Vickrey–Clarke–Groves auction

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A Vickrey–Clarke–Groves (VCG) auction of multiple goods is a sealed-bid auction wherein bidders report their valuations for the items. The auction system assigns the items in a socially optimal manner, while ensuring each bidder receives at most one item. This system charges each individual the harm they cause to other bidders,[1] and ensures that the optimal strategy for a bidder is to bid the true valuations of the objects. It is a generalization of a Vickrey auction for multiple items.

The auction is named after William Vickrey, Edward H. Clarke, and Theodore Groves.

Contents

[edit] A Formal Description

For any set of auctioned items M = \{t_1,\ldots,t_m\} and a set of bidders N = \{b_1,\ldots,b_n\}, let V^M_N the social value of the VCG auction for a given bid-combination. A bidder bi who wins an item tj pays V^{M}_{N \setminus \{b_i\}}-V^{M \setminus \{t_j\}}_{N \setminus \{b_i\}}, which is the social cost of his winning that is incurred by the rest of the agents

Indeed, the set of bidders other than {bi} is N \setminus \{b_i\}. When item tj is available, they could attain welfare V^{M}_{N \setminus \{b_i\}}. The winning of the item by bi reduces the set of available items to M \setminus \{t_j\}, however, so that the attainable welfare is now V^{M \setminus \{t_j\}}_{N \setminus \{b_i\}}. The difference between the two levels of welfare is the payment for tj paid by the winning bidder bi.

The winning bidder who has value A for the item tj derives therefore utility A - \left(V^{M}_{N \setminus \{b_i\}}-V^{M \setminus \{t_j\}}_{N \setminus \{b_i\}}\right).

[edit] Examples

[edit] Example #1

See the example with apples in the introduction of Vickrey Auction.

[edit] Example #2

Assume that there are two bidders, b1 and b2, two items, t1 and t2, and each bidder is allowed to obtain one item. We let vi,j be bidder bi's valuation for item tj. Assume v1,1 = 10, v1,2 = 5, v2,1 = 5, and v2,2 = 3. We see that both b1 and b2 would prefer to receive item t1; however, the socially optimal assignment gives item t1 to bidder b1 (so his achieved value is 10) and item t2 to bidder b2 (so his achieved value is 3). Hence, the total achieved value is 13, which is optimal.

If person b2 were not in the auction, person b1 would still be assigned to t1, and hence no harm was done for that bidder. Hence, b2 is charged nothing.

If person b1 were not in the auction, person b2 would be assigned to t1, and would have valuation 5. b1 thus caused 2 harm to b2, and hence b1 is charged 2.

[edit] Optimality of Truthful Bidding

The following is a proof that bidding one's true valuations for the auctioned items is optimal[2]

For each bidder bi, let vi be her true valuation of an item ti, and suppose (without loss of generality) that b1 wins t1 upon submitting his true valuations.

Note that the size bid of b1 has no effect on her utility as long as she wins the item (see the utility function above). Hence, we assume that b1 does not bid truthfully, and receives item tj because of his non-truthful bidding. In the truthful bidding case, b1 has total utility U_1 = v_1-\left(V^{M}_{N \setminus \{b_1\}}-V^{M \setminus \{t_1\}}_{N \setminus \{b_1\}}\right). In the untruthful bidding case, b1 has total utility U_2 = v_j-\left(V^{M}_{N \setminus \{b_1\}}-V^{M \setminus \{t_j\}}_{N \setminus \{b_1\}}\right). Hence, we must prove that U_1 - U_2 \geq 0, which shows that the utility received from truthful bidding is always at least that received from untruthful bidding.

U_1 - U_2 = v_1 - \left(V^{M}_{N \setminus \{b_1\}} - V^{M \setminus \{t_1\}}_{N \setminus \{b_1\}}\right) - v_j + \left(V^{M}_{N \setminus \{b_1\}}-V^{M \setminus \{t_j\}}_{N \setminus \{b_1\}}\right) = \left[v_1 + V^{M \setminus \{t_1\}}_{N \setminus \{b_1\}}\right] - \left[v_j + V^{M \setminus \{t_j\}}_{N \setminus \{b_1\}}\right]

However, the first term there is the maximum total social value achieved when b1 received t1, and the second term there is the maximum total social value achieved when b1 received tj. However, we assumed that the VCG auction gave b1 item t1; hence, the first term must be greater, and U_1 - U_2 \geq 0.

[edit] More general setting

We can consider a more general setting[3] of the VCG mechanism. Consider a set A of possible outcomes and n bidders which have different valuations for each outcome. This can be expressed as, function

v_i : A \longrightarrow R_+

for each bidder i which expresses the value it has for each alternative. In this auction, each bidder will submit his valuation and the VCG mechanism will choose the alternative a \in A that maximizes

vi(a)
i

and charge prices pi given by:

p_i = h_i(v_{-i}) - \sum_{j \neq i} v_j(a)

where v_{-i} = (v_1, \dots, v_{i-1}, v_{i+1}, \dots, v_n), that is, hi is a function that only depends on the valuation of the other players. This alone gives a truthful mechanism, that is, a mechanism where bidding the true valuation is a dominant strategy.

We could take, for example, hi(v i) = 0, but we would have all prices negative, what might not be desirable - we would rather prefer that players give money to the mechanism than the other way round. The function:

h_i(v_{-i}) = \max_{b \in A} \sum_{j \neq i} v_j(b)

is called Clarke pivot rule. It has some very good properties as:

  • it is individually rational, i.e., v_i (a) - p_i \geq 0. It means that all the players are getting positive utility by participating in the auction. No one is really being forced do bid.
  • it has no positive transfers, i.e., p_i \geq 0. So the mechanism doesn't need to pay anything to the bidders.

[edit] References

  1. ^ von Ahn, Luis (2008-10-07). "Sponsored Search" (PDF). 15–396: Science of the Web Course Notes. Carnegie Mellon University. http://scienceoftheweb.org/15-396/lectures/lecture11.pdf. Retrieved 2008-11-05. 
  2. ^ von Ahn, Luis (2008-10-07). "Assignment 5 Solutions" (PDF). 15–396: Science of the Web Published Solutions. Carnegie Mellon University. http://www.scienceoftheweb.org/15-396/assignments/assignment5_solutions.pdf. Retrieved 2008-11-05. 
  3. ^ Nisan, Roughgarden, Tardos and Vazirani, Algorithmic Game Theory, Cambridge, 2007
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