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Documents authored by Mohan, Divyarthi

Document
APPROX
DOI: 10.4230/LIPIcs.APPROX/RANDOM.2024.5
Asynchronous Majority Dynamics on Binomial Random Graphs

Authors: Divyarthi Mohan and Paweł Prałat

Published in: LIPIcs, Volume 317, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)

Abstract
We study information aggregation in networks when agents interact to learn a binary state of the world. Initially each agent privately observes an independent signal which is correct with probability 1/2+δ for some δ > 0. At each round, a node is selected uniformly at random to update their public opinion to match the majority of their neighbours (breaking ties in favour of their initial private signal). Our main result shows that for sparse and connected binomial random graphs G(n,p) the process stabilizes in a correct consensus in 𝒪(nlog² n/log log n) steps with high probability. In fact, when log n/n ≪ p = o(1) the process terminates at time T^ = (1+o(1))nlog n, where T^ is the first time when all nodes have been selected at least once. However, in dense binomial random graphs with p = Ω(1), there is an information cascade where the process terminates in the incorrect consensus with probability bounded away from zero.
Cite as

Divyarthi Mohan and Paweł Prałat. Asynchronous Majority Dynamics on Binomial Random Graphs. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 317, pp. 5:1-5:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{mohan_et_al:LIPIcs.APPROX/RANDOM.2024.5,
  author =	{Mohan, Divyarthi and Pra{\l}at, Pawe{\l}},
  title =	{{Asynchronous Majority Dynamics on Binomial Random Graphs}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2024)},
  pages =	{5:1--5:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-348-5},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{317},
  editor =	{Kumar, Amit and Ron-Zewi, Noga},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX/RANDOM.2024.5},
  URN =		{urn:nbn:de:0030-drops-209985},
  doi =		{10.4230/LIPIcs.APPROX/RANDOM.2024.5},
  annote =	{Keywords: Opinion dynamics, Social learning, Stochastic processes, Random Graphs, Consensus}
}
Document
Extended Abstract
DOI: 10.4230/LIPIcs.ITCS.2024.57
Communicating with Anecdotes (Extended Abstract)

Authors: Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Markus Mobius, and Divyarthi Mohan

Published in: LIPIcs, Volume 287, 15th Innovations in Theoretical Computer Science Conference (ITCS 2024)

Abstract
We study a communication game between a sender and receiver. The sender chooses one of her signals about the state of the world (i.e., an anecdote) and communicates it to the receiver who takes an action affecting both players. The sender and receiver both care about the state of the world but are also influenced by personal preferences, so their ideal actions can differ. We characterize perfect Bayesian equilibria. The sender faces a temptation to persuade: she wants to select a biased anecdote to influence the receiver’s action. Anecdotes are still informative to the receiver (who will debias at equilibrium) but the attempt to persuade comes at the cost of precision. This gives rise to informational homophily where the receiver prefers to listen to like-minded senders because they provide higher-precision signals. Communication becomes polarized when the sender is an expert with access to many signals, with the sender choosing extreme outlier anecdotes at equilibrium (unless preferences are perfectly aligned). This polarization dissipates all the gains from communication with an increasingly well-informed sender when the anecdote distribution is heavy-tailed. Experts therefore face a curse of informedness: receivers will prefer to listen to less-informed senders who cannot pick biased signals as easily.
Cite as

Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Markus Mobius, and Divyarthi Mohan. Communicating with Anecdotes (Extended Abstract). In 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 287, pp. 57:1-57:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{haghtalab_et_al:LIPIcs.ITCS.2024.57,
  author =	{Haghtalab, Nika and Immorlica, Nicole and Lucier, Brendan and Mobius, Markus and Mohan, Divyarthi},
  title =	{{Communicating with Anecdotes}},
  booktitle =	{15th Innovations in Theoretical Computer Science Conference (ITCS 2024)},
  pages =	{57:1--57:2},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-309-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{287},
  editor =	{Guruswami, Venkatesan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2024.57},
  URN =		{urn:nbn:de:0030-drops-195852},
  doi =		{10.4230/LIPIcs.ITCS.2024.57},
  annote =	{Keywords: Communication game, Equilibrium, Polarization, Signalling}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2020.8
Asynchronous Majority Dynamics in Preferential Attachment Trees

