The Price of Stochastic Anarchy - Computer & Information Science

15 Pages·2007·0.2 MB·English

The Price of Stochastic Anarchy
ChristineChung1,KatrinaLigett2?,KirkPruhs1??,andAaronRoth2
1 DepartmentofComputerScience
UniversityofPittsburgh
{chung,kirk}@cs.pitt.edu
2 DepartmentofComputerScience
CarnegieMellonUniversity
{katrina,alroth}@cs.cmu.edu
Abstract. Weconsiderthesolutionconceptofstochasticstability,andpropose
thepriceofstochasticanarchyasanalternativetothepriceof(Nash)anarchy
for quantifying the cost of selfishness and lack of coordination in games. As a
solutionconcept,theNashequilibriumhasdisadvantagesthatthesetofstochas-
ticallystablestatesofagameavoid:unlikeNashequilibria,stochasticallystable
statesaretheresultofnaturaldynamicsofcomputationallyboundedanddecen-
tralizedagents,andareresilienttosmallperturbationsfromidealplay.Theprice
ofstochasticanarchycanbeviewedasasmoothedanalysisofthepriceofan-
archy,distinguishingequilibriathatareresilienttonoisefromthosethatarenot.
Toillustratetheutilityofstochasticstability,westudytheloadbalancinggame
onunrelatedmachines.ThisgamehasanunboundedlylargepriceofNashanar-
chyevenwhenrestrictedtotwoplayersandtwomachines.Weshowthatinthe
twoplayercase,thepriceofstochasticanarchyis2,andthateveninthegeneral
case,thepriceofstochasticanarchyisbounded.Weconjecturethatthepriceof
stochasticanarchyisO(m),matchingthepriceofstrongNashanarchywithout
requiringplayercoordination.Weexpectthatstochasticstabilitywillbeusefulin
understandingtherelativestabilityofNashequilibriainothergameswherethe
worstequilibriaseemtobeinherentlybrittle.
?PartiallysupportedbyanAT&TLabsGraduateFellowshipandanNSFGraduateResearch
Fellowship.
??Supported in part by NSF grants CNS-0325353, CCF-0448196, CCF-0514058 and IIS-
0534531.
1 Introduction
Quantifyingthepriceof(Nash)anarchyisoneofthemajorlinesofresearchinalgorith-
micgametheory.Indeed,onefourthoftheauthoritativealgorithmicgametheorytext
editedbyNisanetal.[20]iswhollydedicatedtothistopic.ButtheNashequilibrium
solution concept has been widely criticized [15,4,9,10]. First, it is a solution charac-
terizationwithoutaroadmapforhowplayersmightarriveatsuchasolution.Second,
at Nash equilibria, players are unrealistically assumed to be perfectly rational, fully
informed, and infallible. Third, computing Nash equilibria is PPAD-hard for even 2-
player,n-actiongames[6],anditisthereforeconsideredveryunlikelythatthereexists
apolynomialtimealgorithmtocomputeaNashequilibriumeveninacentralizedman-
ner.Thus,itisunrealistictoassumethatselfishagentsingeneralgameswillconverge
precisely to the Nash equilibria of the game, or that they will necessarily converge to
anythingatall.Inaddition,thepriceofNashanarchymetriccomeswithitsownweak-
nesses;itblindlyusestheworstcaseoverallNashequilibria,despitethefactthatsome
equilibriaaremoreresilientthanotherstoperturbationsinplay.
Considering these drawbacks, computer scientists have paid relatively little atten-
tiontoiforhowNashequilibriawillinfactbereached,andevenlesstothequestionof
whichNashequilibriaaremorelikelytobeplayedintheeventplayersdoconvergeto
Nashequilibria.Toaddresstheseissues,weemploythestochasticstabilityframework
fromevolutionarygametheorytostudysimpledynamicsofcomputationallyefficient,
imperfect agents. Rather than defining a-priori states such as Nash equilibria, which
mightnotbereachablebynaturaldynamics,thestochasticstabilityframeworkallows
ustodefineanaturaldynamic,andfromitderivethestablestates.Wedefinetheprice
of stochastic anarchy to be the ratio of the worst stochastically stable solution to the
optimal solution. The stochastically stable states of a game may, but do not necessar-
ily,containallNashequilibriaofthegame,andsothepriceofstochasticanarchymay
bestrictlybetterthanthepriceofNashanarchy.Ingamesforwhichthestochastically
stablestatesareasubsetoftheNashequilibria,studyingtheratiooftheworststochas-
tically stable state to the optimal state can be viewed as a smoothed analysis of the
priceofanarchy,distinguishingNashequilibriathatarebrittletosmallperturbationsin
perfectplayfromthosethatareresilienttonoise.
