Tag Archives: opinion dynamics

If you want to be cited, calibrate your agent-based model: A Reply to Chattoe-Brown

By Marijn A. Keijzer

This is a reply to a previous comment, (Chattoe-Brown 2022).

The social simulation literature has called on its proponents to enhance the quality and realism of their contributions through systematic validation and calibration (Flache et al., 2017). Model validation typically refers to assessments of how well the predictions of their agent-based models (ABMs) map onto empirically observed patterns or relationships. Calibration, on the other hand, is the process of enhancing the realism of the model by parametrizing it based on empirical data (Boero & Squazzoni, 2005). We would expect that presenting a validated or calibrated model serves as a signal of model quality, and would thus be a desirable characteristic of a paper describing an ABM.

In a recent contribution to RofASSS, Edmund Chattoe-Brown provocatively argued that model validation does not bear fruit for researchers interested in boosting their citations. In a sample of articles from JASSS published on opinion dynamics he observed that “the sample clearly divides into non-validated research with more citations and validated research with fewer” (Chattoe-Brown, 2022). Well-aware of the bias and limitations of the sample at hand, Chattoe-Brown calls on refutation of his hypothesis. An analysis of the corpus of articles in Web of Science, presented here, could serve that goal.

To test whether there exists an effect of model calibration and/or validation on the citation counts of papers, I compare citation counts of a larger number of original research articles on agent-based models published in the literature. I extracted 11,807 entries from Web of Science by searching for items that contained the phrases “agent-based model”, “agent-based simulation” or “agent-based computational model” in its abstract.[1] I then labeled all items that mention “validate” in its abstract as validated ABMs and those that mention “calibrate” as calibrated ABMs. This measure if rather crude, of course, as descriptions containing phrases like “we calibrated our model” or “others should calibrate our model” are both labeled as calibrated models. However, if mentioning that future research should calibrate or validate the model is not related to citations counts (which I would argue it indeed is not), then this inaccuracy does not introduce bias.

The shares of entries that mention calibration or validation are somewhat small. Overall, just 5.62% of entries mention validation, 3.21% report a calibrated model and 0.65% fall in both categories. The large sample size, however, will still enable the execution of proper statistical analysis and hypothesis testing.

How are mentions of calibration and validation in the abstract related to citation counts at face value? Bivariate analyses show only minor differences, as revealed in Figure 1. In fact, the distribution of citations for validated and non-validated ABMs (panel A) is remarkably similar. Wilcoxon tests with continuity correction—the nonparametric version of the simple t test—corroborate their similarity (W = 3,749,512, p = 0.555). The differences in citations between calibrated and non-calibrated models appear, albeit still small, more pronounced. Calibrated ABMs are cited slightly more often (panel B), as also supported by a bivariate test (W = 1,910,772, p < 0.001).

Picture 1

Figure 1. Distributions of number of citations of all the entries in the dataset for validated (panel A) and calibrated (panel B) ABMs and their averages with standard errors over years (panels C and D)

Age of the paper might be a more important determinant of citation counts, as panels C and D of Figure 1 suggest. Clearly, the age of a paper should be important here, because older papers have had much more opportunity to get cited. In particular, papers younger than 10 years seem to not have matured enough for its citation rates to catch up to older articles. When comparing the citation counts of purely theoretical models with calibrated and validated versions, this covariate should not be missed, because the latter two are typically much younger. In other words, the positive relationship between model calibration/validation and citation counts could be hidden in the bivariate analysis, as model calibration and validation are recent trends in ABM research.

I run a Poisson regression on the number of citations as explained by whether they are validated and calibrated (simultaneously) and whether they are both. The age of the paper is taken into account, as well as the number of references that the paper uses itself (controlling for reciprocity and literature embeddedness, one might say). Finally, the fields in which the papers have been published, as registered by Web of Science, have been added to account for potential differences between fields that explains both citation counts and conventions about model calibration and validation.

Table 1 presents the results from the four models with just the main effects of validation and calibration (model 1), the interaction of validation and calibration (model 2) and the full model with control variables (model 3).

