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Teaching highly intelligent primary school kids energy system complexity

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By Emile Chappin

An energy system complexity lecture for kids?

I was invited to open the ‘energy theme’ at a primary school with a lecture on energy and wanted to give it a complexity and modelling flavour. And I wondered… can you teach this to a large group of 7-to-12-year-old children, all highly intelligent but so far apart in their development? What works in this setting, and what doesn’t? How long should I make such a lecture? Can I explain and let them feel what research is? Can I do some experiments? Can I share what modelling is? What concepts should I include? What are such kids interested in? What do they know? What would they expect? Many of these questions haunted me for some time, and I thought it would be nice to share my observations from simply going for it.

I outline my learning goals, observations from the first few minutes, approach, some later observations, and main takeaways. I end with a plea for teaching social simulation at primary schools. This initiative is part of the Special Interest Group on Education (http://www.essa.eu.org/sig/sig-education/) of the European Social Simulation Association.

Learning goals

I wanted to provide the following insights to these kids:

  • Energy is everywhere; you can feel, hear, and see it all around you. Even from outer space, you can see where cities are when you look at the earth. All activities you do require some form of energy.
  • Energy comes in different forms and can be converted into other forms.
  • Everyone likes to play games, and we can use games even to do research and perform experiments.
  • Doing research/being a researcher involves asking (sometimes odd) questions, looking very carefully at things, studying how the world works and why and solving problems.
  • You can use computers to perform social simulations that help us think. Not necessarily to answer questions but as tools that help us think about the world, do many experiments and study their implications.

First observations

It is easy to notice that this is a rather ambitious plan. Nevertheless, I learnt very quickly that these kids knew a lot! And that they (may) question everything from every angle. They are keen to understand and eager to share what they know. I was happy I could connect with them quickly by helping them get seated, chit chatting before the start.

My approach

I used symbols/analogies to explain deep concepts and layered the meaning, deepening the understanding layer by layer. I came back to and connected all these layers. This enables kids from different age groups to understand the lecture on their level. An example is that I mentioned early on how I was interested in as a kid in black holes. I explained that black holes were discovered by thinking carefully about how the universe works and that theoretical physicists concluded there might be something like a black hole. It was decades later before a real black hole was photographed. The fact that you can imagine and reason how something may exist that you cannot (yet) observe… that much later has been proven to exist. This is what research can be; it is incredible how this happened. Much later in the talk, I connected this to how you can use the computer to imagine, dream up, and test ideas because, in many cases, it is tough to do in real life.

I asked many questions and listened carefully to the answers. Some answers are way off-topic, and it is essential to guide these kids enough so the story continues, but at the same time, the kids stay on board. An early question was… do you like to play games? It is so lovely to have a group of kids cheering that they want to play games! It provides a connection. Another question I asked was, what is the similarity between a wind turbine and a sheep? Kids laughed at the funny question and picture but also came up with the desired answer (they both need/convert energy). Other creative solutions were that the colours were similar, and the shape had similarities. These are fun answers and also correct!

Because of these questions, kids came up with many great insights and good observations. This was astonishing. Research is looking at something carefully, like a snail. A black hole comes from a collapsing star, and our sun will collapse at some point in time. One kid knew that the object I brought was a kazoo… so I invited him to try imitating the sound of Max Verstappen’s Formula One car. And, of course, I had a few more kazoos, so we made a few reasonable attempts. I went back to 5+ times during the next hour to some of these kids’ great remarks: it helped to keep connected to the kids.

I played the ‘heroes and cowards’ game (similar to the ‘heroes and cowards’ model from the Netlogo library). This was a game as well as an experiment. I announced that it only works if we all follow the rules carefully. I made the kids silently think about what would happen. It worked reasonably well: they could observe the emergent phenomenon of the group cluttering and exploding, although it went somewhat rough.

A fantastic moment was to explain the concept of validity to young kids simply by experiencing it. I pressed on the fact that following the rules was crucial for our experiment to be valid and that stumbling and running was problematic for our outcomes. It was amazing that this landed so well; I was fortunate that the circumstances allowed this.

After playing this game a couple of times, with hilarious moments and chaos, I showed how you could replicate what happened in a simulation in Netlogo. I showed that you could repeat it rapidly and do variations that would be hard to do with the kids. I even showed the essential piece of code. And I remarked that the kids on the computer did listen better to me.

Later observations

We planned to take 60 minutes, observe how far we could go, and adapt. I noticed I could stretch it to 75 minutes, far longer than I thought was possible. I used less material than I thought I would use for 60 minutes. I started relatively slow and with a personal touch. I was happy I had flexible material and could adapt to what the kids shared. I used my intuition and picked up objects that were around that I could use to tell the story.

Some sweet things happened. When I first arrived, one kid played the piano in the general area. He played with much possess, small but intense. I said in the lecture that I heard him play and that I was also into music. Raised hand: ‘Will you play something for us at the end’? Of course, I promised this! During the lecture… I repeatedly promised I would; the question came back many times. I played a song the young piano player liked to hear.

These children were very open and direct. I had expected that but was still surprised by how honest and straightforward. ‘Ow, now I lost my question, this happens to me all the time’. I said: do you know I also have this quite often? It is perfectly normal. It doesn’t matter. If the question comes back, you can raise your hand again. If it doesn’t, then that is also just fine.

My takeaways

  • Do fun things, even if it is not perfectly connected. It helps with the attention span and provides a connection. Using humour helps us all to be open to learning.
  • Ask many questions, and use your intuition when asking questions. Listen to the answers, remember important ones (and who gave them), and refer back to them. If something is off-topic, you can ‘park’ that question and remark or answer it politely without dismissing it.
  • Act things out very dramatically. I acted very brave and very cowardly when introducing the game. I used two kids to show the rules and kept referring to them using their names.
  • Don’t overprepare but make the lecture flexible. Where can you expand? What do you need to do to make the connection, to make it stick?
  • I was happy that the class teachers helped me by asking a crucial question at the end, allowing me to close a couple of circles. Keep the teacher active and involved in the lecture. Invite them beforehand to do so.
  • A helpful hint I received afterwards was to use a whiteboard (or something similar) to develop a visual record of concepts and keywords raised by the kids, e.g., in the form of a mind map.
  • Kids keep surprising you all the way. One asked about NetLogo: ‘Can you install this software on Windows 8?’ It is free. You can try it out yourselves, I said. ‘Can you upgrade windows 8 to windows 10’. Well, this depends on your computer, I said. These kids keep surprising you!
  • You can teach complexity, emergence, and agent-based modelling without using words. But if kids use a term, acknowledge it. In this case: ‘But with AI….’ This is AI. It is worth exploring how to reach and teach children crucial complexity insights at a young age.

