Tag Archives: citation

If You Want To Be Cited, Don’t Validate Your Agent-Based Model: A Tentative Hypothesis Badly In Need of Refutation

By Edmund Chattoe-Brown

As part of a previous research project, I collected a sample of the Opinion Dynamics (hereafter OD) models published in JASSS that were most highly cited in JASSS. The idea here was to understand what styles of OD research were most influential in the journal. In the top 50 on 19.10.21 there were eight such articles. Five were self-contained modelling exercises (Hegselmann and Krause 2002, 58 citations, Deffuant et al. 2002, 35 citations, Salzarulo 2006, 13 citations, Deffuant 2006, 13 citations and Urbig et al. 2008, 9 citations), two were overviews of OD modelling (Flache et al. 2017, 13 citations and Sobkowicz 2009, 10 citations) and one included an OD example in an article mainly discussing the merits of cellular automata modelling (Hegselmann and Flache 1998, 12 citations). In order to get in to the top 50 on that date you had to achieve at least 7 citations. In parallel, I have been trying to identify Agent-Based Models that are validated (undergo direct comparison of real and equivalent simulated data). Based on an earlier bibliography (Chattoe-Brown 2020) which I extended to the end of 2021 for JASSS and articles which were described as validated in the highly cited articles listed above, I managed to construct a small and unsystematic sample of validated OD models. (Part of the problem with a systematic sample is that validated models are not readily searchable as a distinct category and there are too many OD models overall to make reading them all feasible. Also, I suspect, validated models just remain rare in line with the larger scale findings of Dutton and Starbuck (1971, p. 130, table 1) and discouragingly, much more recently, Angus and Hassani-Mahmooei (2015, section 4.5, figure 9). Obviously, since part of the sample was selected by total number of citations, one cannot make a comparison on that basis, so instead I have used the best possible alternative (given the limitations of the sample) and compared articles on citations per year. The problem here is that attempting validated modelling is relatively new while older articles inevitably accumulate citations however slowly. But what I was trying to discover was whether new validated models could be cited at a much higher annual rate without reaching the top 50 (or whether, conversely, older articles could have a high enough total citations to get into the top 50 without having a particularly impressive annual citation rate.) One would hope that, ultimately, validated models would tend to receive more citations than those that were not validated (but see the rather disconcerting related findings of Serra-Garcia and Gneezy 2021). Table 1 shows the results sorted by citations per year.

Article Status Number of JASSS Citations[1] Number of Years[2] Citations Per Year
Bernardes et al. 2002 Validated 1 20 0.05
Bernardes et al. 2001 Validated 2 21 0.096
Fortunato and Castellano 2007 Validated 2 15 0.13
Caruso and Castorina 2005 Validated 4 17 0.24
Chattoe-Brown 2014 Validated 2 8 0.25
Brousmiche et al. 2016 Validated 2 6 0.33
Hegselmann and Flache 1998 Non-Validated 12 24 0.5
Urbig et al. 2008 Non-Validated 9 14 0.64
Sobkowicz 2009 Non-Validated 10 13 0.77
Deffuant 2006 Non-Validated 13 16 0.81
Salzarulo 2006 Non-Validated 13 16 0.81
Duggins 2017 Validated 5 5 1
Deffuant et al. 2002 Non-Validated 35 20 1.75
Flache et al. 2017 Non-Validated 13 5 2.6
Hegselmann and Krause 2002 Non-Validated 58 20 2.9

Table 1. Annual Citation Rates for OD Articles Highly Cited in JASSS (Systematic Sample) and Validated OD Articles in or Cited in JASSS (Unsystematic Sample)

With the notable (and potentially encouraging) exception of Duggins (2017), the most recent validated OD model I have been able to discover in JASSS, the sample clearly divides into non-validated research with more citations and validated research with fewer. The position of Duggins (2017) might suggest greater recent interest in validated OD models. Unfortunately, however, qualitative analysis of the citations suggests that these are not cited as validated models per se (and thus as a potential improvement over non-validated models) but merely as part of general classes of OD model (like those involving social networks or repulsion – moving away from highly discrepant opinions). This tendency to cite validated models without acknowledging that they are validated (and what the implications of that might be) is widespread in the articles I looked at.

