(one-shot) mechanism design with partial revelation
DESCRIPTION
(One-shot) Mechanism Design with Partial Revelation. Nathana ë l Hyafil, Craig Boutilier IJCAI 2007 Department of Computer Science University of Toronto. $$. $$. $$. $$. $$. $$. $$. $$. Bargaining for a Car. Luggage Capacity? Two Door? Cost? Engine Size? Color? Options?. - PowerPoint PPT PresentationTRANSCRIPT
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(One-shot) Mechanism Design with
Partial Revelation
Nathanaël Hyafil, Craig Boutilier
IJCAI 2007
Department of Computer Science
University of Toronto
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Bargaining for a Car
Luggage Capacity?Two Door? Cost?
Engine Size?Color? Options?
$$$$
$$
$$
$$
$$
$$
$$
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Mechanism Design
Mechanism design tackles this:• Design rules of game to induce behavior that
leads to maximization of some objective (e.g., social welfare, revenue, ...)
• Objective value depends on private information held by self-interested agents Elicitation + Incentives
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Partial Revelation Mechanism Design
Problem:• Stating full utility is intractable
• Costs: communication, computational…
Partial Revelation:• what preference info is relevant to decision?
• when is the elicitation cost worth the improvement in decision quality?
• how to deal with incentives ?
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Overview
Mechanism Design Background
Partial Revelation Mechanisms (PRM)
Regret-based PRMs
Partition Optimization
Experimental Results
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Basic Social Choice Setup
Choice of x from outcomes X (e.g. cars)
Agents 1..n: type ti Ti and valuation vi(x, ti)
Type vectors: t TGoal: implement social choice function f: T X
• e.g., social welfare SW(x,t) = vi(x, ti)
Quasi-linear utility:• ui(x, i ,ti ) = vi(x, ti ) - i
Our focus: social welfare maximization
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Basic Mechanism Design
A direct mechanism M consists of three components:
• types Ti
• allocation function m: T X
• payment functions pi : T R
Mechanism is incentive compatible: (IC)• In equilibrium, agents reveal truthfully
Dominant Strategy IC• Regardless of what others report, agent i should
always tell the truth
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Properties
Mechanism is efficient: • maximizes social welfare given reported types:
-efficient: within of optimal social welfare
Mechanism is Individually Rational: (IR)• no agent can lose by participating
-IR: can lose at most
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Direct Mechanisms
Revelation principle: focus on direct mechanisms where agents directly and (in eq.) truthfully reveal their full types
For example, Groves scheme (e.g., VCG):• choose efficient allocation and use payment function:
• incentive compatible in dominant strategies
• efficient, individually rational
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Cost of Full Revelation
Communication costs
Computation costs
Cognitive costs
Privacy costs
INTRACTABLE!
Partial revelation?
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Existing Work on Partial Revelation [Conen,Hudson,Sandholm, Parkes, Nisan&Segal, Blumrosen&Nisan, …]
Full revelation not always necessary for optimal decision(though worst-case is exponential: [Nisan&Segal05])
Most Approaches:• require enough revelation for optimal VCG outcome
• sequential, not one-shot / specific settings (1-item,CAs)
BUT: optimal decision not always worth the costs
• Partial revelation:Trade-off elicitation costs with decision quality
• e.g. Priority games [Blumrosen&Nisan 02]
Can we maintain incentives?
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Overview
Mechanism Design Background
Partial Revelation Mechanisms (PRM)
Regret-based PRMs
Partition Optimization
Experimental Results
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Partial Revelation Mechanisms
A partial type is any subset i Ti
• e.g. v(red,2doors) [50,75], etc…
A one-shot (direct) partial revelation mechanism• set i of partial types, i . (typically partition, not required)
• m: X, chooses allocation m()
• pi: R, sets payment pi()
A truthful strategy: report i s.t. ti i
Goal: • Tradeoff “quality” with revelation/communication costs
• maintain appropriate incentives
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Partial Revelation MD:Negative Results
Partial revelation can’t generally maximize SW
• must allocate under type uncertainty
Roberts: Dominant-IC (affine) SW maximizer,
Partial revelation no Dominant-IC
What are some solutions?
• relax solution concept to BNE / Ex-Post
• relax solution concept to approximate dominant-IC
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Partial Revelation MD:Negative Results
Avoid Roberts by relaxing solution concept? Bayes-Nash Equilibrium
• Theorem: Bayes-Nash IC PRM with certain form of partitions
Trivial mechanism
• Consequences: max expected SW = same as best trivialmax expected revenue = same as best trivial
“Useless”
Ex-Post Equilibrium:Same
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Approximate Incentives
: bound on utility gain
• difference b/w u(best lie) and u(truth)
Considerable costs of manipulation:
• Uncertainty over others’ types
• Valuation + computational costs
If is small enoughFormal, approximate IC practical, exact IC
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Overview
Mechanism Design Background
Partial Revelation Mechanisms (PRM)
Regret-based PRMs
Partition Optimization
Experimental Results
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Regret-based PRMs
In any PRM, how is allocation to be chosen?
x*() is minimax-regret optimal decision for
A regret-based PRM: m()=x*() for all
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Regret-based PRMs: Efficiency
Obs: If MR(x*(),) for all , then regret-
based PRM m is -efficient for truthtelling agents.
