can approximate some of them arbitrarily finely.18

18Simon and Stinchcombe (1989) show that discrete time with a very fine grid can be agood proxy for continuous time. However, in their theory, players have perfect informationabout past activity in the system. Adapting this to our theory, where G includes alldiscrete-time game forms with imperfect information, is far from straightforward.

20

Second, we assume that the lowest type of each agent is never in theallocation, and has a zero transfer. This assumption is borrowed from theRevenue Equivalence Theorem (Myerson, 1981). However, it is a substantiverestriction, and rules out certain cases of interest.

Definition 16. fy admits a finite partition if there exists K ∈ N suchthat, for each i, there exists {θki }Kk=1 such that:

1. θi = θ1i < θ2

i < . . . < θKi = θi.

2. For all θi, θ′i, for all θ−i, if there does not exist k such that θi ≤ θki < θ′i,

then fy(θi, θ−i) = fy(θ′i, θ−i)

The use of a single K for all agents is without loss of generality.All vector inequalities in the following theorem are in the product order.

That is, v ≥ v′ iff for every index i, vi ≥ v′i. Similarly, v > v′ iff for everyindex i, vi > v′i.

Theorem 4. Assume that:

1. fy admits a finite partition.

2. For all i, for all θ−i, i /∈ fy(θi, θ−i).

There exists G and ft such that:

1. G OSP-implements (fy, ft)

2. For all i, for all θ−i, ft,i(θi, θ−i) = 0

if and only if

1. fy is monotone.

2. For all A ⊆ N , for all θN\A, for

ΘA(θN\A) ≡ ∩i∈Aclosure({θA : ∀θ′A\i ≥ θA\i : i /∈ fy(θi, θ′A\i, θN\A)})(9)

(a) ΘA(θN\A) is connected.

(b) There exists i ∈ A such that, if θA > sup{ΘA(θN\A)}, then i ∈fy(θA, θN\A).

The sets defined by Equation 9 are join-semilattices.19 Since their supre-mum is also the supremum of a finite set of partition coordinates, it is welldefined.

19For a proof, see Lemma 8 in Appendix C.

21

5.2 Online Advertising Auctions

We now study an online advertising environment, which generalizes Edelmanet al. (2007).

There are n bidders, and n − 1 advertising positions.20 Each positionhas an associated click-through rate αk, where α1 ≥ α2 ≥ . . . ≥ αn−1 > 0.For convenience, we define position n with αn = 0.

Each bidder’s type is a vector, θi ≡ (θki )nk=1. A bidder with type θi whoreceives position k and transfer t has utility:

ui(k, t, θi) = αkθki + t (10)

The marginal utility of moving to position k from position k′, for typeθi, is

m(k, k′, θi) ≡ αkθki − αk′θk′i (11)

We make the following assumptions on the type space Θ:

A1. Finite:|Θ| <∞ (12)

A2. Higher slots are better :

∀k ≤ n− 1 : ∀θ ∈ Θ : ∀i ∈ N : m(k, k + 1, θi) ≥ 0 (13)

A3. Single-crossing :21

∀k ≤ n− 2 : ∀θ, θ′ ∈ Θ : ∀i, j ∈ N : If m(k, k + 1, θi) > m(k, k + 1, θ′j),

then m(k + 1, k + 2, θi) > m(k + 1, k + 2, θ′j).

(14)

A1 is a technical assumption to accommodate extensive game forms thatmove in discrete steps. A2 and A3 are substantive assumptions. Edelmanet al. (2007) assume that for all k, k′, θki = θk

′i , which entails A2. If α1 >

α2 > . . . > αn−1 > 0, then their assumption also entails A3.In this environment, the Vickrey-Clarke-Groves (VCG) mechanism se-

lects the efficient allocation. Suppose we number each buyer according tothe slot he wins. Then bidder i has VCG payment:

20It is trivial to extend what follows to fewer than n−1 advertising positions, but doingso would add notation.

21This assumption is not identical to the single-crossing assumption in Yenmez (2014).For instance, their condition permits the second inequality in Equation 14 to be weak.

22

−ti =n−1∑k=i

m(k, k + 1, θk+1) (15)

Edelman et al. (2007) produce a generalized English auction that ex postimplements the efficient allocation rule in online advertising auctions. Thegeneralized English auction has a unique perfect Bayesian equilibrium incontinuous strategies. It is not SP, and therefore is not OSP.

Here we produce an alternative ascending auction that OSP-implementsthe efficient allocation rule.

Proposition 3. Assume A1, A2, A3. There exists G that OSP-implementsthe efficient allocation rule and the VCG payments.

Proof. We construct G. Set pn−1 := 0, An−1 = N .For l = 1, . . . , n− 1:

1. Start the price at pn−l.

2. Raise the price in small increments. If the current price is p′n−l, thenext price is:

p′′n−l :=

infθ∈Θ,i∈N

{m(n− l, n− l + 1, θi) : m(n− l, n− l + 1, θi) > p′n−l}(16)

3. At each price, query each agent in An−l (in an arbitrary order), givingher the option to quit.

4. At any price p′n−l, if agent i quits, allocate her slot n−l+1, and chargeevery agent in An−l \ i the price p′n−l.

5. Set

pn−l−1 :=

infθ∈Θ,i∈N

{m(n− l − 1, n− l, θi) : m(n− l, n− l + 1, θi) ≥ p′n−l}(17)

An−l−1 := An−l \ i (18)

It is an obviously dominant strategy for agent i to quit iff the price inround l is weakly greater than m(n− l, n− l + 1, θi).

23

Consider any round l. Payments from previous rounds are sunk costs.Quitting yields slot n − l + 1 at no additional cost, and removes the agentfrom future rounds.

Consider deviations where the earliest point of departure involves quit-ting. The current price p′n−l is weakly less than m(n− l, n− l+ 1, θi). If thetruth-telling strategy has the result that i quits in round l, this outcome isat least as good for i as quitting now. If the truth-telling strategy has theresult that i does not quit in round l, then i is charged some amount lessthan his marginal value for moving up a slot, and the next starting price ispn−l−1 ≤ m(n− l − 1, n− l, θi), so the argument repeats.

Consider deviations where the earliest point of departure involves stayingin. The current price p′n−l is weakly greater than m(n − l, n − l + 1, θi),so this either has the same result as quitting now, or raises i’s positionat marginal cost weakly above i’s marginal utility. This is trivially truefor the current round. Consider the next round, l + 1. If the startingprice pn−l−1 is strictly less m(n − l − 1, n − l, θi), then there exists someθ′ and j such that m(n − l − 1, n − l, θi) > m(n − l − 1, n − l, θ′j). Andm(n− l, n− l+ 1, θi) ≤ p′n−l ≤ m(n− l, n− l+ 1, θ′j), which contradicts A3.Repeating the argument suffices to prove the claim for all rounds l′ ≥ l.

By inspection, this mechanism and the specified strategy profile resultin the efficient allocation and the VCG payments.

Internet transactions conducted by a central auctioneer raise commit-ment problems, and bidders may be legitimately concerned about shill bid-ding. If we consider such auctions as repeated games, reputation can ame-liorate commitment problems, but the set of equilibria can be very large andprevent tractable analysis.

Proposition 3 shows that, even if we do not consider such auctions asrepeated games, there sometimes exist robust mechanisms that rely onlyon bilateral commitments. In the case of advertising auctions, the speedof transactions may require bidders to implement their strategies using au-tomata.

5.3 Top Trading Cycles

We now produce an impossibility result for OSP-implementation in a classicmatching environment. (Shapley and Scarf, 1974)

There are n agents in the market, each endowed with an indivisible good.An agent’s type is a vector θi ∈ Rn. Θ is the set of all n by n matrices of

24

real numbers. An outcome assigns one object to each agent. If agent i isassigned object k, he has utility θki . There are no money transfers.

Following Roth (1982), we assume that the algorithm in question has anarbitrary, fixed way of resolving ties.

Given preferences θ and agents R ⊆ N , a top trading cycle is a set∅ ⊂ R′ ⊆ R whose members can be indexed in a cyclic order:

R′ = {i1, i2, . . . , ir = i0} (19)

such that each agent ik likes ik+1’s good more than any other good inR, resolving ties according to the fixed order.

Definition 17. f is a top trading cycle rule if, for all θ, f(θ) is equal tothe output of the following algorithm:

1. Set R1 := N

2. For l = 1, 2, , . . .:

(a) Choose some top trading cycle R′ ⊆ Rl.(b) Carry out the indicated trades.

(c) Set Rl+1 := Rl \R′.(d) Terminate if Rl+1 = ∅.

Proposition 4. If f is a top trading cycle rule, then there exists G thatSP-implements f .

This result is proved in Roth (1982).

Proposition 5. If n ≥ 3 and f is a top trading cycle rule, then there doesnot exist G that OSP-implements f .

Proof. Saks and Yu (2005) note that SP-implementability is a hereditaryproperty of functions. That is, if f is SP-implementable given domain Θ,then the subfunction f ′ = f with domain Θ′ ⊆ Θ is SP-implementable.By inspection, the same is true for OSP-implementability. Thus, to proveProposition 5, it suffices to produce a subfunction that is not OSP-implementable.

