Some overarching themes on electronic marketplaces Tuomas Sandholm Computer Science Department Carnegie Mellon University

Automated negotiation systems Agents search & make contracts –Through peer-to-peer negotiation or a mediated marketplace –Agents can be real-world parties or software agents that work on behalf of real-world parties Increasingly important in practice due to –Developing communication infrastructure –Electronic commerce on the Internet: Goods, services, information, bandwidth, computation, storage... –Industrial trend toward virtual enterprises & outsourcing

Need to simultaneously handle: Strategyness “CS issues” –Computation complexity –Communication complexity –Privacy (amount of preference info revealed, and to whom)

Some high-level goals Design electronic market mechanisms that lead to economically efficient outcomes via efficient processes Design software agents that act optimally (subject to their computational limitations) on behalf of the users that they represent => move strategic & computational complexity from human to machine

Perspectives Leverage increasing computing (and communication) power to increase economic efficiency –E.g. running more complex mechanisms –E.g. automatically designing the mechanism Capitalize on the piles of information that the parties have about each other - unlike traditional market mechanisms Tackle the new issues that ecommerce has brought about, such as anonymity & cheap pseudonyms (lack of personal relationships and legal enforcement between transaction parties) –E.g. safe exchange mechanisms –E.g. reputation servers –E.g. false-name proof combinatorial markets

Interplay of computing & incentives Executing a mechanism (rules of the game) –Determining the winners in Combinatorial auctions [Sandholm IJCAI-99, AIJ-02, AIJ-03, …] Voting [Bartholdi, Tovey, Trick 89, …] Executing a strategy –How should computationally bounded agents play strategically? Limits on memory [Rubinstein, Papadimitriou, Kalai, Gilboa, …] Costly or limited computing [Sandholm ICMAS-96, IJEC-00; Larson&Sandholm AIJ-01, AAMAS-02, TARK-01, AGENTS-01 workshop, …] Manipulating a mechanism (by determining a beneficial insincere revelation) [Conitzer&Sandholm AAAI-02, IJCAI-03, TARK-03, …] Determining which agents’ preferences should be elicited [Conitzer&Sandholm AAAI-02] Determining how to play (finding the game’s equilibrium) [Gilboa&Zemel GEB-89, Conitzer&Sandholm IJCAI-03, …] Finding a payoff division (e.g., according to the core) [Conitzer&Sandholm IJCAI-03, …] Designing a mechanism automatically [Conitzer&Sandholm UAI-02, ACM-EC-03, …] –Perhaps without knowledge of the prior (or at least not complete knowledge) Computational complexity of

Expressiveness => economic efficiency computational complexity of clearing no need for lookahead Clearing markets with expressive bidding –Bidding with supply/demand curves in multi-unit market –Package bidding –Side constraints –Multiple attributes –How to make combinatorial exchanges fast ? –Approximate clearing while maintaining incentive properties?

Other promising new directions Online clearing Preference elicitation (& other multi-stage mechanisms) Mechanisms with insincere equilibrium play

Randomization can help To keep the adversary at bay –Universal revelation reducers –Online clearing To keep agents at bay (= yield better mechanisms) –Automated mechanism design To reduce computational complexity when desired –Designing a randomized mechanism is in P To increase computational complexity when desired –Randomized cup voting protocol is harder to manipulate than the cup –In voting protocol tweaks paper, if preround pairing is randomly selected after votes are collected, then manipulation is #P-hard instead of NP-hard –Correlated uncertainty about other voters serves the same role as weighted coalitional voters => can get hardness even for a constant number of candidates

A few other research topics Exotic contract types: Leveled commitment contracts [Sandholm&Lesser AAAI-96, GEB-01, Sandholm, Sikka, Norden IJCAI-99, Sandholm&Zhou ICMAS-00, AIJ-02, Andersson&Sandholm AAAI-98, ICMAS-98, JEDC-01] –Open: Are these an optimal backtracking instrument? How should the contract parameters be set to maximize social welfare? Nonmanipulable reputation mechanisms / collaborative filters / recommender systems Safe exchange [Sandholm&Lesser IJCAI-95, IEEE97, Sandholm&Ferrandon ICMAS-00, Sandholm&Wang AAAI-02,…] Coalition formation [Sandholm&Lesser IJCAI-95, AIJ-97, Sandholm et al AAAI-98, AIJ- 99, Tohme&Sandholm J. Logic & Computation 97, Conitzer&Sandholm02, 03, …] Multiagent reinforcement learning [Sandholm&Crites 95, Wang&Sandholm NIPS-02, NIPS-03, Conitzer&Sandholm ICML-03] Resource-bounded reasoning [Sandholm&Lesser ECAI-94 WS, Conitzer&Sandholm IJCAI-03] Game tree search