Abstract
Group decisions are often complicated by a deadline. For example, in committee hiring decisions the deadline might be the next start of a budget, or the beginning of a semester. It may be that if no candidate is supported by a strong majority, the default is to hire no one - an option that may cost dearly. As a result, committee members might prefer to agree on a reasonable, if not necessarily the best, candidate, to avoid unfilled positions. In this paper we propose a model for the above scenario—Consensus Under a Deadline (CUD)—based on a time-bounded iterative voting process. We provide convergence guarantees and an analysis of the quality of the final decision. An extensive experimental study demonstrates more subtle features of CUDs, e.g., the difference between two simple types of committee member behavior, lazy vs. proactive voters. Finally, a user study examines the differences between the behavior of rational voting bots and real voters, concluding that it may often be best to have bots play on the voters’ behalf.
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Notes
Many a joke deals with chains of “I know that he thinks that I know...”, and are in fact based on human inability to handle deep nested beliefs. Comically this is even witnessed in popular culture, e.g., the episode of “Friends”: “The one where everybody finds out”.
Note the difference from the classical concept of possible winners, which refers to expansions of partial preference ballots (see e.g., [55]).
https://github.com/DavidBenYosef/CUDGame.
https://github.com/DavidBenYosef/CUDRunner.
There are precious few works that already do so, such as [52], and the standard approach is to keep both protocol and evaluation aggregations the same.
References
Bannikova, M., Naamani-Dery, L., Obraztsova, S., Rabinovich, Z., Rosenschein, J.S. (2016). Between fairness and a mistrial: Consensus under a deadline. In: The 10th workshop on Advances in Preference Handling (M-PREF)
Boergers, T. (1994). Weak dominance and approximate common knowledge. Journal of Economic Theory, 64, 265–276.
Branzei, S., Caragiannis, I., Morgenstern, J., Procaccia, A. (2013). How bad is selfish voting? In: Proceedings of the 27th AAAI conference on artificial intelligence, 138–144
Camerer, C. F., Ho, T. H., & Chong, J. K. (2004). A cognitive hierarchy of games. The Quarterly Journal of Economics, 119(3), 861–898.
Capuano, N., Chiclana, F., Fujita, H., Herrera-Viedma, E., & Loia, V. (2017). Fuzzy group decision making with incomplete information guided by social influence. IEEE Transactions on Fuzzy Systems, 26(3), 1704–1718.
Cramton, P. C., & Tracy, J. S. (1992). Strikes and holdouts in wage bargaining: Theory and data. American Economic Review, 82(1), 100–121.
Crawford, V. P., Cost-Gomes, M. A., & Iriberri, N. (2013). Structural models of non-equilibrium strategic thinking: Theory, evidence, and applications. Journal of Economic Literature, 51(1), 5–62.
Dery, L., Obraztsova, S., Rabinovich, Z., & Kalech, M. (2019). Lie on the fly: Strategic voting in an iterative preference elicitation process. Group Decision and Negotiation, 28(6), 1077–1107.
Dong, Y., & Zhang, H. (2014). Multiperson decision making with different preference representation structures: A direct consensus framework and its properties. Knowledge-Based Systems, 58, 45–57.
Elkind, E., Markakis, E., Obraztsova, S., Skowron, P. (2015). Equilibria of plurality voting: Lazy and truth-biased voters. In: Proceedings of the 8th international symposium on algorithmic game theory, 110–122
Elkind, E., Grandi, U., Rossi, F., & Slinko, A. (2020). Cognitive hierarchy and voting manipulation in k-approval voting. Mathematical Social Sciences, 108, 193–205.
Fershtman, C., & Seidmann, D. J. (1993). Deadline effects and inefficient delay in bargaining with endogenous commitment. Journal of Economic Theory, 60(2), 306–321.
Gale, D. (1995). Dynamic coordination games. Economic Theory, 5(1), 1–18.
García, S., Fernández, A., Luengo, J., & Herrera, F. (2010). Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences, 180(10), 2044–2064.
Gmytrasiewicz, P. J., & Doshi, P. (2005). A framework for sequential planning in multi-agent settings. Journal of Artificial Intelligence Research, 24, 49–79.
Gvirts, H. Z., & Dery, L. (2019). Alexithymia and reaching group consensus. Cognition and Emotion. https://doi.org/10.1080/02699931.2019.1675600.
