A Simple Threshold Rule Is Sufficient to Explain Sophisticated Collective Decision-Making
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{"title"=>"A simple threshold rule is sufficient to explain sophisticated collective decision-making", "type"=>"journal", "authors"=>[{"first_name"=>"Elva J.H.", "last_name"=>"Robinson", "scopus_author_id"=>"24446575300"}, {"first_name"=>"Nigel R.", "last_name"=>"Franks", "scopus_author_id"=>"7007013081"}, {"first_name"=>"Samuel", "last_name"=>"Ellis", "scopus_author_id"=>"40661340500"}, {"first_name"=>"Saki", "last_name"=>"Okuda", "scopus_author_id"=>"40661765300"}, {"first_name"=>"James A.R.", "last_name"=>"Marshall", "scopus_author_id"=>"57198721878"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"79956363506", "doi"=>"10.1371/journal.pone.0019981", "pui"=>"361809191", "pmid"=>"21629645", "scopus"=>"2-s2.0-79956363506", "issn"=>"19326203", "isbn"=>"1932-6203"}, "id"=>"8a85d992-f7cf-344a-b2d9-6fddcaf96488", "abstract"=>"Decision-making animals can use slow-but-accurate strategies, such as making multiple comparisons, or opt for simpler, faster strategies to find a 'good enough' option. Social animals make collective decisions about many group behaviours including foraging and migration. The key to the collective choice lies with individual behaviour. We present a case study of a collective decision-making process (house-hunting ants, Temnothorax albipennis), in which a previously proposed decision strategy involved both quality-dependent hesitancy and direct comparisons of nests by scouts. An alternative possible decision strategy is that scouting ants use a very simple quality-dependent threshold rule to decide whether to recruit nest-mates to a new site or search for alternatives. We use analytical and simulation modelling to demonstrate that this simple rule is sufficient to explain empirical patterns from three studies of collective decision-making in ants, and can account parsimoniously for apparent comparison by individuals and apparent hesitancy (recruitment latency) effects, when available nests differ strongly in quality. This highlights the need to carefully design experiments to detect individual comparison. We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out. However, by using a simple threshold rule, decision-making groups are able to effectively compare options, without relying on any form of direct comparison of alternatives by individuals. This parsimonious mechanism could promote collective rationality in group decision-making.", "link"=>"http://www.mendeley.com/research/simple-threshold-rule-sufficient-explain-sophisticated-collective-decisionmaking", "reader_count"=>112, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>5, "Student > Doctoral Student"=>4, "Researcher"=>20, "Student > Ph. D. Student"=>38, "Student > Postgraduate"=>3, "Student > Master"=>19, "Other"=>1, "Student > Bachelor"=>9, "Lecturer > Senior Lecturer"=>4, "Professor"=>5}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>5, "Student > Doctoral Student"=>4, "Researcher"=>20, "Student > Ph. D. Student"=>38, "Student > Postgraduate"=>3, "Student > Master"=>19, "Other"=>1, "Student > Bachelor"=>9, "Lecturer > Senior Lecturer"=>4, "Professor"=>5}, "reader_count_by_subject_area"=>{"Unspecified"=>11, "Agricultural and Biological Sciences"=>60, "Business, Management and Accounting"=>1, "Computer Science"=>10, "Earth and Planetary Sciences"=>1, "Engineering"=>4, "Environmental Science"=>2, "Mathematics"=>3, "Medicine and Dentistry"=>1, "Neuroscience"=>3, "Physics and Astronomy"=>3, "Psychology"=>6, "Social Sciences"=>4, "Immunology and Microbiology"=>1, "Linguistics"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>4}, "Physics and Astronomy"=>{"Physics and Astronomy"=>3}, "Psychology"=>{"Psychology"=>6}, "Mathematics"=>{"Mathematics"=>3}, "Unspecified"=>{"Unspecified"=>11}, "Environmental Science"=>{"Environmental Science"=>2}, "Engineering"=>{"Engineering"=>4}, "Neuroscience"=>{"Neuroscience"=>3}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>60}, "Computer Science"=>{"Computer Science"=>10}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Linguistics"=>{"Linguistics"=>2}}, "reader_count_by_country"=>{"Canada"=>1, "French Guiana"=>1, "Belgium"=>1, "United States"=>6, "Japan"=>2, "United Kingdom"=>1, "Portugal"=>2, "Switzerland"=>3, "Germany"=>3}, "group_count"=>8}

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  • {"files"=>["https://ndownloader.figshare.com/files/772227"], "description"=>"<p>Recruitment latencies to the near (poor) and far (good) nests, mean + SE. (A) Empirical data <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981-Robinson1\" target=\"_blank\">[26]</a>; nests presented together, n = 9 colonies. No significant differences in recruitment latencies: GLMM: <i>t</i><sub>39</sub> = 0.08, <i>P</i> = 0.93. (B) Simulation data; nests presented together, n = 9 replicates. No significant differences in recruitment latencies: GLMM: <i>t</i><sub>39</sub> = 0.71, <i>P</i> = 0.48. Sample set of 9 replicates shown; of 100 repeat sets, 90% showed no significant difference in recruitment latencies. (C) Simulation data; nests presented separately, n = 9 replicates. Recruitment latencies to poor nest significantly greater: GLMM: <i>t</i><sub>88</sub> = 2.19, <i>P</i><0.05. Sample set of 9 replicates shown; of 100 repeat sets, 70% showed significantly greater recruitment latencies to poor nest.