automated mechanism design: complexity results stemming from the single-agent setting vincent...
Post on 21-Dec-2015
215 views
TRANSCRIPT
Automated Mechanism Design: Complexity Results Stemming From
the Single-Agent Setting
Vincent Conitzer and Tuomas SandholmComputer Science Department
Carnegie Mellon University
Mechanism design
• Mechanism design = – designing the rules of the game…– so that a (globally) good outcome happens… – although each agent plays strategically to maximize own utility– E.g. auctions, elections
• Setting– Each agent has a type which specifies the agent’s preferences– The designer doesn’t know the types, but has a prior over them– The designer constructs a mechanism (mapping from the
agents’ type reports to outcomes)– The agents report their types to the mechanism, which then
determines the outcome
Constraints on the mechanism• Incentive compatibility constraints: Each agent (for each
type) best off reporting truthfully– Dominant strategies: for any type reports by the other agents, each
agent is best of reporting truthfully– Bayes-Nash equilibrium (weaker): each agent is best off reporting
truthfully when not aware of other agents’ types
• Participation constraints: Each agent (for each type) benefits from participating in the mechanism– Ex post: beneficial for any type reports by the other agents– Ex interim: beneficial when not aware of other agents’ types
• Mechanism design trivializes each agent’s search for a strategy
Available tools
• Sometimes the designer can force the agents to make certain payments– E.g. auction
• Sometimes the mechanism is allowed to be randomized– Each type vector defines a probability distribution
over outcomes
Objective
• The designer has an objective to maximize– Social welfare– Total payments to the designer– Own agenda for the outcome– …
• In general, function of how the outcome relates to the agents’ preferences
Classical mechanism design
• Classical mechanism design has created a number of canonical mechanisms– Vickrey, Clarke, Groves mechanisms; Myerson auction; …
– These obtain a particular goal over a range of settings
• It has also created impossibility results– Gibbard-Satterthwaite; Myerson-Satterthwaite; …
– Show that no mechanism obtains a goal over a range of settings
General preferences
Quasilinear prefs
Difficulties with canonical mechanisms• A single preference aggregation instance comes along
– A particular set of outcomes, players, sets of possible preferences (types), priors over preferences, …
• What if no canonical mechanism covers this instance?– Unusual objective (not social welfare); payments not possible; …– Impossibility results may exist for the general type of setting
• But the particular instance may have additional structure so that good mechanisms do exist => can circumvent impossibility result
• What if a canonical mechanism does cover the setting?– Can we use instance’s structure to get higher objective value?– Can we get stronger nonmanipulability/participation properties?
• Dominant strategies instead of Bayes-Nash equilibrium• Ex-post IR instead of ex-interim
• SOLUTION: hire a mechanism designer for every instance!
A cheaper, faster solution: Automated mechanism design
Solve mechanism design as an optimization problem automatically for the instance at hand
– Inputs: players, outcomes, type space, prior over types, designer’s objective, types of mechanism allowed (payments? randomized?)
– Output: optimal mechanism– Creating a mechanism for the specific setting
(instance) at hand rather than a class of settings
Conitzer & Sandholm, Bayesian Modeling Applications Workshop (UAI-03)
Small example: Divorce arbitration
• Outcomes:
• Each agent is of type high w.p. .2 and type low w.p. .8– Preferences of the high type:
• u(get the painting) = 11,000• u(museum) = 6,000• u(other gets the painting) = 1,000• u(burn) = 0
– Preferences of the low type:• u(get the painting) = 1,200• u(museum) = 1,100• u(other gets the painting) = 1,000• u(burn) = 0
Optimal deterministic mechanism for maximizing the sum of the divorcees’ utilities
high
low
lowhigh
Expected sum of divorcees’ utilities = 5,248
Mechanism
Optimal randomized mechanism for maximizing the sum of the divorcees’ utilities
high
low
lowhigh
.57.43
.55 .45
Expected sum of divorcees’ utilities = 5,510
Optimal randomized mechanism with payments for maximizing the sum of the divorcees’ utilities
high
low
lowhigh
Expected sum of divorcees’ utilities = 5,688
Wife pays 1,000
Optimal randomized mechanism with payments for maximizing the arbitrator’s revenue
high
low
lowhigh
Expected sum of divorcees’ utilities = 0 Arbitrator expects 4,320
Both pay 250Wife pays 13,750
Husband pays 11,250
Conitzer & Sandholm, Bayesian Modeling Applications Workshop (UAI-03)
Automated mechanism design has already created important mechanisms
• Reinvented the revenue-maximizing 1-object auction– Celebrated result in auction theory [Myerson 81]
• Invented revenue-maximizing combinatorial auctions– Recognized tough open research problem
• Public goods problems– Allowed us to model money burning as a loss in social
welfare– Circumvented the Myerson-Satterthwaite impossibility
theorem (satisfied social welfare maximization, budget balance, and voluntary participation)
• Combinatorial public goods problems
Defining the computational problem: Input
• An instance is given by– Set of possible outcomes– Set of agents
• For each agent– set of possible types– probability distribution over these types– utility function converting type/outcome pairs to utilities
– Objective function• Gives a value for each outcome for each combination of agents’ types• E.g. social welfare
– Restrictions on the mechanism• Are side payments allowed?• Is randomization over outcomes allowed?• What concept of nonmanipulability is used?• What participation constraint (if any) is used?