Halpern, J.Y., Moses, Y. (1985). A guide to the modal logics of knowledge and belief: Preliminary draft. In: Proceedings of the 9th international joint conference on artificial intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 480–490
Halpern, J. Y., & Moses, Y. (1990). Knowledge and common knowledge in a distributed environment. Journal of the ACM, 37(3), 549–587.
Herrera-Viedma, E., Alonso, S., Chiclana, F., & Herrera, F. (2007). A consensus model for group decision making with incomplete fuzzy preference relations. IEEE Transactions on Fuzzy Systems, 15(5), 863–877.
Hurkens, S., & Vulkan, N. (2015). Dynamic matching and bargaining with heterogeneous deadlines. International Journal of Game Theory, 44(3), 599–629.
Kamishima, T., Kazawa, H., Akaho, S. (2005). Supervised ordering - an empirical survey. In: Proceedings of the 5th IEEE international conference on data mining, 673–676
Kocher, M. G., & Sutter, M. (2006). Time is money - time pressure, incentives, and the quality of decision-making. Journal of Economic Behavior and Organization, 61(3), 375–392.
Koolyk, A., Lev, O., Rosenschein, J.S. (2016). Convergence and quality of iterative voting under non-scoring rules. In: Proceedings of the 2016 international conference on autonomous agents & multiagent systems, 1329–1330
Koutsoupias, E., Papadimitriou, C. (1999). Worst-case equilibria. In: Proceedings of annual symposium on theoretical aspects of computer science, Springer, 404–413
Kwiek, M. (2014). Conclave. European Economic Review, 70, 258–275.
Lev, O., Obraztsova, S., Polukarov, M., Rabinovich, Z., Rosenschein, J.S. (2015). Farsighted voting dynamics. In: Algorithmic game theory workshop at 24th international joint conference on artificial intelligence
Lim, S. G. S., & Murnighan, J. K. (1994). Phases, deadlines, and the bargaining process. Organizational Behavior and Human Decision Processes, 58(2), 153–171.
Ma, C. T. A., & Manove, M. (1993). Bargaining with deadlines and imperfect player control. Econometrica, 61(6), 1313–39.
Mao, A., Procaccia, A.D., Chen, Y. (2013). Better human computation through principled voting. In: Proceedings of the 27th AAAI conference on artificial intelligence, pp 1142–1148
Mason, W., & Suri, S. (2012). Conducting behavioral research on amazon’s mechanical turk. Behavior Research Methods, 44(1), 1–23.
Mattei, N., Walsh, T. (2013). Preflib: A library for preferences. In: Proceedings of the 3rd international conference on algorithmic theory, p 259–270
Meir, R. (2015). Plurality voting under uncertainty. In: Proceedings of the 29th AAAI conference on artificial intelligence, 2103–2109
Meir, R. (2016). Strong and weak acyclicity in iterative voting. In: International symposium on algorithmic game theory, Springer, 182–194
Meir R, Polukarov M, Rosenschein JS, Jennings NR (2010) Convergence to equilibria of plurality voting. In: Proceedings of the 24th AAAI conference on artificial intelligence, 823–828
Meir R, Lev O, Rosenschein JS (2014) A local-dominance theory of voting equilibria. In: Proceedings of the 15th ACM conference on economics and computation, 313–330
Meir, R., Gal, K., & Tal, M. (2020). Strategic voting in the lab: Compromise and leader bias behavior. Autonomous Agents and Multi-Agent Systems, 34(1), 1–37.
de Melo, C. M., Marsella, S., & Gratch, J. (2018). Social decisions and fairness change when people’s interests are represented by autonomous agents. Autonomous Agents and Multi-Agent Systems, 32(1), 163–187.
Mennle T, Weiss M, Philipp B, Seuken S (2015) The power of local manipulation strategies in assignment mechanisms. In: Proceedings of the 24th international joint conference on artificial intelligence, 82–89
Merlo, A., & Ortalo-Magné, F. (2004). Bargaining over residential real estate: Evidence from england. Journal of Urban Economics, 56(2), 192–216.
Monderer, D., & Samet, D. (1989). Approximating common knowledge with common beliefs. Games and Economic Behavior, 1, 170–190.
Moore, D. A. (2004a). Myopic prediction, self-destructive secrecy, and the unexpected benefits of revealing final deadlines in negotiation. Organizational Behavior and Human Decision Processes, 94(2), 125–139.
