# Envy-free cake-cutting

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An envy-free cake-cutting is a kind of fair cake-cutting. It is a division of a heterogeneous resource ("cake") that satisfies the envy-free criterion, namely, that every partner feels that their allocated share is at least as good as any other share, according to their own subjective valuation.

 Unsolved problem in computer science: What is the runtime complexity of envy-free cake-cutting? (more unsolved problems in computer science)

When there are only 2 partners, the problem is easy and has been solved in Biblical times by the divide and choose protocol. When there are 3 or more partners, the problem becomes much more challenging.

Two major variants of the problem have been studied:

• Connected pieces, e.g. if the cake is a 1-dimensional interval then each partner must receive a single sub-interval. If there are ${\displaystyle n}$ partners, only ${\displaystyle n-1}$ cuts are needed.
• General pieces, e.g. if the cake is a 1-dimensional interval then each partner can receive a union of disjoint sub-intervals.

## Short history

The modern research of the fair cake-cutting problem has started in the 1940s. The first fairness criterion studied was proportional division, and a procedure for n partners has been found soon.

The stronger criterion of envy-freeness was introduced into the cake-cutting problem by George Gamow and Marvin Stern in 1950s.[1]

A procedure for 3 partners and general pieces was found in 1960. A procedure for 3 partners and connected pieces was found only in 1980.

Envy-free division for 4 or more partners has been an open problem until the 1990s, when three procedures for general pieces and a procedure for connected pieces have been published. All these procedures are unbounded - they may require a number of steps which is not bounded in advance. The procedure for connected pieces may even require an infinite number of steps.

Two lower bounds on the run-time complexity of envy-freeness have been published in the 2000s.

In the 2010s, several approximation procedures and procedures for special cases have been published. The question whether bounded-time procedures exist for the case of general pieces had remained open for a long time. The problem was finally solved in 2016. Haris Aziz and Simon Mackenzie presented a discrete envy-free protocol that requires at most ${\displaystyle n^{n^{n^{n^{n^{n}}}}}}$ queries. There is still a very large gap between the lower bound and the procedure. As of August 2016, the exact run-time complexity of envy-freeness is still unknown.

For the case of connected pieces, it was noted that the hardness result assumes that the entire cake must be divided. If this requirement is replaced by the weaker requirement that every partner receives a proportional value (at least 1/n of the total cake value according to their own valuation), then a bounded procedure for 3 partners is known, but it has remained an open problem whether there exist bounded-time procedures for 4 or more partners.

## Connected pieces

### Existence proof

An envy-free division for n agents with connected pieces always exists under the following mild assumptions:[2]

• No agent prefers an empty piece over a non-empty piece.
• The preferences of the agents are continuous.

Note that it is not required that the preferences of the agents are represented by an additive function.

The main concept in the proof is the simplex of partitions. Suppose the cake is the interval [0,1]. Each partition with connected pieces can be uniquely represented by n-1 numbers in [0,1] which represent the cut locations. The union of all partitions is a simplex.

For each partition, each agent has one or more pieces which they weakly prefer. E.g., for the partition represented by "0.3,0.5", one agent may prefer piece #1 (the piece [0,0.3]) while another agent might prefer piece #2 (the piece [0.3,0.5]) while a third agent might prefer both piece #1 and piece #2 (which means that they are indifferent between them but like any of them more than piece #3).

For every agent, the partition simplex is covered by n parts, possibly overlapping at their boundaries, such that for all partitions in part i, the agent prefers piece i. In the interior of part i, the agent prefers only piece i, while in the boundary of part i, the agent also prefers some other pieces. So for every i, there is a certain region in the partition simplex in which at least one agent prefers only piece i. Call this region Ui. Using a certain topological lemma (that is similar to the Knaster–Kuratowski–Mazurkiewicz lemma), it is possible to prove that the intersection of all Ui's is non-empty. Hence, there is a partition in which every piece is the unique preference of an agent. Since the number of pieces equals the number of agents, we can allocate each piece to the agent that prefers it and get an envy-free allocation.

