--- _id: '12767' abstract: - lang: eng text: "Several problems in planning and reactive synthesis can be reduced to the analysis of two-player quantitative graph games. Optimization is one form of analysis. We argue that in many cases it may be better to replace the optimization problem with the satisficing problem, where instead of searching for optimal solutions, the goal is to search for solutions that adhere to a given threshold bound.\r\nThis work defines and investigates the satisficing problem on a two-player graph game with the discounted-sum cost model. We show that while the satisficing problem can be solved using numerical methods just like the optimization problem, this approach does not render compelling benefits over optimization. When the discount factor is, however, an integer, we present another approach to satisficing, which is purely based on automata methods. We show that this approach is algorithmically more performant – both theoretically and empirically – and demonstrates the broader applicability of satisficing over optimization." acknowledgement: We thank anonymous reviewers for valuable inputs. This work is supported in part by NSF grant 2030859 to the CRA for the CIFellows Project, NSF grants IIS-1527668, CCF-1704883, IIS-1830549, the ERC CoG 863818 (ForM-SMArt), and an award from the Maryland Procurement Office. alternative_title: - LNCS article_processing_charge: No author: - first_name: Suguman full_name: Bansal, Suguman last_name: Bansal - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Moshe Y. full_name: Vardi, Moshe Y. last_name: Vardi citation: ama: 'Bansal S, Chatterjee K, Vardi MY. On satisficing in quantitative games. In: 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. Vol 12651. Springer Nature; 2021:20-37. doi:10.1007/978-3-030-72016-2' apa: 'Bansal, S., Chatterjee, K., & Vardi, M. Y. (2021). On satisficing in quantitative games. In 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (Vol. 12651, pp. 20–37). Luxembourg City, Luxembourg: Springer Nature. https://doi.org/10.1007/978-3-030-72016-2' chicago: Bansal, Suguman, Krishnendu Chatterjee, and Moshe Y. Vardi. “On Satisficing in Quantitative Games.” In 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, 12651:20–37. Springer Nature, 2021. https://doi.org/10.1007/978-3-030-72016-2. ieee: S. Bansal, K. Chatterjee, and M. Y. Vardi, “On satisficing in quantitative games,” in 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Luxembourg City, Luxembourg, 2021, vol. 12651, pp. 20–37. ista: 'Bansal S, Chatterjee K, Vardi MY. 2021. On satisficing in quantitative games. 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. TACAS: Tools and Algorithms for the Construction and Analysis of Systems, LNCS, vol. 12651, 20–37.' mla: Bansal, Suguman, et al. “On Satisficing in Quantitative Games.” 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, vol. 12651, Springer Nature, 2021, pp. 20–37, doi:10.1007/978-3-030-72016-2. short: S. Bansal, K. Chatterjee, M.Y. Vardi, in:, 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, Springer Nature, 2021, pp. 20–37. conference: end_date: 2021-04-01 location: Luxembourg City, Luxembourg name: 'TACAS: Tools and Algorithms for the Construction and Analysis of Systems' start_date: 2021-03-27 date_created: 2023-03-26T22:01:09Z date_published: 2021-03-21T00:00:00Z date_updated: 2023-03-28T11:03:11Z day: '21' ddc: - '000' department: - _id: KrCh doi: 10.1007/978-3-030-72016-2 ec_funded: 1 external_id: arxiv: - '2101.02594' file: - access_level: open_access checksum: b020b78b23587ce7610b1aafb4e63438 content_type: application/pdf creator: dernst date_created: 2023-03-28T11:00:33Z date_updated: 2023-03-28T11:00:33Z file_id: '12777' file_name: 2021_LNCS_Bansal.pdf file_size: 747418 relation: main_file success: 1 file_date_updated: 2023-03-28T11:00:33Z has_accepted_license: '1' intvolume: ' 12651' language: - iso: eng month: '03' oa: 1 oa_version: Published Version page: 20-37 project: - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication: 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems publication_identifier: eissn: - 1611-3349 isbn: - '9783030720155' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: On satisficing in quantitative games tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 12651 year: '2021' ... --- _id: '10667' abstract: - lang: eng text: Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction. We consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with infinite time horizon systems. Compared to the existing sampling-based approaches, which are inapplicable to the infinite time horizon setting, we train a separate deterministic neural network that serves as an infinite time horizon safety certificate. In particular, we show that the certificate network guarantees the safety of the system over a subset of the BNN weight posterior's support. Our method first computes a safe weight set and then alters the BNN's weight posterior to reject samples outside this set. Moreover, we show how to extend our approach to a safe-exploration reinforcement learning setting, in order to avoid unsafe trajectories during the training of the policy. We evaluate our approach on a series of reinforcement learning benchmarks, including non-Lyapunovian safety specifications. acknowledgement: This research was supported in part by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award), ERC CoG 863818 (FoRM-SMArt), and the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 665385. alternative_title: - ' Advances in Neural Information Processing Systems' article_processing_charge: No author: - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Ðorđe full_name: Žikelić, Ðorđe last_name: Žikelić - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Thomas A full_name: Henzinger, Thomas A id: 40876CD8-F248-11E8-B48F-1D18A9856A87 last_name: Henzinger orcid: 0000-0002-2985-7724 citation: ama: 'Lechner M, Žikelić Ð, Chatterjee K, Henzinger TA. Infinite time horizon safety of Bayesian neural networks. In: 35th Conference on Neural Information Processing Systems. ; 2021. doi:10.48550/arXiv.2111.03165' apa: Lechner, M., Žikelić, Ð., Chatterjee, K., & Henzinger, T. A. (2021). Infinite time horizon safety of Bayesian neural networks. In 35th Conference on Neural Information Processing Systems. Virtual. https://doi.org/10.48550/arXiv.2111.03165 chicago: Lechner, Mathias, Ðorđe Žikelić, Krishnendu Chatterjee, and Thomas A Henzinger. “Infinite Time Horizon Safety of Bayesian Neural Networks.” In 35th Conference on Neural Information Processing Systems, 2021. https://doi.org/10.48550/arXiv.2111.03165. ieee: M. Lechner, Ð. Žikelić, K. Chatterjee, and T. A. Henzinger, “Infinite time horizon safety of Bayesian neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021. ista: 'Lechner M, Žikelić Ð, Chatterjee K, Henzinger TA. 2021. Infinite time horizon safety of Bayesian neural networks. 35th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, .' mla: Lechner, Mathias, et al. “Infinite Time Horizon Safety of Bayesian Neural Networks.” 35th Conference on Neural Information Processing Systems, 2021, doi:10.48550/arXiv.2111.03165. short: M. Lechner, Ð. Žikelić, K. Chatterjee, T.A. Henzinger, in:, 35th Conference on Neural Information Processing Systems, 2021. conference: end_date: 2021-12-10 location: Virtual name: 'NeurIPS: Neural Information Processing Systems' start_date: 2021-12-06 date_created: 2022-01-25T15:45:58Z date_published: 2021-12-01T00:00:00Z date_updated: 2023-06-23T07:01:11Z day: '01' ddc: - '000' department: - _id: GradSch - _id: ToHe - _id: KrCh doi: 10.48550/arXiv.2111.03165 ec_funded: 1 external_id: arxiv: - '2111.03165' file: - access_level: open_access checksum: 0fc0f852525c10dda9cc9ffea07fb4e4 content_type: application/pdf creator: mlechner date_created: 2022-01-26T07:39:59Z date_updated: 2022-01-26T07:39:59Z file_id: '10682' file_name: infinite_time_horizon_safety_o.pdf file_size: 452492 relation: main_file success: 1 file_date_updated: 2022-01-26T07:39:59Z has_accepted_license: '1' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-nd/3.0/ main_file_link: - open_access: '1' url: https://proceedings.neurips.cc/paper/2021/hash/544defa9fddff50c53b71c43e0da72be-Abstract.html month: '12' oa: 1 oa_version: Published Version project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: 35th Conference on Neural Information Processing Systems publication_status: published quality_controlled: '1' related_material: record: - id: '11362' relation: dissertation_contains status: public status: public title: Infinite time horizon safety of Bayesian neural networks tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) short: CC BY-NC-ND (3.0) type: conference user_id: 2EBD1598-F248-11E8-B48F-1D18A9856A87 year: '2021' ... --- _id: '8793' abstract: - lang: eng text: We study optimal election sequences for repeatedly selecting a (very) small group of leaders among a set of participants (players) with publicly known unique ids. In every time slot, every player has to select exactly one player that it considers to be the current leader, oblivious to the selection of the other players, but with the overarching goal of maximizing a given parameterized global (“social”) payoff function in the limit. We consider a quite generic model, where the local payoff achieved by a given player depends, weighted by some arbitrary but fixed real parameter, on the number of different leaders chosen in a round, the number of players that choose the given player as the leader, and whether the chosen leader has changed w.r.t. the previous round or not. The social payoff can be the maximum, average or minimum local payoff of the players. Possible applications include quite diverse examples such as rotating coordinator-based distributed algorithms and long-haul formation flying of social birds. Depending on the weights and the particular social payoff, optimal sequences can be very different, from simple round-robin where all players chose the same leader alternatingly every time slot to very exotic patterns, where a small group of leaders (at most 2) is elected in every time slot. Moreover, we study the question if and when a single player would not benefit w.r.t. its local payoff when deviating from the given optimal sequence, i.e., when our optimal sequences are Nash equilibria in the restricted strategy space of oblivious strategies. As this is the case for many parameterizations of our model, our results reveal that no punishment is needed to make it rational for the players to optimize the social payoff. acknowledgement: "We are grateful to Matthias Függer and Thomas Nowak for having raised our interest in the problem studied in this paper.\r\nThis work has been supported the Austrian Science Fund (FWF) projects S11405, S11407 (RiSE), and P28182 (ADynNet)." article_processing_charge: No article_type: original author: - first_name: Martin full_name: Zeiner, Martin last_name: Zeiner - first_name: Ulrich full_name: Schmid, Ulrich last_name: Schmid - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X citation: ama: Zeiner M, Schmid U, Chatterjee K. Optimal strategies for selecting coordinators. Discrete Applied Mathematics. 2021;289(1):392-415. doi:10.1016/j.dam.2020.10.022 apa: Zeiner, M., Schmid, U., & Chatterjee, K. (2021). Optimal strategies for selecting coordinators. Discrete Applied Mathematics. Elsevier. https://doi.org/10.1016/j.dam.2020.10.022 chicago: Zeiner, Martin, Ulrich Schmid, and Krishnendu Chatterjee. “Optimal Strategies for Selecting Coordinators.” Discrete Applied Mathematics. Elsevier, 2021. https://doi.org/10.1016/j.dam.2020.10.022. ieee: M. Zeiner, U. Schmid, and K. Chatterjee, “Optimal strategies for selecting coordinators,” Discrete Applied Mathematics, vol. 289, no. 1. Elsevier, pp. 392–415, 2021. ista: Zeiner M, Schmid U, Chatterjee K. 2021. Optimal strategies for selecting coordinators. Discrete Applied Mathematics. 289(1), 392–415. mla: Zeiner, Martin, et al. “Optimal Strategies for Selecting Coordinators.” Discrete Applied Mathematics, vol. 289, no. 1, Elsevier, 2021, pp. 392–415, doi:10.1016/j.dam.2020.10.022. short: M. Zeiner, U. Schmid, K. Chatterjee, Discrete Applied Mathematics 289 (2021) 392–415. date_created: 2020-11-22T23:01:26Z date_published: 2021-01-31T00:00:00Z date_updated: 2023-08-04T11:12:41Z day: '31' ddc: - '510' department: - _id: KrCh doi: 10.1016/j.dam.2020.10.022 external_id: isi: - '000596823800035' file: - access_level: open_access checksum: f1039ff5a2d6ca116720efdb84ee9d5e content_type: application/pdf creator: dernst date_created: 2021-02-04T11:28:42Z date_updated: 2021-02-04T11:28:42Z file_id: '9089' file_name: 2021_DiscreteApplMath_Zeiner.pdf file_size: 652739 relation: main_file success: 1 file_date_updated: 2021-02-04T11:28:42Z has_accepted_license: '1' intvolume: ' 289' isi: 1 issue: '1' language: - iso: eng month: '01' oa: 1 oa_version: Published Version page: 392-415 project: - _id: 25F2ACDE-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: S11402-N23 name: Rigorous Systems Engineering - _id: 25863FF4-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: S11407 name: Game Theory publication: Discrete Applied Mathematics publication_identifier: issn: - 0166218X publication_status: published publisher: Elsevier quality_controlled: '1' scopus_import: '1' status: public title: Optimal strategies for selecting coordinators tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 289 year: '2021' ... --- _id: '9381' abstract: - lang: eng text: 'A game of rock-paper-scissors is an interesting example of an interaction where none of the pure strategies strictly dominates all others, leading to a cyclic pattern. In this work, we consider an unstable version of rock-paper-scissors dynamics and allow individuals to make behavioural mistakes during the strategy execution. We show that such an assumption can break a cyclic relationship leading to a stable equilibrium emerging with only one strategy surviving. We consider two cases: completely random mistakes when individuals have no bias towards any strategy and a general form of mistakes. Then, we determine conditions for a strategy to dominate all other strategies. However, given that individuals who adopt a dominating strategy are still prone to behavioural mistakes in the observed behaviour, we may still observe extinct strategies. That is, behavioural mistakes in strategy execution stabilise evolutionary dynamics leading to an evolutionary stable and, potentially, mixed co-existence equilibrium.' acknowledgement: Authors would like to thank Christian Hilbe and Martin Nowak for their inspiring and very helpful feedback on the manuscript. article_number: e1008523 article_processing_charge: No article_type: original author: - first_name: Maria full_name: Kleshnina, Maria id: 4E21749C-F248-11E8-B48F-1D18A9856A87 last_name: Kleshnina - first_name: Sabrina S. full_name: Streipert, Sabrina S. last_name: Streipert - first_name: Jerzy A. full_name: Filar, Jerzy A. last_name: Filar - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X citation: ama: Kleshnina M, Streipert SS, Filar JA, Chatterjee K. Mistakes can stabilise the dynamics of rock-paper-scissors games. PLoS Computational Biology. 2021;17(4). doi:10.1371/journal.pcbi.1008523 apa: Kleshnina, M., Streipert, S. S., Filar, J. A., & Chatterjee, K. (2021). Mistakes can stabilise the dynamics of rock-paper-scissors games. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1008523 chicago: Kleshnina, Maria, Sabrina S. Streipert, Jerzy A. Filar, and Krishnendu Chatterjee. “Mistakes Can Stabilise the Dynamics of Rock-Paper-Scissors Games.” PLoS Computational Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1008523. ieee: M. Kleshnina, S. S. Streipert, J. A. Filar, and K. Chatterjee, “Mistakes can stabilise the dynamics of rock-paper-scissors games,” PLoS Computational Biology, vol. 17, no. 4. Public Library of Science, 2021. ista: Kleshnina M, Streipert SS, Filar JA, Chatterjee K. 2021. Mistakes can stabilise the dynamics of rock-paper-scissors games. PLoS Computational Biology. 17(4), e1008523. mla: Kleshnina, Maria, et al. “Mistakes Can Stabilise the Dynamics of Rock-Paper-Scissors Games.” PLoS Computational Biology, vol. 17, no. 4, e1008523, Public Library of Science, 2021, doi:10.1371/journal.pcbi.1008523. short: M. Kleshnina, S.S. Streipert, J.A. Filar, K. Chatterjee, PLoS Computational Biology 17 (2021). date_created: 2021-05-09T22:01:38Z date_published: 2021-04-01T00:00:00Z date_updated: 2023-08-08T13:31:08Z day: '01' ddc: - '000' department: - _id: KrCh doi: 10.1371/journal.pcbi.1008523 ec_funded: 1 external_id: isi: - '000639711200001' file: - access_level: open_access checksum: a94ebe0c4116f5047eaa6029e54d2dac content_type: application/pdf creator: kschuh date_created: 2021-05-11T13:50:06Z date_updated: 2021-05-11T13:50:06Z file_id: '9385' file_name: 2021_pcbi_Kleshnina.pdf file_size: 1323820 relation: main_file success: 1 file_date_updated: 2021-05-11T13:50:06Z has_accepted_license: '1' intvolume: ' 17' isi: 1 issue: '4' language: - iso: eng month: '04' oa: 1 oa_version: Published Version project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication: PLoS Computational Biology publication_identifier: eissn: - '15537358' issn: - 1553734X publication_status: published publisher: Public Library of Science quality_controlled: '1' scopus_import: '1' status: public title: Mistakes can stabilise the dynamics of rock-paper-scissors games tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 17 year: '2021' ... --- _id: '9640' abstract: - lang: eng text: 'Selection and random drift determine the probability that novel mutations fixate in a population. Population structure is known to affect the dynamics of the evolutionary process. Amplifiers of selection are population