Moore, D. A. (2004b). The unexpected benefits of final deadlines in negotiation. Journal of Experimental Social Psychology, 40(1), 121–127.
Naamani-Dery L, Obraztsova S, Rabinovich Z, Kalech M (2015) Lie on the fly: Manipulative voters under iterative voting centre. In: Proceedings of the 24th international joint conference on artificial intelligence, 2033–2039
Nagel, R. (1995). Unraveling in guessing games: An experimental study. The American Economic Review, 85(5), 1313–1326.
Obraztsova, S., Markakis, E., Polukarov, M., Rabinovich, Z., Jennings, N.R. (2015). On the convergence of iterative voting: How restrictive should restricted dynamics be? In: Proceedings of the 29th AAAI conference on artificial intelligence, pp 993–999
Papadimitriou, C. (2001). Algorithms, games, and the internet. In: Proceedings of the 33rd annual ACM symposium on theory of computing, pp 749–753
Reijngoud, A., Endriss, U. (2012). Voter response to iterated poll information. In: Proceedings of the 11th international conference on autonomous agents and multiagent systems, 2, 635–644
Reyhani, R., Wilson, M. (2012). Best reply dynamics for scoring rules. In: Proceedings of the 20th european conference on artificial intelligence, 2, 672–677
Roth, A. E., Murnighan, J. K., & Schoumaker, F. (1988). The deadline effect in bargaining : Some experimental evidence. American Economic Review, 78(4), 806–823.
Scheuerman, J., Harman, J.L., Mattei, N., Venable, K.B. (2020). Heuristic strategies in uncertain approval voting environments. In: Proceedings of the 19th international conference on autonomous agents and multiagent systems, 1993–1995
Stahl, D. O., & Wilson, P. W. (1994). Experimental evidence on players’ models of other players. Journal of Economic Behavior and Organization, 25(3), 309–327.
Thompson, D.R., Lev, O., Leyton-Brown, K., Rosenschein, J.S. (2012). Empirical aspects of plurality election equilibria. In: Proceedings of the 4th international workshop on computational social choice, 431–442
Wright, J.R., Leyton-Brown, K. (2010). Beyond equilibrium: predicting human behavior in normal-form games. In: Proceedings of the 24th AAAI international conference on artificial intelligence, 901–907
Wu, J., Chiclana, F., Fujita, H., & Herrera-Viedma, E. (2017). A visual interaction consensus model for social network group decision making with trust propagation. Knowledge-Based Systems, 122, 39–50.
Xia, L., & Conitzer, V. (2011). Determining possible and necessary winners under common voting rules given partial orders. Journal of Artificial Intelligence Research, 41, 25–67.
Yager, R. R., & Alajlan, N. (2015). An intelligent interactive approach to group aggregation of subjective probabilities. Knowledge-Based Systems, 83, 170–175.
Yosef, D.B., Naamani-Dery, L., Obraztsova, S., Rabinovich, Z., Bannikova, M. (2017). Haste makes waste: A case to favour voting bots. In: Proceedings of the international conference on web intelligence, 419–425
Zhang, H., Dong, Y., Chiclana, F., & Yu, S. (2019). Consensus efficiency in group decision making: A comprehensive comparative study and its optimal design. European Journal of Operational Research, 275(2), 580–598.
Zou, J., Meir, R., Parkes, D. (2015). Strategic voting behavior in doodle polls. In: Proceedings of the 18th ACM conference on computer supported cooperative work & social computing, 464–472
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Appendices
A proofs of theorems, lemmata and formal statements
Theorem 1
In any \(CUD(\mathcal {F}^{IMaj}_\sigma , \tau )\) with \(\sigma \in (\frac{n}{2},n]\) and consistent utility functions, either the default alternative is set at \(t=0\), or a valid alternative becomes the winner at time \(t\in [0:\tau ]\).
Proof
Proof of Theorem 1.
First, we prove by contradiction that if there are possible winners at the beginning of the process, then the process converges with some valid alternative as a winner. This implies that at stage \(\tau \) the set of possible winners is not empty, nor is it empty at the final stage 0, since there is a winner. Suppose that it does not hold: the process does not converge, even if there were possible winners at the beginning. In other words, at the final step \(t=0\) the set of possible winners is empty, although at the initial step \(t=\tau \) it is not empty. This can happen if, at some time step between the beginning at \(t=\tau \) and the end at \(t=0\), the set of possible winners becomes empty (including the time step \(t=0\)). Consider the time step \(\tau '\), such that for all \(\tau ''>\tau '\) the set of possible winners is not empty, while for \(\tau '\) it is empty.