The evolutionary game theory literature on stochastic stability studies n-player
gamesthatareplayedrepeatedly.Ineachround,eachplayerobservesheractionandits
outcome,andthenusessimplerulestoselectheractionforthenextroundbasedonly
on her size-restricted memory of the past rounds. In any round, players have a small
probability of deviating from their prescribed decision rules. The state of the game is
thecontentsofthememoriesofalltheplayers.Thestochasticallystablestatesinsucha
gamearethestateswithnon-zeroprobabilityinthelimitofthisrandomprocess,asthe
probability of error approaches zero. The play dynamics we employ in this paper are
theimitationdynamicsstudiedbyJosephsonandMatros[16].Underthesedynamics,
eachplayerimitatesthestrategythatwasmostsuccessfulforherinrecentmemory.
Toillustratetheutilityofstochasticstability,westudythepriceofstochasticanar-
chyoftheunrelatedloadbalancinggame[2,1,11].Toourknowledge,wearethefirst
to quantify the loss of efficiency in any system when the players are in stochastically
stableequilibria.Intheloadbalancinggameonunrelatedmachines,evenwithonlytwo
players and two machines, there are Nash equilibria with arbitrarily high cost, and so
the price of Nash anarchy is unbounded. We show that these equilibria are inherently
1
brittle, and that for two players and two machines, the price of stochastic anarchy is
2. This result matches the strong price of anarchy [1] without requiring coordination
(atstrongNashequilibria,playershavetheabilitytocoordinatebyformingcoalitions).
Wefurthershowthatinthegeneraln-player,m-machinegame,thepriceofstochastic
anarchyisbounded.Morepreciselythepriceofstochasticanarchyisupperboundedby
thenmthn-stepFibonaccinumber.Wealsoshowthatthepriceofstochasticanarchy
isatleastm+1.
Ourworkprovidesnewinsightintotheequilibriaoftheloadbalancinggame.Un-
like some previous work on dynamics for games, our work does not seek to propose
practicaldynamicswithfastconvergence;rather,weusesimpledynamicsasatoolfor
understanding the inherent relative stability of equilibria. Instead of relying on player
coordinationtoavoidtheNashequilibriawithunboundedcost(asisdoneinthestudy
of strong equilibria), we show that these bad equilibria are inherently unstable in the
face of occasional uncoordinated mistakes. We conjecture that the price of stochastic
anarchyisclosertothelinearlowerbound,parallelingthepriceofstronganarchy.
In light of our results, we believe the techniques in this paper will be useful for
understandingtherelativestabilityofNashequilibriainothergamesforwhichtheworst
equilibria are brittle. Indeed, for a variety of games in the price of anarchy literature,
theworstNashequilibriaofthelowerboundinstancesarenotstochasticallystable.
1.1 RelatedWork
Wegiveabriefsurveyofrelatedworkinthreeareas:alternativestoNashequilibriaas
asolutionconcept,stochasticstability,andtheunrelatedloadbalancinggame.
Recently,severalpapershavenotedthattheNashequilibriumisnotalwaysasuit-
ablesolutionconceptforcomputationallyboundedagentsplayinginarepeatedgame,
and have proposed alternatives. Goemans et al. [15] study players who sequentially
play myopic best responses, and quantify the price of sinking that results from such
play.FabrikantandPapadimitriou[9]proposeamodelinwhichagentsplayrestricted
finite automata. Blum et al. [4,3] assume only that players’ action histories satisfy a
propertycallednoregret,andshowthatformanygames,theresultingsocialcostsare
noworsethanthoseguaranteedbypriceofanarchyresults.
