## Abstract

Previous work on voter control, which refers to situations where a chair seeks to change the outcome of an election by deleting, adding, or partitioning voters, takes for granted that the chair knows all the voters’ preferences and that all votes are cast simultaneously. However, elections are often held sequentially and the chair thus knows only the previously cast votes and not the future ones, yet needs to decide instantaneously which control action to take. We introduce a framework that models *online voter control in sequential elections*. We show that the related problems can be much harder than in the standard (non-online) case: For certain election systems, even with efficient winner problems, online control by deleting, adding, or partitioning voters is \(\mathrm {PSPACE}\)-complete, even if there are only two candidates. In addition, we obtain (by a new characterization of coNP in terms of weight-bounded alternating Turing machines) completeness for \({\mathrm {coNP}}\) in the deleting/adding cases with a bounded deletion/addition limit, and we obtain completeness for \({\mathrm {NP}}\) in the partition cases with an additional restriction. We also show that for plurality, online control by deleting or adding voters is in \({\mathrm {P}}\), and for partitioning voters is \({\mathrm {coNP}}\)-hard.

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An exception is a paper by Fitzsimmons et al. [16] that is, regarding their earliest appearing versions, more recent than the present paper, and studies a mixed model involving both a chair and manipulators, in which the manipulative voters set their votes after control action by the chair. (We mention in passing that if one looks beyond the study of control, uncertainty appears in many election, selection, and preference-aggregation settings, see, e.g., the book [39] and, as one among many possible examples, the work of Mattei et al. [33].)

Actually, as our previous example suggested, our model is a bit more flexible and allows one to ask such questions starting at an intermediate point at which some actions have already been taken, potentially by a different admissions staff member.

Note that this maxi-min-inspired (but with more quantifiers) approach is really about alternating quantifiers. We are asking if there exists a current action of the chair, such that for all potential revealed vote values that come between now and the next time the chair has to decide on an action, there exists a next action by the chair, such that for all \(\ldots \) \(\ldots \) the chair reaches her goal.

Why do we provide an ordering \(\sigma \) rather than just providing as a list the set of candidates who are good enough to count as reaching our goal? For the decision-problem version of online voter control, which is our formulation here, providing such a set would be just as good. But by making \(\sigma \) a part of the input, we make the model compatible, for the future, with the interesting optimization problem of trying to find the most preferred candidate within \(\sigma \) for which the chair can ensure that there is among the winner set one of the candidates in the segment from that candidate to the top candidate in \(\sigma \).

Also, to avoid any confusion, we note that in our “

*d*chooses an upper (constructive case) or lower (destructive case) segment of the candidates” approach, the non-online version’s situation that the destructive goal “opposing” a constructive goal is specified in the same way not longer holds (although we could have defined things in a less natural way so that that would hold). That is, in the non-online setting, the distinguished candidate*d*in the constructive case is saying who the chair wants to win, and in the destructive case is saying who the chair wants to not win;*d*in one case is defined in the problem definition to denote the beloved candidate and in the other case is defined to denote the despised candidate. However, in our case, we are giving an order \(\sigma \), and it would be perverse and confusing to have > mean one thing for constructive and another for destructive. And so, as we have defined things, if the chair’s stated ordering \(\sigma \) is \(v_1> v_2> v_3> v_4 > v_5\) and \(d=v_2\), in the constructive case that means that the chair wants at least one of \(v_1\) or \(v_2\) to win. To state the destructive-case goal—which in some sense is the “flip” of that constructive-case goal—of having neither \(v_1\) nor \(v_2\) be a winner, one would give as the chair’s ordering \(v_5> v_4> v_3> v_2 > v_1\) and \(d=v_2\), since this specifies that \(v_2\) and \(v_1\) are the chair’s two most despised candidates and are the ones the chair wants to prevent from being winners.These comments simply refer to the way various “opposite” goals happen to be expressed. None of the above is saying that the constructive

*problem*(viewed as a set) and the destructive*problem*(viewed as a set) are each other’s complements. Due to the quantification involved regarding the actions being taken such as by the chair, that is not true.The statement of Theorem 1 holds even for election systems whose winner problems are in \(\mathrm {PSPACE}\).

Recall that each path of a polynomial-time alternating Turing machine has as its individual (leaf) value either Accept or Reject, and the overall action of the Turing machine is determined by the thought-experiment of applying the existential and universal node actions of the machine to those leaf values, resulting in an Accept or Reject at the root that determines the machine’s acceptance or rejection on the given input.

Are elections with just one candidate ever interesting in the real world? We feel they sometimes are. For example, a popular referendum—or for that matter a vote in a legislature on a bill—is essentially an up-or-down vote on one “candidate.” So is a vote on whether to recall an elected official, or to impeach a judge, or to ratify a person who has been nominated for a sports hall of fame.

Sure enough,

*u*’s top choice could be one of those candidates that end up having only few votes, so adding*u*could be a wasted addition that will block some future good addition in some vote sequences, but in the worst case all future voters put first a candidate disliked by the chair; so our action is fine within the quantifier structure of the problem.

