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Costly valuation computation/information acquisition in auctions: Strategy, counterspeculation, and deliberation equil

Costly valuation computation/information acquisition in auctions: Strategy, counterspeculation, and deliberation equilibrium. Tuomas Sandholm Computer Science Department Carnegie Mellon University. $ 2,000. $ 1,700. Contract: Task transferred. TRACONET, 1990-91. Auction.

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Costly valuation computation/information acquisition in auctions: Strategy, counterspeculation, and deliberation equil

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  1. Costly valuation computation/information acquisition in auctions: Strategy, counterspeculation, and deliberation equilibrium Tuomas Sandholm Computer Science Department Carnegie Mellon University

  2. $ 2,000 $ 1,700 Contract: Task transferred TRACONET, 1990-91 Auction [Sandholm NOAS-91, AAAI-93]

  3. Bidders may need to compute their valuations for (bundles of) goods • In many (even private-values quasilinear) applications, e.g. • Vehicle routing problem in transportation exchanges • Manufacturing scheduling problem in procurement • Value of a bundle of items (tasks, resources, etc) = value of solution with those items - value of solution without them • Our models apply to information gathering as well

  4. Software agents for auctions • Software agents exist that bid on behalf of user • We want to enable agents to not only bid in auctions, but also determine the valuations of the items • Agents use computational resources to compute valuations • Valuation determination can involve computing on NP-complete problems (scheduling, vehicle routing, etc.) • Optimal solutions may not be possible to determine due to limitations in agents’ computational abilities (i.e. agents have bounded rationality)

  5. Bounded rationality • Work in economics has largely focused on descriptive models • Some models based on limited memory in repeated games [Papadimitriou, Rubinstein, …] • Some AI work has focused on models that prescribe how computationally limited agents should behave [Horvitz; Russell & Wefald; Zilberstein & Russell; Sandholm & Lesser; Hansen & Zilberstein, …] • Simplifying assumptions • Myopic deliberation control • Asymptotic notions of bounded optimality • Conditioning on performance but not path of an algorithm • Simplifications can work well in single agent settings, but any deviation from full normativity can be catastrophicin multiagent settings Incorporate deliberation (computing) actions into agents’ strategies => deliberation equilibrium

  6. Simple model: can pay c to find one’s own valuation => Vickrey auction no longer has a dominant strategy [Sandholm ICMAS-96, International J. of Electronic Commerce 2000] Thrm. In a private value Vickrey auction with uncertainty about an agent’s own valuation, a risk-neutral agent’s best strategy can depend on others. E.g. two bidders (1 and 2) bid for a good. v1 uniform between 0 and 1; v2 deterministic, 0 ≤ v2 ≤ 0.5 Agent 1 bids 0.5 and gets item at price v2: Say agent 1 has the choice of paying c to find out v1. Then agent 1 will bid v1 and get the item iff v1 ≥ v2 (no loss possibility, but c invested) Same model studied more recently in the literature on “information acquisition in auctions” [Compte and Jehiel 01, Rezende 02, Rasmussen 06]

  7. Quest for a general fully normative model Auctioneer bid(result) bid(result) Agent Agent Deliberation controller (uses performance profile) Deliberation controller (uses performance profile) Compute! result Compute! result Domain problem solver (anytime algorithm) Domain problem solver (anytime algorithm)

  8. Normative control of deliberation • In our setting agents have • Limited computing, or • Costly computing • Agents must decide how to use their limited resources in an efficient manner • Agents have anytime algorithms and use performance profiles to control their deliberation

  9. Anytime algorithms can be used to approximate valuations • Solution improves over time • Can usually “solve” much larger problem instances than complete algorithms can • Allow trading off computing time against quality • Decision is not just which bundles to evaluate, but how carefully • Examples • Iterative refinement algorithms: Local search, simulated annealing • Search algorithms: Depth first search, branch and bound

  10. Performance profiles of anytime algorithms • Statistical performance profiles characterize the quality of an algorithm’s output as a function of computing time • There are different ways of representing performance profiles • Earlier methods were not normative: they do not capture all the possible ways an agent can control its deliberation • Can be satisfactory in single agent settings, but catastrophic in multiagent systems