Although we believe this to be the first work studying stochastic stability in the
computer science literature, computer scientists have recently employed other tools
from evolutionary game theory. Fisher and Vo¨cking [13] show that under replicator
dynamics in the routing game studied by Roughgarden and Tardos [22], players con-
verge to Nash. Fisher et al. [12] went on to show that using a simultaneous adaptive
samplingmethod,playconvergesquicklytoaNashequilibrium.Forathoroughsurvey
of algorithmic results that have employed or studied other evolutionary game theory
techniquesandconcepts,seeSuri[23].
Stochasticstabilityanditsadaptivelearningmodelasstudiedinthispaperwerefirst
defined by Foster and Young [14], and differ from the standard game theory solution
conceptofevolutionarilystablestrategies(ESS).ESSarearefinementofNashequilib-
ria, and so do not always exist, and are not necessarily associated with a natural play
dynamic.Incontrast,agamealwayshasstochasticallystablestatesthatresult(bycon-
struction) from natural dynamics. In addition, ESS are resilient only to single shocks,
whereasstochasticallystablestatesareresilienttopersistentnoise.
2
Stochastic stability has been widely studied in the economics literature (see, for
example, [24,17,19,5,7,21,16]). We discuss in Sect. 2 concepts from this body of
literaturethatarerelevanttoourresults.WerecommendYoung[25]foraninformative
andreadableintroductiontostochasticstability,itsadaptivelearningmodel,andsome
relatedresults.Ourworkdiffersfrompriorworkinstochasticstabilityinthatitisthe
first to quantify the social utility of stochastically stable states, the price of stochastic
anarchy.
Wealsonoteaconnectionbetweenthestochasticallystablestatesofthegameand
thesinksofagame,recentlyintroducedbyGoemansetal.asanotherwayofstudying
thedynamicsofcomputationallyboundedagents.Inparticular,thestochasticallystable
states of a game under the play dynamics we consider correspond to a subset of the
sink equilibria, and so provide a framework for identifying the stable sink equilibria.
In potential games, the stochastically stable states of the play dynamics we consider
correspondtoasubsetoftheNashequilibria,thusprovidingamethodforidentifying
whichoftheseequilibriaarestable.
Inthispaper,westudythepriceofstochasticanarchyinloadbalancing.Even-Dar
etal.[1]showthatwhenplayingtheloadbalancinggameonunrelatedmachines,any
turn-takingimprovementdynamicsconvergetoNash.Andelmanetal.[1]observethat
thepriceofNashanarchyinthisgameisunboundedandtheyshowthatthestrongprice
ofanarchyislinearinthenumberofmachines.Fiatetal.[11]tightentheirupperbound
tomatchtheirlowerboundatastrongpriceofanarchyofexactlym.
2 ModelandBackground
We now formalize (from Young [24]) the adaptive play model and the definition of
stochastic stability. We then formalize the play dynamics that we consider. We also
provide in this section the results from the stochastic stability literature that we will
lateruseforourresults.
2.1 AdaptivePlayandStochasticStability
LetG=(X,π)beagamewithnplayers,whereX =Qn X representsthestrategy
j=1 i
setsX foreachplayeri,andπ =Qn π representsthepayofffunctionsπ :X →R
i j=1 i i
foreachplayer.Gisplayedrepeatedlyforsuccessivetimeperiodst = 1,2,...,andat
each time step t, player i plays some action st ∈ X . The collection of all players’
i i
actionsattimetdefinesaplayprofileSt = (St,St,...,St).Wewishtomodelcom-
1 2 n
putationallyefficientagents,andsoweimaginethateachagenthassomefinitememory
ofsize z, andthat after timestep t, all playersremember ahistory consistingof ase-
quenceofplayprofilesht =(St−z+1,St−z+2,...,St)∈(X)z.
