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# Avrim Blum Carnegie Mellon University Joint work with Maria-Florina Balcan and Yishay Mansour

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Guiding dynamics in potential games. Avrim Blum Carnegie Mellon University Joint work with Maria-Florina Balcan and Yishay Mansour. [Cornell CSECON 2009]. [This talk based on results in “Improved Equilibria via Public Service Advertising”, SODA’09 and “The Price of Uncertainty”, ACM-EC’09]. - PowerPoint PPT Presentation
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Guiding dynamics Guiding dynamics in potential games in potential games Avrim Blum Carnegie Mellon University Joint work with Maria-Florina Balcan and Yishay Mansour [Cornell CSECON 2009] [This talk based on results in “Improved Equilibria via Public Service Advertising”, SODA’09 and “The Price of Uncertainty”, ACM-EC’09]
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Guiding dynamics in Guiding dynamics in potential gamespotential games

Avrim BlumCarnegie Mellon University

Joint work with Maria-Florina Balcan and Yishay Mansour

[Cornell CSECON 2009]

[This talk based on results in “Improved Equilibria via Public Service Advertising”, SODA’09 and “The Price of Uncertainty”, ACM-EC’09]

In some places, everyone throws their trash on the street. In some, everyone puts their trash in the trash can.

In some places, everyone drives their own car. In some, everybody uses and pays for good public transit.

A nice formal example is fair cost-sharing. n players in weighted directed graph G. Player i wants to get from si to ti, and they share cost of edges they use with others.s

t

1n

Good equilibrium: all use edge of cost 1. (cost 1/n per player)

Bad equilibrium: all use edge of cost n. (cost 1 per player)

A nice formal example is fair cost-sharing. n players in weighted directed graph G. Player i wants to get from si to ti, and they share cost of edges they use with others.

…1 1 1 1

s1 sn

t

0 00

k ¿ n

cars

Shared transit

High-level questionsHigh-level questions1. Can a helpful authority encourage

behavior to move from bad to good?– Model as having some limited powers of

persuasion

…1 1 1 1

s1 sn

t

0 00

k ¿ n

cars

Shared transit

High-level questionsHigh-level questions1. Can a helpful authority encourage

behavior to move from bad to good?– Model as having some limited powers of

persuasion

2. In reverse direction, if we get people into a good equilibrium (and players are selfish,

reasonably myopic, etc) then like to think behavior will stay there.

High-level questionsHigh-level questions1. Can a helpful authority encourage

behavior to move from bad to good?– Model as having some limited powers of

persuasion

2. If game has small fluctuations in costs, or a few Byzantine players, (when) could behavior spiral out of control?

Direction 1: guiding from bad to Direction 1: guiding from bad to goodgood

0. n players begin in some arbitrary configuration.

1. Authority launches public-service advertising campaign, proposing joint action s*.

…1 1 1 1

s1 sn

t

0 00

k k

Direction 1: guiding from bad to Direction 1: guiding from bad to goodgood

0. n players begin in some arbitrary configuration.

1. Authority launches public-service advertising campaign, proposing joint action s*. Each player i pays attention and follows with probability . Call these the receptive players

…1 1 1 1

s1 sn

t

0 00

k

1. Authority launches public-service advertising campaign, proposing joint action s*.

Direction 1: guiding from bad to Direction 1: guiding from bad to goodgood

1. Authority launches public-service advertising campaign, proposing joint action s*. Each player i pays attention and follows with probability . Call these the receptive players2. Remaining (non-receptive) players fall to some arbitrary equilibrium for themselves, given play of receptive players.3. Campaign wears off. Entire set of players follows best-response dynamics from then on.

0. n players begin in some arbitrary configuration.