  11. Variance introduced by different problem instances Solution quality Computing time Performance profiles Deterministic performance profile Solution quality Optimum Computing time [Horvitz 87, 89, Dean & Boddy 89]

  12. Ignores conditioning on the path Table-based representation of uncertainty in performance profiles [Zilberstein & Russell IJCAI-91, AIJ-96] Conditioning on solution quality so far [Hansen & Zilberstein AAAI-96] Solution quality Computing time

  13. Performance profile tree [Larson & Sandholm TARK-01] 5 P(B|A) B 4 4 10 A • Normative • Allows conditioning on path of solution quality • Allows conditioning on path of other solution features • Allows conditioning on problem instance features (different trees to be used for different classes) • Constructed from statistics on earlier runs 0 3 Solution quality C P(C|A) 6 15 2 5 20

  14. Performance profile tree… • Can be augmented to model • Randomized algorithms • Agent not knowing which algorithms others are using • Agent having uncertainty about others’ problem instances • Agent can emulate different scenarios of others 5 p(0) 4 4 Random node 10 Value node 3 0 6 p(1) 2 15 Our results hold in this augmented setting 20

  15. Roles of computing • Computing by an agent • Improves the solution to the agent’s own problem(s) • Reduces uncertainty as to what future computing steps will yield • Improves the agent’s knowledge about others’ valuations • Improves the agent’s knowledge about what problems others may have computed on and what solutions others may have obtained • Our results apply to different settings • Computing increases the valuation (reduces cost) • Computing refines the valuation estimate

  16. “Strategic computing” • Good estimates of the other bidders’ valuations can allow an agent to tailor its bids to achieve higher utility • Definition. Strong strategic computing:Agent uses some of its deliberation resources to compute on others’ problems • Definition. Weak strategic computing:Agent uses information from others’ performance profiles • How an agent should allocate its computation (based on results it has obtained so far) can depend on how others allocate their computation • “Deliberation equilibrium”

  17. no yes no yes Theorems on strategic computing Auction mechanism Counter-speculation by rational agents ? Strategic computing ? Limited computing Costly computing Single item for sale First price sealed-bid yes yes yes Dutch(1st price descending) yes yes yes Vickrey(2nd price sealed bid) no English (1st price ascending) no Multiple items for sale Generalized Vickrey On which <bidder, bundle> pair to allocate next computation step ? no yes yes If performance profiles are deterministic, only weak strategic computing can occur  New normative deliberation control method uncovered a new phenomenon

  18. Costly computing in English auctions • For rational bidders, straightforward bidding is ex post eq. • Thrm: If at most one performance profile is stochastic, no strong strategic computing occurs in equilibrium • Thrm: If at least two performance profiles are stochastic, strong strategic computing can occur in equilibrium • Despite the fact that agents learn about others’ valuations by waiting and observing others’ bids • Passing & resuming computation during the auction is allowed • Proof. Consider an auction with two bidders: • Agent 1 can compute for free • Agent 2 incurs cost 1 for each computing step

  19. Performance profiles of the proof Agent 1’s problem Agent 2’s problem p(high2) p(high1) high1 high2 0 0 0 low1 low2 1-p(high1) 1-p(high2) low2 < low1 < high2 < high1 Since computing one step on 2’s problem does not yield any information, we can treat computing for two steps on 2’s problem atomically

  20. Proof continued… • Agent 1 has straightforward (ex post eq.) strategy: • Compute only on own problem & increment bid whenever • Agent 1 does not have the highest bid and • Highest bid is lower than agent 1’s valuation • Agent 2’s strategy: • CASE 1: bid1 > low1 • Agent 2 knows that agent 1 has valuation high1 • Agent 2 cannot win, and thus has no incentive to compute or bid • CASE 2: bid1< low2 • Agent 2 continues to increment its own bid • No need to compute since it knows that its valuation is at least low2 • CASE 3: low1  bid1  low2 • If Agent 2 bids, he should bid bid1+  • His strategy depends on the performance profiles…