Consider the following subset of Θ′ ⊂ Θ. Take agents a, b, c, with en-dowed goods A,B,C. a has only two possible types, θa and θ′a, such that

Either B �a C �a A �a . . .or C �a B �a A �a . . .

(20)

25

We make the symmetric assumption for b and c.We now argue by contradiction. Take any G pruned with respect to the

truthful strategy profiles, such that G OSP-implements f ′ = f for domainΘ′.22 Consider some history h at which P (h) = a with a non-singletonaction set. This cannot come before all such histories for b and c.

Suppose not, and suppose B �a C. If a chooses the action correspondingto B �a C, and faces opponent strategies corresponding to C �b A andB �c A, then a receives good A. If a chooses the action correspondingto C �a B, and faces opponent strategies corresponding A �c B, then areceives good C. Thus, it is not an obviously dominant strategy to choosethe action corresponding to B �a C. So a cannot be the first to have anon-singleton action set.

By symmetry, this argument applies to b and c as well. So all of theaction sets for a, b, and c are singletons, and G does not OSP-implement f ′,a contradiction.

Proposition 5 implies that no implementation of top trading cycles canbe deduced to be strategy-proof without contingent reasoning. This is well,since few would claim that the main result of Roth (1982) is obvious.

6 Conclusion

In this paper, we produced a compact definition of obviously strategy-proofmechanisms. This uses a novel notion: We define the earliest points ofdeparture of two strategies. Appendix B shows that this notion is latent inour usual definition of strategy-proofness.

Using a formal model of cognitive limitations, we proved that a strat-egy is obviously dominant if and only if it can be deduced to be weaklydominant without contingent reasoning. We proved that a choice rule isOSP-implementable if and only if it can be supported by bilateral commit-ments.

For binary allocation problems, we characterized the OSP mechanismsand the OSP-implementable allocation rules. We produced one possibilityresult for a case with multi-minded bidders, and one impossibility result fora classic matching algorithm.

Much remains to be done. There are many classic results for SP-implementation,where OSP-implementation is an open question. In this paper, we consid-ered a cognitive model that rules out contingent reasoning. It remains to be

22Lemma 2 establishes that, if f ′ is OSP-implementable, then such a pruned G exists.

26

seen whether there exists a tractable model of agents who can use contingentreasoning, but of limited complexity. Finally, this paper has characterizedone natural partial commitment metagame, but there exist others; and itwould be valuable to find a way to think rigorously and generally aboutpartial commitment.

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A δc has full support

Defining “full support” is a subtle issue. The support of a probability dis-tribution is the smallest closed set whose complement has probability zero.However, our definition of extensive game forms permits A(h) to be un-countably infinite, and does not specify a topology on A(h). The topologywe need is defined using utilities:

Definition 18. δc has full support if

∀i, h, Si, S−i, dc, θi : ∀ε > 0 :

Pd′c∼δc(|uGi (h, Si, S−i, d

′c, θi)− uGi (h, Si, S−i, dc, θi)| < ε) > 0

(21)

Consider the values of uGi (h, Si, S−i, dc, θi) that can be achieved for somerealization of dc. Full support requires that there does not exist an open ballin the achievable utilities that has probability zero under δc. Equivalently,the smallest closed set whose complement has probability zero is the entirespace of achievable utilities.

29

B Weak dominance restated using earliest pointsof departure

Definition 19. In G for agent i with preferences θi, Si is weakly domi-nant including chance realizations if:

∀S′i : ∀S′−i : ∀dc :

uGi (h∅, S′i, S′−i, dc, θi) ≤ uGi (h∅, Si, S

′−i, dc, θi)

(22)

Proposition 6. Si is weakly dominant including chance realizationsif and only if

∀S′i : ∀Ii ∈ α(Si, S′i) : ∀h ∈ Ii : ∀S′−i : ∀dc :

uGi (h, S′i, S′−i, dc, θi) ≤ uGi (h, Si, S

′−i, dc, θi)

(23)

Proof.

Lemma 1. The following statement holds for any Si. If there exists h ∈Ii, S−i, dc such that h is a subhistory of zG(h∅, Si, S−i, dc), then for all h′ ∈Ii, there exists S′−i, d

′c such that h′ is a subhistory of zG(h∅, Si, S

′−i, d

′c).

This holds because, if there is some history in h′ ∈ Ii that is neverreached if Si is played, then perfect recall requires that h and h′ be indifferent information sets.

First we show that if Equation 22 does not hold, then Equation 23 doesnot hold. Suppose Equation 22 does not hold. That is

∃S′i, S′−i, dc : uGi (θi, h∅, S′i, S′−i, dc) > uGi (θi, h∅, Si, S

′−i, dc) (24)

Thus, zG(h∅, Si, S′−i, dc) 6= zG(h∅, S

′i, S′−i, dc). So there exists a subhis-

tory h of zG(h∅, Si, S′−i, dc) where h ∈ Ii and Ii ∈ αi(Si, S′i). (S′i, Ii, h, S

′−i, dc)

is a counterexample to Equation 23.Now the other direction. Suppose Equation 23 is false. Then

∃S′i, Ii ∈ α(Si, S′i), h ∈ Ii, S−i, dc :

uGi (θi, h, S′i, S−i, dc) > uGi (θi, h, Si, S−i, dc)

(25)

By Lemma 1, we can find some Si, dc that brings us to h. That is, thereexists Si, dc such that h is a subhistory of zG(h∅, Si, Si, dc).

Now we specify a synthesis of (S−i, dc) and (S−i, dc). Define (S−i, dc) asfollows:

30

1. If there is a subhistory h′ of h such that h′ ∈ I ′j for j 6= i, then

Sj(I′j) = Si(I

′j)

2. For all other I ′j such that j 6= i: Sj(h′) = Sj(h

′)

3. If there is a subhistory h′ of h such that P (h′) = c, then dc(h′) = dc(h

′)

4. For all other h′ such that P (h′) = c: dc(h′) = dc(h

′)

Notice that no I ′j can contain both proper subhistories of h and propersuperhistories of h, by the perfect recall assumption. j remembers informa-tion set I ′j , and can tell whether he has visited it before.

Since Ii ∈ α(Si, S′i), it follows by construction that zG(h∅, S

′i, S−i, dc) =

zG(h, S′i, S−i, dc) and zG(h∅, Si, S−i, dc) = zG(h, Si, S−i, dc). Thus,

uG(θi, h∅, S′i, S−i, dc) = uG(θi, h, S

′i, S−i, dc)

> uGi (θi, h, Si, S−i, dc) = uGi (θi, h∅, Si, S−i, dc)(26)

C Proofs omitted from the main text

C.1 Theorem 1

Proof. First we prove the “if” direction. Fix agent 1 and preferences θ1.Suppose that S1 is not obviously dominant in game G. We need to demon-strate that there exists G′′ that generates the same experiences as G, suchthat λ(S1) is not weakly dominant in G′′. We proceed by construction.

Let (S′1, I1, hinf , Sinf

−1, dinfc , hsup, Ssup

−1 , dsupc ) be some tuple that falsifies Equa-

tion 4. Using the same technique as in the proof of Proposition 6 (see Ap-pendix B), we can choose (S1, S

inf−1, d

infc , S′1, S

sup−1 , d

supc ) such that (S1, S

inf−1, d

infc )

causes hinf to be on the path of play, and (S′1, Ssup−1 , d

supc ) causes hsup to be

on the path of play.First, let G′ be the same as G, except that all players other than 1

have perfect information: All their information sets are singleton. SinceDefinition 1 makes no reference to other players’ information partitions, G′

generates the same experiences for 1.

31

Second, let G′′ be the same as G′, except that Player 2 has a new infor-mation set at the start of the game.23 If 2 plays L at this information set,then the game proceeds exactly as in G′. If 2 plays R at this informationset, then play proceeds as follows: Let R = h1. Initialize k = 1, and applythe following algorithm:

1. P (hk) = 1.

2. hk ∈ Ik1 , where Ik1 is the (bijected partner of the) kth information setalong ψ1(I1).24

3. If the algorithm has reached I1 (i.e. if we have reached the point whereS1 and S′1 disagree), then stop the algorithm. Else:

4. Let hk+1 be the history where 1, at hk, played the same action as thatspecified in ψ1(I1).

5. If 1 plays a different action from that specified in ψ1(I1), then playproceeds as from any h′ 6= hk, where h′ ∈ Ik1 .

6. Repeat the above steps for k + 1.

When the algorithm stops, call that information set I ′′1 . This is thebijected partner of I1 in G, the first point of difference.

Now we define what happens at the history following R which is in I ′′1 .Call this history hα. At hα, if 1 plays S1(I1), then play proceeds as thoughfrom hinf in G′. If 1 plays S′1(I1), then play proceeds as though from hsup

in G′. If 1 plays a /∈ S1(I1) ∪ S′1(I1), then 1 proceeds as though from anyh ∈ I1 in G′.