Teaching social simulation in primary schools

I plea that it is worth the effort to inspire children at a young age with crucial insights into what research is, into complexity, and into using social simulation. In this specific lecture, I only briefly touched on the use of social simulation (right at the end). It is a fantastic gift to help someone see complexity unfold before their eyes and to catch a glimpse of the tools that show the ingredients of this complexity. And it is a relatively small step towards unravelling social behaviour through social simulations. I’m tempted to conclude that you could teach young children a basic understanding of social simulation with relatively small educational modules. Even if it is implicit through games and examples, they may work effectively if placed carefully in the social environment that the different age groups typically face. Showing social structures emerging from behavioural rules. Illustrating different patterns emerging due to stochasticity and changes in assumptions. Dreaming up basic (but distinct) codified decision rules about actual (social) behaviour you see around you. If this becomes an immersive experience, such educational modules have the potential to contribute to an intuitive understanding of what social simulations are and how they can be used. Children may be inspired to learn to see and understand emergent phenomena around them from an early age; they may become the thinkers of tomorrow. And for the kids I met on this occasion: I’d be amazed if none of them became researchers one day. I hope that if you get the chance, you also give it a go and share your experience! I’m keen to hear and learn!

Chappin, E. (2023) Teaching highly intelligent primary school kids energy system complexity. Review of Artificial Societies and Social Simulation, 19 Apr 2023. https://rofasss.org/2023/04/19/teachcomplex

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)

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Yes, but what did they actually do? Review of: Jill Lepore (2020) “If Then: How One Data Company Invented the Future”

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By Nick Gotts

ngotts@gn.apc.org

Jill Lepore (2020) If Then: How One Data Company Invented the Future. John Murray. ISBN: 978-1-529-38617-2 (2021 pbk edition). [Link to book]

This is a most frustrating book. The company referred to in the subtitle is the Simulmatics Corporation, which collected and analysed data on public attitudes for politicians, retailers and the US Department of Defence between 1959 and 1970. Lepore says it carried out “simulation”, but is never very clear about what “simulation” meant to the founders of Simulmatics, what algorithms were involved, or how these algorithms used data. The history of Simulmatics is narrated along with that of US politics and the Vietnam War during its period of operation; the company worked for John Kennedy’s presidential campaign in 1960, although the campaign was shy about admitting this. There is much of interest in this historical context, but the book is marred by the apparent limitations of Lepore’s technical knowledge, her prejudices against the social and behavioural sciences (and in particular the use of computers within them), and irritating “tics” such as the frequent repetition of “If/Then”. There are copious notes, and an index, but no bibliography.

Lepore insists that human behaviour is not predictable, whereas both everyday observation and the academic study of human sciences and history show that on both individual and collective levels it is partially predictable – if it were not, social life would be impossible – and partially unpredictable; she also claims that there is a general repudiation of the importance of history among social and behavioural scientists and in “Silicon Valley”, and seems unaware that many historians and other humanities researchers use mathematics and even computers in their work.

Information about Simulmatics’ uses of computers is in fact available from contemporary documents which its researchers published. In the case of Kennedy’s presidential campaign (de Sola Pool and Abelson 1961, de Sola Pool 1963), the “simulation” involved was the construction of synthetic populations in order to amalgamate polling data from past (1952, 1954, 1956, 1958) American election campaigns. Americans were divided into 480 demographically defined “voter types” (e.g. “Eastern, metropolitan, lower-income, white, Catholic, female Democrats”), and the favourable/unfavourable/neither polling responses of members of these types to 52 specific “issues” (examples given include civil rights, anti-Communism, anti-Catholicism, foreign aid) were tabulated. Attempts were then made to “simulate” 32 of the USA’s 50 states by calculating the proportions of the 480 types in those states and assuming the frequency of responses within a voter type would be the same across states. This produced a ranking of how well Kennedy could be expected to do across these states, which matched the final results quite well. On top of this work an attempt was made to assess the impact of Kennedy’s Catholicism if it became an important issue in the election, but this required additional assumptions on how members of nine groups cross-classified by political and religious allegiance would respond. It is not clear that Kennedy’s campaign actually made any use of Simulmatics’ work, and there is no sense in which political dynamics were simulated. By contrast, in later Simulmatics work not dealt with by Lepore, on local referendum campaigns about water fluoridation (Abelson and Bernstein 1963), an approach very similar to current work in agent-based modelling was adopted. Agents based on the anonymised survey responses of individuals both responded to external messaging, and interacted with each other, to produce a dynamically simulated referendum campaign. It is unclear why Lepore does not cover this very interesting work. She does cover Simulmatics’ involvement in the Vietnam War, where their staff interviewed Vietnamese civilians and supposed “defectors” from the National Liberation Front of South Vietnam (“Viet Cong”) – who may in fact simply have gone back to their insurgent activity afterwards; but this work does not appear to have used computers for anything more than data storage.

In its work on American national elections (which continued through 1964) Simulmatics appears to have wildly over-promised given the data that it would have had available, subsequently under-performed, and failed as a company as a result; from this, indeed, today’s social simulators might take warning. Its leaders started out as “liberals” in American terms, but appear to have retained the colonialist mentality generally accompanying this self-identification, and fell into and contributed to the delusions of American involvement in the Vietnam War – although it is doubtful whether the history of this involvement would have been significantly different if the company had never existed. The fact that Simulmatics was largely forgotten, as Lepore recounts, hints that it was not, in fact, particularly influential, although interesting as the venue of early attempts at data analytics of the kind which may indeed now threaten what there is of democracy under capitalism (by enabling the “microtargeting” of specific lies to specific portions of the electorate), and at agent-based simulation of political dynamics. From a personal point of view, I am grateful to Lepore for drawing my attention to contemporary papers which contain far more useful information than her book about the early use of computers in the social sciences.