### Procedures

For three agents, an envy-free division can be found using several different procedures:

These are continuous procedures - they rely on people moving knives continuously and simultaneously. They cannot be executed in a finite number of discrete steps.

For n agents, an envy-free division can be found by Simmons' cake-cutting protocol. The protocol uses a simplex of partitions similar to the one used in Stromquist's existence proof. It generates a sequence of partitions which converges to an envy-free partition. The convergence might take infinitely many steps.

It is not a coincidence that all these algorithms may require infinitely many queries. As we show in the following subsection, it may be impossible to find an envy-free cake-cutting with connected pieces with a finite number of queries.

### Hardness result

An envy-free division with connected pieces for 3 or more agents cannot be found by a finite protocol. [3] The reason this result doesn't contradict the previously mentioned algorithms is that they are not finite in the mathematical sense.[4]

The impossibility proof uses a rigid measure system (RMS) - a system of n value measures, for which an envy-free division must cut the cake at very specific locations. Then, finding an envy-free division reduces to finding these specific locations. Assuming the cake is the real interval [0,1], finding an envy-free division using queries to the agents is equivalent to finding a real number in the interval [0,1] using yes/no questions. This might require an infinite number of questions.

A RMS for 3 agents can be constructed as follows. Let x, y, s, and t be parameters satisfying:

• ${\displaystyle 0
• ${\displaystyle 0
• ${\displaystyle s+2t=1}$.

Construct a set of three measures with these two properties:

1. The density of each measure is always strictly between 2/2 and 2 (so for a given piece, the agents' valuations differ by a factor of less than 2).
2. The values of the pieces determined by x and y are as in the table:
Agent [0,x] [x,y] [y,1]
A t t s
B s t t
C t s t

Then, every envy-free division among Alice Bob and Carl must have cuts at x and y (there are exactly two such divisions), since all other options lead to envy:

• If cuts are made to the left of x and to the right of y, then Alice and Bob both insist on getting the middle piece.
• If cuts are made to the right of x and to the left of y, then no agent would accept the middle piece.
• If cuts are made to the right of x and to the right of y (say, at x*>x and y*>y), then both Alice and Carl prefer the leftmost piece to the rightmost piece, so Bob must agree to accept the rightmost piece. This means that Bob must value the piece [x,x*] at least twice as much as [y,y*]. But because of the restriction on the value densities, this means that both Alice and Carl value [x,x*] more than [y,y*], so they insist on the leftmost piece.
• The fourth case (cuts to the left of x and to the left of y) is analogous.

For every RMS, every agent i and every constant ε>0, there is a slightly different RMS with the following properties:

• The new value measure of agent i is completely identical to his old value measure;
• The new value measures of the other two agents are identical to their old value measure everywhere except in an ε-neighbourhood of x and y.

This implies that, if all queries answered so far were outside the ε-neighbourhood of x and y, then agent i has no way to know whether we are in the old RMS or in the new RMS.

Equipped with this knowledge, an adversary can trick every envy-free division protocol to go on forever:

1. Start with any RMS, e.g. with parameters x=1/3, y=2/3, s=0.3 and t=0.35.
2. As long as the protocol makes cuts at points other than x and y, let it continue.
3. Whenever the protocol asks agent i to make a cut at x or y, switch to a different RMS with x'≠x and y'≠y, making sure that the values are the same for all previously made cuts.

Thus the protocol can never make cuts at the correct x and y required for an envy-free division.

### Approximations

Since envy-free cake-cutting with connected pieces cannot be done in finite time, cake-cutters have tried to find work-arounds.

One work-around is looking for divisions which are only approximately-envy-free. There are two ways to define the approximation - in units of length or in units of value.

Length-based approximation uses the following definitions:

• A partition of a cake is represented by the n lengths of intervals it creates. So (0.2,0.5,0.3) is a partition of the unit interval to three sub-intervals with lengths 0.2, 0.5 and 0.3 (it is generated by cutting the unit interval at 0.2 and at 0.7).
• A multi-partition is a set of several different partitions.
• A multi-partition X is called envy-free if there exists a permutation of the partners such that, for every i, there exists an element of X such that the i-th partner prefers the i-th segment.
• A δ-multi-partition is a multi-partition in which, for each pair of partitions, the difference between each of their coordinates is at most δ. For example: {(0.2+δ,0.5,0.3), (0.2,0.5+δ,0.3), (0.2,0.5,0.3+δ)}.