Consider the preceding iteration of our game protocol, i.e., the time step \(\tau '+1\). There are two possible scenarios: (i) no voter changes the vote, and (ii) some voter changes the vote.
First, we assume that at the time step \(\tau '+1\) there are no voters who wish to change their votes. Since at \(\tau '+1\) the set of possible winners is not empty, for all the voters the utility is not zero under the current strategy. Since their utility at time \(\tau '+1\) reflects the outcome of time \(\tau '\), the set of possible winners could not become empty at time step \(\tau '\).
Second, if the second scenario has occurred, at the time step \(\tau '+1\) there are some voters who do change their vote. The only reason for a voter to change her vote is to improve her utility by switching away from an alternative that is either no longer in the set of possible winners, or will be removed from the set at the next time-slice. Such a switch, however, necessitates that the voter’s newly chosen alternative remains in the set of possible winners at the next time-slice. Otherwise, the voter’s utility from this new choice would not be positive, and no decision switch would occur. In particular, this implies that the set of possible winners is not empty. The obtained contradiction proves that if the set of possible winners is not empty at \(\tau \), it cannot become empty at 0, therefore there is a valid alternative that is declared as a winner.
What remains is to show consistency of our protocol, i.e., that if at the time step \(\tau \) the set of possible winners is empty, it must be empty at the time step 0 as well. Denote \(c \in C^+\) an alternative that at time step \(\tau \) has the maximum score \(s_c\) among all other alternatives. Since the set of possible winners is empty at the time step \(\tau \), it implies that \(\sigma -s_c \ge \tau +1\). Even if at each step until the deadline there will be a voter that changes her vote for this alternative c, at time 0 this alternative will have obtained only \(\tau \) more votes. As a result, the score of the alternative c at time step 0 can be at most \(s_c+\tau \), which would imply that \(\sigma -s_c-\tau \ge 1\) and \(c\not \in \widehat{W}(\mathbf {s},0)\). As the score of c is maximal possible at time \(t=\tau \), the same holds for all other alternatives. Hence, no alternative is a member of \(\widehat{W}(\mathbf {s},0)\)—it is empty.
Lemma 1
Let \(c \notin \widehat{W}\) at step t, then \(c \notin \widehat{W}\) at any step \(t'<t\).
Proof
Proof of Lemma 1.
Given that \(s_c^t<\sigma -t\) and at each step the candidate c can get at most one vote, then \(s_c^{t'}<s_c^t+(t-t')<\sigma -t+(t-t')=\sigma -t'\) for any \(t'<t\). Therefore, \(c \notin \widehat{W}\) at step \(t'\).
Lemma 2
If \(s_c^{t+1}<s_c^t\), then \(c \in \widehat{W}(s^t,t)\).
Proof
Proof of Lemma 2.
Given that \(s_c^{t+1}<s_c^t\), there exists a voter, i, who changed her vote in favor of c at time step t. Let \(c'\) denote a candidate that she voted for at the preceding time step \(t+1\), and let us assume that the lemma’s conclusion does not hold. That is, let us assume that \(c \notin \widehat{W}(s^t,t)\).
Notice that, except c, no candidate increased his score from the time step \(t+1\) to the time step t. This is because only one voter was given the chance to change her vote, and thus \(s^t=s^{t+1}-c'+c\). As a result, \(\widehat{W}(s^{t+1},t)\supseteq \widehat{W}(s^t,t)\). Therefore, either (but not both) of the following holds:
-
\(a_i(\mathbf {top}_i(\widehat{W}(s^{t+1},t)), \mathbf {top}_i(\widehat{W}(s^t,t)))\)
-
\(\mathbf {top}_i(\widehat{W}(s^{t+1},t))= \mathbf {top}_i(\widehat{W}(s^t,t))\).
A voter changes her vote only if it increases her utility, and conditions above indicate that a lazy voter’s utility (Definition 1) will not change between \(s^{t+1}\) and \(s^t\). Thus, voter i can not be a lazy voter.