References

Abelson, R.P. and Bernstein, A. (1963) A Computer Simulation Model of Community Referendum Controversies. The Public Opinion Quarterly Vol. 27, No. 1 (Spring, 1963), pp. 93-122. Stable URL http://www.jstor.com/stable/2747294.

de Sola Pool, I. (1963) AUTOMATION: New Tool For Decision Makers. Challenge Vol. 11, No. 6 (MARCH 1963), pp. 26-27. Stable URL https://www.jstor.org/stable/40718664.

de Sola Pool, I. and Abelson, R.P. (1961) The Simulmatics Project. The Public Opinion Quarterly, Vol. 25, No. 2 (Summer, 1961), pp. 167-183. Stable URL https://www.jstor.org/stable/2746702.

Gotts, N. (2023) Yes, but what did they actually do? Review of: Jill Lepore (2020) "If Then: How One Data Company Invented the Future". Review of Artificial Societies and Social Simulation, 9 Mar 2023. https://rofasss.org/2023/03/09/ReviewofJillLepore

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)

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Why we are failing at connecting opinion dynamics to the empirical world

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By Dino Carpentras

ETH Zürich – Department of Humanities, Social and Political Sciences (GESS)

The big mystery

Opinion dynamics (OD) is field dedicated to studying the dynamic evolution of opinions and is currently facing some extremely cryptic mysteries. Since 2009 there have been multiple calls for OD models to be strongly grounded in empirical data (Castellano et al., 2009, Valori et al., 2012, Flache et al., 2017; Dong et al., 2018), however the number of articles moving in this direction is still extremely limited. This is especially puzzling when compared with the increase in the number of publications in this field (see Fig 1). Another surprising issue, which extends also beyond OD, is that validated models are not cited as often as we would expect them to be (Chattoe-Brown, 2022; Kejjzer, 2022).

Some may argue that this could be explained by a general lack of people interested in the empirical side of opinion dynamics. However, the World seems in desperate need of empirically grounded OD models that could help us in shape policies on topics such as vaccination and climate change. Thus, it is very surprising to see that almost nobody is interested in meeting such a big and pressing demand.

In this short piece, I will share my experience both as a writer and as a reviewer for empirical OD papers, as well as the information I gathered from discussions with other researchers in similar roles. This will help us understand much better what is going on in the world of empirical OD and, more in general, in the empirical parts of agent-based modelling (ABM) related to psychological phenomena.

fig 1 rofasss

Publications containing the term “opinion dynamics” in abstract or title. Total 2,527. Obtained from dimensions.ai

Theoretical versus empirical OD

The main issue I have noticed with works in empirical OD is that these papers do not conform to the standard framework of ABM papers. Indeed, in “classical” ABM we usually try to address research questions like:

  1. Can we develop a toy model to show how variables X and Y are linked?
  2. Can we explain some macroscopic phenomenon as the result of agents’ interaction?
  3. What happens to the outputs of a popular model if we add a new variable?

However, empirical papers do not fit into this framework. Indeed, empirical ABM papers ask questions such as:

  1. How accurate are the predictions made by a certain model when compared with data?
  2. How close is the micro-dynamic to the experimental data?
  3. How can we refine previous models to improve their predicting ability?

Unfortunately, many reviewers do not view the latter questions as genuine research inquiries, ending up in pushing the authors to modify their papers to meet the first set of questions.

For instance, my empirical works often receive the critique that “the research question is not clear”, even though the question was explicitly stated in the main text, abstract and even in the title (See, for example “Deriving An Opinion Dynamics Model From Experimental Data”, Carpentras et al. 2022). Similarly, once a reviewer acknowledged that the experiment presented in the paper was an interesting addition to it, but they requested me to demonstrate why it was useful. Notice that, also in this case, the paper was on developing a model from the dynamical behavior observed in an experiment; therefore, the experiment was not just “an add on”, but core of the paper. I also have reviewed some empirical OD papers where the authors are asked, by other reviewers, to showcase how their model informs us about the world in a novel way.

As we will see in a moment, this approach does not just make authors’ life harder, but it also generates a cascade of consequences on the entire field of opinion dynamics. But to better understand our world, let us move first to a fictitious scenario.

A quick tale of natural selection of researcher

Let us now imagine a hypothetical world where people have almost no knowledge of the principles of physics. However, to keep the thought experiment simple, let us also suppose they have already developed the peer-review process. Of course, this fictious scenario is far from being realistic, but it should still help us understand what is going on with empirical OD.

In this world, a scientist named Alice writes a paper suggesting that there is an upward force when objects enter water. She also shows that many objects can float on water, therefore “validating” her model. The community is excited about this new paper which took Alice 6 months to write.

Now, consider another scientist named Bob. Bob, inspired by Alice’s paper, in 6 months conducts a series of experiments demonstrating that when an object is submerged in water, it experiences an upward force that is proportional to its submerged volume. This pushes knowledge forward as Bob does not just claim that this force exists, but he shows how this force has some clear quantitative relationship to the volume of the object.

However, when reviewers read Bob’s work, they are unimpressed. They question the novelty of his research and fail to see the specific research question he is attempting to address. After all, Alice already showed that this force exists, so what is new in this paper? One of the reviewers suggests that Bob should show how his study may impact their understanding of the world.

As a result, Bob spends an additional six months to demonstrate that he could technically design a floating object made out of metal (i.e. a ship). He also describes the advantages for society if such an object was invented. Unfortunately, one of the reviewers is extremely skeptical as metal is known to be extremely heavy and should not float in water, and requests additional proof.

After multiple revisions, Bob’s work is eventually published. However, the publication process takes significantly longer than Alice’s work, and the final version of the paper addresses a variety of points, including empirical validation, the feasibility of constructing a metal boat, and evidence to support this claim. Consequently, the paper becomes densely technical, making it challenging for most people to read and understand.

At the end, Bob is left with a single paper which is hardly readable (and therefore citable), while Alice, in the meanwhile, published many other easier-to-read papers having a much bigger impact.