## References

Borodin, A., & El-Yaniv, R. (1998).

*Online computation and competitive analysis*. Cambridge: Cambridge University Press.Baumeister, D., Erdélyi, G., Erdélyi, O., & Rothe, J. (2013). Computational aspects of manipulation and control in judgment aggregation.

*Proceedings of the 3rd international conference on algorithmic decision theory*(Vol. 8176, pp. 71–85). Lecture Notes in Computer Science, Berlin: Springer.Buhrman, H., & Hitchcock, J. (2008, June). NP-hard sets are exponentially dense unless coNP\(\,\subseteq \,\)NP/poly. In

*Proceedings of the 23rd annual IEEE conference on computational complexity*(pp. 1–7). IEEE Computer Society Press, Los Alamitos, CA.Baumeister, D., & Rothe, J. (2016). Preference aggregation by voting. In J. Rothe (Ed.),

*Economics and computation: An introduction to algorithmic game theory, computational social choice, and fair division*(pp. 197–325). Berlin: Springer.Bartholdi III, J., Tovey, C., & Trick, M. (1992). How hard is it to control an election?

*Mathematical and Computer Modeling*,*16*(8/9), 27–40.Cai, J., Chakaravarthy, V., Hemaspaandra, L., & Ogihara, M. (2005). Competing provers yield improved Karp–Lipton collapse results.

*Information and Computation*,*198*(1), 1–23.Chandra, A., Kozen, D., & Stockmeyer, L. (1981). Alternation.

*Journal of the ACM*,*26*(1), 114–133.Desmedt, Y., & Elkind, E. (2010, June). Equilibria of plurality voting with abstentions. In

*Proceedings of the 11th ACM conference on electronic commerce*(pp. 347–356). ACM Press, New York.Dwork, C., Kumar, R., Naor, M., & Sivakumar, D. (2001, March). Rank aggregation methods for the web. In

*Proceedings of the 10th international world wide web conference*(pp. 613–622). ACM Press, New York.Dekel, E., & Piccione, M. (2001). Sequential voting procedures in symmetric binary elections.

*Journal of Political Economy*,*108*(1), 34–55.Erdélyi, G., Fellows, M., Rothe, J., & Schend, L. (2015). Control complexity in Bucklin and fallback voting: A theoretical analysis.

*Journal of Computer and System Sciences*,*81*(4), 632–660.Erdélyi, G., Nowak, M., & Rothe, J. (2009). Sincere-strategy preference-based approval voting fully resists constructive control and broadly resists destructive control.

*Mathematical Logic Quarterly*,*55*(4), 425–443.Ephrati, E., & Rosenschein, J. (1997). A heuristic technique for multi-agent planning.

*Annals of Mathematics and Artificial Intelligence*,*20*(1–4), 13–67.Fraenkel, A., Garey, M., Johnson, D., Schaefer, T., & Yesha, Y. (1978, October). The complexity of checkers on an \({N}\)x\({N}\) board—Preliminary report. In

*Proceedings of the 19th IEEE symposium on foundations of computer science*(pp. 55–64). IEEE Computer Society, Los Alamitos, CA.Faliszewski, P., Hemaspaandra, E., & Hemaspaandra, L. (2010). Using complexity to protect elections.

*Communications of the ACM*,*53*(11), 74–82.Fitzsimmons, Z., Hemaspaandra, E., & Hemaspaandra, L. (2013, August). Control in the presence of manipulators: Cooperative and competitive cases. In

*Proceedings of the 23rd international joint conference on artificial intelligence*(pp. 113–119). AAAI Press, Menlo Park, CA.Faliszewski, P., Hemaspaandra, E., & Hemaspaandra, L. (2014). The complexity of manipulative attacks in nearly single-peaked electorates.

*Artificial Intelligence*,*207*, 69–99.Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2009). Llull and Copeland voting computationally resist bribery and constructive control.

*Journal of Artificial Intelligence Research*,*35*, 275–341.Faliszewski, P., Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2009). A richer understanding of the complexity of election systems. In Ravi, S., & Shukla, S. (Eds.),

*Fundamental problems in computing: Essays in honor of Professor Daniel J. Rosenkrantz*(pp. 375–406). Berlin: Springer.Faliszewski, P., & Rothe, J. (2016). Control and bribery in voting. In F. Brandt, V. Conitzer, U. Endriss, J. Lang, & A. Procaccia (Eds.),

*Handbook of computational social choice*(pp. 146–168). Cambridge: Cambridge University Press.Ghosh, S., Mundhe, M., Hernandez, K., & Sen, S. (1999). Voting for movies: The anatomy of recommender systems. In

*Proceedings of the 3rd annual conference on autonomous agents*(pp. 434–435). ACM Press, New York.Homan, C., & Hemaspaandra, L. (2009). Guarantees for the success frequency of an algorithm for finding Dodgson-election winners.

*Journal of Heuristics*,*15*(4), 403–423.Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2007). Anyone but him: The complexity of precluding an alternative.

*Artificial Intelligence*,*171*(5–6), 255–285.Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2009). Hybrid elections broaden complexity-theoretic resistance to control.