Table 1. Poisson regression on the number of citations

# Citations
(1) (2) (3)
Validated -0.217*** -0.298*** -0.094***
(0.012) (0.014) (0.014)
Calibrated 0.171*** 0.064*** 0.076***
(0.014) (0.016) (0.016)
Validated x Calibrated 0.575*** 0.244***
(0.034) (0.034)
Age 0.154***
(0.0005)
Cited references 0.013***
(0.0001)
Field included No No Yes
Constant 2.553*** 2.556*** 0.337**
(0.003) (0.003) (0.164)
Observations 11,807 11,807 11,807
AIC 451,560 451,291 301,639
Note: *p<0.1; **p<0.05; ***p<0.01

The results from the analyses clearly suggest a negative effect of model validation and a positive effect of model calibration on the likelihood of being cited. The hypothesis that was so “badly in need of refutation” (Chattoe-Brown, 2022) will remain unrefuted for now. The effect does turn positive, however, when the abstract makes mention of calibration as well. In both the controlled (model 3) and uncontrolled (model 2) analyses, combining the effects of validation and calibration yields a positive coefficient overall.[2]

The controls in model 3 substantially affect the estimates from the three main factors of interest, while remaining in expected directions themselves. The age of a paper indeed helps its citation count, and so does the number of papers the item cites itself. The fields, furthermore, take away from the main effects somewhat, too, but not to a problematic degree. In an additional analysis, I have looked at the relationship between the fields and whether they are more likely to publish calibrated or validated models and found no substantial relationships. Citation counts will differ between fields, however. The papers in our sample are more often cited in, for example, hematology, emergency medicine and thermodynamics. The ABMs in the sample coming from toxicology, dermatology and religion are on the unlucky side of the equation, receiving less citations on average. Finally, I have also looked at papers published in JASSS specifically, due to the interest of Chattoe-Brown and the nature of this outlet. Surprisingly, the same analyses run on the subsample of these papers (N=376) showed a negative relationship between citation counts and model calibration/validation. Does the JASSS readership reveal its taste for artificial societies?

In sum, I find support for the hypothesis of Chattoe-Brown (2022) on the negative relationship between model validation and citations counts for papers presenting ABMs. If you want to be cited, you should not validate your ABM. Calibrated ABMs, on the other hand, are more likely to receive citations. What is more, ABMs that were both calibrated and validated are most the most successful papers in the sample. All conclusions were drawn considering (i.e. controlling for) the effects of age of the paper, the number of papers the paper cited itself, and (citation conventions in) the field in which it was published.

While the patterns explored in this and Chattoe-Brown’s recent contribution are interesting, or even puzzling, they should not distract from the goal of moving towards realistic agent-based simulations of social systems. In my opinion, models that combine rigorous theory with strong empirical foundations are instrumental to the creation of meaningful and purposeful agent-based models. Perhaps the results presented here should just be taken as another sign that citation counts are a weak signal of academic merit at best.

Data, code and supplementary analyses

All data and code used for this analysis, as well as the results from the supplementary analyses described in the text, are available here: https://osf.io/x9r7j/

Notes

[1] Note that the hyphen between “agent” and “based” does not affect the retrieved corpus. Both contributions that mention “agent based” and “agent-based” were retrieved.

[2] A small caveat to the analysis of the interaction effect is that the marginal improvement of model 2 upon model 1 is rather small (AIC difference of 269). This is likely (partially) due to the small number of papers that mention both calibration and validation (N=77).

Acknowledgements

Marijn Keijzer acknowledges IAST funding from the French National Research Agency (ANR) under the Investments for the Future (Investissements d’Avenir) program, grant ANR-17-EURE-0010.