The parameter δ represents the tolerance of the partners in units of length. E.g., if land is divided and the partners agree that a difference of 0.01 meter in the location of the border is not relevant to them, then it makes sense to look for a 0.01-multi-partition which is envy-free. Deng at al[5] present a modification of Simmons' cake-cutting protocol which finds an envy-free δ-multi-partition using ${\displaystyle O[(1/\delta )^{n-2}]}$ queries. Moreover, they prove a lower bound of ${\displaystyle \Omega [(1/\delta )^{n-2}/2^{(n-1)(n-2)}]}$ queries. Even when the utility functions are given explicitly by polynomial-time algorithms, the envy-free cake-cutting problem is PPAD-complete.

Value-based approximation uses the following definitions:

• A partition X is called ε-envy-free if there exists a permutation of the partners such that, for every i, the value of the i-th piece plus ε is at least as much as the value of any other piece: ${\displaystyle \forall i,j:V_{i}(X_{i})+\epsilon \geq V_{i}(X_{j})}$.
• A value measure is called Lipschitz-continuous if there exists a constant K such that, for any pair of intervals, the difference of values between them is at most K times the length of their symmetric difference ${\displaystyle \exists K:\forall j:|V_{i}(X_{i})-V_{i}(X_{j})|\leq K\cdot \mathrm {length} (X_{i}\setminus X_{j}\cup X_{j}\setminus X_{i})}$.

If all value measures are Lipschitz-continuous, then the two approximation definitions are related. Let ${\displaystyle \epsilon =2K\delta }$. Then, every partition in an envy-free δ-multi-partition is ε-envy-free.[5] Hence, Deng et al.'s results prove that, if all partners have Lipschitz-continuous valuations, an ε-envy-free partition can be found with ${\displaystyle \Theta [(1/\epsilon )^{n-2}]}$ queries.

Offline calculation is a second work-around that finds a totally-envy-free division but only for a restricted class of valuations. If all value measures are polynomials of degree at most d, there is an algorithm which is polynomial in n and d.[6] Given d, the algorithm asks the agents d+1 evaluation queries, which give d+1 points in the graph of the value measure. It is known that d+1 points are sufficient to interpolate a polynomial of degree d. Hence, the algorithm can interpolate the entire value measures of all agents, and find an envy-free division offline. The number of required queries is ${\displaystyle O(n^{2}d)}$.

Free disposal is a third work-around that keeps the requirement that the division be totally envy-free and works for all value measures, but drops the requirement that the entire cake must be divided. I.e, it allows to divide a subset of the cake and discard the remainder. Without further requirements the problem is trivial, since it is always envy-free to give nothing to all agents. Thus, the real goal is to give each agent a strictly positive value. Every cake allocation can be characterized by its level of proportionality, which is the value of the least fortunate agent. An envy-free division of the entire cake is also a proportional division, and its proportionality level is at least ${\displaystyle 1/n}$, which is the best possible. But when free disposal is allowed, an envy-free division may have a lower proportionality level, and the goal is to find an envy-free division with the highest possible proportionality. The currently known bounds are:

• For 3 agents the proportionality is ${\displaystyle 1/3}$, i.e, an envy-free and proportional division is attainable in bounded time.[7]
• For 4 agents the proportionality is ${\displaystyle 1/7}$.[7]
• For n agents, the proportionality is ${\displaystyle 1/(3n)}$.[8]

It is not known whether there exists a bounded-time envy-free and proportional division procedure for 4 or more partners with connected pieces.

