1. Mechanism design for computationally limited agents (previous slide deck discussed the case where valuation determination was complex) Tuomas Sandholm Computer Science Department Carnegie Mellon University

2. Part I Mechanisms that are computationally (worst-case) hard to manipulate

3. Voting mechanisms that are worst-case hard to manipulate Bartholdi, Tovey, and Trick. 1989. The computational difficulty of manipulating an election, Social Choice and Welfare, 1989. Bartholdi and Orlin. Single Transferable Vote Resists Strategic Voting, Social Choice and Welfare, 1991. Conitzer, V., Sandholm, T., Lange, J. 2007. When are elections with few candidates hard to manipulate? JACM. Conitzer, V. and Sandholm, T. 2003. Universal Voting Protocol Tweaks to Make Manipulation Hard. International Joint Conference on Artificial Intelligence (IJCAI).Universal Voting Protocol Tweaks to Make Manipulation Hard. Elkin & Lipmaa …

4. 2 nd -chance mechanism [in paper “Computationally Feasible VCG Mechanisms” by Nisan & Ronen, EC-00]Computationally Feasible VCG Mechanisms (Interesting unrelated fact: Any VCG mechanism that is maximal in range is incentive compatible) Observation: only way an agent can improve its utility in a VCG mechanism where an approximation algorithm is used is by helping the algorithm find a higher-welfare allocation Second-chance mechanism: let each agent i submit a valuation fn v i and an appeal fn l i : V->V. Mechanism (using alg k) computes k(v), k(l i (v)), k(l 2 (v)), … and picks the among those the allocation that maximizes welfare. Pricing based on unappealed v.

5. Other mechanisms that are worst-case hard to manipulate O’Connell and Stearns. 2000. Polynomial Time Mechanisms for Collective Decision Making, SUNYA-CS-00-1 …

6. Part II Usual-case hardness of manipulation

7. Problems with mechanisms that are worst-case hard to manipulate Worst-case hardness does not imply hardness in practice If agents cannot find a manipulation, they might still not tell the truth –Solution: mechanisms like the one in Part III of this slide deck.

8. Impossibility of usual-case hardness For voting: –Procaccia & Rosenschein JAIR-97 Assumes constant number of candidates Impossibility of avg-case hardness for Junta distributions (that seem hard) –Conizer & Sandholm AAAI-06 Any voting rule, any number of candidates, weighted voters, coalitional manipulation Thm. cannot be usually hard to manipulate if –It is weakly monotone (either c2 does not win, or if everyone ranks c2 first and c1 last then c1 does not win), and –If there exists a manipulation by the manipulators, then with high probability the manipulators can only decide between two candidates –Elections can be Manipulated Often by E. Friedgut, G. Kalai, and N. Nisan. Draft, 2007.Elections can be Manipulated Often For 3 candidates Shows that randomly selected manipulations work with non-vanishing probability –Still open directions available Multi-stage voting protocols Combining randomization and manipulation hardness… Open for other settings

9. Problems with mechanisms that are worst-case hard to manipulate Worst-case hardness does not imply hardness in practice If agents cannot find a manipulation, they might still not tell the truth –Solution: mechanisms like the one in Part III of this slide deck.

10. Part III Based on “Computational Criticisms of the Revelation Principle” by Conitzer & Sandholm

11. Mechanism design One outcome must be selected Select based on agents’ reported preferences (or types) mechanism outcome types Agents report strategically, rather than truthfully Mechanism is called truthful if truthful = strategic

12. mechanism outcome types The revelation principle Key tool in mechanism design If there exists mechanism that performs well when agents act strategically, then there exists a truthful mechanism that performs just as well P1P1 P2P2 P3P3 types new mechanism

13. Computational criticisms of the revelation principle Revelation principle says nothing about computational implications of using direct, truthful mechanisms Does restricting oneself to such mechanisms lead to computational hassles? YES If the participating agents have computational limits, does restricting oneself to such mechanisms lead to loss in objective (e.g. social welfare)? YES

14. Criticizing one-step mechanisms Theorem. There are settings where: –Executing the optimal single-step mechanism requires an exponential amount of communication and computation –There exists an entirely equivalent two-step mechanism that only requires a linear amount of communication and computation Holds both for dominant strategies and Bayes-Nash implementation

15. Criticizing truthful mechanisms Theorem. There are settings where: –Executing the optimal truthful (in terms of social welfare) mechanism is NP-complete –There exists an insincere mechanism, where The center only carries out polynomial computation Finding a beneficial insincere revelation is NP-complete for the agents If the agents manage to find the beneficial insincere revelation, the insincere mechanism is just as good as the optimal truthful one Otherwise, the insincere mechanism is strictly better (in terms of s.w.) Holds both for dominant strategies and Bayes-Nash implementation

16. Proof (in story form) k of the n employees are needed for a project Head of organization must decide, taking into account preferences of two additional parties: –Head of recruiting –Job manager for the project Some employees are “old friends”: Head of recruiting prefers at least one pair of old friends on team (utility 2) Job manager prefers no old friends on team (utility 1) Job manager sometimes (not always) has private information on exactly which k would make good team (utility 3) –(n choose k) + 1 types for job manager (uniform distribution)

17. Proof (in story form)… Recruiting: +2 utility for pair of friends Job manager: +1 utility for no pair of friends, +3 for the exactly right team (if exists) Claim: if job manager reports specific team preference, must give that team in optimal truthful mechanism Claim: if job manager reports no team preference, optimal truthful mechanism must give team without old friends to the job manager (if possible) –Otherwise job manager would be better off reporting type corresponding to such a team Thus, mechanism must find independent set of k employees, which is NP-complete

18. Proof (in story form)… Recruiting: +2 utility for pair of friends Job manager: +1 utility for no pair of friends, +3 for the exactly right team (if exists) Alternative (insincere!) mechanism: –If job manager reports specific team preference, give that team –Otherwise, give team with at least one pair of friends Easy to execute To manipulate, job manager needs to solve (NP-complete) independent set problem –If job manager succeeds (or no manipulation exists), get same outcome as best truthful mechanism –Otherwise, get strictly better outcome

19. Criticizing truthful mechanisms… Suppose utilities can only be computed by (sometimes costly) queries to oracle Then get similar theorem: –Using insincere mechanism, can shift burden of exponential number of costly queries to agent –If agent fails to make all those queries, outcome can only get better oracle u(t, o)? u(t, o) = 3

20. Is there a systematic approach? Previous result is for very specific setting How do we take such computational issues into account in general in mechanism design? What is the correct tradeoff? –Cautious: make sure that computationally unbounded agents would not make mechanism worse than best truthful mechanism (like previous result) –Aggressive: take a risk and assume agents are probably somewhat bounded Recent results on these manipulation-optimal mechanisms in [Othman & Sandholm SAGT-09]