Solving the mystery of empirical opinion dynamics

The previous sections helped us in understanding the following points: (1) validation and empirical grounding are often not seen as a legitimate research goal by many members of the ABM community. (2) This leads to bigger struggle when trying to publish this kind of research, and (3) reviewers often try to push the paper into the more classic research questions, possibly resulting in a monster-paper which tries to address multiple points all at once. (4) This also generates lower readability and so less impact.

So to sum it up: empirical OD gives you the privilege of working much more to obtain way less. This, combined with the “natural selection” of the “publish or perish” explains the scarcity of publications in this field, as authors need either to adapt to more standard ABM formulas or to “perish.” I also personally know an ex-researcher who tried to publish empirical OD until he got fed up and left the field.

Some clarifications

Let me make clear that this is a bit of a simplification and that, of course, it is definitely possible to publish empirical work in opinion dynamics even without “perishing.” However, choosing this instead of the traditional ABM approach strongly enhances the difficulty. This is a little like running while carrying extra weight: it is still possible that you will win the race, but the weight strongly decreases the probability of this happening.

I also want to say that while here I am offering an explanation of the puzzles I presented, I do not claim that this is the only possible explanation. Indeed, I am sure that what I am offering here is only part of the full story.

Finally, I want to clarify that I do not believe anyone in the system has bad intentions. Indeed, I think reviewers are in good faith when suggesting empirically-oriented papers to take a more classical approach. However, even with good intentions, we are creating a lot of useless obstacles for an entire research field.

Trying to solve the problem

To address this issue, in the past I have suggested dividing ABM researchers into theoretical and empirically oriented (Carpentras, 2020). The division of research into two streams could help us in developing better standards for both developing toy models and for empirical ABMs.

To give you a practical example, my empirical ABM works usually receive long and detailed comments about the model properties and almost no comment on the nature of the experiment or data analysis. Am I that good in these last two steps? Or maybe reviewers in ABM focus very little on the empirical side of empirical ABMs? While the first explanation would be flattering for me, I am afraid that the reality is better depicted by the second option.

With this in mind, together with other members of the community, we have created a special interest group for Experimental ABM (see http://www.essa.eu.org/sig/sig-experimental-abm/). However, for this to be successful, we really need people to recognize the distinction between these two fields. We need to acknowledge that empirically-related research questions are still valid and not push papers towards the more classical approach.

I really believe empirical OD will raise, but how this will happen is still to decide. Will it be at the cost of many researchers facing bigger struggle or will we develop a more fertile environment? Or maybe some researchers will create an entire new niche outside of the ABM community? The choice is up to us!

References

Carpentras, D., Maher, P. J., O’Reilly, C., & Quayle, M. (2022). Deriving An Opinion Dynamics Model From Experimental Data. Journal of Artificial Societies & Social Simulation, 25(4). https://www.jasss.org/25/4/4.html

Carpentras, D. (2020) Challenges and opportunities in expanding ABM to other fields: the example of psychology. Review of Artificial Societies and Social Simulation, 20th December 2021. https://rofasss.org/2021/12/20/challenges/

Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Reviews of modern physics, 81(2), 591. DOI: 10.1103/RevModPhys.81.591

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, 1 Feb 2022. https://rofasss.org/2022/02/01/citing-od-models/

Dong, Y., Zhan, M., Kou, G., Ding, Z., & Liang, H. (2018). A survey on the fusion process in opinion dynamics. Information Fusion, 43, 57-65. DOI: 10.1016/j.inffus.2017.11.009

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://www.jasss.org/20/4/2.html

Keijzer, M. (2022). If you want to be cited, calibrate your agent-based model: a 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

Valori, L., Picciolo, F., Allansdottir, A., & Garlaschelli, D. (2012). Reconciling long-term cultural diversity and short-term collective social behavior. Proceedings of the National Academy of Sciences, 109(4), 1068-1073. DOI: 10.1073/pnas.1109514109

Carpentras, D. (2023) Why we are failing at connecting opinion dynamics to the empirical world. Review of Artificial Societies and Social Simulation, 8 Mar 2023. https://rofasss.org/2023/03/08/od-emprics

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)

Content

The inevitable “layering” of models to extend the reach of our understanding

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By Bruce Edmonds

“Just as physical tools and machines extend our physical abilities, models extend our mental abilities, enabling us to understand and control systems beyond our direct intellectual reach” (Calder  & al. 2018)

Motivation

There is a modelling norm that one should be able to completely understand one’s own model. Whilst acknowledging there is a trade-off between a model’s representational adequacy and its simplicity of formulation, this tradition assumes there will be a “sweet spot” where the model is just tractable but also good enough to be usefully informative about the target of modelling – in the words attributed to Einstein, “Everything should be made as simple as possible, but no simpler”1. But what do we do about all the phenomena where to get an adequate model2 one has to settle for a complex one (where by “complex” I mean a model that we do not completely understand)? Despite the tradition in Physics to the contrary, it would be an incredibly strong assumption that there are no such phenomena, i.e. that an adequate simple model is always possible (Edmonds 2013).

There are three options in these difficult cases.

  • Do not model the phenomena at all until we can find an adequate model we can fully understand. Given the complexity of much around us this would mean to not model these for the foreseeable future and maybe never.
  • Accept inadequate simpler models and simply hope that these are somehow approximately right3. This option would allow us to get answers but with no idea whether they were at all reliable. There are many cases of overly simplistic models leading policy astray (Adoha & Edmonds 2017; Thompson 2022), so this is dangerous if such models influence decisions with real consequences.
  • Use models that are good for our purpose but that we only partially understand. This is the option examined in this paper.

When the purpose is empirical the last option is equivalent to preferring empirical grounding over model simplicity (Edmonds & Moss 2005).

Partially Understood Models

In practice this argument has already been won – we do not completely understand many computer simulations that we use and rely on. For example, due to the chaotic nature of the dynamics of the weather, forecasting models are run multiple times with slightly randomised inputs and the “ensemble” of forecasts inspected to get an idea of the range of different outcomes that could result (some of which might be qualitatively different from the others)4. Working out the outcomes in each case requires the computational tracking of a huge numbers of entities in a way that is far beyond what the human mind can do5. In fact, the whole of “Complexity Science” can be seen as different ways to get some understanding of systems for which there is no analytic solution6.