### Variants

Most procedures for cake-cutting with connected pieces assume that the cake is a 1-dimensional interval and the pieces are 1-dimensional sub-intervals. Often, it is desirable that the pieces have a certain geometric shape, such as a square. With such constraints, it may be impossible to divide the entire cake (e.g., a square cannot be divided to two squares), so we must allow free disposal. As explained above, when free disposal is allowed, the procedures are measured by their level of proportionality - the value that they guarantee to all agents. The following results are currently known:[9]

• For two partners sharing a square cake and wanting square pieces the proportionality is ${\displaystyle 1/4}$, and this is the best that can be guaranteed even without envy.
• For three partners sharing a square cake and wanting square pieces the proportionality is ${\displaystyle 1/10}$; the best that can be guaranteed without envy is ${\displaystyle 1/6}$.

## Disconnected pieces

### Procedures

For 3 partners, the Selfridge–Conway discrete procedure makes an envy-free division with at most 5 cuts. Other procedures using moving knives require fewer cuts:

For 4 partners, The Brams–Taylor–Zwicker procedure makes an envy-free division with at most 11 cuts.[11] For 5 partners, a procedure by Saberi and Wang makes an envy-free division with a bounded number of cuts.[12] Both these procedures use Austin's procedure for two partners and general fractions as an initial step. Hence, these procedures should be considered infinite - they cannot be completed using a finite number of steps.

For 4 or more partners, there are three algorithms which are finite but unbounded - there is no fixed bound on the number of cuts required.[13] There are three such algorithms:

Although the protocols are different, the main idea behind them is similar: Divide the cake to a finite but unbounded number of "crumbs", each of which is so small that its value for all partners is negligible. Then combine the crumbs in a sophisticated way to get the desired division. William Gasarch has compared the three unbounded algorithms using ordinal numbers.[15]

The question of whether envy-free cake-cutting can be done in bounded time for 4 or more partners had been open for many years. It was finally solved in 2016 by Hariz Aziz and Simon Mackenzie. Initially they developed a bounded-time algorithm for 4 partners.[16] Then they extended their algorithm to handle any number of partners.[8] Their algorithm requires at most ${\displaystyle n^{n^{n^{n^{n^{n}}}}}}$ queries. While this number is bounded, it is very far from the lower bound of ${\displaystyle \Omega (n^{2})}$. So the question of how many queries are required for envy-free cake-cutting is still open.

### Approximations and partial solutions

A reentrant variant of the last diminisher protocol finds an additive approximation to an envy-free division in bounded time. Specifically, for every constant ${\displaystyle \epsilon >0}$, it returns a division in which the value of each partner is at least the largest value minus ${\displaystyle \epsilon }$, in time ${\displaystyle n^{2}/\epsilon }$.

If all value measures are piecewise-linear, there is an algorithm which is polynomial in the size of the representation of the value functions.[17] The number of queries is ${\displaystyle O(n^{6}k\ln {k})}$, where ${\displaystyle k}$ is the number of discontinuities in the derivatives of the value density functions.

### Hardness result

Every envy-free procedure for n people requires at least Ω(n2) queries.[18] The proof relies on a careful analysis of the amount of information the algorithm has on each partner.

A. Assume that the cake is the 1-dimensional interval [0,1], and that the value of the entire cake for each of the partners is normalized 1. In each step, the algorithm asks a certain partner either to evaluate a certain interval contained in [0,1], or to mark an interval with a specified value. In both cases, the algorithm gathers information only about intervals whose end-points were mentioned in the query or in the reply. Let's call these endpoints landmarks. Initially the only landmarks of i are 0 and 1 since the only thing the algorithm knows about partner i is that vi([0,1])=1. If the algorithm asks partner i to evaluate the interval [0.2,1], then after the reply the landmarks of i are {0,0.2,1}. The algorithm can calculate vi([0,0.2]), but not the value of any interval whose endpoint is different than 0.2. The number of landmarks increases by at most 2 in each query. In particular, the value of the interval [0,0.2] might be concentrated entirely near 0, or entirely near 0.2, or anywhere in between.