We can tradeoff efficiency for elicitation effort• More elicitation effort more refined ’s
smaller
Incentives?
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Regret-based PRMs: Incentives
Can generalize Groves payments
• fi (-i): arbitrary type in -i and hi (-i) an arbitrary function of -i
Theorem: Let m be a regret-based PRM with • partial types and a
• partial Groves payment scheme.
If MR(x*(),) for all , then m is
-dominant incentive compatible
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Approximate Incentives and IR
Can generalize Clark payments to get -IR
A Clark-style regret-based PRM gives• approximate Efficiency• approximate Incentive Compatibility• approximate Individual Rationality • (Increased revenue from flexible payments)
Allows tradeoff “quality” vs revelation costs• as long as we can find a good set of partial
types
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Overview
Mechanism Design Background
Partial Revelation Mechanisms (PRM)
Regret-based PRMs
Partition Optimization
Experimental Results
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(One-shot) Partial Type Optimization
Designing PRM: must pick partial types• we focus on bounds on utility parameters
• Use regret-based heuristics to estimate VOI
i :
p1
p2
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The Mechanism Tree
(1,… i,…n )Worst-case
Heuristic: Split 1
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The Mechanism Tree
(’1,… i,…n ) (’’1,… i,…n )
(1,… i,…n )Worst-case
Heuristic: Split i
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The Mechanism Tree
(’1,… i,…n ) (’’1,… i,…n )
(’1,… ’i,… ) (’1,… ’’i,… )
(1,… i,…n )
More details necessary to make it tractable
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Empirical Results
Negotiation problem• 1 buyer, 1 seller, 4 boolean attributes
• valuation/cost given by factored model (GAI)16 values/costs specified by 8 parameters
Compare:• uniform partitioning vs. regret-based heuristic
• worst-case and expected (uniform prior)
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Empirical Results
average = 706.5 vs 11 bits
(40% savings)
worst = 905.5 vs 11 bits (50% savings)
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Empirical Results
Mechanism accounts for all types• Initial regret: 50-146% of optimal
• (depending on actual type vector)
With 11 bits (1.4 bits/param , 0.7 bits/good): • 20-56% of optimal (regret) vs 30-86% (uniform)
• 60% reduction of vs 38%
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Contributions
Negative Results• Exact incentives “useless”
Regret-based PRMs• Trade-off “quality” with revelation costs
Partial Types Optimization• Avoid exponential blow-up• Use regret to guide elicitation effectively
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Current + Future Work
Sequential PRMs (Hyafil Boutilier AAAI 06)
Formal model manipulation and revelation costs
formal, exact IC
explicit revelation/quality trade-off
Partial Revelation Automated Mech Design• General objective functions
• include “execution costs”
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QUESTIONS?
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Regret-based PRMs: Rationality
Can generalize Clark payments as well
• fi (-i): arbitrary type in -I
Thm: Let m be a regret-based PRM with • partial types and a
• partial Clark payment scheme.
If MR(x*(),) for all , then m is
-individually rational.
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(One-shot) Partial Type Optimization
Designing PRM: must pick partial types• we focus on bounds on utility parameters
A simple greedy approach• Let be current partial type vectors (initially {T} )
• Let =(1,… i,…n ) be partial type vector with greatest MMR
• Choose agent i and suitable split of partial type i into ’i and ’’i
• Replace all [i ] by pair of vectors: i ’i ;’’i
• Repeat until bound is acceptable
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The Mechanism Tree
(’1,… i,…n ) (’’1,… i,…n )
(’1,… ’i,… ) (’1,… ’’i,… ) (’’1,… ’i,… ) (’’1,… ’’i,… )
(1,… i,…n )Worst-case
Heuristic: Split 1Heuristic: Split i
*
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A More Refined Approach
Simple model has drawbacks• exponential blowup (“naïve” resolution)
• split of i useful in reducing regret in one partial type vector , but is applied at all partial type vectors
Refinement: variable resolution• apply split only at leaves where it is “useful”
Ignore on other leaveskeeps tree from blowing up, saves computation
• new splits traded off against “cached” splits
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Naïve vs. Variable Resolution
i
p1
p2
i
p1
p2
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Heuristic for Choosing Splits
Adapted from single agent preference elicitation techniques: Current Solution Strategy
Let be partial type vector with max MR• optimal solution x* regret-maximizing witness xw
• intuition: focus on parameters that contribute to regret reducing u.b. on xw or increasing l.b. on x* helps
• But: have to account for both “answers”
• Here: also consider second best MR