…1 1 1 1

s1 sn

t

0 00

k

Direction 1: guiding from bad to Direction 1: guiding from bad to goodgood

1. Authority launches public-service advertising campaign, proposing joint action s*. Each player i pays attention and follows with probability . Call these the receptive players2. Remaining (non-receptive) players fall to some arbitrary equilibrium for themselves, given play of receptive players.3. Campaign wears off. Entire set of players follows best-response dynamics from then on.

0. n players begin in some arbitrary configuration.

Note #1: if =1, can just propose best Nash equilibrium. Key issue: what if < 1?

Direction 1: guiding from bad to Direction 1: guiding from bad to goodgood

1. Authority launches public-service advertising campaign, proposing joint action s*. Each player i pays attention and follows with probability . Call these the receptive players2. Remaining (non-receptive) players fall to some arbitrary equilibrium for themselves, given play of receptive players.3. Campaign wears off. Entire set of players follows best-response dynamics from then on.

0. n players begin in some arbitrary configuration.

Note #2: Can replace 2 with poly(n) steps of best-response for non-receptive players.

Fair Cost SharingFair Cost Sharing

If only an probability of players following the advice, then we get within O(log(n)/) of OPT.

(PoS = log(n), PoA = n)

- In any NE for non-receptive players, any such player i can’t improve by switching to his path Pi

OPT in OPT.

- Advertiser proposes OPT (any apx also works)

#receptives on edge e

- Calculate total cost of these guaranteed options.

Rearrange sum...

…111 1s1 sn

t

0 00

k

Fair Cost SharingFair Cost Sharing

If only an probability of players following the advice, then we get within O(log(n)/) of OPT.

(PoS = log(n), PoA = n)

- Calculate total cost of these guaranteed options.

Rearrange sum...

- Finally, use: X ~ Bi(n,p)

- Take expectation, add back in cost of receptives: get O(OPT/).(End of phase 2)

Fair Cost SharingFair Cost Sharing

If only an probability of players following the advice, then we get within O(log(n)/) of OPT.

(PoS = log(n), PoA = n)

- Finally, use: X ~ Bi(n,p)

- Take expectation, add back in cost of receptives: get O(OPT/).(End of phase 2)

- Finally, in last phase, std potential argument shows behavior cannot get worse by more than an additional log(n) factor.(End of phase 3)

Cost Sharing, ExtensionCost Sharing, Extension+ linear delays:

- Problem: can’t argue as if remaining NR players didn’t exist since they add to delays

- Define shadow game: pure linear latency fns. Offset defined by equilib at end of phase 2.

# users on e at end of phase 2

- Behavior at end of phase 2 is equilib for this game too.

- Show

- This has good PoA.

Party affiliation gamesParty affiliation games• Given graph G, each edge labeled + or -.• Vertices have two actions: RED or BLUE.

Pay 1 for each + edge with endpoint of different color, and each – edge with endpoint of same color.

• Special cases:

+

+

+

--

• All + edges is consensus game. • All – edges is cut-game.

+1 to keep ratios finite

Party affiliation gamesParty affiliation games(PoS = 1, PoA = (n2))

- Threshold behavior: for > ½, can get ratio O(1), but for < ½, ratio stays (n2). (assume degrees (log

n)).

- Consensus game, two cliques, with relatively sparse between them. Players “locked” into

place.

Lower bound:

Degree (1/2 - )n/8 across cut

Party affiliation gamesParty affiliation games(PoS = 1, PoA = (n2))

- Threshold behavior: for > ½, can get ratio O(1), but for < ½, ratio stays (n2). (assume degrees (log

n)).

- Split nodes into those incurring low-cost vs those incurring high-cost under OPT.

- Show that low-cost will switch to behavior in OPT. For high-cost, don’t care.

- Cost only improves in final best-response process.

Upper bound:

High-level questionsHigh-level questions1. Can a helpful authority encourage

behavior to move from bad to good?– Model as having some limited powers of

persuasion

2. If game has small fluctuations in costs, or a few byzantine players, could behavior spiral out of control?

Direction #2Direction #2

A few ways this could happen:– Small changes cause good equilibria to disappear,

only bad ones left. (economy?)– Bad behavior by a few players causes pain for all

(nukes)– Neither of above, but instead through more subtle

interaction with dynamics…

If game has small fluctuations in costs, or a few Byzantine players, could behavior spiral out of control?