structures that increase the fixation probability of beneficial mutants compared to well-mixed populations. Over the past 15 years, extensive research has produced remarkable structures called strong amplifiers which guarantee that every beneficial mutation fixates with high probability. But strong amplification has come at the cost of considerably delaying the fixation event, which can slow down the overall rate of evolution. However, the precise relationship between fixation probability and time has remained elusive. Here we characterize the slowdown effect of strong amplification. First, we prove that all strong amplifiers must delay the fixation event at least to some extent. Second, we construct strong amplifiers that delay the fixation event only marginally as compared to the well-mixed populations. Our results thus establish a tight relationship between fixation probability and time: Strong amplification always comes at a cost of a slowdown, but more than a marginal slowdown is not needed.' acknowledgement: 'K.C. acknowledges support from ERC Start grant no. (279307: Graph Games), ERC Consolidator grant no. (863818: ForM-SMart), Austrian Science Fund (FWF) grant no. P23499-N23 and S11407-N23 (RiSE). M.A.N. acknowledges support from Office of Naval Research grant N00014-16-1-2914 and from the John Templeton Foundation.' article_number: '4009' article_processing_charge: No article_type: original author: - first_name: Josef full_name: Tkadlec, Josef id: 3F24CCC8-F248-11E8-B48F-1D18A9856A87 last_name: Tkadlec orcid: 0000-0002-1097-9684 - first_name: Andreas full_name: Pavlogiannis, Andreas id: 49704004-F248-11E8-B48F-1D18A9856A87 last_name: Pavlogiannis orcid: 0000-0002-8943-0722 - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Martin A. full_name: Nowak, Martin A. last_name: Nowak citation: ama: Tkadlec J, Pavlogiannis A, Chatterjee K, Nowak MA. Fast and strong amplifiers of natural selection. Nature Communications. 2021;12(1). doi:10.1038/s41467-021-24271-w apa: Tkadlec, J., Pavlogiannis, A., Chatterjee, K., & Nowak, M. A. (2021). Fast and strong amplifiers of natural selection. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-021-24271-w chicago: Tkadlec, Josef, Andreas Pavlogiannis, Krishnendu Chatterjee, and Martin A. Nowak. “Fast and Strong Amplifiers of Natural Selection.” Nature Communications. Springer Nature, 2021. https://doi.org/10.1038/s41467-021-24271-w. ieee: J. Tkadlec, A. Pavlogiannis, K. Chatterjee, and M. A. Nowak, “Fast and strong amplifiers of natural selection,” Nature Communications, vol. 12, no. 1. Springer Nature, 2021. ista: Tkadlec J, Pavlogiannis A, Chatterjee K, Nowak MA. 2021. Fast and strong amplifiers of natural selection. Nature Communications. 12(1), 4009. mla: Tkadlec, Josef, et al. “Fast and Strong Amplifiers of Natural Selection.” Nature Communications, vol. 12, no. 1, 4009, Springer Nature, 2021, doi:10.1038/s41467-021-24271-w. short: J. Tkadlec, A. Pavlogiannis, K. Chatterjee, M.A. Nowak, Nature Communications 12 (2021). date_created: 2021-07-11T22:01:15Z date_published: 2021-06-29T00:00:00Z date_updated: 2023-08-10T14:05:09Z day: '29' ddc: - '510' department: - _id: KrCh doi: 10.1038/s41467-021-24271-w ec_funded: 1 external_id: isi: - '000671752100003' pmid: - '34188036' file: - access_level: open_access checksum: 5767418926a7f7fb76151de29473dae0 content_type: application/pdf creator: cziletti date_created: 2021-07-19T13:02:20Z date_updated: 2021-07-19T13:02:20Z file_id: '9692' file_name: 2021_NatCoom_Tkadlec.pdf file_size: 628992 relation: main_file success: 1 file_date_updated: 2021-07-19T13:02:20Z has_accepted_license: '1' intvolume: ' 12' isi: 1 issue: '1' language: - iso: eng month: '06' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 2581B60A-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '279307' name: 'Quantitative Graph Games: Theory and Applications' - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' - _id: 2584A770-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P 23499-N23 name: Modern Graph Algorithmic Techniques in Formal Verification - _id: 25832EC2-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: S 11407_N23 name: Rigorous Systems Engineering publication: Nature Communications publication_identifier: eissn: - '20411723' publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Fast and strong amplifiers of natural selection tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 12 year: '2021' ...