Of course, this raises the question of what is meant by “understand” a model, for this is not something that is formally defined. This could involve many things, including the following.

  1. That the micro-level – the individual calculations or actions done by the model each time step – is understood. This is equivalent to understanding each line of the computer code.
  2. That some of the macro-level outcomes that result from the computation of the whole model is understood in terms of partial theories or “rules of thumb”.
  3. That all the relevant macro-level outcomes can be determined to a high degree of accuracy without simulating the model (e.g. by a mathematical model).

Clearly, level (1) is necessary for most modelling purposes in order to know the model is behaving as intended. The specification of this micro-level is usually how such models are made, so if this differs from what was intended then this would be a bug. Thus this level would be expected of most models7. However, this does not necessarily mean that this is at the finest level of detail possible – for example, we usually do not bother about how random number generators work, but simply rely on its operation, but in this case we have very good level (3) of understanding for these sub-routines.

At the other extreme, a level (3) understanding is quite rare outside the realm of physics. In a sense, having this level of understanding makes the model redundant, so would probably not be the case for most working models (those used regularly)8. As discussed above, there will be many kinds of phenomena for which this level of understanding is not feasible.

Clearly, what many modelers find useful is a combination of levels (1) & (2) – that is, the detailed, micro-level steps that the model takes are well understood and the outcomes understood well enough for the intended task. For example, when using a model to establish a complex explanation9 (of some observed pattern in data using certain mechanisms or structures) then one might understand the implementation of the candidate mechanisms and verify that the outcomes fit the target pattern for a range of parameters, but not completely understand the detail of the causation involved. There might well be some understanding, for example how robust this is to minor variations in the initial conditions or the working of the mechanisms involved (e.g. by adding some noise to the processes). A complete understanding might not be accessible but this does not stop an explanation being established (although a  better understanding is an obvious goal for future research or avenue for critiques of the explanation).

Of course, any lack of a complete, formal understanding leaves some room for error. The argument here is not deriding the desirability of formal understanding, but is against prioritising that over model adequacy. Also the lack of a formal, level (3), understanding of a model does not mean we cannot take more pragmatic routes to checking it. For example: performing a series of well-designed simulation experiments that intend to potentially refute the stated conclusions, systematically comparing to other models, doing a thorough sensitivity analysis and independently reproducing models can help ensure their reliability. These can be compared with engineering methods – one may not have a proof that a certain bridge design is solid over all possible dynamics, but practical measures and partial modelling can ensure that any risk is so low as to be negligible. If we had to wait until bridge designs were proven beyond doubt, we would simply have to do without them.

Layering Models to Leverage some Understanding

As a modeller, if I do not understand something my instinct is to model it. This instinct does not change if what I do not understand is, itself, a model. The result is a model of the original model – a meta-model. This is, in fact, common practice. I may select certain statistics summarising the outcomes and put these on a graph; I might analyse the networks that have emerged during model runs; I may use maths to approximate or capture some aspect of the dynamics; I might cluster and visualise the outcomes using Machine Learning techniques; I might make a simpler version of the original and compare them. All of these might give me insights into the behaviour of the original model. Many of these are so normal we do not think of this as meta-modelling. Indeed, empirically-based models are already, in a sense, meta-models, since the data that they represent are themselves a kind of descriptive model of reality (gained via measurement processes).

This meta-modelling strategy can be iterated to produce meta-meta-models etc. resulting in “layers” of models, with each layer modelling some aspect of the one “below” until one reaches the data and then what the data measures. Each layer should be able to be compared and checked with the layer “below”, and analysed by the layer “above”.

An extended example of such layering was built during the SCID (Social Complexity of Immigration and Diversity) project10 and illustrated in Figure 1. In this a complicated simulation (Model 1) was built to incorporate some available data and what was known concerning the social and behavioural processes that lead people to bother to vote (or not). This simulation was used as a counter-example to show how assumptions about the chaining effect of interventions might be misplaced (Fieldhouse et al. 2016). A much simpler simulation was then built by theoretical physicists (Model 2), so that it produced the same selected outcomes over time and aa range of parameter values. This allowed us to show that some of the features in the original (such as dynamic networks) were essential to get the observed dynamics in it (Lafuerza et al. 2016a). This simpler model was in turn modelled by an even simpler model (Model 3) that was amenable to an analytic model (Model 4) that allowed us to obtain some results concerning the origin of a region of bistability in the dynamics (Lafuerza et al. 2016b).

Layering fig 1

Figure 1. The Layering of models that were developed in part of the SCID project

Although there are dangers in such layering – each layer could introduce a new weakness – there are also methodological advantages, including the following. (A) Each model in the chain (except model 4) is compared and checked against both the layer below and that above. Such multiple model comparisons are excellent for revealing hidden assumptions and unanticipated effects. (B) Whilst previously what might have happened was a “heroic” leap of abstraction from evidence and understanding straight to Model 3 or 4, here abstraction happens over a series of more modest steps, each of which is more amenable to checking and analysis. When you stage abstraction the introduced assumptions are more obvious and easier to analyse.

One can imagine such “layering” developing in many directions to leverage useful (but indirect) understanding, for example the following.

  • Using an AI algorithm to learn patterns in some data (e.g. medical data for disease diagnosis) but then modelling its working to obtain some human-accessible understanding of how it is doing it.
  • Using a machine learning model to automatically identify the different “phase spaces” in model results where qualitatively different model behaviour is exhibited, so one can then try to simplify the model within each phase.
  • Automatically identifying the processes and structures that are common to a given set of models to facilitate the construction of a more general, ‘umbrella’ model that approximates all the outcomes that would have resulted from the set, but within a narrower range of conditions.

As the quote at the top implies, we are used to settling for partial control of what machines do because it allows us to extend our physical abilities in useful ways. Each time we make their control more indirect, we need to check that this is safe and adequate for purpose. In the cars we drive there are ever more layers of electronic control between us and the physical reality it drives through which we adjust to – we are currently adjusting to more self-drive abilities. Of course, the testing and monitoring of these systems is very important but that will not stop the introduction of layers that will make them safer and more pleasant to drive.