*Mathematical Logic Quarterly*,*55*(4), 397–424.Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2012, August). Controlling candidate-sequential elections. In

*Proceedings of the 20th European conference on artificial intelligence*(pp. 905–906). IOS Press, Amsterdam.Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2012, August). Online voter control in sequential elections. In

*Proceedings of the 20th European conference on artificial intelligence*(pp. 396–401). IOS Press, Amsterdam.Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2013, January). The complexity of online manipulation of sequential elections. In

*Proceedings of the 14th conference on theoretical aspects of rationality and knowledge*(pp. 111–120).Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2014). The complexity of online manipulation of sequential elections.

*Journal of Computer and System Sciences*,*80*(4), 697–710.Hemaspaandra, E., Hemaspaandra, L., & Rothe, J. (2013). The complexity of manipulative actions in single-peaked societies. In J. Rothe (Ed.),

*Economics and computation: An introduction to algorithmic game theory, computational social choice, and fair division*(pp. 327–360). Berlin: Springer.Hopcroft, J., & Ullman, J. (1979).

*Introduction to automata theory, languages, and computation*. Reading, MA: Addison-Wesley.Hemaspaandra, L., & Williams, R. (2012). An atypical survey of typical-case heuristic algorithms.

*SIGACT News*,*43*(4), 71–89.Lichtenstein, D., & Sipser, M. (1980). GO is polynomial-space hard.

*Journal of the ACM*,*27*(2), 393–401.Mattei, N., Goldsmith, J., Klapper, A., & Mundhenk, M. (2015). On the complexity of bribery and manipulation in tournaments with uncertain information.

*Journal of Applied Logic*,*13*(4, Part 2), 557–581.McCabe-Dansted, J., Pritchard, G., & Slinko, A. (2008). Approximability of Dodgson’s rule.

*Social Choice and Welfare*,*31*(2), 311–330.Oren, J., & Lucier, B. (2014, July). Online (budgeted) social choice. In

*Proceedings of the 28th AAAI conference on artificial intelligence*(pp. 1456–1462). AAAI Press, Menlo Park, CA.Papadimitriou, C. (1994).

*Computational complexity*. Reading, MA: Addison-Wesley.Parkes, D., & Procaccia, A. (2013, July). Dynamic social choice with evolving preferences. In

*Proceedings of the 27th AAAI conference on artificial intelligence*(pp. 767–773). AAAI Press, Menlo Park, CA.Rothe, J., & Schend, L. (2013). Challenges to complexity shields that are supposed to protect elections against manipulation and control: A survey.

*Annals of Mathematics and Artificial Intelligence*,*68*(1–3), 161–193.Rossi, F., Venable, K., & Walsh, T. (2011).

*A short introduction to preferences: Between artificial intelligence and social choice*., Synthesis Lectures on Artificial Intelligence and Machine Learning San Rafael: Morgan & Claypool Publishers.Simon, H. (1969).

*The sciences of the artificial*(3rd ed., 1996). Cambridge: MIT Press.Sloth, B. (1993). The theory of voting and equilibria in noncooperative games.

*Games and Economic Behavior*,*5*(1), 152–169.Storer, J. (1983). On the complexity of chess.

*Journal of Computer and System Sciences*,*27*(1), 77–100.Tennenholtz, M. (2004, July). Transitive voting. In

*Proceedings of the 5th ACM conference on electronic commerce*(pp. 230–231). ACM Press, New York.Xia, L., & Conitzer, V. (2010, July). Stackelberg voting games: Computational aspects and paradoxes. In

*Proceedings of the 24th AAAI conference on artificial intelligence*(pp. 697–702). AAAI Press, Menlo Park, CA.Yang, Y., & Dimitrov, D. (2016, June). How hard is it to control a group? Technical Report, arXiv:1606.03366 [cs.GT], Computing Research Repository.

## Acknowledgments

Parts of this paper appeared in preliminary form in ECAI-2012 [26]. We are very grateful to the referees and editor for their comments and suggestions. The authors’ work was supported in part by Grants ARC-DP110101792, DFG-RO-1202/15-1, and NSF-CCF-{0915792,1101452,1101479}, and by COST Action IC1205, Friedrich Wilhelm Bessel Research Awards from the Alexander von Humboldt Foundation, the NRW-MIWF project “Online Partizipation,” and the SFF grant “Cooperative Normsetting” from HHU Düsseldorf. Work done in part while E. and L. Hemaspaandra were visiting Heinrich-Heine-Universität Düsseldorf, and while J. Rothe was visiting the University of Rochester.

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A preliminary version of parts of this paper has appeared in the Proceedings of the 20th European Conference on Artificial Intelligence [26].

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Hemaspaandra, E., Hemaspaandra, L.A. & Rothe, J. The complexity of online voter control in sequential elections.
*Auton Agent Multi-Agent Syst* **31**, 1055–1076 (2017). https://doi.org/10.1007/s10458-016-9349-1

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DOI: https://doi.org/10.1007/s10458-016-9349-1