  21. Decision node for agent 2 -3 high2 Chance node for agent 1’s performance profile -3 Compute on 2’s low2 Chance node for agent 2’s performance profile Bid high2 -1 -1 low2 Bid high2 high2-low1-1 Withdraw low2-low1-1 low2 -1 high1 Compute on 1’s -3 high2-low1-3 low1 Withdraw -2 high1 Compute on 1’s -3 -3 low1 Bid high1 -2 -2 low1 Agent 2’s utility low2 < low1 < high2 < high1 Decision problem of agent 2 in CASE 3 high1 Withdraw -1 high2 high2-low1-3 Compute on 2’s -3 low2 Compute on 1’s problem low1 Compute on 2’s problem high2 Bid high1 -2 low1 high2-low1-2 low2 Bid high1 Withdraw 0 -2 Withdraw high2 high2-low1 low1 low2 low2-low1 0

  22. 1 0.8 0.6 0.4 0.2 0 0 0.2 0.4 0.6 0.8 1 Under what conditions does strong strategic computing occur? low2 =3, low1 =12, high2 =22, high1 =30 Probability that agent 2 will have its high valuation Probability that agent 1 will have its high valuation

  23. Other variants we solved • Agents cannot pass on computing during the auction & resume computing later during the auction • Can make a difference in English auctions with costly computing, but strong strategic computing is still possible in equilibrium • Agents can/cannot compute after the auction • 2-agent bargaining (again with performance profile trees) • Larson, K. and Sandholm, T. 2001. Bargaining with Limited Computation: Deliberation Equilibrium.Artificial Intelligence, 132(2), 183-217. • Larson, K. and Sandholm, T. 2002. An Alternating Offers Bargaining Model for Computationally Limited Agents. In Proceedings of theFirst International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), Bologna, Italy, July.

  24. Conclusions on this part • Agents (human or software) participating in auctions may need to compute valuations under computational limitations • This adds other possibilities to the agents’ strategies • Modeled computing normatively as part of each agent’s strategy • Performance profile tree • Deliberation equilibrium • Showed under which auction mechanisms and which models of bounded rationality strategic computing can/cannot occur • Deliberation resources may be used strategically • Strong strategic vs. weak strategic computing • Deep interaction between incentives and computing • Dominant strategy mechanisms can become strategy-prone • Even English auction with costly computing • Results apply to info acquisition as well

  25. Current & future research • Using our deliberation control method in systems • Manufacturing planning, trucking [Larson & Sandholm AAMAS-04, AAAI] • Networks, … • Miscomputing ratio [Larson & Sandholm AAMAS-03] • New (auction) mechanisms • Game-theoretically engineered to work well under models of bounded rationality • Our normative deliberation control method = basis for new design principles ? • Our results show that even the most common mechanism design principles (e.g., revelation principle) cease to hold

  26. Designing mechanisms for agents whose valuation deliberation is limited or costly [Larson & Sandholm AAMAS-05]

  27. Mechanism desiderata • Preference formation-independent • Mechanism should not be involved in agents’ preference formation process • (otherwise revelation principle applies trivially) • I.e., agents communicate to auctioneer in terms of valuation (or expected valuations) • Deliberation-proof • In equilibrium, no agent should have incentive to strategically deliberate • Non-misleading • In equilibrium, no agent should follow a strategy that causes others to believe that its true preferences are impossible • E.g. agent should not want to report a valuation and willingness to pay higher than his true valuation • <= truthful (equivalence in the case of direct mechanisms) • Thm. There exists no direct or indirect mechanism (where any agent can affect the allocation regardless of others’ revelations) that satisfies all these 3 properties

  28. Ongoing work on overcoming the impossibility • Restricted settings • Not too much asymmetry – tends to avoid strong strategic computing • Relaxing properties (but not Non-Misleading) • Relax Deliberation-Proof: Encourage strategic deliberation • Incentives for the right (cheap) agents to compute & share right information? • Some agents as “experts” [Ito et al. AAMAS-03] • Relax Preference-Formation Independent • Mechanism guides deliberation • Revealing only some info about agents’ deliberative capabilities? • Related to “search” & sequential preference elicitation • Generalizing [Cremer et al. 03] to multi-step info gathering & to gathering info about other agents as well • [Larson AAMAS-06] studies mechanism design for the case where agents can only deliberate on their own valuations

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