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Aggregating value ranges: preference elicitation and truthfulness

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Abstract

We study the case where agents have preferences over ranges (intervals) of values, and we wish to elicit and aggregate these preferences. For example, consider a set of climatologist agents who are asked for their predictions for the increase in temperature between 2009 and 2100. Each climatologist submits a range, and from these ranges we must construct an aggregate range. What rule should we use for constructing the aggregate range? One issue in such settings is that an agent (climatologist) may misreport her range to make the aggregate range coincide more closely with her own (true) most-preferred range. We extend the theory of single-peaked preferences from points to ranges to obtain a rule (the median-of-ranges rule) that is strategy-proof under a condition on preferences. We then introduce and analyze a natural class of algorithms for approximately eliciting a median range from multiple agents. We also show sufficient conditions under which such an approximate elicitation algorithm still incentivizes agents to answer truthfully. Finally, we consider the possibility that ranges can be refined when the topic is more completely specified (for example, the increase in temperature on the North Pole given the failure of future climate pacts). We give a framework and algorithms for selectively specifying the topic further based on queries to agents.

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References

  1. Barberà S., Massó J., Serizawa S. (1998) Strategy-proof voting on compact ranges. Games and Economic Behavior 25(2): 272–291

    Article  MATH  MathSciNet  Google Scholar 

  2. Black D. (1948) On the rationale of group decision-making. Journal of Political Economy 56(1): 23–34

    Article  Google Scholar 

  3. Border K. C., Jordan J. S. (1983) Straightforward elections, unanimity and phantom voters. The Review of Economic Studies 50(1): 153–170

    Article  MATH  MathSciNet  Google Scholar 

  4. Boutilier, C. (2002). A POMDP formulation of preference elicitation problems. In Proceedings of the 18th national conference on artificial intelligence (AAAI) (pp. 239–246). Edmonton, Canada.

  5. Braziunas, D., & Boutilier, C. (2005). Local utility elicitation in GAI models. In Proceedings of the 21st annual conference on uncertainty in artificial intelligence (UAI) (pp. 42–49). Edinburgh, UK.

  6. Chajewska, U., Getoor, L., Norman, J., & Shahar, Y. (1998). Utility elicitation as a classification problem. In Proceedings of the 14th conference on uncertainty in artificial intelligence (UAI) (pp. 79–88). Madison, WI, USA.

  7. Chajewska, U., Koller, D., & Parr, R. (2000). Making rational decisions using adaptive utility elicitation. In Proceedings of the 17th national conference on artificial intelligence (AAAI) (pp. 363–369). Austin, TX.

  8. Conitzer V. (2009) Eliciting single-peaked preferences using comparison queries. Journal of Artificial Intelligence Research 35: 161–191

    MATH  MathSciNet  Google Scholar 

  9. Conitzer, V., & Sandholm, T. (2002). Vote elicitation: Complexity and strategy-proofness. In Proceedings of the 18th national conference on artificial intelligence (AAAI) (pp. 392–397). Edmonton, Canada.

  10. Conitzer, V., & Sandholm, T. (2005). Communication complexity of common voting rules. In Proceedings of the sixth ACM conference on electronic commerce (EC) (pp. 78–87). Vancouver, Canada.

  11. Gibbard A. (1973) Manipulation of voting schemes: A general result. Econometrica 41: 587–602

    Article  MATH  MathSciNet  Google Scholar 

  12. Pini, M. S., Rossi, F., Venable, K. B., & Walsh, T. (2005). Aggregating partially ordered preferences: Possibility and impossibility results. In Proceedings of the tenth conference on theoretical aspects of rationality and knowledge (TARK), Singapore.

  13. Pini, M. S., Rossi, F., Venable, K. B., & Walsh T. (2007). Incompleteness and incomparability in preference aggregation. In Proceedings of the 20th international joint conference on artificial intelligence (IJCAI), Hyderabad, India.

  14. Rossi, F., Pini, M. S., Venable, K. B., & Walsh, T. (2006). Strategic voting when aggregating partially ordered preferences. In Proceedings of the fifth international conference on autonomous agents and multi-agent systems (AAMAS) (pp. 685–687). Hakodate, Japan.

  15. Sandholm T., Boutilier C. (2006) Preference elicitation in combinatorial auctions. In: Cramton P., Shoham Y., Steinberg R. (eds) Combinatorial auctions. (Chap. 10). MIT Press, Cambridge, pp 233–263

    Google Scholar 

  16. Satterthwaite M. (1975) Strategy-proofness and Arrow’s conditions: Existence and correspondence theorems for voting procedures and social welfare functions. Journal of Economic Theory 10: 187–217

    Article  MATH  MathSciNet  Google Scholar 

  17. Vu, H., & Haddawy, P. (1997). Problem-focused incremental elicitation of multi-attribute utility models. In Proceedings of the 13th conference on uncertainty in artificial intelligence (UAI) (pp. 215–222). San Francisco, CA, USA.

  18. Vu, H., & Haddawy, P. (1998) Towards case-based preference elicitation: Similarity measures on preference structures. In Proceedings of the 14th conference on uncertainty in artificial intelligence (UAI) (pp. 193–201).

  19. Xia, L., & Conitzer, V. (2008). Determining possible and necessary winners under common voting rules given partial orders. In Proceedings of the 23rd national conference on artificial intelligence (AAAI) (pp. 196–201). Chicago, IL, USA.

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Acknowledgments

We thank the reviewers for helpful comments. This work is supported by NSF Grant IIS-0812113 and an Alfred P. Sloan Fellowship.

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  1. Duke University, Durham, NC, USA

    Joseph Farfel & Vincent Conitzer

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  1. Joseph Farfel
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  2. Vincent Conitzer
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Correspondence to Joseph Farfel.

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Farfel, J., Conitzer, V. Aggregating value ranges: preference elicitation and truthfulness. Auton Agent Multi-Agent Syst 22, 127–150 (2011). https://doi.org/10.1007/s10458-009-9118-5

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  • DOI: https://doi.org/10.1007/s10458-009-9118-5

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