B. An interval between two consecutive landmarks of partner i is called a landmark-interval of partner i, When the algorithm decides to allocate a piece of cake to partner i, it must allocate a piece whose total value for i is at least as large as any landmark-interval of i. The proof is by contradiction: suppose there is a certain landmark-interval J whose value for i is more than the value actually allocated to i. Some other partner, say j, will necessarily receive some part of the landmark-interval J. By paragraph A, it is possible that all the value of interval J is concentrated inside the share allocated to partner j. Thus, i envies j and the division is not envy-free.

C. Suppose all partners answer all queries as if their value measure is uniform (i.e. the value of an interval is equal to its length). By paragraph B, the algorithm may assign a piece to partner i, only if it is longer than all landmark-intervals of i. At least n/2 partners must receive an interval with a length of at most 2/n; hence all their landmark-intervals must have a length of at most 2/n; hence they must have at least n/2 landmark-intervals; hence they must have at least n/2 landmarks.

D. Each query answered by partner i involves at most two new endpoints, so it increases the number of landmarks of i by at most 2. Hence, in the case described by paragraph C, the algorithm must ask each of n/2 partners, at least n/4 queries. The total number of queries is thus at least n2/8 = Ω(n2).

It is an open question whether a bounded algorithm exists for arbitrary valuation functions. Thus there is a huge gap between the lower bound of Ω(n2) and the best currently known algorithm which is finite but unbounded.

### Existence proofs and variants

In addition to the general existence proofs implied by the algorithms described above, there are proofs for the existence of envy-free partitions with additional properties:

Both proofs work only for additive and non-atomic value measures, and rely on the ability to give each partner a large number of disconnected pieces.

## Weighted-envy-free division

A common generalization of the envy-free criterion is that each of the partners has a different entitlement. I.e., for every partner i there is a weight wi describing the fraction of the cake that they are entitled to receive (the sum of all wi is 1). Then a weighted-envy-free division is defined as:

For every agent i with value measure V, and for every other agent k:
V(Xi) / V(Xk) ≥ wi / wk

I.e., every partner thinks that their allocation relative to their entitlement is at least as large as any other allocation relative to the other partner's entitlement.

When all weights are the same (and equal to 1/n), this definition reduces to the standard definition of envy-freeness.

When the pieces may be disconnected, a weighted envy-free division can be found constructively using the Robertson-Webb protocol, for any set of weights.

But when the pieces must be connected, only non-constructive existence proofs are reported. A weighted envy-free division is known to exist in the following cases (each case generalizes the previous case):

## Dividing a 'bad' cake

In some cases, the 'cake' to be divided has a negative value. For example, it might be piece of lawn that has to be mowed, or a piece of wasteland that has to be cleaned. Then, the cake is a 'heterogeneous bad' rather than a 'heterogeneous good'.

Some procedures for envy-free cake-cutting can be adapted to work for a bad cake, but the adaptation is often not trivial. See envy-free chore division for more details.