Weassumethateachplayerihassomeefficientlycomputablefunctionp :(X)z×
i
X → Rthat,givenaparticularhistory,inducesasampleableprobabilitydistribution
i
P
over actions (for all players i and histories h, p (h,a) = 1). We write p for
Q a∈Xi i
p .Wewishtomodelimperfectagentswhomakemistakes,andsoweimaginethat
i i
attimeteachplayeriplaysaccordingtop withprobability1−(cid:15),andwithprobability
i
(cid:15)playssomeactioninX uniformlyatrandom.3Thatis,forallplayersi,forallactions
i
3Themistakeprobabilitiesneednotbeuniformrandom—allthatwerequireisthatthedistri-
butionhassupportonallactionsinX .
i
3
a∈X ,Pr[st =a]=(1−(cid:15))p (ht,a)+ (cid:15) .Thedynamicswehavedescribeddefine
i i i |Xi|
aMarkovprocessPG,p,(cid:15) withfinitestatespaceH = (X)z correspondingtothefinite
histories.Fornotationalsimplicity,wewillwritetheMarkovprocessasP(cid:15)whenthere
isnoambiguity.
Thepotentialsuccessorsofahistorycanbeobtainedbyobservinganewplaypro-
file,and“forgetting”theleastrecentplayprofileinthecurrenthistory.
Definition2.1. For any S0 ∈ X, A history h0 = (St−z+2,St−z+3,...,St,S0) is a
successorofhistoryht =(St−z+1,St−z+2,...,St).
TheMarkovprocessP(cid:15) hastransitionprobabilityp(cid:15) ofmovingfromstateh =
h,h0
(S1,...,Sz)tostateh0 =(T1,...,Tz):
(Qn (1−(cid:15))p (h,Tz)+ (cid:15) ifh0isasuccessorofh;
p(cid:15) = i=1 i i |Xi|
h,h0 0 otherwise.
We will refer to P0 as the unperturbed Markov process. Note that for (cid:15) > 0,
p(cid:15) > 0 for every history h and successor h0, and that for any two histories h and
h,h0
hˆ notnecessarilyasuccessorofh,thereisaseriesofz historiesh ,...,h suchthat
1 z
h =h,h =hˆ,andforall1<i≤z,h isasuccessorofh .Thusthereispositive
1 z i i−1
probability of moving between any h and any hˆ in z steps, and so P(cid:15) is irreducible.
Similarly,thereisapositiveprobabilityofmovingbetweenanyhandanyhˆ inz+1
steps,andsoP(cid:15)isaperiodic.Therefore,P(cid:15)hasauniquestationarydistributionµ(cid:15).
The stochastically stable states of a particular game and player dynamics are the
stateswithnonzeroprobabilityinthelimitofthestationarydistribution.
Definition2.2 (Foster and Young [14]). A state h is stochastically stable relative to
P(cid:15)iflim µ(cid:15)(h)>0.
(cid:15)→0
Intuitively,weshouldexpectaprocessP(cid:15) tospendalmostallofitstimeatitsstochas-
ticallystablestateswhen(cid:15)issmall.
Whenaplayeriplaysatrandomratherthanaccordingtop ,wecallthisamistake.
i
Definition2.3 (Young [24]). Suppose h0 = (St−z+1,...,St) is a successor of h. A
mistakeinthetransitionbetweenhandh0 isanyelementSt suchthatp (h,St) = 0.
i i i
Notethatmistakesoccurwithprobability≤(cid:15).
We can characterize the number of mistakes required to get from one history to
another.
Definition2.4 (Young [24]). For any two states h,h0, the resistance r(h,h0) is the
minimumtotalnumberofmistakesinvolvedinthetransitionh→h0ifh0isasuccessor
ofh.Ifh0isnotasuccessorofh,thenr(h,h0)=∞.
Notethatthetransitionsofzeroresistanceareexactlythosethatoccurwithpositive
probabilityintheunperturbedMarkovprocessP0.
Definition2.5. WerefertothesinksofP0asrecurrentclasses.Inotherwords,arecur-
rentclassofP0isasetofstatesC ⊆H suchthatanystateinC isreachablefromany
otherstateinC andnostateoutsideC isaccessiblefromanystateinsideC.
4
WemayviewthestatespaceH asthevertexsetofadirectedgraph,withanedge
fromhtoh0ifh0isasuccessorofh,withedgeweightr(h,h0).