Obviously, there is plenty wrong with this analysis. Even looking at citations per annum we are arguably still partially sampling on the dependent variable (articles selected for being widely cited prove to be widely cited!) and the sample of validated OD models is unsystematic (though in fairness the challenges of producing a systematic sample are significant.[3]) But the aim here is to make a distinctive use of RoFASSS as a rapid mode of permanent publication and to think differently about science. If I tried to publish this in a peer reviewed journal, the amount of labour required to satisfy reviewers about the research design would probably be prohibitive (even if it were possible). As a result, the case to answer about this apparent (and perhaps undesirable) pattern in data might never see the light of day.

But by publishing quickly in RoFASSS without the filter of peer review I actively want my hypothesis to be rejected or replaced by research based on a better design (and such research may be motivated precisely by my presenting this interesting pattern with all its imperfections). When it comes to scientific progress, the chance to be clearly wrong now could be more useful than the opportunity to be vaguely right at some unknown point in the future.

Acknowledgements

This analysis was funded by the project “Towards Realistic Computational Models Of Social Influence Dynamics” (ES/S015159/1) funded by ESRC via ORA Round 5 (PI: Professor Bruce Edmonds, Centre for Policy Modelling, Manchester Metropolitan University: https://gtr.ukri.org/projects?ref=ES%2FS015159%2F1).

Notes

[1] Note that the validated OD models had their citations counted manually while the high total citation articles had them counted automatically. This may introduce some comparison error but there is no reason to think that either count will be terribly inaccurate.

[2] Including the year of publication and the current year (2021).

[3] Note, however, that there are some checks and balances on sample quality. Highly successful validated OD models would have shown up independently in the top 50. There is thus an upper bound to the impact of the articles I might have missed in manually constructing my “version 1” bibliography. The unsystematic review of 47 articles by Sobkowicz (2009) also checks independently on the absence of validated OD models in JASSS to that date and confirms the rarity of such articles generally. Only four of the articles that he surveys are significantly empirical.

References

Angus, Simon D. and Hassani-Mahmooei, Behrooz (2015) ‘“Anarchy” Reigns: A Quantitative Analysis of Agent-Based Modelling Publication Practices in JASSS, 2001-2012’, Journal of Artificial Societies and Social Simulation, 18(4), October, article 16, <http://jasss.soc.surrey.ac.uk/18/4/16.html>. doi:10.18564/jasss.2952

Bernardes, A. T., Costa, U. M. S., Araujo, A. D. and Stauffer, D. (2001) ‘Damage Spreading, Coarsening Dynamics and Distribution of Political Votes in Sznajd Model on Square Lattice’, International Journal of Modern Physics C: Computational Physics and Physical Computation, 12(2), February, pp. 159-168. doi:10.1140/e10051-002-0013-y

Bernardes, A. T., Stauffer, D. and Kertész, J. (2002) ‘Election Results and the Sznajd Model on Barabasi Network’, The European Physical Journal B: Condensed Matter and Complex Systems, 25(1), January, pp. 123-127. doi:10.1142/S0129183101001584

Brousmiche, Kei-Leo, Kant, Jean-Daniel, Sabouret, Nicolas and Prenot-Guinard, François (2016) ‘From Beliefs to Attitudes: Polias, A Model of Attitude Dynamics Based on Cognitive Modelling and Field Data’, Journal of Artificial Societies and Social Simulation, 19(4), October, article 2, <https://www.jasss.org/19/4/2.html>. doi:10.18564/jasss.3161