In words, this is what we have just done: We have constructed a G′′

where Player 2 has the option to secretly move 1 into a ‘parallel’ game.In this parallel game, if 1 follows S1, then 1 is mechanically directed to (ahistory that is equivalent to) hinf . If 1 follows S′1, then 1 is mechanicallydirected to hsup. If 1 follows neither S1 nor S′1, then 1 is put into a part of thegame tree that can only lead to experiences for 1 that are also experiencesfor 1 in game G′. By construction, G′′ generates the same experiences for 1as G′.

23If |N | = 1, so that there is no second player, then all the steps in this proof work usingchance moves instead. We use Player 2 here, merely to illustrate that these equivalencesstill hold if the environment G is instead defined to be extensive game forms without chancemoves.

24That is, 1’s information sets are such that, if 1 has not yet deviated from S1, then 1cannot tell if 2 played L or R.

32

Now we show that λ(S1) is not weakly dominant in G′′. To do this,we define (S−1, dc) as follows: 2 plays R at the start of the game. Athistories following (hα, S1(I1)), play proceeds according to (Sinf

−1, dinfc ). At

histories following (hα, S′1(I1)), play proceeds according to (Ssup

−1 , dsupc ). At

other histories, it does not matter for our purposes how play proceeds.Thus, if 1 plays λ(S1), he reaches outcome g(ZG(h, S1, S

inf−1, d

infc )), whereas

if he plays λ(S′1), he reaches outcome g(ZG(h, S1, Ssup−1 , d

supc )), which he

strictly prefers. It follows that λ(S1) is not weakly dominant in G′′, since

uG′′

1 (h∅, λ(S1), S−1, dc, θ1) < uG′′

1 (h∅, λ(S′1), S−1, dc, θ1) (27)

But notice that we have not yet specified the chance function δc for G′′.Now we specify that, whenever P (h′) = c, δc(h

′) places probability 1 − εon dc(h

′), and probability ε divided evenly on the other actions at h′.25 Bycontinuity of E[uG

′′1 ] in ε, there exists ε > 0 such that

Eδc [uG′′

1 (h∅, λ(S1), S−1, dc, θ1)] < Eδc [uG′′

1 (h∅, λ(S′1), S−1, dc, θ1)] (28)

which demonstrates that λ(S1) is not weakly dominant in G′′.Now the “only if” direction. Given (G,S1), suppose some (G′, λ(S1)) in

its equivalence class such that λ(S1) is not weakly dominant in G′. We wantto prove that S1 is not obviously dominant in G.

For some (S′1, S′−1, δc):

Eδc [uG′

1 (h∅, λ(S1), S′−1, dc, θ1)] < Eδc [uG′

1 (h∅, S′1, S′−1, dc, θ1)] (29)

It follows that there exists d′c such that:

uG′

1 (h∅, λ(S1), S′−1, d′c, θ1) < uG

′1 (h∅, S

′1, S′−1, d

′c, θ1) (30)

Consider the histories zG′(h∅, λ(S1), S′−1, d

′c) and zG

′(h∅, S

′1, S′−1, d

′c). The

first difference between them is just after some h′ ∈ I ′1, when Player 1 choosesa different action. Let Ii ≡ λ−1(I ′i). Notice that Ii ∈ α(S1, λ

−1(S′1)).Since G generates the same experiences as G′, there exists some (S−1, dc)

such that g(zG(h∅, S1, S−1, dc)) = g′(zG′(h∅, λ(S1), S′−1, d

′c)). Similarly, there

exists some (S−1, dc) such that g(zG(h∅, λ−1(S′1), S−1, dc)) = g′(zG

′(h∅, S

′1, S′−1, d

′c)).

Both ψ1(zG′(h∅, λ(S1), S′−1, d

′c)) and ψ1(zG

′(h∅, S

′1, S′−1, d

′c)) include I ′1.

Consequently, there exists a subhistory h of zG(h∅, S1, S−1, dc) and a sub-history h of zG(h∅, λ

−1(S′1), S−1, dc) such that h, h ∈ Ii.25If there are infinite actions at h′, we specify that δc(h

′) is a linear (ε, 1−ε) combinationof any probability measure with full support and an atom of mass on dc(h

′).

33

We have found a deviation λ−1(S′1) and an information set Ii ∈ α(S1, λ−1(S′1))

such that h, h ∈ Ii and

uG1 (h, S1, S−1, dc, θ1) < uG1 (h, λ−1(S′1), S−1, dc, θ1) (31)

Thus, S1 is not obviously dominant in G.

C.2 Theorem 2

Proof. First we prove the “only if” direction. Suppose f is OSP-implementable.Let G and ((Sθii )i∈N )θ∈Θ be some mechanism and strategy profiles that OSP-implement f .

Pick any i ∈ N . Each terminal history z in G has some associatedexperience ψi(z) and outcome x. For each information set Ii ∈ Ii, weassociate a unique message m = β(Ii). For each action a ∈ A(Ii), weassociate a unique reply r = β(a). We now define

Γi = {γi : ¬∃z ∈ Z : (β(ψi(z)), g(z)) = γi} (32)

This implicitly defines the required bilateral commitment Ψi0. We now

define strategy S∗0 , which ‘runs’ game G. S∗0 is defined as follows:

1. Set h = h∅, the beginning history in G

2. If P (h) = j, for j ∈ N , then:

• Send j message and reply set (m,R) corresponding to the infor-mation set such that h ∈ Ij and the actions A(Ij) in G.

• Upon receiving reply r ∈ R, set h := (h, a) for a correspondingto r.

• Go to Step 2.

3. Else if P (h) = c, then:

• Randomize across available actions in A(h) according to measureδc in G.

• Set h := (h, a) for the chosen action.

• Go to Step 2.

4. If h ∈ Z, then announce outcome g(z) as required by G.

34

We now show that, for any θi, the strategy Sθii , that plays in the bilateral

commitment game whatever Sθii plays in G, is a best response to any SN\iand any S0 ∈ Ψi

0.Suppose not. Then there exists some SN\i and S0 ∈ Ψi

0 such that devia-

tion S′i yields a strictly higher expected payoff than Sθii . We can pick someS0 that never (for any realization) produces γi ∈ Γi. Consider the modifiedstrategy S′0 ∈ Ψi

0, which is equal to S0 with probability 1 − ε and is equalto S∗0 with probability ε.26 For small ε > 0, S′i still yields a strictly higher

expected payoff than Sθii .We now produce G′ that generates the same experiences for i as G, where

λ(Sθii ) is not weakly dominant. Let G′ be the extensive game such that:

1. The extensive game form in G′ is the same as the extensive game formin the bilateral commitment game, EXCEPT:

2. Whenever Player 0 is called to play in the bilateral commitment game,chance is called to play in G′.

3. S′0 in the bilateral commitment game is the same as δ′c in G′.

4. Any subtrees such that all the terminal nodes correspond to γi ∈ Γiare removed from G′.

At every terminal history in G′, i’s experience ψ′i(z′) and outcome g′(z′)

correspond to some communication sequence γi under S′0, which correspondsto that same communication sequence γi under S∗0 , which corresponds tosome experience ψi(z) and that same outcome in G. The same holds inreverse too, since by construction S∗0 can generate every communicationsequence not in Γi. Moreover, notice that if there was any subhistory h′

in G′ with an experience that did not have a partner in G, then therewould exist a terminal history with that property, and vice versa. Thus, G′

generates the same experiences for i as G.Summarizing: G′ generates the same experiences for i as G, but λ(Sθii )

is not weakly dominant in G′. Thus, by Theorem 1, Sθii is not obviouslydominant in G, a contradiction.

Repeating the above procedure for every agent produces the bilateralcommitments (Ψi

0)i∈N that support f .

26Note that this is not a behavioral strategy. We can represent this as the Plannerhaving a personal information set, where he flips an ε-weighted coin that decides his statevariable at the start of the game.

35

We now prove the “if” direction. Suppose there exist bilateral com-mitments (Ψi

0)i∈N that support f , with requisite strategy profiles S0, SN ,and prohibited set Γi. Since for all S′N , µ(Γi, S0, S

′N ) = 0, we can pick

S0 such that no realization produces γi ∈ Γi. We now construct G thatOSP-implements f .

As before, let G be the extensive game such that:

1. Whenever Player 0 is called to play in the bilateral commitment game,chance is called to play in G.

2. S0 in the bilateral commitment game is the same as δc in G.

3. Any subtrees such that all the terminal nodes correspond to γi ∈ Γiare removed from G.

Once more, let β(·) be the bijection from information sets and actionsin G to communication sequences (mk, Rk)

tk=1 and responses r under S0.

In G, for all i, θi, let Sθii play the same as Sθii in the partial commitmentgame.

Suppose that, for some i, θi, Sθii is not obviously dominant in G. Then,

by Theorem 1, we can find G′ that generates the same experiences whereλ(Sθii ) is not weakly dominant.

So consider G′ and S′−i such that agent i with type θi has a strictly prof-

itable deviation. We can equivalently construct strategy S′0, which ‘runs’game G′. We omit the details as they are essentially identical to the pre-vious construction. S′0 ∈ Ψi

0, since every γi produced by S′0 corresponds tosome (ψ′i(z

′), g′(z′)) in G′, which corresponds (by λ−1) to some (ψi(z), g(z))in G, which corresponds (by β) to some γi produced by S0, which (by con-struction) never produces γi ∈ Γi.