</p>", "links"=>[], "tags"=>["empirical", "simulated", "recruitment", "latency", "higher", "further"], "article_id"=>442592, "categories"=>["Neuroscience", "Ecology", "Evolutionary Biology"], "users"=>["Elva J. H. Robinson", "Nigel R. Franks", "Samuel Ellis", "Saki Okuda", "James A. R. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019981.g004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_between_empirical_and_simulated_recruitment_latency_results_where_the_higher_quality_is_further_away_/442592", "title"=>"Comparison between empirical and simulated recruitment latency results where the higher quality is further away.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-05-24 00:43:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/772548"], "description"=>"1<p>This includes <i>r</i>, the probability of re-discovering the same nest.</p>2<p>Numbers of ants are colony specific for recruitment latency simulations; see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone-0019981-g005\" target=\"_blank\">Fig. 5</a>.</p>3<p>These acceptance threshold distributions and error rates correspond to quality-dependent nest acceptance probabilities of 0.76 for the good nest and 0.24 for the poor nest. See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981.s003\" target=\"_blank\">Text S1</a> for details.</p>", "links"=>[], "tags"=>["simulations", "monte-carlo"], "article_id"=>442920, "categories"=>["Neuroscience", "Ecology", "Evolutionary Biology"], "users"=>["Elva J. H. Robinson", "Nigel R. Franks", "Samuel Ellis", "Saki Okuda", "James A. R. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019981.t001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameterisation_used_in_simulations_of_Monte_Carlo_model_/442920", "title"=>"Parameterisation used in simulations of Monte-Carlo model.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-05-24 00:48:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/772460"], "description"=>"<p>Expected time for an ant to accept any site across varying qualities of site A, calculated from equation 2. Dashed line: only site A present. Solid line: two nests present; site B fixed at P(accept nest B) = 0.88 (estimated from data, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981.s003\" target=\"_blank\">Text S1</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981.s001\" target=\"_blank\">Fig. S1</a>). For two nests present, either site is equally likely to be discovered first. Nest rediscovery probability <i>r</i> = 0.7 (estimated from data, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981.s003\" target=\"_blank\">Text S1</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981.s004\" target=\"_blank\">Table S1</a>). The reduced effect of site A's quality on expected decision time is robust to variations in site B's quality, except where this becomes low (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981.s001\" target=\"_blank\">Fig. S1</a>).</p>", "links"=>[], "tags"=>["markov"], "article_id"=>442828, "categories"=>["Neuroscience", "Ecology", "Evolutionary Biology"], "users"=>["Elva J. H. Robinson", "Nigel R. Franks", "Samuel Ellis", "Saki Okuda", "James A. R. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019981.g006", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Analytical_results_of_Markov_Chain_model_/442828", "title"=>"Analytical results of Markov Chain model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-05-24 00:47:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/772516"], "description"=>"a<p>Active ants are the ants that discover one or both of the new nests before quorum is reached and nest-mate carrying begins.</p>b<p>Empirical results are from 3 colonies <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981-Mallon1\" target=\"_blank\">[20]</a>.</p>c<p>Simulation results given as mean and 95% confidence intervals from 100 replicates.</p>", "links"=>[], "tags"=>["simulation"], "article_id"=>442891, "categories"=>["Neuroscience", "Ecology", "Evolutionary Biology"], "users"=>["Elva J. H. Robinson", "Nigel R. Franks", "Samuel Ellis", "Saki Okuda", "James A. R. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019981.t002", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Empirical_and_simulation_results_for_apparent_comparison_of_nests_/442891", "title"=>"Empirical and simulation results for apparent comparison of nests.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2011-05-24 00:48:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/772011"], "description"=>"<p>Dashed outline = old destroyed nest; shading = good nests. (A) Good nest 120 cm from old nest; poor nest 30 cm <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981-Robinson1\" target=\"_blank\">[26]</a>. (B) Equidistant good and poor nests 45 cm from old nest <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981-Mallon1\" target=\"_blank\">[20]</a>. (C) Three equidistant new nests 36 cm from old nest. Dashed lines indicate points at which tandem-runs were recorded.</p>", "links"=>[], "tags"=>["emigration"], "article_id"=>442380, "categories"=>["Neuroscience", "Ecology", "Evolutionary Biology"], "users"=>["Elva J. H. Robinson", "Nigel R. Franks", "Samuel Ellis", "Saki Okuda", "James A. R. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019981.