Caruso, Filippo and Castorina, Paolo (2005) ‘Opinion Dynamics and Decision of Vote in Bipolar Political Systems’, arXiv > Physics > Physics and Society, 26 March, version 2. doi:10.1142/S0129183105008059

Chattoe-Brown, Edmund (2014) ‘Using Agent Based Modelling to Integrate Data on Attitude Change’, Sociological Research Online, 19(1), February, article 16, <https://www.socresonline.org.uk/19/1/16.html>. doi:0.5153/sro.3315

Chattoe-Brown Edmund (2020) ‘A Bibliography of ABM Research Explicitly Comparing Real and Simulated Data for Validation: Version 1’, CPM Report CPM-20-216, 12 June, <http://cfpm.org/discussionpapers/256>

Deffuant, Guillaume (2006) ‘Comparing Extremism Propagation Patterns in Continuous Opinion Models’, Journal of Artificial Societies and Social Simulation, 9(3), June, article 8, <https://www.jasss.org/9/3/8.html>.

Deffuant, Guillaume, Amblard, Frédéric, Weisbuch, Gérard and Faure, Thierry (2002) ‘How Can Extremism Prevail? A Study Based on the Relative Agreement Interaction Model’, Journal of Artificial Societies and Social Simulation, 5(4), October, article 1, <https://www.jasss.org/5/4/1.html>.

Duggins, Peter (2017) ‘A Psychologically-Motivated Model of Opinion Change with Applications to American Politics’, Journal of Artificial Societies and Social Simulation, 20(1), January, article 13, <http://jasss.soc.surrey.ac.uk/20/1/13.html>. doi:10.18564/jasss.3316

Dutton, John M. and Starbuck, William H. (1971) ‘Computer Simulation Models of Human Behavior: A History of an Intellectual Technology’, IEEE Transactions on Systems, Man, and Cybernetics, SMC-1(2), April, pp. 128-171. doi:10.1109/TSMC.1971.4308269

Flache, Andreas, Mäs, Michael, Feliciani, Thomas, Chattoe-Brown, Edmund, Deffuant, Guillaume, Huet, Sylvie and Lorenz, Jan (2017) ‘Models of Social Influence: Towards the Next Frontiers’, Journal of Artificial Societies and Social Simulation, 20(4), October, article 2, <http://jasss.soc.surrey.ac.uk/20/4/2.html>. doi:10.18564/jasss.3521

Fortunato, Santo and Castellano, Claudio (2007) ‘Scaling and Universality in Proportional Elections’, Physical Review Letters, 99(13), 28 September, article 138701. doi:10.1103/PhysRevLett.99.138701

Hegselmann, Rainer and Flache, Andreas (1998) ‘Understanding Complex Social Dynamics: A Plea For Cellular Automata Based Modelling’, Journal of Artificial Societies and Social Simulation, 1(3), June, article 1, <https://www.jasss.org/1/3/1.html>.

Hegselmann, Rainer and Krause, Ulrich (2002) ‘Opinion Dynamics and Bounded Confidence Models, Analysis, and Simulation’, Journal of Artificial Societies and Social Simulation, 5(3), June, article 2, <http://jasss.soc.surrey.ac.uk/5/3/2.html>.

Salzarulo, Laurent (2006) ‘A Continuous Opinion Dynamics Model Based on the Principle of Meta-Contrast’, Journal of Artificial Societies and Social Simulation, 9(1), January, article 13, <http://jasss.soc.surrey.ac.uk/9/1/13.html>.

Serra-Garcia, Marta and Gneezy, Uri (2021) ‘Nonreplicable Publications are Cited More Than Replicable Ones’, Science Advances, 7, 21 May, article eabd1705. doi:10.1126/sciadv.abd1705

Sobkowicz, Pawel (2009) ‘Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality’, Journal of Artificial Societies and Social Simulation, 12(1), January, article 11, <http://jasss.soc.surrey.ac.uk/12/1/11.html>.