Consequently, there exist S′N\i and S′0 ∈ Ψi0 such that Sθii is not a best

response, a contradiction. This concludes the proof of the “if” direction.

C.3 Theorem 3

Proof.

Lemma 2. Let G ≡ P(G, (Sθ)θ∈Θ), and (Sθ)θ∈Θ be (Sθ)θ∈Θ restricted to G.If (G, (Sθ)θ∈Θ) OSP-implements (fy, ft), then (G, (Sθ)θ∈Θ) OSP-implements(fy, ft).

36

Let S and S be the sets of possible strategies under G and G respec-tively. Since (G, (Sθ)θ∈Θ) OSP-implements (fy, ft), it follows that, for allS ∈ (Sθ)θ∈Θ.

∀i : ∀S′i ∈ Si : ∀Ii ∈ α(Si, S′i) :

infh∈Ii,S′−i∈S−i,dc

uGi (θi, h, Si, S′−i, dc) ≥ sup

h∈Ii,S′−i∈S−i,dcuGi (θi, h, S

′i, S′−i, dc)

(33)

By inspection, S ⊆ S. Consequently, the above inequality implies

∀i : ∀S′i ∈ Si : ∀Ii ∈ α(Si, S′i) :

infh∈Ii,S′−i∈S−i,dc

uGi (θi, h, Si, S′−i, dc) ≥ sup

h∈Ii,S′−i∈S−i,dcuGi (θi, h, S

′i, S′−i, dc)

(34)

which proves Lemma 2.Take any (G, (Sθ)θ∈Θ) that implements (fy, ft). For any history h, we

define

Θh ≡ {θ ∈ Θ : h is a subhistory of zG(∅, Sθ)} (35)

Θh,i ≡ {θi : ∃θ−i : (θi, θ−i) ∈ Θh} (36)

For information set Ii, we define

ΘIi ≡ ∪h∈IiΘh (37)

ΘIi,i ≡ {θi : ∃θ−i : (θi, θ−i) ∈ ΘIi} (38)

Θ1Ii,i ≡ {θi : ∃θ−i : (θi, θ−i) ∈ ΘIi and i ∈ fy(θi, θ−i)} (39)

Θ0Ii,i ≡ {θi : ∃θ−i : (θi, θ−i) ∈ ΘIi and i /∈ fy(θi, θ−i)} (40)

Some observations about this construction:

1. Since player i’s strategy depends only on his own type, ΘIi,i = Θh,i

for all h ∈ Ii.

37

2. ΘIi,i = Θ1Ii,i∪Θ0

Ii,i

3. Since SP requires 1i∈fy(θ) weakly increasing in θi, Θ1Ii,i

dominates Θ0Ii,i

in the strong set order.

Lemma 3. Suppose (G, (Sθ)θ∈Θ) OSP-implements (fy, ft), where G = 〈N,H,P, (Ii)i∈N , g〉.For all i, for all Ii ∈ Ii, if:

1. θi < θ′i

2. θi ∈ Θ1Ii,i

3. θ′i ∈ Θ0Ii,i

then Sθii (Ii) = Sθ′ii (Ii).

Equivalently, for any Ii, there exists a∗Ii such that for all θi ∈ Θ1Ii,i∩Θ0

Ii,i,

Sθii (Ii) = a∗Ii.

Suppose not. Take (i, Ii, θi, θ′i) constituting a counterexample to Lemma

3. Since θi ∈ Θ1Ii,i

, there exists h ∈ Ii and S−i such that i ∈ gy(zG(h, Sθii , S−i)).

Fix ti ≡ gt,i(zG(h, Sθii , S−i)). Since θ′i ∈ Θ0

Ii,i, there exists h′ ∈ Ii and S′−i

such that i /∈ gy(zG(h′, S

θ′ii , S

′−i)). Fix t′i ≡ gt,i(z

G(h′, Sθ′ii , S

′−i)). Since

Sθii (Ii) 6= Sθ′ii (Ii) and θi ∪ θ′i ⊆ ΘIi,i, Ii ∈ α(Sθii , S

θ′ii ). Thus, OSP requires

that

ui(θi, h, Sθii , S−i) ≥ ui(θi, h

′, Sθ′ii , S

′−i) (41)

which implies

θi + ti ≥ t′i (42)

and

ui(θ′i, h, S

θii , S−i) ≤ ui(θ

′i, h′, S

θ′ii , S

′−i) (43)

which implies

θ′i + ti ≤ t′i (44)

But θ′i > θi, so

θ′i + ti > t′i (45)

a contradiction. This proves Lemma 3. The last statement follows as acorollary of the rest.

38

Lemma 4. Suppose (G, (Sθ)θ∈Θ) OSP-implements (fy, ft) and P(G, (Sθ)θ∈Θ) =G. Take any Ii such that Θ1

Ii,i∩Θ0

Ii,i6= ∅, and associated a∗Ii.

1. If there exists θi ∈ Θ0Ii,i

such that Sθii (Ii) 6= a∗Ii, then there exists t0isuch that:

(a) For all θi ∈ Θ0Ii,i

such that Sθii (Ii) 6= a∗Ii, for all h ∈ Ii, for all

S−i, gt,i(zG(h, Sθii , S−i)) = t0i .

(b) For all θi ∈ ΘIi,i such that Sθii (Ii) = a∗Ii, for all h ∈ Ii, for all

S−i, if i /∈ gy(zG(h, Sθii , S−i)), then gt,i(zG(h, Sθii , S−i)) = t0i .

2. If there exists θi ∈ Θ1Ii,i

such that Sθii (Ii) 6= a∗Ii, then there exists t1isuch that:

(a) For all θi ∈ Θ1Ii,i

such that Sθii (Ii) 6= a∗Ii, for all h ∈ Ii, for all

S−i, gt,i(zG(h, Sθii , S−i)) = t1i .

(b) For all θi ∈ ΘIi,i such that Sθii (Ii) = a∗Ii, for all h ∈ Ii, for all

S−i, if i ∈ gy(zG(h, Sθii , S−i)), then gt,i(zG(h, Sθii , S−i)) = t1i .

Take any type θ′i ∈ Θ0Ii,i

such that Sθ′ii (Ii) 6= a∗Ii . Take any type θ′′i ∈ Θ0

Ii,i

such that Sθ′′ii (Ii) = a∗Ii . (By Θ1

Ii,i∩ Θ0

Ii,i6= ∅ there exists at least one such

type.) Notice that Ii ∈ α(Sθ′ii , S

θ′′ii ).

By Lemma 3, θ′i /∈ Θ1Ii,i

, and the game is pruned. Thus,

∀h ∈ Ii : ∀S−i : i /∈ gy(zG(h, Sθ′ii , S−i)) (46)

Since θ′′i ∈ Θ0Ii,i

,

∃h ∈ Ii : ∃S−i : i /∈ gy(zG(h, Sθ′′ii , S−i)) (47)

OSP requires that type θ′i does not want to (inf-sup) deviate. Thus,

infh∈Ii,S−i

gt,i(zG(h, S

θ′ii , S−i)) ≥

suph∈Ii,S−i

{gt,i(zG(h, Sθ′′ii , S−i)) : i /∈ gy(zG(h, S

θ′′ii , S−i))}

(48)

OSP also requires that type θ′′i does not want to (inf-sup) deviate. Thisimplies

39

infh∈Ii,S−i

{gt,i(zG(h, Sθ′′ii , S−i)) : i /∈ gy(zG(h, S

θ′′ii , S−i))}

≥ suph∈Ii,S−i

gt,i(zG(h, S

θ′ii , S−i))

(49)

The RHS of Equation 48 is weakly greater than the LHS of Equation49. The RHS of Equation 49 is weakly greater than the LHS of Equation48. Consequently all four terms are equal. Moreover, this argument applies

to every θ′i ∈ Θ0Ii,i

such that Sθ′ii (Ii) 6= a∗Ii , and every θ′′i ∈ Θ0

Ii,isuch that

Sθ′′ii (Ii) = a∗Ii . Since the game is pruned, θ′′i satisfies (1b) iff θ′′i ∈ Θ0

Ii,iand

Sθ′′ii (Ii) = a∗Ii . This proves part 1 of Lemma 4. Part 2 follows by symmetry;

we omit the details since they involve only small notational changes to theabove argument.

Lemma 5. Suppose (G, (Sθ)θ∈Θ) OSP-implements (fy, ft) and P(G, (Sθ)θ∈Θ) =G. Take any Ii such that Θ1

Ii,i∩Θ0

Ii,i6= ∅, and associated a∗Ii. Let t1i and t0i

be defined as before.

1. If there exists θi ∈ Θ0Ii,i

such that Sθii (Ii) 6= a∗Ii, then for all (h ∈Ii, Si, S−i), if i ∈ gy(z

G(h, Si S−i)), then gt,i(zG(h, Si, S−i)) ≤ t0i −

sup{θi ∈ Θ0Ii,i

: Sθii (Ii) 6= a∗Ii}.