Authors: Maryam Bahrani, Nicole Immorlica, Divyarthi Mohan, and S. Matthew Weinberg

Published in: LIPIcs, Volume 168, 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)

Abstract
We study information aggregation in networks where agents make binary decisions (labeled incorrect or correct). Agents initially form independent private beliefs about the better decision, which is correct with probability 1/2+δ. The dynamics we consider are asynchronous (each round, a single agent updates their announced decision) and non-Bayesian (agents simply copy the majority announcements among their neighbors, tie-breaking in favor of their private signal). Our main result proves that when the network is a tree formed according to the preferential attachment model [Barabási and Albert, 1999], with high probability, the process stabilizes in a correct majority within O(n log n/log log n) rounds. We extend our results to other tree structures, including balanced M-ary trees for any M.
Cite as

Maryam Bahrani, Nicole Immorlica, Divyarthi Mohan, and S. Matthew Weinberg. Asynchronous Majority Dynamics in Preferential Attachment Trees. In 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 168, pp. 8:1-8:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{bahrani_et_al:LIPIcs.ICALP.2020.8,
  author =	{Bahrani, Maryam and Immorlica, Nicole and Mohan, Divyarthi and Weinberg, S. Matthew},
  title =	{{Asynchronous Majority Dynamics in Preferential Attachment Trees}},
  booktitle =	{47th International Colloquium on Automata, Languages, and Programming (ICALP 2020)},
  pages =	{8:1--8:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-138-2},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{168},
  editor =	{Czumaj, Artur and Dawar, Anuj and Merelli, Emanuela},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2020.8},
  URN =		{urn:nbn:de:0030-drops-124156},
  doi =		{10.4230/LIPIcs.ICALP.2020.8},
  annote =	{Keywords: Opinion Dynamics, Information Cascades, Preferential Attachment, Majority Dynamics, non-Bayesian Asynchronous Learning, Stochastic Processes}
}
Document
DOI: 10.4230/LIPIcs.ESA.2017.15
Improved Algorithm for Dynamic b-Matching

Authors: Sayan Bhattacharya, Manoj Gupta, and Divyarthi Mohan

Published in: LIPIcs, Volume 87, 25th Annual European Symposium on Algorithms (ESA 2017)

Abstract
Recently there has been extensive work on maintaining (approximate) maximum matchings in dynamic graphs. We consider a generalisation of this problem known as the maximum b-matching: Every node v has a positive integral capacity b_v, and the goal is to maintain an (approximate) maximum-cardinality subset of edges that contains at most b_v edges incident on every node v. The maximum matching problem is a special case of this problem where b_v = 1 for every node v. Bhattacharya, Henzinger and Italiano [ICALP 2015] showed how to maintain a O(1) approximate maximum b-matching in a graph in O(log^3 n) amortised update time. Their approximation ratio was a large (double digit) constant. We significantly improve their result both in terms of approximation ratio as well as update time. Specifically, we design a randomised dynamic algorithm that maintains a (2+epsilon)-approximate maximum $b$-matching in expected amortised O(1/epsilon^4) update time. Thus, for every constant epsilon in (0, 1), we get expected amortised O(1) update time. Our algorithm generalises the framework of Baswana, Gupta, Sen [FOCS 2011] and Solomon [FOCS 2016] for maintaining a maximal matching in a dynamic graph.
Cite as

Sayan Bhattacharya, Manoj Gupta, and Divyarthi Mohan. Improved Algorithm for Dynamic b-Matching. In 25th Annual European Symposium on Algorithms (ESA 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 87, pp. 15:1-15:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{bhattacharya_et_al:LIPIcs.ESA.2017.15,
  author =	{Bhattacharya, Sayan and Gupta, Manoj and Mohan, Divyarthi},
  title =	{{Improved Algorithm for Dynamic b-Matching}},
  booktitle =	{25th Annual European Symposium on Algorithms (ESA 2017)},
  pages =	{15:1--15:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-049-1},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{87},
  editor =	{Pruhs, Kirk and Sohler, Christian},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2017.15},
  URN =		{urn:nbn:de:0030-drops-78443},
  doi =		{10.4230/LIPIcs.ESA.2017.15},
  annote =	{Keywords: dynamic data structures, graph algorithms}
}
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