Urbig, Diemo, Lorenz, Jan and Herzberg, Heiko (2008) ‘Opinion Dynamics: The Effect of the Number of Peers Met at Once’, Journal of Artificial Societies and Social Simulation, 11(2), March, article 4, <http://jasss.soc.surrey.ac.uk/11/2/4.html>.


Chattoe-Brown, E. (2022) If You Want To Be Cited, Don’t Validate Your Agent-Based Model: A Tentative Hypothesis Badly In Need of Refutation. Review of Artificial Societies and Social Simulation, 1st Feb 2022. https://rofasss.org/2022/02/01/citing-od-models


 

Does It Take Two (And A Creaky Search Engine) To Make An Outstation? Hunting Highly Cited Opinion Dynamics Articles in the Journal of Artificial Societies and Social Simulation (JASSS)

By Edmund Chattoe-Brown

In an important article, Squazzoni and Casnici (2013) raise the issue of how social simulation (as manifested in the Journal of Artificial Societies and Social Simulation – hereafter JASSS – the journal that has probably published the most of this kind of research for longest) cites and is cited in the wider scientific community. They discuss this in terms of social simulation being a potential “outstation” of social science (but better integrated into physical science and computing). This short note considers the same argument in reverse. As an important site of social simulation research, is it the case that JASSS is effectively representing research done more widely across the sciences?

The method used to investigate this was extremely simple (and could thus easily be extended and replicated). On 28.08.21, using the search term “opinion dynamics” in “all fields”, all sources from Web of Science (www.webofknowledge.com, hereafter WOS) that were flagged as “highly cited” were selected as a sample. For each article (only articles turned out to be highly cited), the title was searched in JASSS and the number of hits recorded. Common sense was applied in this search process to maximise the chances of success. So if a title had two sub clauses, these were searched jointly as quotations (to avoid the “hits” being very sensitive to the reproduction of punctuation linking clauses.) In addition, the title of the journal in which the article appeared was searched to give a wider sense of how well the relevant journal is known is JASSS.

However, now we come to the issue of the creaky search engine (as well as other limitations of quick and dirty searches). Obviously searching for the exact title will not find variants of that title with spelling mistakes or attempts to standardise spelling (i. e. changing behavior to behaviour). Further, it turns out that the Google search engine (which JASSS uses) does not promise the consistency that often seems to be assumed for it (http://jdebp.uk/FGA/google-result-counts-are-a-meaningless-metric.html). For example, when I searched for “SIAM Review” I mostly got 77 hits, rather often 37 hits and very rarely 0 or 1 hits. (PDFs are available for three of these outcomes from the author but the fourth could not be reproduced to be recorded in the time available.) This result occurred when another search took place seconds after the first so it is not, for example, a result of substantive changes to the content of JASSS. To deal with this problem I tried to confirm the presence of a particular article by searching jointly for all its co-authors. Mostly this approach gave a similar result (but where it does not it is noted in the table below). In addition, wherever there were a relatively large number of hits for a specific search, some of these were usually not the ones intended. (For example no hit on the term “global challenges” actually turned out to be for the journal Global Challenges.) In addition, JASSS often gives an oddly inconsistent number of hits for a specific article: It may appear as PDF and HTML as well as in multiple indices or may occur just once. (This discouraged attempts to go from hits to the specific number of unique articles citing these WOS sources. As it turns out, this additional detail would have added little to the headline result.)

The term “opinion dynamics” was chosen somewhat arbitrarily (for reasons connected with other research) and it is not claimed that this term is even close to a definitive way of capturing any models connected with opinion/attitude change. Nonetheless, it is clear that the number of hits and the type of articles reported on WOS (which is curated and quality controlled) are sufficient (and sufficiently relevant) for this to be a serviceable search term to identify a solid field of research in JASSS (and elsewhere). I shall return to this issue.