2. If there exists θi ∈ Θ1Ii,i

such that Sθii (Ii) 6= a∗Ii, then for all (h ∈Ii, Si, S−i), if i /∈ gy(zG(h, Si S−i)), then gt,i(z

G(h, Si, S−i)) ≤ inf{θi ∈Θ1Ii,i

: Sθii (Ii) 6= a∗Ii}+ t1i .

Suppose that part 1 of Lemma 5 does not hold. Fix (h ∈ Ii, Si, S−i)such that i ∈ gy(zG(h, Si S−i)) and gt,i(z

G(h, Si, S−i)) > t0i −sup{θi ∈ Θ0Ii,i

:

Sθii (Ii) 6= a∗Ii}. Since G is pruned, we can find some θ′i ∈ ΘIi,i such that for

every Ii ∈ {I ′i ∈ Ii : Ii occurs in ψ(I ′i)}, Sθ′ii (Ii) = Si(Ii). Fix that θ′i.

Fix θ′′i ∈ Θ0Ii,i

such that Sθii (Ii) 6= a∗Ii and θ′′i ≥ sup{θi ∈ Θ0Ii,i

: Sθii (Ii) 6=a∗Ii} − ε. Since G is pruned and θ′′i /∈ Θ1

Ii,i(by Lemma 3), it must be that

Sθ′′ii (Ii) 6= S

θ′ii (Ii).

By construction, Ii ∈ α(Sθ′ii , S

θ′′ii ).

OSP requires that, for all h′′ ∈ Ii, S′′−i:

ui(θ′′i , h′′, S

θ′′ii , S

′′−i) ≥ ui(θ′′i , h, S

θ′ii , S−i) (50)

40

which entails

t0i ≥ θ′′i + gt,i(zG(h, Si, S−i)) (51)

which entails

t0i − sup{θi ∈ Θ0Ii,i : Sθii (Ii) 6= a∗Ii}+ ε ≥ gt,i(zG(h, Si, S−i)) (52)

But, by hypothesis,

t0i − sup{θi ∈ Θ0Ii,i : Sθii (Ii) 6= a∗Ii} < gt,i(z

G(h, Si, S−i)) (53)

Since this argument holds for all ε > 0, we can pick ε small enough tocreate a contradiction. This proves part 1 of Lemma 5. Part 2 follows bysymmetry.

Lemma 6. Suppose (G, (Sθ)θ∈Θ) OSP-implements (fy, ft) and P(G, (Sθ)θ∈Θ) =G. Take any Ii such that |Θ1

Ii,i∩Θ0

Ii,i| > 1 and associated a∗Ii.

1. If there exists θi ∈ Θ0Ii,i

such that Sθii (Ii) 6= a∗Ii, then for all θ′i ∈ Θ1Ii,i

,

Sθ′ii (Ii) = a∗Ii.

2. If there exists θi ∈ Θ1Ii,i

such that Sθii (Ii) 6= a∗Ii, then for all θ′i ∈ Θ0Ii,i

,

Sθ′ii (Ii) = a∗Ii.

Suppose part 1 of Lemma 6 does not hold. Fix Ii, and choose θ′i < θ′′isuch that θ′i ∪ θ′′i ⊆ Θ1

Ii,i∩ Θ0

Ii,i. Fix θ′′′i ∈ Θ1

Ii,isuch that S

θ′′′ii (Ii) 6= a∗Ii .

By Lemma 3, if θ′′′i ∈ Θ0Ii,i

, then Sθ′′′ii (Ii) = a∗Ii , a contradiction. Thus,

θ′′′i ∈ Θ1Ii,i\ Θ0

Ii,i, and since Θ1

Ii,idominates Θ0

Ii,iin the strong set order,

θ′′i < θ′′′i .Since θ′i ∈ Θ1

Ii,i, there exists h′ ∈ Ii and θ′−i such that (θ′i, θ

′−i) ∈ ΘIi and

i ∈ gy(zG(h′, S

θ′ii , S

θ′−i−i )). By Lemma 4, there exists ai ∈ Ai(Ii) such that

ai 6= a∗Ii and choosing ai ensures i /∈ y and ti = t0i . Thus, by G SP

θ′i + gt,i(zG(h′, S

θ′ii , S

θ′−i−i )) ≥ t0i (54)

By θ′′i ∈ Θ0Ii,i

, there exists h′′ ∈ Ii and θ′′−i such that i /∈ gy(zG(h′′, Sθ′′ii , S

θ′′−i−i )).

By Lemma 4

gt,i(zG(h′′, S

θ′′ii , S

θ′′−i−i )) = t0i (55)

41

ByG SP, i ∈ gy(zG(h′, Sθ′′′ii , S

θ′−i−i )) and gt,i(z

G(h′, Sθ′′′ii , S

θ′−i−i )) = gt,i(z

G(h′, Sθ′ii , S

θ′−i−i )).

Notice that Ii ∈ α(Sθ′′ii , S

θ′′′ii ). Thus, OSP requires that θ′′i does not want

to (inf-sup) deviate to θ′′′i ’s strategy, which entails:

gt,i(zG(h′′, S

θ′′ii , S

θ′′−i−i )) ≥ θ′′i + gt,i(z

G(h′, Sθ′′′ii , S

θ′−i−i )) (56)

t0i ≥ θ′′i + gt,i(zG(h′, S

θ′′′ii , S

θ′−i−i ))

> θ′i + gt,i(zG(h′, S

θ′ii , S

θ′−i−i ))

(57)

which contradicts Equation 54.Part 2 follows by symmetry. This proves Lemma 6.

Lemma 7. Suppose (G, (Sθ)θ∈Θ) OSP-implements (fy, ft) and P(G, (Sθ)θ∈Θ) =G.

For all Ii, if |Θ1Ii,i∩ Θ0

Ii,i| ≤ 1 and |A(Ii)| ≥ 2, then there exists t1i and

t0i such that:

1. For all θi ∈ ΘIi,i, h ∈ Ii, S−i:

(a) If i /∈ gy(zG(h, Sθii , S−i)) then gt,i(zG(h, Sθii , S−i)) = t0i

(b) If i ∈ gy(zG(h, Sθii , S−i)) then gt,i(zG(h, Sθii , S−i)) = t1i

2. If |Θ1Ii,i| > 0 and |Θ0

Ii,i| > 0, then t1i = − inf{θi ∈ Θ1

Ii,i}+ t0i

By G pruned and |A(Ii)| ≥ 2, ΘIi,i 6= ∅.Consider the case where Θ1

Ii,i= ∅. Pick some θ′i ∈ Θ0

Ii,iand some

h′ ∈ Ii, S′−i. Fix t0i ≡ gt,i(z

G(h′, Sθ′ii , S

′−i)). Suppose there exists some

(θ′′i , θ′′−i) ∈ ΘIi such that ft,i(θ

′′i , θ′′−i) = t0i

′′ 6= t0i . Pick h′′ ∈ Ii in the terminal

history zG(∅, Sθ′′ii , S

θ′′−i−i ). By Equation 8, for all θi ∈ ΘIi,i, ft,i(θi, θ

′′−i) = t0i

′′.

By G pruned and |A(Ii)| ≥ 2, we can pick θ′′′i ∈ Θ0Ii,i

such that Sθ′′′ii (Ii) 6=

Sθ′ii (Ii). Notice that Ii ∈ α(S

θ′′′ii (Ii), S

θ′ii ). If t0i

′′> t0i , then

ui(θ′i, h′, S

θ′ii , S

′−i) = t0i < t0i

′′= ui(θ

′i, h′′, S

θ′′′ii , S

θ′′−i−i ) (58)

so (G,Sθ′ii ) is not OSP for (i, θ′i). If t0i

′′< t0i , then

ui(θ′′′i , h

′, Sθ′ii , S

′−i) = t0i > t0i

′′= ui(θ

′′′i , h

′′, Sθ′′′ii , S

θ′′−i−i ) (59)

42

so (G,Sθ′′′ii ) is not OSP for (i, θ′′′i ). By contradiction, this proves Lemma

7 for this case. A symmetric argument proves Lemma 7 for the case whereΘ0Ii,i

= ∅.Note that, if Lemma 7 holds at some information set Ii, it holds at all

information sets I ′i that follow Ii. Thus, we need only consider some earliestinformation set I∗i at which |Θ1

I∗i ,i∩Θ0

I∗i ,i| ≤ 1 and |A(I∗i )| ≥ 2.

Now we consider the case where Θ1I∗i ,i6= ∅ and Θ0

I∗i ,i6= ∅.

At every prior information set Ii prior to I∗i , |Θ1Ii,i∩ Θ0

Ii,i| ≥ 2. Since

Θ1I∗i ,i6= ∅ and Θ0

I∗i ,i6= ∅, by Lemma 6, I∗i is reached by some interval of

types all taking the same action. Thus sup{θi ∈ Θ0I∗i ,i} = inf{θi ∈ Θ1

I∗i ,i}.