Now, combining the fact that \(s^t=s^{t+1}-c'+c\) with our attempt to assume that \(c \notin \widehat{W}(s^t,t)\), we conclude that no candidate in the set \(\widehat{W}(s^t,t)\) has a score higher that he has in \(s^{t+1}\). More formally, \(\forall \widehat{c}\in \widehat{W}(s^t,t),\ \ s^t_{\widehat{c}}\le s^{t+1}_{\widehat{c}}\). As a result, switching from \(c'\) to c would not have been the preferred move of a proactive voter (Definition 2), as it does not change the utility of the set of possible outcomes.
We conclude that if the set of voters consists of lazy and/or proactive voters, then the assumption \(c\notin \widehat{W}(s^t,t)\) leads to a contradiction of no voter having an incentive to change her vote. Thus, \(c\in \widehat{W}(s^t,t)\) must hold.
Lemma 3
If a voter j at the time step t votes for candidate \(c \in \widehat{W}(s^t,t)\), then \(c=\mathbf {top}_i(\widehat{W}(s^t,t))\).
Proof
Proof of Lemma 3.
First notice that all voters initially vote for their top choice, thus the lemma’s conclusion holds for \(t=\tau \). However, let us assume, to the contrary, that the lemma does not hold in general. In particular, it would imply that there is a time step t such that for any \(t'>t\) (i.e., preceding steps) the statement of the lemma is fulfilled, and at step t there is a voter j such that: (i) she votes for \(c\in \widehat{W}(s^t,t)\); (ii) there is a candidate \(c' \succ _{j} c\) in the set \(\widehat{W}(s^t,t)\).
Lemma 1 implies that \(\widehat{W}(s^t,t) \subseteq \widehat{W}(s^{t+1},t+1).\) If voter j did not change her vote at time t, then by maximality of t holds \(c=\mathbf {top}_i\in \widehat{W}(s^{t+1},t+1)\) and it implies that \(c=\mathbf {top}_i\in \widehat{W}(s^t,t)\), which we assumed not to hold. Thus, j must have changed her vote at time step t. However, since there is \(c' \succ _{j} c\) in the set \(\widehat{W}(s^t,t)\), both lazy and proactive consistent utility (see Definitions 1, 2) from c is lower than it is from \(c'\) at time step t, which contradicts optimality of choice in voting decisions (see Game Protocol 1, line 8).
Lemma 4
If there is a voter that changes her ballot at time t from voting for c to voting for \(c'\), then \(s_c^{t+1} \le s_{c'}^{t+1}\).
Proof
Proof of Lemma 4.
Let us assume the opposite to the lemma’s conclusion, that is \(s_{c}^{t+1}>s_{c'}^{t+1}\) and consequently, \(s_c^{t+1} \ge s_{c'}^{t+1}+1=s_{c'}^t\). As a result, according to Lemma 2, \(c' \in \widehat{W}(s^t,t)\). However, since \(s_{c'}^t\) is sufficient to become a possible winner at time t and \(s_c^{t+1}\ge s_{c'}^t\), holds that \(c \in \widehat{W} (s^{t+1},t)\). Note that, according to Lemma 1, the above implies that \(c,c' \in \widehat{W}(s^{t+1},t+1)\).
Now, Lemma 3 implies that if a voter votes for a candidate in the set \(\widehat{W}(s^{t+1},t+1)\), then she prefers this candidate over all other possible winners. Therefore, similarly to the proof of Lemma 2, neither a lazy nor a proactive voter would change their votes. The obtained contradiction proves the lemma.
Corollary 1
Let \(\mathbf {a}=(a_1,\ldots ,a_n)\) be the truthful profile, let \(\tau \) be the deadline time, and let \(\mathbf {b}\) be the ballot profile induced by \(\mathbf {a}\), i.e., \(b_i=\mathbf {top}_i(C)\). CUD stops with some \(w\in C^+\) if and only if there is an alternative \(c\in C^+\) so that \(\mathbf {sc}_c(\mathbf {b})\ge \sigma -\tau \).
Proof
Proof of corollary 1.
First, let us assume the right-hand side of the “if and only if” statement, and show that CUD stops with a non-default alternative. The condition \(\mathbf {sc}_c(\mathbf {b})\ge \sigma -\tau \) implies that c is a possible winner by the definition of \(\widehat{W}(\mathbf {b},\tau )\). Thus, at time step \(t=\tau \) the set of possible winners contains at least one alternative. As a result, by Theorem 1, the process converges with some (non-default) alternative chosen as the winner.