Observation2.6. WeobservethattherecurrentclassesH ,H ,...,whereeachH ⊆
1 2 i
H,havethefollowingproperties:
1. Fromeveryvertexh∈H,thereisapathofcost0tooneoftherecurrentclasses.
2. For each H and for every pair of vertices h,h0 ∈ H , there is a path of cost 0
i i
betweenhandh0.
3. ForeachH ,everyedge(h,h0)withh∈H ,h0 6∈H haspositivecost.
i i i
Let r denote the cost of the shortest path between H and H in the graph de-
i,j i j
scribedabove.WenowconsiderthecompletedirectedgraphGwithvertexset{H ,H ,...}
1 2
inwhichtheedge(H ,H )hasweightr .LetT beadirectedminimum-weightspan-
i j i,j i
ningin-treeofG rootedatvertexH .(Anin-treeisadirectedtreewhereeachedgeis
i
orientedtowardtheroot.)ThestochasticpotentialofH isdefinedtobethesumofthe
i
edgeweightsinT .
i
Youngprovesthefollowingtheoremcharacterizingstochasticallystablestates:
Theorem2.7 (Young[24]).Inanyn-playergameGwithfinitestrategysetsandany
setofactiondistributionsp,thestochasticallystablestatesofPG,p,(cid:15) aretherecurrent
classesofminimumstochasticpotential.
2.2 ImitationDynamics
In this paper, we study agents who behave according to a slight modification of the
imitationdynamicsintroducedbyJosephsonandMatros[16].(Wenotethatthismodi-
ficationisofnoconsequencetotheresultsofJosephsonandMatros[16]thatwepresent
below.)Playeriusingimitationdynamicsparameterizedbyσ ∈ Nchooseshisaction
attimet+1accordingtothefollowingmechanism:
1. PlayeriselectsasetY ofσ playprofilesuniformlyatrandomfromthez profiles
inhistoryh .
t
2. ForeachplayprofileS ∈ Y,irecallsthepayoffπ (S)heobtainedfromplaying
i
actionS .
i
3. Player i plays the action among these that corresponds to his highest payoff; that
is,heplaystheith componentofargmax π (S).Inthecaseofties,heplaysa
S∈Y i
highest-payoffactionatrandom.
The value σ is a parameter of the dynamics that is taken to be n ≤ σ ≤ z/2. These
dynamicscanbeinterpretedasmodelingasituationinwhichateachtimestep,players
arechosenatrandomfromapoolofidenticalplayers,whoeachplayedinasubsetofthe
lastz rounds.Theplayersarecomputationallysimple,andsodonotcounterspeculate
the actions of their opponents, instead playing the action that has worked the best for
theminrecentmemory.
WewillsaythatahistoryhismonomorphicifthesameactionprofileS hasbeen
repeatedforthelastzrounds:h=(S,S,...,S).JosephsonandMatros[16]provethe
followingusefulfact:
Proposition2.8. A set of states is a recurrent class of the imitation dynamics if and
onlyifitisasingletonsetconsistingofamonomorphicstate.
5
Since the stochastically stable states are a subset of the recurrent classes, we can
associatewitheachstochasticallystablestateh = (S,...,S)theuniqueactionprofile
S itcontains.Thisallowsustonowdefinethepriceofstochasticanarchywithrespect
toimitationdynamics.Forbrevity,wewillrefertothisthroughoutthepaperassimply
thepriceofstochasticanarchy.
Definition2.9. GivenagameG = (X,π)withasocialcostfunctionγ : X → R,the
priceofstochasticanarchyofGisequaltomax γ(S) ,whereOPTistheplayprofile
γ(OPT)
thatminimizesγandthemaxistakenoverallplayprofilesSsuchthath=(S,...,S)
isstochasticallystable.
Given a game G, we define the better response graph of G: The set of vertices
correspondstothesetofactionprofilesofG,andthereisanedgebetweentwoaction
profilesS andS0 ifandonlyifthereexistsaplayerisuchthatS0 differsfromS only
inplayeri’saction,andplayeridoesnotdecreasehisutilitybyunilaterallydeviating
fromS toS0.JosephsonandMatros[16]provethefollowingrelationshipbetweenthis
i i
betterresponsegraphandthestochasticallystablestatesofagame:
Theorem2.10. IfVisthesetofstochasticallystablestatesunderimitationdynamics,
then V = {S : (S ,...,S ) ∈ V} is either a strongly connected component of the
i i i
betterresponsegraphofG,oraunionofstronglyconnectedcomponents.