The same is true of our modelling, which we will need to apply in ever more layers in order to leverage useful understanding which would not be accessible otherwise. Yes, we will need to use practical methods to test their fitness for purpose and reliability, and this might include the complete verification of some components (where this is feasible), but we cannot constrain ourselves to only models we completely understand.

Concluding Discussion

If the above seems obvious, then why am I bothering to write this? I think for a few reasons. Firstly, to answer the presumption that understanding one’s model must have priority over all other considerations (such as empirical adequacy) so that sometimes we must accept and use partially understood models. Secondly, to point out that such layering has benefits as well as difficulties – especially if it can stage abstraction into more verifiable steps and thus avoid huge leaps to simple but empirically-isolated models. Thirdly, because such layering will become increasingly common and necessary.

In order to extend our mental reach further, we will need to develop increasingly complicated and layered modelling. To do this we will need to accept that our understanding is leveraged via partially understood models, but also to develop the practical methods to ensure their adequacy for purpose.

Notes

[1] These are a compressed version of his actual words during a 1933 lecture, which were: “It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience.” (Robinson 2018)
[2] Adequate for whatever our purpose for it is (Edmonds & al. 2019).
[3]The weasel words I once heard from a Mathematician excusing an analytic model he knew to be simplistic were: that, although he knew it was wrong, it was useful for “capturing core dynamics” (though how he knew that they were not completely wrong eludes me).
[4] For an introduction to this approach read the European Centre for Medium-Range Weather Forecasts’ fact sheet on “Ensemble weather forecasting” at: https://www.ecmwf.int/en/about/media-centre/focus/2017/fact-sheet-ensemble-weather-forecasting
[5] In principle, a person could do all the calculations involved in a forecast but only with the aid of exterior tools such as pencil and paper to keep track of it all so it is arguable whether the person doing the individual calculations has an “understanding” of the complete picture. Lewis Fry Richardson, who pioneered the idea of numerical forecasting of weather in the 1920s, did a 1-day forecast by hand to illustrate his method (Lynch 2008), but this does not change the argument.
[6] An analytic solution is when one can obtain a closed-form equation that characterises all the outcomes by manipulating the mathematical symbols in a proof. If one has to numerically calculate outcomes for different initial conditions and parameters this is a computational solution.
[7] For purely predictive models, whose purpose is only to anticipate an unknown value to a useful level of accuracy, this is not strictly necessary. For example, how some AI/Machine learning models work may not clear at the micro-level, but as long as it works (successfully predicts) this does not matter – even if its predictive ability is due to a bug.
[8] Models may still be useful in this case, for example to check the assumptions made in the matching mathematical or other understanding.
[9] For more on this use see (Edmonds et al. 2019).
[10] For more about this project see http://cfpm.org/scid

Acknowledgements

Bruce Edmonds is supported as part of the ESRC-funded, UK part of the “ToRealSim” project, 2019-2023, grant number ES/S015159/1 and was supported as part of the EPSRC-funded “SCID” project 2010-2016, grant number EP/H02171X/1.

References

Calder, M., Craig, C., Culley, D., de Cani, R., Donnelly, C.A., Douglas, R., Edmonds, B., Gascoigne, J., Gilbert, N. Hargrove, C., Hinds, D., Lane, D.C., Mitchell, D., Pavey, G., Robertson, D., Rosewell, B., Sherwin, S., Walport, M. and Wilson, A. (2018) Computational modelling for decision-making: where, why, what, who and how. Royal Society Open Science, DOI:10.1098/rsos.172096.

Edmonds, B. (2013) Complexity and Context-dependency. Foundations of Science, 18(4):745-755. DOI:10.1007/s10699-012-9303-x

Edmonds, B. and Moss, S. (2005) From KISS to KIDS – an ‘anti-simplistic’ modelling approach. In P. Davidsson et al. (Eds.): Multi Agent Based Simulation 2004. Springer, Lecture Notes in Artificial Intelligence, 3415:130–144. DOI:10.1007/978-3-540-32243-6_11

Edmonds, B., le Page, C., Bithell, M., Chattoe-Brown, E., Grimm, V., Meyer, R., Montañola-Sales, C., Ormerod, P., Root H. & Squazzoni. F. (2019) Different Modelling Purposes. Journal of Artificial Societies and Social Simulation, 22(3):6. DOI:10.18564/jasss.3993

Fieldhouse, E., Lessard-Phillips, L. & Edmonds, B. (2016) Cascade or echo chamber? A complex agent-based simulation of voter turnout. Party Politics. 22(2):241-256.  DOI:10.1177/1354068815605671

Lafuerza, LF, Dyson, L, Edmonds, B & McKane, AJ (2016a) Simplification and analysis of a model of social interaction in voting, European Physical Journal B, 89:159. DOI:10.1140/epjb/e2016-70062-2

Lafuerza L.F., Dyson L., Edmonds B., & McKane A.J. (2016b) Staged Models for Interdisciplinary Research. PLoS ONE, 11(6): e0157261. DOI:10.1371/journal.pone.0157261

Lynch, P. (2008). The origins of computer weather prediction and climate modeling. Journal of Computational Physics, 227(7), 3431-3444. DOI:10.1016/j.jcp.2007.02.034

Robinson, A. (2018) Did Einstein really say that? Nature, 557, 30. DOI:10.1038/d41586-018-05004-4

Thompson, E. (2022) Escape from Model Land. Basic Books. ISBN-13: 9781529364873

Edmonds, B. (2023) The inevitable “layering” of models to extend the reach of our understanding. Review of Artificial Societies and Social Simulation, 9 Feb 2023. https://rofasss.org/2023/02/09/layering

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)

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Socio-Cognitive Systems – a position statement

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By Frank Dignum1, Bruce Edmonds2 and Dino Carpentras3

1Department of Computing Science, Faculty of Science and Technology, Umeå University, frank.dignum@umu.se
2Centre for Policy Modelling, Manchester Metropolitan University, bruce@edmonds.name
3Department of Psychology, University of Limerick, dino.carpentras@gmail.com

In this position paper we argue for the creation of a new ‘field’: Socio-Cognitive Systems. The point of doing this is to highlight the importance of a multi-levelled approach to understanding those phenomena where the cognitive and the social are inextricably intertwined – understanding them together.