Now, let us deal with the opposite direction of the Theorem’s implication. Let CUD stop with some \(w \in C^+\). Then this candidate w achieved \(\sigma \) votes in no more than \(\tau \) steps. At each step he could get 1 vote at most, that is, he achieved no more than \(\tau \) votes. Thus, for the initial score of w it must hold that \(\mathbf {sc}_w(\mathbf {b})\ge \sigma -\tau \).
Theorem 2
Let \(\mathbf {a}=(a_1,\ldots ,a_n)\) be the truthful profile, let \(\tau \) be the deadline time, and let \(\mathbf {b}\) be the ballot profile induced by \(\mathbf {a}\), i.e., \(b_i=\mathbf {top}_i(C)\)). If there is an alternative \(c\in C^+\) so that \(\mathbf {sc}_c(\mathbf {b})\ge \max \left\{ \left\lfloor \frac{n}{2}\right\rfloor +1,\sigma -\tau \right\} \) then CUD terminates with c as the winner.
Proof
Proof of Theorem 2.
Note that at any step such that all voters whose top-choice is candidate c, vote for c, for any other candidate it is true that \(s_{c'}^t \le n- s_c^t \le \left\lfloor \frac{n}{2}\right\rfloor \) and \(s_{c'}^t \le s_c^t -1\).
Thus, if CUD terminates with a winner other than c, it implies that c loses some votes of those whose top-choice is c.
Consider t such that for every \(t' \ge t\) all voters whose top-choice is c vote for c and at step t one of them changes her vote to \(c'\). Thus, given that no one of them has changed their vote before, \(s_{c'}^t<s_c^t\) which contradicts Lemma 4. That is, there is no such t.
Therefore, candidate c retains the same number of votes, \(\mathbf {sc}_c^\tau \), until step 0, which implies that at that last step he has more votes than any other candidate.
Theorem 3
Let \(\mathbf {a}\) be the truthful profile of voters participating in a CUD. Assuming that it is well-defined for the CUD instance, the following bounds can be placed on the additive Price of Anarchy, \(PoA^+\), depending on the ratio of the deadline timeout \(\tau \) and the number of voters n:
-
1.
If \(\tau \le \sigma - \left\lfloor \frac{n}{2}\right\rfloor \), then
-
2.
If \(\sigma -\left\lfloor \frac{n}{2}\right\rfloor<\tau <\sigma \), then
-
3.
If \(\tau \ge \sigma \), then
Proof
Proof of Theorem 3.
Case when \(\tau \le \sigma - \big \lfloor \frac{n}{2}\big \rfloor \).
Note that, even if at each step every voter would change her vote in favour of the same alternative c, this alternative c would get no more than \(\tau \) points.
Note that, there can be at most two alternatives with score \(\big \lfloor \frac{n}{2}\big \rfloor \). If c is the only one, then he is the winner, hence \(PoA^+(\mathbf {a})=0\). Suppose there are two such alternatives: w and c. If they have equal scores at \(\tau \): \(s_w^\tau =s_c^\tau =\big \lfloor \frac{n}{2}\big \rfloor \), then, whoever wins, \(PoA^+(\mathbf {a})=0\). Another possibility is that there are two such alternatives, w and c, such that w has more points, i.e., \(s_w^\tau =s_c^\tau +1\), and all other alternatives have 0 points. Alternative c would win only if every supporter of w would change to c, which would take all \(\tau \) stages. But, from those who initially voted for w, no voter would change her vote to c, since they are better off by keeping their votes for w. Hence, w will win, and consequently, \(PoA^+(\mathbf {a})=0\).