Goemans et al. [15] introduce the notion of sink equilibria and a corresponding
notion of the “price of sinking”, which is the ratio of the social welfare of the worst
sink equilibrium to that of the social optimum. We note that the strongly connected
componentsofthebetterresponsegraphofGcorrespondtothesinkequilibria(under
sequentialbetter-responseplay)ofG,andsoTheorem2.10impliesthatthestochasti-
callystablestatesunderimitationdynamicscorrespondtoasubsetofthesinksofthe
betterresponsegraphofG,andwegetthefollowingcorollary:
Corollary2.11. ThepriceofstochasticanarchyofagameGunderimitationdynamics
isatmostthepriceofsinkingofG.
3 LoadBalancing:GameDefinitionandPriceofNashAnarchy
The load balancing game on unrelated machines models a set of agents who wish to
schedule computing jobs on a set of machines. The machines have different strengths
andweaknesses(forexample,theymayhavedifferenttypesofprocessorsordiffering
amounts of memory), and so each job will take a different amount of time to run on
each machine. Jobs on a single machine are executed in parallel such that all jobs on
anygivenmachinefinishatthesametime.Thus,eachagentwhoscheduleshisjobon
machineM endurestheloadonmachineM ,wheretheloadisdefinedtobethesumof
i i
therunningtimesofalljobsscheduledonM .Agentswishtominimizethecompletion
i
timefortheirjobs,andsocialcostisdefinedtobethemakespan:themaximumloadon
anymachine.
Formally,aninstanceoftheloadbalancinggameonunrelatedmachinesisdefined
byasetofnplayersandmmachinesM ={M ,...,M }.Theactionspaceforeach
1 m
player is X = M. Each player i has some cost c on machine j. Denote the cost
i i,j
6
P
of machine Mj for action profile S by Cj(S) = is.t.Si=jci,j. Each player i has
utility function π (S) = −C (S). The social cost of an action profile S is γ(S) =
i si
max C (S). We define OPT to be the action profile that minimizes social cost:
j∈M j
OPT = argmin γ(S). Without loss of generality, we will always normalize so
S∈X
thatγ(OPT)=1.
The coordination ratio of a game (also known as the price of anarchy) was intro-
duced by Koutsoupias and Papadimitriou [18], and is intended to quantify the loss of
efficiencyduetoselfishnessandthelackofcoordinationamongrationalagents.Given
agameGandasocialcostfunctionγ,itissimpletoquantifytheOPTgamestateS:
OPT=argminγ(S).Itislessclearhowtomodelrationalselfishagents.Inmostprior
workithasbeenassumedthatselfishagentsplayaccordingtoaNashequilibrium,and
thepriceofanarchyhasbeendefinedastheratioofthecostoftheworst(purestrategy)
NashstatetoOPT.Inthispaper,werefertothismeasureasthepriceofNashanarchy,
todistinguishitfromthepriceofstochasticanarchy,whichwedefinedinSect.2.2.
Definition3.1. For a game G with a set of Nash equilibrium states E, the price of
(Nash)anarchyismax γ(S) .
S∈E γ(OPT)
Weshowherethatevenwithonlytwoplayersandtwomachines,theloadbalancing
game on unrelated machines has a price of Nash anarchy that is unbounded by any
functionofmandn.Considerthetwo-player,two-machinegamewithc =c =1
1,1 2,2
andc = c = 1/δ,forsome0 < δ < 1.ThentheplayprofileOPT = (M ,M )
1,2 2,1 1 2
is a Nash equilibrium with cost 1. However, observe that the profile S∗ = (M ,M )
2 1
isalsoaNashequilibrium,withcost1/δ(sincebydeviating,playerscanonlyincrease
their cost from 1/δ to 1/δ +1). The price of anarchy of the load balancing game is
therefore1/δ,whichcanbeunboundedlylarge,althoughm=n=2.