## Summary tables

Summary by result
Name Type Cake Pieces
#partners (n)
#queries #cuts envy
proportionality[22]
Comments
Divide and choose Discrete proc Any Connected 2 2 1 (optimal) None 1/2
Stromquist Moving-knife proc Interval Connected 3 2 (optimal) None 1/3
Selfridge–Conway Discrete proc Any Disconnected 3 9 5 None 1/3
Brams–Taylor–Zwicker Moving-knife proc Any Disconnected 4 11 None 1/4
Saberi–Wang[12] Discrete proc Any Disconnected 4 Bounded Bounded None 1/4 Free disposal
Aziz–Mackenzie[16] Discrete proc Any Disconnected 4 203 584 None 1/4
Saberi–Wang[12] Moving-knife proc Any Disconnected 5 Bounded None 1/5
Stromquist Existence Interval Connected n n-1 None 1/n
Simmons Discrete proc Interval Connected n n-1 None 1/n
Deng-Qi-Saberi Discrete proc Interval Connected n ${\displaystyle \left({\frac {1}{\epsilon }}\right)^{n-2}}$ n-1 Additive ${\displaystyle \epsilon }$ ${\displaystyle \left({\tfrac {1}{n}}\right)-\epsilon }$ Only when valuations are Lipschitz-continuous
Branzei[6] Discrete proc Interval Connected n ${\displaystyle n^{2}d}$ ? None 1/n Only when value densities are polynomial with degree at most d.
Waste-Makes-Haste Discrete proc Interval Connected 3 9 4 None 1/3 Free disposal
Waste-Makes-Haste Discrete proc Any Connected 4 16 6 None 1/7 Free disposal
Waste-Makes-Haste Discrete proc Any Connected n ${\displaystyle 2^{n}}$ ${\displaystyle 2^{n-1}-1}$ None ${\displaystyle {\frac {1}{2^{n-1}}}}$ Free disposal
Aziz-Mackenzie ConnectedPieces [8] Discrete proc Any Connected n ${\displaystyle n^{n+1}}$ ${\displaystyle n^{n+1}}$ None ${\displaystyle {\frac {1}{3n}}}$ Free disposal
Brams-Taylor Discrete proc Any Disconnected n Unbounded Unbounded None 1/n
Robertson-Webb Discrete proc Any Disconnected n Unbounded Unbounded None 1/n Weighted-envy-free.
Pikhurko[14] Discrete proc Any Disconnected n Unbounded Unbounded None 1/n
Aziz–Mackenzie[8] Discrete proc Any Disconnected n ${\displaystyle n^{n^{n^{n^{n^{n}}}}}}$ ${\displaystyle n^{n^{n^{n^{n^{n}}}}}}$ None 1/n
Reentrant last diminisher Discrete proc Interval Disonnected n ${\displaystyle n^{2}/\epsilon }$ ? Additive ${\displaystyle \epsilon }$ 1/n
Kurokawa-Lai-Procaccia[17] Discrete proc Interval Disonnected n ${\displaystyle n^{6}k\ln k}$ ? None 1/n Only when value densities are piecewise linear with at most k discontinuities.
Weller Existence Any Disconnected n None 1/n Pareto efficient.
2-D Discrete proc Square Connected and Square 2 2 2 None 1/4 Free disposal
2-D Moving-knife proc Square Connected and Square 3 6 None 1/10 Free disposal

Summary by number of agents and type of pieces:

# agents Connected pieces General pieces
2 Divide and choose
3 Stromquist moving-knives procedure (infinite time);
Waste-makes-haste (bounded-time, bounded cuts, free disposal, proportional)
Selfridge–Conway discrete procedure (bounded-time, at most 5 cuts).
4 Waste-makes-haste (bounded-time, bounded cuts, free disposal, proportionality 1/7). Brams–Taylor–Zwicker moving knives procedure (infinite time, at most 11 cuts).
Saberi–Wang discrete procedure[12] (bounded time, bounded cuts, free disposal, proportional).
Aziz–Mackenzie discrete procedure[16] (bounded time, bounded cuts, proportional).
5 Saberi–Wang moving-knives procedure[12] (infinite time, bounded cuts).
n Simmons' protocol (infinite time)
Deng-Qi-Saberi (approximately envy-free, exponential time).
Waste-makes-haste (fully envy-free, exponential time, free-disposal, exponential proportionality)
Aziz-Mackenzie ConnectedPieces [8] (fully envy-free, exponential time, free-disposal, linear proportionality)
Brams and Taylor (1995);
Robertson and Webb (1998).
- Both algorithms require a finite but unbounded number of cuts.

Aziz-Mackenzie discrete procedure[8] (bounded time, bounded cuts, proportional).

Hardness All algorithms for n ≥ 3 must be infinite (Stromquist, 2008). All algorithms must use at least Ω(n2) steps (Procaccia, 2009).
Variants A weighted envy-free division exists for arbitrary weights (Idzik, 1995),
even when the cake and pieces are simplexes (Idzik and Ichiishi, 1996),
and even with non-additive preferences (Dall'Aglio and Maccheroni, 2009).
Robertson-Webb can find weighted-envy-free divisions for arbitrary weights.
A perfect division exists (Dubins&Spanier, 1961).
An envy-free and efficient cake-cutting exists (Weller's theorem).

## See also

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