Case when \(\sigma -\left\lfloor \frac{n}{2}\right\rfloor<\tau <\sigma \)
Let \(\omega \) denote the plurality winner at the time step \(t=\tau \) and c denote the winner at the time step \(t=0\), and let us assume the contrary to the Theorem, i.e., \(PoA^+(\mathbf {a})>\left\lfloor \frac{n}{2}\right\rfloor +\tau -\sigma \). In particular, it would mean that \(\omega \ne c\) and
In \(\tau \) steps candidate c obtains at most \(\tau \) votes and becomes a winner, that is \(s_c^\tau +\tau \ge \sigma \). Therefore, \(s_c^\tau \ge \sigma -\tau \). Combining this with Eq. 4, we obtain:
Thus, \(s_{\omega }^\tau >\left\lfloor \frac{n}{2}\right\rfloor \) and, consequently, \(s_{\omega }^\tau \ge \left\lfloor \frac{n}{2}\right\rfloor +1\). However, according to Theorem 2, this means that \(\omega \) is the declared winner of the CUD at time step \(t=0\). Which contradicts key part of our assumption: \(c\ne \omega \). We thus must conclude that \(PoA^+(\mathbf {a}) \le \left\lfloor \frac{n}{2}\right\rfloor +\tau -\sigma \), as is per the Theorem.
Case \(\tau \ge \sigma \).
Notice again that if \(PoA^+ \ne 0\) then the winner at the time step \(t=\tau \) (denoted by \(\omega \)) and the winner at the time step \(t=0\) (denoted by c) must be different. Now, Theorem 2 implies that at the time step \(t=\tau \) the truthful profile winner, \(\omega \), can have at most \(\left\lfloor \frac{n}{2}\right\rfloor \) votes, otherwise it must be the winner at the time step \(t=0\) as well.
At the same time, it must be that \(s_c^\tau \ge 1\). Otherwise, no voter would be able to switch to c at any time, as there will be at least one other candidate with a higher score than c (the current winner) and, thus, Lemma 4 would prevent the switch. Since possible winners never lose votes, but only gain them, which means that the score of c will never drop to zero either. Thus, \(PoA^+(a) \le \left\lfloor \frac{n}{2}\right\rfloor -1\), as required.
Lemma 5
The last two bounds in Theorem 3are tight. For all \(\tau \) and n that satisfy the conditions of Eqs. 2and 3, there exists a truthful profile \(\mathbf {a}\) such that the corresponding bound holds as an equality.
Proof
Proof of Lemma 5.
Table 8 provides an example of a voting profile that proves that the bounds in Case 2 (\(\sigma -\left\lfloor \frac{n}{2}\right\rfloor<\tau <\sigma \)) of Theorem 3 are tight. All voters are grouped into 3 Blocks. Voters in Block-1 prefer the candidate c over \(c_1\) and over all other candidates; voters in Block-2 prefer candidate \(\omega \) over c and over all other candidates; finally, each voter in Block-3 prefers some distinct candidate (but not \(\omega \)) over c and over all other candidates. We assume that there are \(\sigma -\tau \) voters in Block-1, \(\left\lfloor \frac{n}{2}\right\rfloor \) voters in Block-2, and the rest of the voters are in Block-3. It is assumed that \(\sigma -\tau \ge 2\), so, there are at least 2 voters in Block-1.
Notice that we can indeed construct such a preference profile given that \(\sigma - \left\lfloor \frac{n}{2}\right\rfloor <\tau \), that is, \(\sigma - \tau < \left\lfloor \frac{n}{2}\right\rfloor \). In particular, the size of Block-3 is \(k=n-\left\lfloor \frac{n}{2}\right\rfloor -\sigma + \tau \), and, therefore, every candidate from \(\{c_1,c_2,...c_k\}\) appears as a top-choice only once.
We assume that \(\sigma - \tau \ge 2\) and n is odd. Then, \(\widehat{W}(s^{\tau },\tau )=\{c,w\}\). Thus, at any time step prior to \(\sigma - \left\lfloor \frac{n}{2}\right\rfloor \) voters from the Block-3 will want to change their vote to c, since they currently vote for a candidate outside the set of possible winner \(\widehat{W}\) and for all of these voters \(c\succ w\). No candidate from Block-1 or Block-2 will want to change their votes during that time.
As a result, for \(t=\left\lfloor \frac{n}{2}\right\rfloor -\sigma + \tau \) holds \(s_c^{t}=s_w^{t}=\left\lfloor \frac{n}{2}\right\rfloor \). After this step all voters from Block 1 and all except one from Block 3 vote for c, and all the voters from Block 2 vote for w. At the next step, \(t-1\), all the voters will want to change their vote since the set of possible winners \(\widehat{W}(s^{t-1},t-1)\) can only contain candidates with \(\left\lfloor \frac{n}{2}\right\rfloor +1\) votes.