4 UpperBoundonPriceofStochasticAnarchy
Theloadbalancinggameisanordinalpotentialgame[8],andsothesinksofthebetter-
response graph correspond to the pure strategy Nash equilibria. We therefore have by
Corollary2.11thatthestochasticallystablestatesareasubsetofthepurestrategyNash
equilibriaofthegame,andthepriceofstochasticanarchyisatmostthepriceofanarchy.
Wehavenotedthateveninthetwo-person,two-machineloadbalancinggame,theprice
ofanarchyisunbounded(evenforpurestrategyequilibria).Therefore,asawarmup,we
boundthepriceofstochasticanarchyofthetwo-player,two-machinecase.
4.1 TwoPlayers,TwoMachines
Theorem4.1. Inthetwo-player,two-machineloadbalancinggameonunrelatedma-
chines,thepriceofstochasticanarchyis2.
Note that the two-player, two-machine load balancing game can have at most two
strictpurestrategyNashequilibria.(Forbrevityweconsiderthecaseofstrictequilibria.
The argument for weak equilibria is similar). Note also that either there is a unique
Nashequilibriumat(M ,M )or(M ,M ),ortherearetwoatN = (M ,M )and
1 1 2 2 1 1 2
N =(M ,M ).
2 2 1
AnactionprofileN ParetodominatesN0ifforeachplayeri,C (N)≤C (N0).
Ni Ni0
7
Lemma4.2. IftherearetwoNashequilibria,andN ParetodominatesN ,thenonly
1 2
N isstochasticallystable(andviceversa).
1
Proof. Note that if N Pareto dominates N , then it also Pareto dominates (M ,M )
1 2 1 1
and (M ,M ), since each is a unilateral deviation from a Nash equilibrium for both
2 2
players.Considerthemonomorphicstate(N ,...,N ).Ifbothplayersmakesimulta-
2 2
neous mistakes at time t to N , then by assumption, N will be the action profile in
1 1
h = (N ,...,N ,N ) with lowest cost for both players. Therefore, with positive
t+1 2 2 1
probability,bothplayerswilldrawsamplesoftheirhistoriescontainingtheactionpro-
fileN ,andthereforeplayit,untilh = (N ,...,N ).Therefore,thereisanedge
1 t+z 1 1
inG fromh = {N ,...,N }toh0 = {N ,...,N }ofresistance2.However,thereis
2 2 1 1
no edge from h0 to any other state in G with resistance < σ. Recall our initial obser-
vation that in fact, N Pareto dominates all other action profiles. Therefore, no set of
1
mistakeswillyieldanactionprofilewithhigherpayoffthanN foreitherplayer,andso
1
toleavestateh0willrequireatleastσmistakes(sothatsomeplayermaydrawasample
from their history that contains no instance of action profile h). Therefore, given any
minimumspanningtreeofG rootedath,wemayaddanedge(h,h0)ofweight2,and
removetheoutgoingedgefromh0,whichwehaveshownmusthavecost≥σ.Thisisa
minimumspanningtreerootedath0 withstrictlylowercost.Wehavethereforeshown
that h0 has strictly lower stochastic potential than h, and so by Theorem 2.7, h is not
stochasticallystable.SinceatleastoneNashequilibriummustbestochasticallystable,
h0 =(N ,...,N )istheuniquestochasticallystablestate. ut
1 1
Proof (ofTheorem4.1).IfthereisonlyoneNashequilibrium(M ,M )or(M ,M ),
1 1 2 2
thenitmustbetheonlystochasticallystablestate(sinceinpotentialgamesthesearea
nonemptysubsetofthepurestrategyNashequilibria),andmustalsobeOPT.Inthis
case,thepriceofanarchyisequaltothepriceofstochasticanarchy,andis1.Therefore,
wemayassumethattherearetwoNashequilibria,N andN .IfN Paretodominates
1 2 1
N ,thenN mustbeOPT(sinceloadbalancingisapotentialgame),andbyLemma
2 1
4.2,N istheonlystochasticallystablestate.Inthiscase,thepriceofstochasticanarchy
1
is1(strictlylessthanthe(possiblyunbounded)priceofanarchy).Asimilarargument
holds if N Pareto dominates N . Therefore, we may assume that neither N nor N
2 1 1 2
Paretodominatetheother.