ModelModel• Players follow best (or better) response

dynamics.• Costs of resources can fluctuate between

moves: cit 2 [ci/(1+), ci(1+)]

(alternatively, one or more Byzantine players who move between time steps)

• Play begins in a low-cost state.• How bad can things get?

Price-of-Uncertainty() of game = maximum ratio of eventual social cost to initial cost.

ModelModel• Players follow best (or better) response

dynamics.• Costs of resources can fluctuate between

moves: cit 2 [ci/(1+), ci(1+)]

Price-of-Uncertainty() of game = maximum ratio of eventual social cost to initial cost.

ModelModel• Players follow best (or better) response dynamics.• Costs of resources can fluctuate between moves:

cit 2 [ci/(1+), ci(1+)]

Price-of-Uncertainty() of game = maximum ratio of eventual social cost to initial cost.

One way to look at this:• Define graph: one node for each state. Edge u ! v if

perturbation can cause BR to move from u to v.• What do the reachable sets

look like?

Set-cover gamesSet-cover games• Special case of fair cost-sharing

• n players, m resources, with costs c1,…,cm. Each player has some allowable resources

• Each player chooses some allowable resource.• Players split cost with all others choosing same

one.

c1 c2 c3 cm

Main resultsMain resultsSet-cover games:Set-cover games:• If = O(1/nm) then PoU = O(log n).• However, for any constant > 0, PoU = (n).• Also, a single Byzantine player can take state

from a PNE of cost O(OPT) to one of cost (n¢OPT).

Main resultsMain resultsGeneral fair-cost-sharing games:General fair-cost-sharing games:• If many players for each (si,ti) pair (ni = (m)),

then PoU = O(1) even for constant >0.• Open for general number of players.

Matroid congestion games: Matroid congestion games: (strategy sets are bases of matroid. E.g., set-cover where choose k resources)

• If = O(1/nm) then PoU = O(log n) for fair cost-sharing.

• In general, if = O(1/nm) then PoU = O(GAP).In both cases, require best-response. Better-

response not enough. (unlike set-cover)

Also results for other classes of games too.Also results for other classes of games too.

Set-Cover games Set-Cover games (upper bound)(upper bound)

For upper bound, think of players in sets as a stack of chips.

• View ith position in stack j as having cost cj/i. Load chips with value equal to initial cost.

• When player moves from j to k, move top chip. Cost of position goes up by at most (1+)2.

cj ck

• At most mn different positions. So, following the path of any chip and removing loops, cost of final set is at most (1+)2nm times its value.

So, if = O(1/nm) then PoU = O(log n).

Matroid gamesMatroid gamesIn matroid games, can think of each player

as controlling a set of chips.

• Nice property of best response in matroids:– Can always order the move so that each

individual chip is doing better-response.• Apply previous argument.• Fails for better-response.

– Here, can get player to do kind of binary counting, bad even for exponentially-small .

Open questions and directionsOpen questions and directionsLooking at: how can we help players find their

way to a good state?

Getting to good states: nice line of work on how players might be able to do it all by themselves. [Blume, Young, Shamma, Marden, Beggs…]

• Noisy best-response / noisy adaptive play.• Distribution in limit favors good states, like

simulated annealing.• But, time could be exponential (subway).

And how dangerous could small fluctuations be in knocking them out?

Open questions and directionsOpen questions and directions

Getting to good states: nice line of work on how players might be able to do it all by themselves. [Blume, Young, Shamma, Marden, Beggs…]

• Noisy best-response / noisy adaptive play.• Distribution in limit favors good states, like

simulated annealing.• But, time could be exponential (subway).