The results, shown in the table below are striking on several counts. (All these sources are fully cited in the references at the end of this article.) Most noticeably, JASSS is barely citing a significant number of articles that are very widely cited elsewhere. Because these are highly cited in WOS this cannot be because they are too new or too inaccessible. The second point is the huge discrepancy in citation for the one article on the WOS list that appears in JASSS itself (Flache et al. 2017). Thirdly, although some of these articles appear in journals that JASSS otherwise does not cite (like Global Challenges and Dynamic Games and Applications) others appear in journals that are known to JASSS and generally cited (like SIAM Review).

Reference WOS Citations Article Title Hits in JASSS Journal Title Hits in JASSS
Acemoglu and Ozdaglar (2011) 301 0 (1 based on joint authors) 2
Motsch and Tadmor (2014) 214 0 77
Van Der Linden et al. (2017) 191 0 6 (but none for the journal)
Acemoğlu et al. (2013) 186 1 2 (but 1 article)
Proskurnikov et al. (2016) 165 0 9
Dong et al. (2017) 147 0 48 (but rather few for the journal)
Jia et al. (2015) 118 0 77
Dong et al. (2018) 117 0 (1 based on joint authors) 48 (but rather few for the journal)
Flache et al. (2017) 86 58 (17 based on joint authors) N/A
Urena et al. (2019) 72 0 6
Bu et al. (2020) 56 0 5
Zhang et al. (2020) 55 0 33 (but only some of these are for the journal)
Xiong et al. (2020) 28 0 1
Carrillo et al. (2020) 13 0 0

One possible interpretation of this result is simply that none of the most highly cited articles in WOS featuring the term “opinion dynamics” happen to be more than incidentally relevant to the scientific interests of JASSS. On consideration, however, this seems a rather improbable coincidence. Firstly, these articles were chosen exactly because they are highly cited so we would have to explain how they could be perceived as so useful generally but specifically not in JASSS. Secondly, the same term (“opinion dynamics”) consistently generates 254 hits in JASSS, suggesting that the problem isn’t a lack of overlap in terminology or research interests.

This situation, however, creates a problem for more conclusive explanation. The state of affairs here is not that these articles are being cited and then rejected on scientific grounds given the interests of JASSS (thus providing arguments I could examine). It is that they are barely being cited at all. Unfortunately, it is almost impossible to establish why something is not happening. Perhaps JASSS authors are not aware of these articles to begin with. Perhaps they are aware but do not see the wider scientific value of critiquing them or attempting to engage with their irrelevance in print.

But, given that the problem is non citation, my concern can be made more persuasive (perhaps as persuasive as it can be given problems of convincingly explaining an absence) by investigating the articles themselves. (My thanks are due to Bruce Edmonds for encouraging me to strengthen the argument in this way.) There are definitely some recurring patterns in this sample. Firstly, a significant proportion of the articles are highly mathematical and, therefore (as Agent-Based Modelling often criticises) rely on extreme simplifying assumptions and toy examples. Even here, however, it is not self-evident that such articles should not be cited in JASSS merely because they are mathematical. JASSS has itself published relatively mathematical articles and, if an article contains a mathematical model that could be “agentised” (thus relaxing its extreme assumptions) which is no less empirical than similar models in JASSS (or has particularly interesting behaviours) then it is hard to see why this should not be discussed by at least a few JASSS authors. A clear example of this is provided by Acemoğlu et al. (2013) which argues that existing opinion dynamics models fail to produce the ongoing fluctuations of opinion observed in real data (see, for example, Figures 1-3 in Chattoe-Brown 2014 which also raises concerns about the face validity of popular social simulations of opinion dynamics). In fact, the assumptions of this model could easily be questioned (and real data involves turning points and not just fluctuations) but the point is that JASSS articles are not citing it and rejecting it based on argument but simply not citing it. A model capable of generating ongoing opinion fluctuations (however imperfect) is simply too important to the current state of opinion dynamics research in social simulation not to be considered at all. Another (though less conclusive) example is Motsch and Tadmor (2014) which presents a model suggesting (counter intuitively) that interaction based on heterophily can better achieve consensus than interaction based on homophily. Of course one can reject such an assumption on empirical grounds but JASSS is not currently doing that (and in fact the term heterophily is unknown in the journal except for the title of a cited article.)