Withoutlossofgenerality,assumethatN isOPT,andthatinN = (M ,M ),
1 1 1 2
M is the maximally loaded machine. Suppose that M is also the maximally loaded
2 2
machine in N . (The other case is similar.) Together with the fact that N does not
2 1
ParetodominateN ,thisgivesusthefollowing:
2
c ≤c
1,1 2,2
c ≤c
2,1 2,2
c ≥c
1,2 2,2
FromthefactthatbothN andN areNashequilibria,weget:
1 2
c +c ≥c
1,1 2,1 2,2
c +c ≥c
1,1 2,1 1,2
Inthiscase,thepriceofanarchyamongpurestrategyNashequilibriais:
c c +c c +c c
1,2 ≤ 1,1 2,1 ≤ 1,1 2,1 =1+ 2,1
c c c c
2,2 2,2 1,1 1,1
8
Similarly,wehave:
c c +c c +c c
1,2 ≤ 1,1 2,1 ≤ 1,1 2,1 =1+ 1,1
c c c c
2,2 2,2 2,1 2,1
Combining these two inequalities, we get that the price of Nash anarchy is at most
1+min(c /c ,c /c ) ≤ 2.Sincethepriceofstochasticanarchyisatmostthe
1,1 2,1 2,1 1,1
priceofanarchyoverpurestrategies,thiscompletestheproof. ut
4.2 GeneralCase:nPlayers,mMachines
Theorem4.3. The general load balancing game on unrelated machines has price of
stochasticanarchyboundedbyafunctionΨ dependingonlyonnandm,and
Ψ(n,m)≤m·F (nm+1),
(n)
whereF (i)denotestheithn-stepFibonaccinumber.4
(n)
Toprovethisupperbound,weshowthatanysolutionworsethanourupperbound
cannotbestochasticallystable.Toshowthisimpossibility,wetakeanyarbitrarysolu-
tionworsethanourupperboundandshowthattheremustalwaysbeaminimumcost
in-tree in G rooted at a different solution that has strictly less cost than the minimum
costin-treerootedatthatsolution.WethenapplyProposition2.8andTheorem2.7.The
proofproceedsbyaseriesoflemmas.
Definition4.4. ForanymonomorphicNashstateh = (S,...,S),lettheNashGraph
ofhbeadirectedgraphwithvertexsetM anddirectededges(M ,M )ifthereissome
i j
playeriwithS =M andOPT =M .LettheclosureM¯ ofmachineM ,betheset
i i i j i i
ofstatesreachablefromM byfollowing0ormoreedgesoftheNashgraph.
i
Lemma4.5. InanymonomorphicNashstateh=(S,...,S),ifthereisamachineM
i
suchthatC (S)>m,theneverymachineM ∈M¯ hascostC (S)>1.
i j i j
Proof. Supposethiswerenotthecase,andthereexistsanM ∈ M¯ withC (S) ≤ 1.
j i j
Since M ∈ M¯ , there exists a simple path (M = M ,M ,...,M = M ) with
j i i 1 2 k j
k ≤ m.SinceS isaNashequilibrium,itmustbethecasethatC (S) ≤ 2because
k−1
by the definition of the Nash graph, the directed edge from M to M implies that
k−1 k
there is some player i with S = M , but OPT = M . Since 1 = γ(OPT) ≥
i k−1 i k
C (OPT) ≥ c ,ifplayerideviatedfromhisactioninNashprofileS toS0 = M ,
k i,k i k
he would experience cost C (S)+c ≤ 1+1 = 2. Since he cannot benefit from
k i,k
deviating (by definition of Nash), it must be that his cost in S, C (S) ≤ 2. By the
k−1
sameargument,itmustbethatC (S)≤3,andbyinduction,C (S)≤k ≤m. ut
k−2 1
Lemma4.6. ForanymonomorphicNashstateh = (S,...,S) ∈ G withγ(S) > m,
thereisanedgefromhtosomeg = (T,...,T)whereγ(T) ≤ mwithedgecost≤ n
inG.
1 ifi≤n;
4F (i)= F (i)∈o(2i)foranyfixedn.
(n) ( ij=i−nF(n)(j) otherwise. (n)
P
9