Defining the computational problem: Output
• The algorithm should produce– a mechanism
• A mechanism maps combinations of agents’ revealed types to outcomes– Randomized mechanism maps to probability distributions over outcomes
– Also specifies payments by agents (if side payments are allowed)
– … which• is nonmanipulable (according to the given concept)
– By revelation principle, we can focus on truth-revealing direct-revelation mechanisms w.l.o.g.
• satisfies the given participation constraint (if any)
• maximizes the expectation of the objective function
Laying out the computational complexity of AMD
• So: how hard is AMD?• Many different variants
– Social welfare/payment maximizing/designer’s agenda for outcome
– Payments allowed/not allowed
– Deterministic mechanisms/randomized mechanisms
– Ex interim IR/ex post IR/no IR
– Dominant strategies/Bayes-Nash equilibrium
– …
• The above already gives 3*2*2*3*2 = 72 variants• Trick: hardness results in specific settings imply hardness
in more general settings
Incentive compatibility constraints coincide with 1 (reporting) agent
Dominant strategies:
Reporting truthfully is optimal for any types the others report
Bayes-Nash equilibrium:
Reporting truthfully is optimal
in expectation over the other agents’ (true) types
21 22
11 o5 o9
12 o3 o2
21 22
11 o5 o9
12 o3 o2
P(21)u1(11,o5) +
P(22)u1(11,o9)
P(21)u1(11,o3) +
P(22)u1(11,o2)
u1(11,o5) u1(11,o3)
AND
u1(11,o9) u1(11,o2)
21
11 o5
11 o3
u1(11,o5) u1(11,o3)
P(21)u1(11,o5) P(21)u1(11,o3)
With only 1 reporting agent, the constraints
are the same!
Individual rationality constraints coincide with 1 (reporting) agent
Ex post:
Participating never hurts (for any reported types for the other
agents)
Ex interim:
Participating does not hurt in expectation over the other
agents’ (true) types
21 22
11 o5 o9
12 o3 o2
21 22
11 o5 o9
12 o3 o2
P(21)u1(11,o5) +
P(22)u1(11,o9) 0
u1(11,o5) 0
AND
u1(11,o9) 0
21
11 o5
11 o3
u1(11,o5) 0
P(21)u1(11,o5) 0
With only 1 reporting agent, the constraints
are the same!
How hard is AMD withdeterministic mechanisms?
NP-complete (even with one player)
Solvable in polynomial time (multiple players)
• Maximizing social welfare (without payments)
• Designer’s own agenda (without payments) [EC03]
• General payment-independent objectives
(with payments)• Maximizing expected
revenue [EC03]
• Maximizing social welfare (with payments)
(VCG)
Sketch of NP-hardness proofs: MINSAT
• In the MINSAT problem we seek to minimize the number of satisfied clauses in a formula in conjunctive normal form
• For example, (X1X2) (X1X3) (X2X3)• Say X1=false, X2=false, X3=false
– (X1X2) (X1X3) (X2X3) (two satisfied)
• Better: X1=true, X2=true, X3=false– (X1X2) (X1X3) (X2X3) (one satisfied)
• This problem is NP-complete [Kohli et al. 94]– We independently derived a reduction from MAX2SAT
Sketch of NP-hardness proofs: the reduction
• Given a MINSAT instance (a CNF formula, e.g. (X1X2) (X1X3) (X2X3)), we construct a single-agent deterministic AMD instance where– For every variable v, there is a type v, and outcomes o+v, o-v
– For every clause c, there is a type c, and outcome oc
– A good mechanism will always select one of o+v, o-v for type v
– To maximize the objective, would like to select oc for type c, but only possible if the mechanism never selects ol for lc
• E.g. for incentive compatibility reasons; details omitted here
• If we consider picking o+v for type v “setting v to true”, and picking o-v for type v “setting v to false”, then– Satisfying a clause preventing the best outcome for the type corresponding to that
clause– Want to minimize the number of satisfied clauses!
How hard is AMD withrandomized mechanisms?
• Everything becomes doable in polynomial time!!!• Use linear programming
– Probabilities of outcomes for given type reports are variables– Players’ payments for given type reports are variables
• Objectives under discussion are linear• IR constraints are linear
• IC constraints are linear
• Forcing the probabilities to be 0 or 1 also gives a MIP formulation for AMD with deterministic mechanisms
Ex post
Ex interim
Dominantstrategies
Bayes-Nashequilibrium
Conclusions• In automated mechanism design, mechanisms are designed on the fly for the setting
at hand– Applicable in settings not covered by classical mechanisms– Can outperform classical mechanisms– Circumvents impossibility results about general mechanisms
• We have shown that:– Designing deterministic mechanisms is typically NP-complete even with only 1 agent– Designing randomized mechanisms is in P for any constant number of agents (using LP)
• Future research:– New algorithms for greater scalability– New applications/bringing to industry– Using AMD as a tool in classical mechanism design