What goes on ‘in the head’ and what goes on ‘in society’ are complex questions. Each of these deserves serious study on their own – motivating whole fields to answer them. However, it is becoming increasingly clear that these two questions are deeply related. Humans are fundamentally social beings, and it is likely that many features of their cognition have evolved because they enable them to live within groups (Herrmann et al. 20007). Whilst some of these social features can be studied separately (e.g. in a laboratory), others only become fully manifest within society at large. On the other hand, it is also clear that how society ‘happens’ is complicated and subtle and that these processes are shaped by the nature of our cognition. In other words, what people ‘think’ matters for understanding how society ‘is’ and vice versa. For many reasons, both of these questions are difficult to answer. As a result of these difficulties, many compromises are necessary in order to make progress on them, but each compromise also implies some limitations. The main two types of compromise consist of limiting the analysis to only one of the two (i.e. either cognition or society)[1]. To take but a few examples of this.

  1. Neuro-scientists study what happens between systems of neurones to understand how the brain does things and this is so complex that even relatively small ensembles of neurones are at the limits of scientific understanding.
  2. Psychologists see what can be understood of cognition from the outside, usually in the laboratory so that some of the many dimensions can be controlled and isolated. However, what can be reproduced in a laboratory is a limited part of behaviour that might be displayed in a natural social context.
  3. Economists limit themselves to the study of the (largely monetary) exchange of services/things that could occur under assumptions of individual rationality, which is a model of thinking not based upon empirical data at the individual level. Indeed it is known to contradict a lot of the data and may only be a good approximation for average behaviour under very special circumstances.
  4. Ethnomethodologists will enter a social context and describe in detail the social and individual experience there, but not generalise beyond that and not delve into the cognition of those they observe.
  5. Other social scientists will take a broader view, look at a variety of social evidence, and theorise about aspects of that part of society. They (almost always) do not include individual cognition into account in these and do not seek to integrate the social and the cognitive levels.

Each of these in the different ways separate the internal mechanisms of thought from the wider mechanisms of society or limits its focus to a very specific topic. This is understandable; what each is studying is enough to keep them occupied for many lifetimes. However, this means that each of these has developed their own terms, issues, approaches and techniques which make relating results between fields difficult (as Kuhn, 1962, pointed out).

SCS Picture 1

Figure 1: Schematic representation of the relationship between the individual and society. Individuals’ cognition is shaped by society, at the same time, society is shaped by individuals’ beliefs and behaviour.

This separation of the cognitive and the social may get in the way of understanding many things that we observe. Some phenomena seem to involve a combination of these aspects in a fundamental way – the individual (and its cognition) being part of society as well as society being part of the individual. Some examples of this are as follows (but please note that this is far from an exhaustive list).

  • Norms. A social norm is a constraint or obligation upon action imposed by society (or perceived as such). One may well be mistaken about a norm (e.g. whether it is ok to casually talk to others at a bus stop), thus it is also a belief – often not told to one explicitly but something one needs to infer from observation. However, for a social norm to hold it also needs to be an observable convention. Decisions to violate social norms require that the norm is an explicit (referable) object in the cognitive model. But the violation also has social consequences. If people react negatively to violations the norm can be reinforced. But if violations are ignored it might lead to a norm disappearing. How new norms come about, or how old ones fade away, is a complex set of interlocking cognitive and social processes. Thus social norms are a phenomena that essentially involves both the social and the cognitive (Conte et al. 2013).
  • Joint construction of social reality. Many of the constraints on our behaviour come from our perception of social reality. However, we also create this social reality and constantly update it. For example, we can invent a new procedure to select a person as head of department or exit a treaty and thus have different ways of behaving after this change. However, these changes are not unconstrained in themselves. Sometimes the time is “ripe for change”, while at other times resistance is too big for any change to take place (even though a majority of the people involved would like to change). Thus what is socially real for us depends on what people individually believe is real, but this depends in complex ways on what other people believe and their status. And probably even more important: the “strength” of a social structure depends on the use people make of it. E.g. a head of department becomes important if all decisions in the department are deferred to the head. Even though this might not be required by university or law.
  • Identity. Our (social) identity determines the way other people perceive us (e.g. a sports person, a nerd, a family man) and therefore creates expectations about our behaviour. We can create our identities ourselves and cultivate them, but at the same time, when we have a social identity, we try to live up to it. Thus, it will partially determine our goals and reactions and even our feeling of self-esteem when we live up to our identity or fail to do so. As individuals we (at least sometimes) have a choice as to our desired identity, but in practice, this can only be realised with the consent of society. As a runner I might feel the need to run at least three times a week in order for other people to recognize me as runner. At the same time a person known as a runner might be excused from a meeting if training for an important event. Thus reinforcing the importance of the “runner” identity.
  • Social practices. The concept already indicates that social practices are about the way people habitually interact and through this interaction shape social structures. Practices like shaking hands when greeting do not always have to be efficient, but they are extremely socially important. For example, different groups, countries and cultures will have different practices when greeting and performing according to the practice shows whether you are part of the in-group or out-group. However, practices can also change based on circumstances and people, as it happened, for example, to the practice of shaking hands during the covid-19 pandemic. Thus, they are flexible and adapting to the context. They are used as flexible mechanisms to efficiently fit interactions in groups, connecting persons and group behaviour.

As a result, this division between cognitive and the social gets in the way not only of theoretical studies, but also in practical applications such as policy making. For example, interventions aimed at encouraging vaccination (such as compulsory vaccination) may reinforce the (social) identity of the vaccine hesitant. However, this risk and its possible consequences for society cannot be properly understood without a clear grasp of the dynamic evolution of social identity.