Fix θi ∈ Θ0I∗i ,i

such that θi ≥ sup{θi ∈ Θ0I∗i ,i} − ε. Choose corresponding

h ∈ I∗i and θ−i ∈ ΘI∗i ,−i such that i /∈ gy(zG(h, S θii , S

θ−i−i )). Define t0i ≡

gt,i(zG(h, S θii , S

θ−i−i )).

Fix θi ∈ Θ1I∗i ,i

such that θi ≤ inf{θi ∈ Θ1I∗i ,i} + ε. Choose corresponding

h ∈ I∗i and θ−i ∈ ΘI∗i ,−i such that i ∈ gy(zG(h, S θii , S

θ−i−i )). Define t1i ≡

gt,i(zG(h, S θii , S

θ−i−i )).

Suppose there exists some (θ′i, θ′−i) ∈ ΘI∗i

such that i /∈ fy(θ′i, θ′−i) and

ft,i(θ′i, θ′−i) = t0i

′ 6= t0i . Since sup{θi ∈ Θ0I∗i ,i} = inf{θi ∈ Θ1

I∗i ,i}, it follows

that for all θ−i ∈ ΘI∗i ,−i, inf{θi : i ∈ fy(θi, θ−i)} = inf{θi ∈ Θ1I∗i ,i}. Thus, by

Equation 8, for all θi ∈ ΘI∗i ,i: ft,i(θi, θ

′−i) = −1i∈fy(θi,θ′−i)

inf{θi ∈ Θ1I∗i ,i}+t0i

′.

Fix h′ ∈ I∗i , in the terminal history zG(∅, Sθ′ii , S

θ′−i−i ).

By A(Ii) ≥ 2, we can pick some θ′′i ∈ ΘI∗i ,isuch that S

θ′′ii (Ii) 6= S θii (Ii).

Notice that I∗i ∈ α(Sθ′′ii , S

θii ). Either θ′′i ∈ Θ0

I∗ior θ′′i ∈ Θ1

I∗i\ Θ0

I∗i. Suppose

θ′′i ∈ Θ0I∗i

. Suppose t0i′> t0i . By OSP,

ui(θi, h, Sθii , S

θ−i−i ) ≥ ui(θi, h′, S

θ′′ii , S

θ′−i−i ) (60)

which entails

t0i ≥ t0i′+ 1i∈fy(θ′′i ,θ

′−i)

(θi − inf{θi ∈ Θ1I∗i ,i})

≥ t0i′+ 1fy(θ′′i ,θ

′−i)∈Yi(−ε)

≥ t0i′ − ε

(61)

and we can pick ε small enough to constitute a contradiction. Supposet0i′< t0i . By OSP

43

ui(θ′′i , h, S

θii , S

θ−i−i ) ≤ ui(θ′′i , h′, S

θ′′ii , S

θ′−i−i ) (62)

which entails

t0i ≤ t0i′+ 1i∈fy(θ′′i ,θ

′−i)

(θ′′i − inf{θi ∈ Θ1I∗i ,i})

= t0i′+ 1i∈fy(θ′′i ,θ

′−i)

(θ′′i − sup{θi ∈ Θ0I∗i ,i}) ≤ t0i

′ (63)

which is a contradiction.The case that remains is θ′′i ∈ Θ1

I∗i\ Θ0

I∗i. Then i ∈ fy(θ

′′i , θ′−i) and

ft,i(θ′′i , θ′−i) = − inf{θi ∈ Θ1

I∗i ,i}+ t0i

′. Suppose t0i

′> t0i . OSP requires:

ui(θi, h, Sθii , S

θ−i−i ) ≥ ui(θi, h′, S

θ′′ii , S

θ′−i−i ) (64)

which entails

t0i ≥ θi − inf{θi ∈ Θ1I∗i ,i}+ t0i

′

≥ t0i′ − ε

(65)

and we can pick ε small enough to constitute a contradiction.

Suppose t0i′< t0i . Since S

θ′′ii (Ii) 6= S θii (Ii), either S θii (Ii) 6= S θii (Ii) or

S θii (Ii) 6= Sθ′′ii (Ii). Moreover, ft,i(θi, θ

′i) = −1fy(θi,θ′−i)∈Yi

inf{θi ∈ Θ1I∗i ,i}+ t0i

′.

Suppose S θii (Ii) 6= S θii (Ii). OSP requires:

ui(θi, h′, S θii , S

θ′−i−i ) ≥ ui(θi, h, S θii , S

θ−i−i ) (66)

which entails

1i∈fy(θi,θ′−i)(θi − inf{θi ∈ Θ1

I∗i ,i}) + t0i

′ ≥ t0i (67)

which entails

1i∈fy(θi,θ′−i)ε+ t0i

′ ≥ t0i (68)

and we can pick ε small enough to yield a contradiction. Suppose

S θii (Ii) 6= Sθ′′ii (Ii). By Equation 8, ft,i(θi, θ

′−i) = −1i∈fy(θi,θ′−i)

inf{θi ∈Θ1I∗i ,i}+ t0i

′, and ft,i(θ

′′i , θ−i) = − inf{θi ∈ Θ1

I∗i ,i}+ t0i . OSP requires

ui(θi, h′, S θii , S

θ′−i−i ) ≥ ui(θi, h, S

θ′′ii , S

θ−i−i ) (69)

44

which entails

1i∈fy(θi,θ′−i)(θi − inf{θi ∈ Θ1

I∗i ,i}) + t0i

′ ≥ (θi − inf{θi ∈ Θ1I∗i ,i}) + t0i (70)

which entails

1i∈fy(θi,θ′−i)ε+ t0i

′ ≥ ε+ t0i (71)

which entails

t0i′ ≥ t0i (72)

a contradiction. By the above argument, for all Ii satisfying the assump-tions of Lemma 7, there is a unique transfer t0i for all terminal histories zpassing through Ii such that i /∈ gy(z). Equation 8 thus implies that thereis a unique transfer t1i for all terminal histories z passing through Ii suchthat i ∈ gy(z). Moreover, t1i = − inf{θi ∈ Θ1

Ii,i}+ t0i . This proves Lemma 7.

Now to bring this all together.27 Take any (G, (Sθ)θ∈Θ) that OSP-implements (fy, ft). Define G ≡ P(G, (Sθ)θ∈Θ) and (Sθ)θ∈Θ as (Sθ)θ∈Θ

restricted to G. By Lemma 2, (G, (Sθ)θ∈Θ) OSP-implements (fy, ft).We now characterize (G, (Sθ)θ∈Θ). For any player i, consider any in-

formation set I∗i such that |A(I∗i )| ≥ 2, and for all prior information setsI ′i ∈ ψi(I∗i )\I∗i , |A(I ′i)| = 1. By Lemma 3, there is a unique action a∗I∗i

taken

by all types in Θ1I∗i ,i∩Θ0

I∗i ,i.

Either |Θ1I∗i ,i∩Θ0

I∗i ,i| > 1 or |Θ1

I∗i ,i∩Θ0

I∗i ,i| ≤ 1.

If |Θ1I∗i ,i∩Θ0

I∗i ,i| > 1, then by Lemma 6, G pruned and |A(Ii)| ≥ 2,

1. EITHER: There exists θi ∈ Θ0I∗i ,i

such that Sθii (I∗i ) 6= a∗I∗i, and for all

θ′i ∈ Θ1I∗i ,i

, Sθ′ii (I∗i ) = a∗I∗i

.

2. OR: There exists θi ∈ Θ1I∗i ,i

such that Sθii (I∗i ) 6= a∗I∗i, and for all θ′i ∈

Θ0I∗i ,i

, Sθ′ii (I∗i ) = a∗I∗i

.

In the first case, then by Lemma 4, there is some t0i such that, for all(Si, S−i), for all h ∈ Ii, if i /∈ gy(zG(h, Si, S−i), then gt,i(z

G(h, Si, S−i) = t0i .Moreover, we can define a ‘going price’ at all information sets I ′i such thatI∗i ∈ ψi(I ′i):

27To any reader who has made it this far, thank you!

45

t1i (I′i) ≡ min

I′′i ∈ψi(I′i)[t0i − sup{θi ∈ Θ0

I′′i ,i: Sθii (I ′′i ) 6= a∗I′′i

}] (73)

Notice that this function falls monotonically as we move along the gametree; for any I ′i, I

′′i such that I ′i ∈ ψi(I

′′i ), t1i (I

′i) ≥ t1i (I

′′i ). Moreover, by

construction, at any I ′i, I′′i such that I ′′i is the immediate successor of I ′i in

i’s experience, if t1i (I′i) > t1i (I

′′i ), then there exists a ∈ A(I ′′i ) that yields

y /∈ Yi, and by Lemma 4 this yields transfer t0i .Lemma 5 and SP together imply that, at any terminal history z if the

going price at i’s last information set I ′i was some t1i (I′i), and i ∈ gy(z),

then gt,i(z) = t1i (I′i). If gt,i(z) < t1i (I

′i), then θi such that t0i − t1i (I

′i) <

θi < t0i −gt,i(z) would have a profitable deviation at the last information setwhere t1i fell.