Ties among voters who wish to change their vote are broken randomly, and with probability \(\frac{\left\lfloor \frac{n}{2}\right\rfloor +1}{n}\) a voter will be chosen who will change her vote to c. This will make c the only possible winner, which, more formally, means that \(\widehat{W}(s^{\left\lfloor \frac{n}{2}\right\rfloor -\sigma +\tau -1},\)
\(\left\lfloor \frac{n}{2}\right\rfloor -\sigma +\tau -1)=\{c\}\). Hence, c will be the winner of the entire election process, the final winner. Given that c and w are the only possible winners, and w is a Plurality winner at time \(\tau \), \(PoA^+=\left\lfloor \frac{n}{2}\right\rfloor -\sigma +\tau \).
Table 9 provides an example of a voting profile that proves that the bounds in the Case 3 (\(\tau \ge \sigma \)) of Theorem 3 are tight.
Once again, all voters are grouped into three blocks. Voters of Block-1 prefer candidate \(\omega \) over c and over all other candidates; Block-2 voters prefer candidate c over \(c_1\) and over all other candidates; each voter in Block-3 prefers some distinct candidate (but not \(\omega \)) over candidate c and other candidates. Let there be \(\left\lfloor \frac{n}{2}\right\rfloor \) voters in Block-1, a single voter in Block-2, and the rest of the voters grouped into Block-3. We will assume that n is odd, so that the number of voters in Block-3 is \(k=\left\lfloor \frac{n}{2}\right\rfloor \).
Now, notice that at the time step \(t=\sigma -1\) voters from Block-2 and Block-3 will want to change their vote. In particular, voters from Block-3 will want to change their votes to c, and, because ties among willing voters are broken uniformly at random, with probability \(\frac{\left\lfloor \frac{n}{2}\right\rfloor }{\left\lfloor \frac{n}{2}\right\rfloor +1}\) a voter from Block-3 will be granted the opportunity and change her vote in favour of c. Thus, \(\widehat{W}(s^{\tau -1},\tau -1)=\{w,c\}\). Analogously to the previously investigated profile, both w and c can be the final winner. Hence, \(PoA^+\ge \left\lfloor \frac{n}{2}\right\rfloor -1\). Furthermore, combining this conclusion with an application of Theorem 3 to the constructed profile, we obtain that \(PoA^+= \left\lfloor \frac{n}{2}\right\rfloor -1\) for the constructed profile. This yields the tightness of Theorem 3 as required. \(\square \)
Remarks on condorcet winners
In addition to the proofs of our main results, we would like to venture some additional thoughts on refinements of our protocol. Specifically, we would like to make a few notes about its relationship to Condorcet winners.
Lemma 6
Protocol1 is not Condorcet-consistent.
Proof
Consider a preference profile, where an alternative \(c\in C\) is the second choice for all voters, and the top choices divide equally among \({a_1,a_2,a_3}\subseteq C\setminus \{c\}\). Furthermore, assume that c is not the default alternative.
It is easy to see that c is a Condorcet winner. However, since the initial score of c is zero and, as a consequence of Lemma 4, no voter will change her ballot in favour of c, and it will persistently remain outside of the set of possible winners. Hence, Protocol 1 will either result in a default or any other alternative in \(C\setminus \{c\}\). That is, Protocol 1 is not Condorcet-consistent.
However, it is marginally easier for a Condorcet winner to survive our Protocol and become its winner, as the following augmentation of Theorem 2 states.
Conjecture 1
Let \(\mathbf {a}=(a_1,\ldots ,a_n)\) be the truthful profile, let \(\tau \) be the deadline time, and let \(\mathbf {b}\) be the ballot profile induced by \(\mathbf {a}\), i.e., \(b_i=\mathbf {top}_i(C)\)). Let \(c\in C^+\) be a Condorcet winner under the profile \(\mathbf {a}\). If in addition holds that \(\mathbf {sc}_c(\mathbf {b})\ge \max \left\{ \left\lceil \frac{n}{2}\right\rceil -1,\sigma -\tau \right\} \) then CUD terminates with c as the winner.
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Bannikova, M., Dery, L., Obraztsova, S. et al. Reaching consensus under a deadline. Auton Agent Multi-Agent Syst 35, 9 (2021). https://doi.org/10.1007/s10458-020-09490-7
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DOI: https://doi.org/10.1007/s10458-020-09490-7