Secondly, there are also a number of articles which, while not providing important results seem no less plausible or novel than typical OD articles that are published in JASSS. For example, Jia et al. (2015) add self-appraisal and social power to a standard OD model. Between debates, agents amend the efficacy they believe that they and others have in terms of swaying the outcome and take that into account going forward. Proskurnikov et al. (2016) present the results of a model in which agents can have negative ties with each other (as well as the more usual positive ones) and thus consider the coevolution of positive/negative sentiments and influence (describing what they call hostile camps i. e. groups with positive ties to each other and negative ties to other groups). This is distinct from the common repulsive effect in OD models where agents do not like the opinions of others (rather than disliking the others themselves.)

Finally, both Dong et al. (2017) and Zhang et al. (2020) reach for the idea (through modelling) that experts and leaders in OD models may not just be randomly scattered through the population as types but may exist because of formal organisations or accidents of social structure: This particular agent is either deliberately appointed to have more influence or happens to have it because of their network position.

On a completely different tack, two articles (Dong et al. 2018 and Acemoglu and Ozdaglar 2011) are literature reviews or syntheses on relevant topics and it is hard to see how such broad ranging articles could have so little value to OD research in JASSS.

It will be admitted that some of the articles in the sample are hard to evaluate with certainty. Mathematical approaches often seem to be more interested in generating mathematics than in justifying its likely value. This is particularly problematic when combined with a suggestion that the product of the research may be instrumental algorithms (designed to get things done) rather than descriptive ones (designed to understand social behaviour). An example of this is several articles which talk about achieving consensus without really explaining whether this is a technical goal (for example in a neural network) or a social phenomenon and, if the latter, whether this places constraints on what it legitimate: You can reach consensus by debate but not by shooting dissenters!

But as well as specific ideas in specific models, this sample of articles also suggest a different emphasis from those currently found within JASSS OD research. For example, there is much more interest in deliberately achieving consensus (and the corresponding hazards of manipulation or misinformation impeding that.) Reading these articles collectively gives a sense that JASSS OD models are very much liberal democratic: Agents honestly express their views (or at most are somewhat reticent to protect themselves.) They decently expect the will of the people to prevail. They do not lie strategically to sway the influential, spread rumours to discredit the opinions of opponents or flood the debate with bots. Again, this darker vision is no more right a priori than the liberal democratic one but JASSS should at least be engaging with articles modelling (or providing data on – see Van Der Linden et al. 2017) such phenomena in an OD context. (Although misinformation is mentioned in some OD articles in JASSS it does not seem to be modelled. There also seems to be another surprising glitch in the search engine which considers the term “fake news” to be a hit for misinformation!) This also puts a new slant on an ongoing challenge in OD research, identifying a plausible relationship between fact and opinion. Is misinformation a different field of research (on the grounds that opinions can never be factually wrong) or is it possible for the misinformed to develop mis-opinions? (Those that they would change if what they knew changed.) Is it really the case that Brexiteers, for example, are completely indifferent to the economic consequences which will reveal themselves or did they simply have mistaken beliefs about how high those costs might turn out to be which will cause them to regret their decision at some later stage?

Thus to sum up, while some of the articles in the sample can be dismissed as either irrelevant to JASSS or having a potential relevance that is hard to establish, the majority cannot reasonably be regarded in this way (and a few are clearly important to the existing state of OD research.) While we cannot explain why these articles are not in fact cited, we can thus call into question one possible (Panglossian) explanation for the observed pattern (that they are not cited because they have nothing to contribute).

Apart from the striking nature of the result and its obvious implication (if social simulators want to be cited more widely they need to make sure they are also citing the work of others appropriately) this study has two wider (related) implications for practice.