In the second case, then by Lemma 4, there is some t1i such that, for all(Si, S−i), for all h ∈ I∗i , if i ∈ gy(zG(h, Si, S−i), then gt,i(z

G(h, Si, S−i) = t1i .Moreover, we can define a ‘going price’ at all information sets I ′i such thatI∗i ∈ ψi(I ′i):

t0i (I′i) ≡ min

I′′i ∈ψi(I′i)[t1i + inf{θi ∈ Θ0

I′′i ,i: Sθii (I ′′i ) 6= a∗I′′i

}] (74)

Once more,

1. This function falls monotonically as we move along the game tree.

2. At any I ′i, I′′i such that I ′′i is the immediate successor of I ′i in i’s experi-

ence, if t0i (I′i) > t0i (I

′′i ), then there exists a ∈ A(I ′′i ) that yields y ∈ Yi,

and transfer t1i .

3. For any z, if the going price at i’s last information set I ′i was somet0i (I

′i), and i /∈ gy(z), then gt,i(z) = t0i (I

′i).

Part (1.c.iii) and (2.c.iii) of Theorem 3 follow from Lemma 7. The aboveconstructions suffice to prove Theorem 3 for cases where |Θ1

I∗i ,i∩Θ0

I∗i ,i| > 1.

Cases where |Θ1I∗i ,i∩Θ0

I∗i ,i| ≤ 1 are dealt with by Lemma 7.

C.4 Theorem 4

Proof. Consider the sets used to construct ΘA(θN\A).

{θA : ∀θ′A\i ≥ θA\i : i /∈ fy(θi, θ′A\i, θN\A)} (75)

46

These are the type profiles θA = (θi, θA\i) such that, if all agents in A \ ihave types at least as high as θA\i and all agents in N \A have types θN\A,then the allocation rule requires that type θi is not satisfied.

Lemma 8. For all A ⊆ N , for all θN\A, ΘA(θN\A) is a join-semilatticewith respect to the product order on ΘA.

Take any θ′′A, θ′′′A ∈ ΘA(θN\A). We want to show that θ′′A∨θ′′′A ∈ ΘA(θN\A).

For all i ∈ A,

θ′′A, θ′′′A ∈ closure({θA : ∀θ′A\i ≥ θA\i : i /∈ fy(θi, θ′A\i, θN\A)}) (76)

The set on the RHS is upward-closed with respect to the product orderon θA\i.

Consider θA ≡ θ′′A∨θ′′′A . Its ith element has the property: θi = max{θ′′i , θ′′′i }.WLOG, suppose θ′′i ≥ θ′′′i . Then, since θA\i ≥ θ′′A\i,

θA ∈ closure({θA : ∀θ′A\i ≥ θA\i : i /∈ fy(θi, θ′A\i, θN\A)}) (77)

Since the above argument holds for all i ∈ A, θ′′A∨ θ′′′A ∈ ΘA(θN\A). Thisconcludes the proof of Lemma 8.

First we prove the “if” direction. We do this by constructing G (and thecorresponding strategy profiles). ft is specified implicitly.

Fix, for each i, the partition points {θki }Kk=1. Initialize k0 := (1, 1, . . . , 1),where k0

i denotes the ith element of this vector. Each agent i chooses

whether to stay in the auction, at price θk0ii . i quits iff i has type θ

k0ii .

Set A0 to be the agents that do not quit. (These are the active bidders.)Set S0 := ∅. (These are the satisfied bidders.)

At each stage, we define θQN\Al−1 ≡ {θ

kl−1ii }i∈N\Al−1 . These are the

recorded type (intervals) of the agents who are no longer active.For l = 1, 2, . . .:

1. If Al−1 = ∅, then terminate the algorithm at allocation y = Sl−1.

2. If

(θkl−1ii )i∈Al−1 = sup{ΘAl−1(θQ

N\Al−1)} (78)

then

(a) Choose agent i ∈ Al−1 such that, if θAl−1 > (θkl−1j

j )j∈Al−1 , then

i ∈ fy(θAl−1 , θQN\Al−1).

47

(b) Charge that agent the price θkl−1ii .

(c) Ask that agent to report k > kl−1i such that θi ∈ (θk−1

i , θki ]. Set(klj)j∈N such that:

klj :=

{k if j = i

kl−1j otherwise.

(79)

(d) Set Al := Al−1 \ i(e) Set Sl := Sl−1 ∪ i(f) Skip to stage l + 1.

3. Choose i ∈ Al−1 such that (klj)j∈N satisfies

klj :=

{kl−1j + 1 if j = i

kl−1j otherwise.

(80)

and

(θkljj )j∈Al−1 ∈ ΘAl−1(θQ

N\Al−1) (81)

4. Offer agent i the option to quit. Agent i quits iff his type is less than

or equal to θklii .

5. If agent i does not quit, set Al := Al−1.

6. If agent i quits, set Al := Al−1 \ i.

7. Set Sl := Sl−1

8. Go to stage l + 1.

The above algorithm defines an auction with monotonically ascendingprices, where an agent has the option to quit (for a transfer of zero) wheneverher price rises. i makes a payment equal to her going price at the first pointwhere she ‘clinches the object’ - i.e. when she is guaranteed to be in thefinal allocation. When i clinches the object, i is also asked to report hertype - this may affect the payoffs of the agents that remain active, but doesnot affect her.28

28We could remove this feature by restricting attention to non-bossy allocation rules,where if changing i’s type changes the allocation, then it also changes whether i is satisfied.However, the canonical results for SP do not assume non-bossiness of fy, and we do not doso here. Alternatively, we could rule out such OSP mechanisms by instead requiring fullimplementation. However, the canonical monotonicity results for SP hold only for weakimplementation.

48

By inspection, it is an obviously dominant strategy for i to stay in theauction if the price is less than her type, to quit if the price is greater thanor equal to her type and to report her type truthfully at the point when sheclinches the object.

It remains to show that the algorithm is well-defined for all type profiles,and that whenever it terminates, the resulting allocation agrees with fy. Inparticular, we must show that in Steps (2a) and (3), we can pick agent isatisfying the requirements of the algorithm.

Step (2a) is well-defined by assumption.

Lemma 9. Under the above algorithm, for all l, (θklii )i∈Al ∈ ΘAl(θ

QN\Al)

We prove Lemma 9 by induction. It hold for l = 0 by the assumptionthat for all i, for all θ−i, i /∈ fy(θi, θ−i). Suppose it holds for l − 1. We nowprove that it holds for l (assuming, of course, that the algorithm does notterminate in Step 1 of iteration l).

Suppose (θkl−1ii )i∈Al−1 = sup{ΘAl−1(θQ

N\Al−1)}, so that Step 2 of the al-

gorithm is triggered in iteration l. Since (θkl−1ii )i∈Al−1 ∈ ΘAl−1(θQ

N\Al−1), we

know that for all j ∈ Al−1:

(θkl−1ii )i∈Al−1 ∈ closure({θAl−1 : ∀θ′Al−1\j ≥ (θ

kl−1ii )i∈Al−1\j :

j /∈ fy(θkl−1j

j , θ′Al−1\j , θQN\Al−1)})

(82)

The set on the RHS of Equation 82 is upward closed with respect to the

product order on θAl−1\j . Al ⊂ Al−1 and (θ

klii )i∈Al = (θ

kl−1ii )i∈Al . Moreover,

for the agent i who just clinched the object, θklii > θ

kl−1ii . Consequently, for

all j ∈ Al

(θklii )i∈Al ∈ closure({θAl : ∀θ′Al\j ≥ (θ

klii )i∈Al\j :

j /∈ fy(θkljj , θ

′Al\j , θ

QN\Al)})

(83)

Since this holds for each set in the intersection that defines ΘAl(θQN\Al),

this entails that (θklii )i∈Al ∈ ΘAl(θ

QN\Al).

49

Suppose (θkl−1ii )i∈Al−1 6= sup{ΘAl−1(θQ

N\Al−1)}, so that we reach Step 3

of the algorithm in iteration l. Then, provided Step 3 is well-defined (i.e.we can pick i satisfying our requirements),

(θkljj )j∈Al−1 ∈ ΘAl−1(θQ

N\Al−1) (84)

Al ⊆ Al−1, and if i ∈ N \Al, then θQi = θklii . Thus,

(θkljj )j∈Al ∈ ΘAl(θ

QN\Al) (85)

So we need only show that Step 3 is well-defined for iteration l, giventhat Lemma 9 holds for l−1. This will simultaneously prove Lemma 9, anddemonstrate that Step 3 is well-defined throughout.

We know that

(θkl−1j

j )j∈Al−1 ∈ ΘAl−1(θQN\Al−1) (86)

and

(θkl−1j

j )j∈Al−1 6= sup{ΘAl−1(θQN\Al−1)} (87)

Let A be the set of all agents in Al−1 such that θkl−1j

j is less than the jth

element of sup{ΘAl−1(θQN\Al−1)}.