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Arenas_used_in_emigration_experiments_showing_nest_locations_/442380", "title"=>"Arenas used in emigration experiments, showing nest locations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-05-24 00:39:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/772154"], "description"=>"<p>Number of ants continuing to visit the same nest (stay) or going on to visit the other nest (switch), depends on the quality of the first nest visited. (A) Empirical results, 9 colonies, mean + SD. (χ<sup>2</sup><sub>1</sub> = 86.6, <i>P</i><0.001), reproduced from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone-0019981-g002\" target=\"_blank\">Figure 2a</a>, Robinson et al. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981-Robinson1\" target=\"_blank\">[26]</a> with permission from Royal Society Publishing. (B) Simulation results, 9 replicates, mean + SD (χ<sup>2</sup><sub>1</sub> = 42.0, <i>P</i><0.001). A sample set of 9 replicates are shown here for comparability with the empirical data. Running 100 repeat sets gave the same pattern, statistically significant in 100% of cases.</p>", "links"=>[], "tags"=>["empirical", "simulated", "emigration"], "article_id"=>442518, "categories"=>["Neuroscience", "Ecology", "Evolutionary Biology"], "users"=>["Elva J. H. Robinson", "Nigel R. Franks", "Samuel Ellis", "Saki Okuda", "James A. R. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019981.g003", "stats"=>{"downloads"=>2, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_empirical_and_simulated_emigration_behavior_/442518", "title"=>"Comparison of empirical and simulated emigration behavior.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-05-24 00:41:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/387699", "https://ndownloader.figshare.com/files/387741", "https://ndownloader.figshare.com/files/387762", "https://ndownloader.figshare.com/files/387865"], "description"=>"<div><p>Decision-making animals can use slow-but-accurate strategies, such as making multiple comparisons, or opt for simpler, faster strategies to find a ‘good enough’ option. Social animals make collective decisions about many group behaviours including foraging and migration. The key to the collective choice lies with individual behaviour. We present a case study of a collective decision-making process (house-hunting ants, <em>Temnothorax albipennis</em>), in which a previously proposed decision strategy involved both quality-dependent hesitancy and direct comparisons of nests by scouts. An alternative possible decision strategy is that scouting ants use a very simple quality-dependent threshold rule to decide whether to recruit nest-mates to a new site or search for alternatives. We use analytical and simulation modelling to demonstrate that this simple rule is sufficient to explain empirical patterns from three studies of collective decision-making in ants, and can account parsimoniously for apparent comparison by individuals and apparent hesitancy (recruitment latency) effects, when available nests differ strongly in quality. This highlights the need to carefully design experiments to detect individual comparison. We present empirical data strongly suggesting that best-of-n comparison is not used by individual ants, although individual sequential comparisons are not ruled out. However, by using a simple threshold rule, decision-making groups are able to effectively compare options, without relying on any form of direct comparison of alternatives by individuals. This parsimonious mechanism could promote collective rationality in group decision-making.</p> </div>", "links"=>[], "tags"=>["decision-making"], "article_id"=>136568, "categories"=>["Neuroscience", "Ecology", "Evolutionary Biology"], "users"=>["Elva J. H. Robinson", "Nigel R. Franks", "Samuel Ellis", "Saki Okuda", "James A. R. Marshall"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0019981.s001", "https://dx.doi.org/10.1371/journal.pone.0019981.s002", "https://dx.doi.org/10.1371/journal.pone.0019981.s003", "https://dx.doi.org/10.1371/journal.pone.0019981.s004"], "stats"=>{"downloads"=>3, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_Simple_Threshold_Rule_Is_Sufficient_to_Explain_Sophisticated_Collective_Decision_Making/136568", "title"=>"A Simple Threshold Rule Is Sufficient to Explain Sophisticated Collective Decision-Making", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-05-24 01:49:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/772092"], "description"=>"<p>A simulated ant continues searching until it encounters a nest of a quality (<i>b</i>) exceeding the ant's individual threshold (<i>a</i>), taking into account assessment error (<i>ε</i>). Ants may revisit the same nest (with probability <i>r</i>), and do not have any memory of previously visited nests.</p>", "links"=>[], "tags"=>["ecology", "neuroscience", "Evolutionary biology"], "article_id"=>442460, "categories"=>["Neuroscience", "Ecology", "Evolutionary Biology"], "users"=>["Elva J. H. Robinson", "Nigel R. Franks", "Samuel Ellis", "Saki Okuda", "James A. R. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019981.g002", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_of_model_/442460", "title"=>"Schematic of model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-05-24 00:41:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/772352"], "description"=>"<p>Empirical and simulated recruitment latencies (solid line = good nest; dashed line = poor nest). The latency between entry and recruitment (mean ± SD), and the number of ants analysed, are shown next to the corresponding survivorship curve. (A–F) Empirical results with nests presented separately, reproduced from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone-0019981-g004\" target=\"_blank\">Figure 4</a>, Mallon et al. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019981#pone.0019981-Mallon1\" target=\"_blank\">[20]</a> with permission of Springer Science and Business Media. Recruitment latencies to the poor nest are significantly greater in four of six colonies (generalised logrank test). (G–L) Simulated recruitment latencies, nests presented separately. Recruitment latencies to the poor nest are significantly greater. Empirical number of ants is matched for each colony. Sample graphs are shown; running 100 replicates of each gives the same pattern of results, with significant differences between recruitment latencies in 95% (Colony 5) or 100% (Colonies 1–4 and 6) of simulations. (M–R) Simulated recruitment latencies, nests presented together. There are no longer any significant differences between recruitment latencies. Empirical number of ants is matched for each colony. Sample graphs are shown; running 100 replicates of each gives the same pattern of results, with no significant difference between recruitment latencies in 96% (Colony 1), 93% (Colony 2), 90% (Colony 3) 90% (Colony 4), 96% (Colony 5) 93% (Colony 6) of simulations.</p>", "links"=>[], "tags"=>["empirical", "simulated", "recruitment", "latency", "nests"], "article_id"=>442717, "categories"=>["Neuroscience", "Ecology", "Evolutionary Biology"], "users"=>["Elva J. H. Robinson", "Nigel R. Franks", "Samuel Ellis", "Saki Okuda", "James A. R. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0019981.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_between_empirical_and_simulated_recruitment_latency_results_where_new_nests_are_equidistant_/442717", "title"=>"Comparison between empirical and simulated recruitment latency results where new nests are equidistant.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-05-24 00:45:17"}

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  • {"unique-ip"=>"11", "full-text"=>"10", "pdf"=>"5", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2014", "month"=>"10"}
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  • {"unique-ip"=>"15", "full-text"=>"15", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2016", "month"=>"11"}
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  • {"unique-ip"=>"9", "full-text"=>"9", "pdf"=>"3", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"1"}
  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"2"}
  • {"unique-ip"=>"26", "full-text"=>"25", "pdf"=>"0", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"3"}
  • {"unique-ip"=>"17", "full-text"=>"19", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"1"}
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  • {"unique-ip"=>"21", "full-text"=>"24", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"6"}
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  • {"unique-ip"=>"26", "full-text"=>"27", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"8"}
  • {"unique-ip"=>"33", "full-text"=>"37", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"2", "cited-by"=>"0", "year"=>"2018", "month"=>"11"}
  • {"unique-ip"=>"25", "full-text"=>"27", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"9"}
  • {"unique-ip"=>"25", "full-text"=>"25", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2018", "month"=>"12"}
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  • {"unique-ip"=>"22", "full-text"=>"25", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
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  • {"unique-ip"=>"20", "full-text"=>"19", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
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  • {"unique-ip"=>"22", "full-text"=>"20", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
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  • {"unique-ip"=>"16", "full-text"=>"17", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
  • {"unique-ip"=>"30", "full-text"=>"37", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"30", "full-text"=>"115", "pdf"=>"5", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"3", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"22", "full-text"=>"21", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
  • {"unique-ip"=>"10", "full-text"=>"15", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
  • {"unique-ip"=>"4", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"4", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
  • {"unique-ip"=>"11", "full-text"=>"9", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}
  • {"unique-ip"=>"34", "full-text"=>"40", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}
  • {"unique-ip"=>"22", "full-text"=>"25", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2020", "month"=>"11"}
  • {"unique-ip"=>"8", "full-text"=>"9", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"12"}
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Relative Metric

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