Firstly, systematic literature reviewing (see, for example, Hansen et al. 2019 – not published in JASSS) needs to be better enforced in social simulation: “Systematic literature review” gets just 7 hits in JASSS. It is not enough to cite just what you happen to have read or models that resemble your own, you need to be citing what the community might otherwise not be aware of or what challenges your own model assumptions. (Although, in my judgement, key assumptions of Acemoğlu et al. 2013 are implausible I don’t think that I could justify non subjectively that they are any more implausible than those of those of the Zaller-Deffuant model – Malarz et al. 2011 – given the huge awareness discrepancy which the two models manifest in social simulation.)

Secondly, we need to rethink the nature of literature reviewing as part of progressive research. I have used “opinion dynamics” here not because it is the perfect term to identify all models of opinion and attitude change but because it throws up enough hits to show that this term is widely used in social simulation. Because I have clearly stated my search term, others can critique it and extend my analysis using other relevant terms like “opinion change” or “consensus formation”. A literature review that is just a bunch of arbitrary stuff cannot be critiqued or improved systematically (rather than nit-picked for specific omissions – as reviewers often do – and even then the critique can’t tell what should have been included if there are no clearly stated search criteria.) It should not be possible for JASSS (and the social simulation community it represents) simply to disregard articles as potentially important in their implications for OD as Acemoğlu et al. (2013). Even if this article turned out to be completely wrong-headed, we need to have enough awareness of it to be able to say why before setting it aside. (Interestingly, the one citation it does receive in JASSS can be summarised as “there are some other model broadly like this” with no detailed discussion at all – and thus no clear statement of how the model presented in the citing article adds to previous models – but uninformative citation is a separate problem.)

Acknowledgements

This article as part of “Towards Realistic Computational Models of Social Influence Dynamics” a project funded through ESRC (ES/S015159/1) by ORA Round 5.

References

Acemoğlu, Daron and Ozdaglar, Asuman (2011) ‘Opinion Dynamics and Learning in Social Networks’, Dynamic Games and Applications, 1(1), March, pp. 3-49. doi:10.1007/s13235-010-0004-1

Acemoğlu, Daron, Como, Giacomo, Fagnani, Fabio and Ozdaglar, Asuman (2013) ‘Opinion Fluctuations and Disagreement in Social Networks’, Mathematics of Operations Research, 38(1), February, pp. 1-27. doi:10.1287/moor.1120.0570

Bu, Zhan, Li, Hui-Jia, Zhang, Chengcui, Cao, Jie, Li, Aihua and Shi, Yong (2020) ‘Graph K-Means Based on Leader Identification, Dynamic Game, and Opinion Dynamics’, IEEE Transactions on Knowledge and Data Engineering, 32(7), July, pp. 1348-1361. doi:10.1109/TKDE.2019.2903712

Carrillo, J. A., Gvalani, R. S., Pavliotis, G. A. and Schlichting, A. (2020) ‘Long-Time Behaviour and Phase Transitions for the Mckean–Vlasov Equation on the Torus’, Archive for Rational Mechanics and Analysis, 235(1), January, pp. 635-690. doi:10.1007/s00205-019-01430-4

Chattoe-Brown, Edmund (2014) ‘Using Agent Based Modelling to Integrate Data on Attitude Change’, Sociological Research Online, 19(1), February, article 16, <http://www.socresonline.org.uk/19/1/16.html&gt;. doi:10.5153/sro.3315

Dong, Yucheng, Ding, Zhaogang, Martínez, Luis and Herrera, Francisco (2017) ‘Managing Consensus Based on Leadership in Opinion Dynamics’, Information Sciences, 397-398, August, pp. 187-205. doi:10.1016/j.ins.2017.02.052

Dong, Yucheng, Zhan, Min, Kou, Gang, Ding, Zhaogang and Liang, Haiming (2018) ‘A Survey on the Fusion Process in Opinion Dynamics’, Information Fusion, 43, September, pp. 57-65. doi:10.1016/j.inffus.2017.11.009