Now, we define (θ′j)j∈A. For each j ∈ A, θ′j = (θkl−1j

j + θkl−1j +1

j )/2. Nowwe define two disjoint open sets:

ΘLAl−1 = {θAl−1 : θA < (θ′j)j∈A} (88)

ΘHAl−1 = [closure(ΘL

Al−1)]C (89)

The sets ΘAl−1(θQN\Al−1)∩ΘL

Al−1 and ΘAl−1(θQN\Al−1)∩ΘH

Al−1 are disjoint

nonempty sets, and are open in the subspace topology. By connectedness,there exists some θ′′

Al−1 ∈ ΘAl−1(θQN\Al−1) \ (ΘL

Al−1 ∪ΘHAl−1). Fix some θ′′

Al−1 .

This has at least one dimension i ∈ A such that θ′′i = (θkl−1ii + θ

kl−1i +1i )/2.

Define θ′′′Al−1 = (θ

kl−1j

j )j∈Al−1 ∨ θ′′Al−1 . By Lemma 8, ΘAl−1(θQN\Al−1) is a

join-semilattice. Thus, θ′′′Al−1 ∈ ΘAl−1(θQ

N\Al−1).

By construction,

50

(θkl−1j

j )j∈A ≤ θ′′′A< (θ

kl−1j +1

j )j∈A (90)

θ′′′Al−1\A = (θ

kl−1j

j )j∈(Al−1\A) (91)

Moreover, θ′′′Al−1 has at least one dimension i ∈ A such that θ′′′i = (θ

kl−1ii +

θkl−1i +1i )/2. Since fy admits a finite partition and ΘAl−1(θQ

N\Al−1) is closed,

it follows that for

klj :=

{kl−1j + 1 if j = i

kl−1j otherwise.

(92)

(θkljj )j∈Al−1 ∈ ΘAl−1(θQ

N\Al−1) (93)

This proves Lemma 9.Now we show that whenever the algorithm terminates, it agrees with fy.By the assumption that for all i, for all θ−i, i /∈ fy(θi, θ−i), it follows that

all the agents that quit at price θ1i = θi are never satisfied (i.e. i /∈ fy(θ∗)

for the true type profile θ∗).By construction, after any iteration l, the bidders that remain active Al

have true types (θ∗i )i∈Al that strictly exceed the going prices (θklii )i∈Al . The

bidders that are inactive have their types recorded (as accurately as we needgiven the finite partition) in the vector θQ

N\Al .

Suppose Step 1 is not activated and Step 2 is activated, in iteration l.Then, based on the information revealed up to iteration l− 1, we know thatthe chosen bidder i is such that i ∈ fy(θ∗). Thus, for all l, Sl ⊆ fy(θ∗).

Suppose neither Step 1 nor Step 2 is activated in iteration l. By Lemma

9, (θkl−1j

j )j∈Al−1 ∈ ΘAl−1(θQN\Al−1). Consider the chosen bidder i whose price

is incremented. The new price vector satisfies:

klj :=

{kl−1j + 1 if j = i

kl−1j otherwise.

(94)

and

(θkljj )j∈Al−1 ∈ ΘAl−1(θQ

N\Al−1) (95)

Define (θ′′j )j∈Al−1 ≡ .5(θkl−1j

j )j∈Al−1 + .5(θkljj )j∈Al−1 .

51

By (θkljj )j∈Al−1 ∈ ΘAl−1(θQ

N\Al−1),

(θkljj )j∈Al−1 ∈

closure({θAl−1 : ∀θ′Al−1\i ≥ θAl−1\i : i /∈ fy(θi, θ′Al−1\i, θN\Al−1)})(96)

The set on the RHS is (by fy monotone) downward-closed with respectto Θi. Thus,

(θ′′j )j∈Al−1 ∈closure({θAl−1 : ∀θ′Al−1\i ≥ θAl−1\i : i /∈ fy(θi, θ′Al−1\i, θN\Al−1)})

(97)

Thus, we can choose (θ′′′j )j∈Al−1 ∈ {θAl−1 : ∀θ′Al−1\i ≥ θAl−1\i : i /∈

fy(θi, θ′Al−1\i, θ

QN\Al−1)} such that |(θ′′′j )j∈Al−1 − (θ′′j )j∈Al−1 | < ε, where ε is

strictly less than half of the length of the smallest interval in the finitepartition.{θAl−1 : ∀θ′

Al−1\i ≥ θAl−1\i : i /∈ fy(θi, θ′Al−1\i, θQN\Al−1)} is upward closed

with respect to the product order on ΘAl−1\i. Thus, from the propertiesof (θ′′′j )j∈Al−1 , and the assumption that fy admits a finite partition, we

conclude that, for all θi ∈ (θkl−1ii , θ

klii ], for all θ′

Al−1\i > (θkl−1j

j )j∈Al−1\i, i /∈fy(θi, θ

′Al−1\i, θ

QN\Al−1).

Thus, whenever some bidder i’s going price rises in iteration l, the typeswho quit are those that, based on the information revealed so far, are re-quired by the allocation rule not to be satisfied. For all l, for the true typeprofile θ∗, (Al)C ∩ (Sl)C ⊆ fy(θ∗)C .

Gathering results: For all l, Sl = (Al)C∩Sl ⊆ fy(θ∗) and (At)C∩(Sl)C ⊆fy(θ

∗)C . Thus, whenever Al = ∅, fy(θ∗) = Sl. This completes the proof ofthe “if” direction.

Now the “only if” direction. G OSP-implements (fy, ft), so fy is SP-implementable. Thus, fy is monotone.

For all i, type θi is never satisfied, and always has a zero transfer. Thus,by Theorem 3, we can restrict our attention to monotone price mechanismsthat satisfy the “Either” clause in Definition 15 - i.e. every agent faces anascending price associated with being satisfied, and a fixed outside option(call this an ascending price mechanism, or APM). Suppose we have someG that OSP-implements (fy, ft). Moreover, suppose G is pruned, so that Gis an APM.

52

Take any A ⊆ N and θN\A. We now show that ΘA(θN\A) is connected.Let p : [0, 1] → ΘA be the price path under G faced by agents in A, whenthe type profile for the agents in A is sup{ΘA(θN\A)} and the type profilefor the agents in N \ A is θN\A. Let z be the terminal history that resultsfrom that type profile, and let l be the number of elements of that sequence.Let h1, h2, . . . , hl be the subhistories of z. (If z is infinitely long, instead letl be the index of some finite history such that all agents have only singletonaction sets afterwards.)

Formally, p is defined as follows: Start f(0) = (θi)i∈A. For each sub-history hm, let p(ml ) be equal to the prices faced by agents in A at hm.For all points in r ∈ (m−1

l , ml ), p(r) = (1 − β)p(m − 1) + βp(m), forβ = (r − m−1

l )/(1/l).By inspection, p is a continuous function. Moreover, since at any point

when an agent i quits under G, i /∈ fy(θ∗) based on the information revealedso far, for all r, p(r) ∈ ΘA(θN\A). Thus, p is a path from ΘA(θN\A) to

sup{ΘA(θN\A)}.By Lemma 8, ΘA(θN\A) is a join semi-lattice. We can generate a path p′

from any θ′A ∈ ΘA(θN\A) to sup{ΘA(θN\A)}, by defining p′(r) ≡ θ′A ∨ p(r).Thus, ΘA(θN\A) is path-connected, which implies that it is connected.

We now show that there exists i ∈ A such that, if θA > sup{ΘA(θN\A)},then i ∈ fy(θA, θN\A). If we cannot choose some θA > sup{ΘA(θN\A)}, then

this holds vacuously. Thus, fix some θ′A > sup{ΘA(θN\A)}.Let z be the terminal history in G when the type profile is (θ′A, θN\A).Suppose there does not exist i ∈ A such that such that, for all θA >

sup{ΘA(θN\A)}, i ∈ fy(θA, θN\A). By definition of sup{ΘA(θN\A)}, there

also does not exist i ∈ A such that such that, for all θA > sup{ΘA(θN\A)},i /∈ fy(θA, θN\A).

Thus, the price path for agents in A along history z (defined as before)is not such that p(r) ≤ sup{ΘA(θN\A)} for all r ∈ [0, 1]. Thus, there must

be a first point along z where the price path is not in {ΘA(θN\A)}. Considerthe agent i whose price was incremented at that point.

For all j ∈ A, the relevant set in the intersection that defines {ΘA(θN\A)}is upward-closed with respect to the product order on A \ j. Thus, whenthe price first leaves {ΘA(θN\A)} at some subhistory ht, it must be that

p(t

l) /∈ closure({θA : ∀θ′A\i ≥ θA\i : i /∈ fy(θi, θ′A\i, θN\A)}) (98)

The complement of the set on the RHS of Equation 98 is open. Thus,for some ε > 0, an open ε-ball around p( tl ) is a subset of the complement of

53

the RHS.Consequently, we can choose some θ′′i strictly greater than i’s old price,

and strictly less than i’s new going price, and some θ′′A\i strictly greater (in

the product order) that the going prices forA\i, such that i ∈ fy(θ′′i , θ′′A\i, θN\A).

Since G is an APM, the actions of types (θ′′i , θ′′A\i) and the actions of types

θ′A are indistinguishable prior to that point. Thus, G does not result in theprescribed outcome for type profile (θ′′i , θ

′′A\i, θN\A), a contradiction.

This completes the proof of the “only-if” direction.

54