References

Boero, R., & Squazzoni, F. (2005). Does empirical embeddedness matter? Methodological issues on agent-based models for analytical social science. Journal of Artificial Societies and Social Simulation, 8(4), 1–31. https://www.jasss.org/8/4/6.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

Flache, A., Mäs, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S., & Lorenz, J. (2017). Models of social influence: towards the next frontiers. Journal of Artificial Societies and Social Simulation, 20(4). https://doi.org/10.18564/jasss.3521


Keijzer, M. (2022) If you want to be cited, calibrate your agent-based model: Reply to Chattoe-Brown. Review of Artificial Societies and Social Simulation, 9th Mar 2022. https://rofasss.org/2022/03/09/Keijzer-reply-to-Chattoe-Brown


 

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


 

A Forgotten Contribution: Jean-Paul Grémy’s Empirically Informed Simulation of Emerging Attitude/Career Choice Congruence (1974)

By Edmund Chattoe-Brown

Since this is new venture, we need to establish conventions. Since JASSS has been running since 1998 (twenty years!) it is reasonable to argue that something un-cited in JASSS throughout that period has effectively been forgotten by the ABM community. This contribution by Grémy is actually a single chapter in a book otherwise by Boudon (a bibliographical oddity that may have contributed to its neglect. Grémy also appears to have published mostly in French, which may also have had an effect. An English summary of his contribution to simulation might be another useful item for RofASSS.) Boudon gets 6 hits on the JASSS search engine (as of 31.05.18), none of which mention simulation and Gremy gets no hits (as does Grémy: unfortunately it is hard to tell how online search engines “cope with” accents and thus whether this is a “real” result).

Since this book is still readily available as a mass-market paperback, I will not reprise the argument of the simulation here (and its limitations relative to existing ABM methodology could be a future RofASSS contribution). Nonetheless, even approximately empirical modelling in the mid-seventies is worthy of note and the article is early to say other important things (for example about simulation being able to avoid “technical assumptions” – made for solubility rather than realism).

The point of this contribution is to draw attention to an argument that I have only heard twice (and only found once in print) namely that we should look at the form of real data as an initial justification for using ABM at all (please correct me if there are earlier or better examples). Grémy (1974, p. 210) makes the point that initial incongruities between the attitudes that people hold (altruistic versus selfish) and their career choices (counsellor versus corporate raider) can be resolved in either direction as time passes (he knows this because Boudon analysed some data collected by Rosenberg at two points from US university students) as well as remaining unresolved and, as such, cannot readily be explained by some sort of “statistical trend” (that people become more selfish as they get older or more altruistic as they become more educated). He thus hypothesises (reasonably it seems to me) that the data requires a model of some sort of dynamic interaction process that Grémy then simulates, paying some attention to their survey results both in constraining the model and analysing its behaviour.

This seems to me an important methodological practice to rescue from neglect. (It is widely recognised anecdotally that people tend to use the research methods they know and like rather than the ones that are suitable.) Elsewhere (Chattoe-Brown 2014), inspired by this argument, I have shown how even casually accessed attitude change data really looks nothing like the output of the (very popular) Zaller-Deffuant model of opinion change (very roughly, 228 hits in JASSS for Deffuant, 8 for Zaller and 9 for Zaller-Deffuant though hyphens sometimes produce unreliable results for online search engines too.) The attitude of the ABM community to data seems to be rather uncomfortable. Perhaps support in theory and neglect in practice would sum it up (Angus and Hassani-Mahmooei 2015, Table 5 in section 4.5). But if our models can’t even “pass first base” with existing real data (let alone be calibrated and validated) should we be too surprised if what seems plausible to us does not seem plausible to social scientists in substantive domains (and thus diminishes their interest in ABM as a “real method?”) Even if others in the ABM community disagree with my emphasis on data (and I know that they do) I think this is a matter that should be properly debated rather than just left floating about in coffee rooms (as such this is what we intend RofASSS to facilitate). As W. C. Fields is reputed to have said (though actually the phrase appears to have been common currency), we would wish to avoid ABM being just “Another good story ruined by an eyewitness”.

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):16.

Chattoe-Brown, Edmund (2014) ‘Using Agent Based Modelling to Integrate Data on Attitude Change’, Sociological Research Online, 19(1):16.

Gremy, Jean-Paul (1974) ‘Simulation Techniques’, in Boudon, Raymond, The Logic of Sociological Explanation (Harmondsworth: Penguin), chapter 11:209-227.


Chattoe-Brown, E. (2018) A Forgotten Contribution: Jean-Paul Grémy’s Empirically Informed Simulation of Emerging Attitude/Career Choice Congruence (1974). Review of Artificial Societies and Social Simulation, 1st June 2018. https://rofasss.org/2018/06/01/ecb/