Flache, Andreas, Mäs, Michael, Feliciani, Thomas, Chattoe-Brown, Edmund, Deffuant, Guillaume, Huet, Sylvie and Lorenz, Jan (2017) ‘Models of Social Influence: Towards the Next Frontiers’, Journal of Artificial Societies and Social Simulation, 20(4), October, article 2, <http://jasss.soc.surrey.ac.uk/20/4/2.html&gt;. doi:10.18564/jasss.3521

Hansen, Paula, Liu, Xin and Morrison, Gregory M. (2019) ‘Agent-Based Modelling and Socio-Technical Energy Transitions: A Systematic Literature Review’, Energy Research and Social Science, 49, March, pp. 41-52. doi:10.1016/j.erss.2018.10.021

Jia, Peng, MirTabatabaei, Anahita, Friedkin, Noah E. and Bullo, Francesco (2015) ‘Opinion Dynamics and the Evolution of Social Power in Influence Networks’, SIAM Review, 57(3), pp. 367-397. doi:10.1137/130913250

Malarz, Krzysztof, Gronek, Piotr and Kulakowski, Krzysztof (2011) ‘Zaller-Deffuant Model of Mass Opinion’, Journal of Artificial Societies and Social Simulation, 14(1), 2, <https://www.jasss.org/14/1/2.html&gt;. doi:10.18564/jasss.1719

Motsch, Sebastien and Tadmor, Eitan (2014) ‘Heterophilious Dynamics Enhances Consensus’, SIAM Review, 56(4), pp. 577-621. doi:10.1137/120901866

Proskurnikov, Anton V., Matveev, Alexey S. and Cao, Ming (2016) ‘Opinion Dynamics in Social Networks With Hostile Camps: Consensus vs. Polarization’, IEEE Transactions on Automatic Control, 61(6), June, pp. 1524-1536. doi:10.1109/TAC.2015.2471655

Squazzoni, Flaminio and Casnici, Niccolò (2013) ‘Is Social Simulation a Social Science Outstation? A Bibliometric Analysis of the Impact of JASSS’, Journal of Artificial Societies and Social Simulation, 16(1), 10, <http://jasss.soc.surrey.ac.uk/16/1/10.html&gt;. doi:10.18564/jasss.2192

Ureña, Raquel, Chiclana, Francisco, Melançon, Guy and Herrera-Viedma, Enrique (2019) ‘A Social Network Based Approach for Consensus Achievement in Multiperson Decision Making’, Information Fusion, 47, May, pp. 72-87. doi:10.1016/j.inffus.2018.07.006

Van Der Linden, Sander, Leiserowitz, Anthony, Rosenthal, Seth and Maibach, Edward (2017) ‘Inoculating the Public against Misinformation about Climate Change’, Global Challenges, 1(2), 27 February, article 1600008. doi:10.1002/gch2.201600008

Xiong, Fei, Wang, Ximeng, Pan, Shirui, Yang, Hong, Wang, Haishuai and Zhang, Chengqi (2020) ‘Social Recommendation With Evolutionary Opinion Dynamics’, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(10), October, pp. 3804-3816. doi:10.1109/TSMC.2018.2854000

Zhang, Zhen, Gao, Yuan and Li, Zhuolin (2020) ‘Consensus Reaching for Social Network Group Decision Making by Considering Leadership and Bounded Confidence’, Knowledge-Based Systems, 204, 27 September, article 106240. doi:10.1016/j.knosys.2020.106240


Chattoe-Brown, E. (2021) Does It Take Two (And A Creaky Search Engine) To Make An Outstation? Hunting Highly Cited Opinion Dynamics Articles in the Journal of Artificial Societies and Social Simulation (JASSS). Review of Artificial Societies and Social Simulation, 19th August 2021. https://rofasss.org/2021/08/19/outstation/