Computational models and systems provide a way of trying to understand the cognitive and the social together. For computational modellers, there is no particular reason to confine themselves to only the cognitive or only the social because agent-based systems can include both within a single framework. In addition, the computational system is a dynamic model that can represent the interactions of the individuals that connect the cognitive models and the social models. Thus the fact that computational models have a natural way to represent the actions as an integral and defining part of the socio-cognitive system is of prime importance. Given that the actions are an integral part of the model it is well suited to model the dynamics of socio-cognitive systems and track changes at both the social and the cognitive level. Therefore, within such systems we can study how cognitive processes may act to produce social phenomena whilst, at the same time, as how social realities are shaping the cognitive processes. Caarley and Newell (1994) discusses what is necessary at the agent level for sociality, Hofested et al. (2021) talk about how to understand sociality using computational models (including theories of individual action) – we want to understand both together. Thus, we can model the social embeddedness that Granovetter (1985) talked about – going beyond over- or under-socialised representations of human behaviour. It is not that computational models are innately suitable for modelling either the cognitive or the social, but that they can be appropriately structured (e.g. sets of interacting parts bridging micro-, meso- and macro-levels) and include arbitrary levels of complexity. Lots of models that represent the social have entities that stand for the cognitive, but do not explicitly represent much of that detail – similarly much cognitive modelling implies the social in terms of the stimuli and responses of an individual that would be to other social entities, but where these other entities are not explicitly represented or are simplified away.

Socio-Cognitive Systems (SCS) are: those models and systems where both cognitive and social complexity are represented with a meaningful level of processual detail.

A good example of an application where this appeared of the biggest importance was in simulations for the covid-19 crisis. The spread of the corona virus on macro level could be given by an epidemiological model, but the actual spreading depended crucially on the human behaviour that resulted from individuals’ cognitive model of the situation. In Dignum (2021) it was shown how the socio-cognitive system approach was fundamental to obtaining better insights in the effectiveness of a range of covid-19 restrictions.

Formality here is important. Computational systems are formal in the sense that they can be unambiguously passed around (i.e. unlike language, it is not differently re-interpreted by each individual) and operate according to their own precisely specified and explicit rules. This means that the same system can be examined and experimented on by a wider community of researchers. Sometimes, even when the researchers from different fields find it difficult to talk to one another, they can fruitfully cooperate via a computational model (e.g. Lafuerza et al. 2016). Other kinds of formal systems (e.g. logic, maths) are geared towards models that describe an entire system from a birds eye view. Although there are some exceptions like fibred logics Gabbay (1996), these are too abstract to be of good use to model practical situations. The lack of modularity and has been addressed in context logics Giunchiglia, F., & Ghidini, C. (1998). However, the contexts used in this setting are not suitable to generate a more general societal model. It results in most typical mathematical models using a number of agents which is either one, two or infinite (Miller and Page 2007), while important social phenomena happen with a “medium sized” population. What all these formalisms miss is a natural way of specifying the dynamics of the system that is modelled, while having ways to modularly describe individuals and the society resulting from their interactions. Thus, although much of what is represented in Socio-Cognitive Systems is not computational, the lingua franca for talking about them is.

The ‘double complexity’ of combining the cognitive and the social in the same system will bring its own methodological challenges. Such complexity will mean that many socio-cognitive systems will be, themselves, hard to understand or analyse. In the covid-19 simulations, described in (Dignum 2021), a large part of the work consisted of analysing, combining and representing the results in ways that were understandable. As an example, for one scenario 79 pages of graphs were produced showing different relations between potentially relevant variables. New tools and approaches will need to be developed to deal with this. We only have some hints of these, but it seems likely that secondary stages of analysis – understanding the models – will be necessary, resulting in a staged approach to abstraction (Lafuerza et al. 2016). In other words, we will need to model the socio-cognitive systems, maybe in terms of further (but simpler) socio-cognitive systems, but also maybe with a variety of other tools. We do not have a view on this further analysis, but this could include: machine learning, mathematics, logic, network analysis, statistics, and even qualitative approaches such as discourse analysis.

An interesting input for the methodology of designing and analysing socio-cognitive systems is anthropology and specifically ethnographical methods. Again, for the covid-19 simulations the first layer of the simulation was constructed based on “normal day life patterns”. Different types of persons were distinguished that each have their own pattern of living. These patterns interlock and form a fabric of social interactions that overall should satisfy most of the needs of the agents. Thus we calibrate the simulation based on the stories of types of people and their behaviours. Note that doing the same just based on available data of behaviour would not account for the underlying needs and motives of that behaviour and would not be a good basis for simulating changes. The stories that we used looked very similar to the type of reports ethnographers produce about certain communities. Thus further investigating this connection seems worthwhile.

For representing the output of the complex socio-cognitive systems we can also use the analogue of stories. Basically, different stories show the underlying (assumed) causal relations between phenomena that are observed. E.g. seeing an increase in people having lunch with friends can be explained by the fact that a curfew prevents people having dinner with their friends, while they still have a need to socialize. Thus the alternative of going for lunch is chosen more often. One can see that the explaining story uses both social as well as cognitive elements to describe the results. Although in the covid-19 simulations we have created a number of these stories, they were all created by hand after (sometimes weeks) of careful analysis of the results. Thus for this kind of approach to be viable, new tools are required.

Although human society is the archetypal socio-cognitive system, it is not the only one. Both social animals and some artificial systems also come under this category. These may be very different from the human, and in the case of artificial systems completely different. Thus, Socio-Cognitive Systems is not limited to the discussion of observable phenomena, but can include constructed or evolved computational systems, and artificial societies. Examination of these (either theoretically or experimentally) opens up the possibility of finding either contrasts or commonalities between such systems – beyond what happens to exist in the natural world. However, we expect that ideas and theories that were conceived with human socio-cognitive systems in mind might often be an accessible starting point for understanding these other possibilities.

In a way, Socio-Cognitive Systems bring together two different threads in the work of Herbert Simon. Firstly, as in Simon (1948) it seeks to take seriously the complexity of human social behaviour without reducing this to overly simplistic theories of individual behaviour. Secondly, it adopts the approach of explicitly modelling the cognitive in computational models (Newell & Simon 1972). Simon did not bring these together in his lifetime, perhaps due to the limitations and difficulty of deploying the computational tools to do so. Instead, he tried to develop alternative mathematical models of aspects of thought (Simon 1957). However, those models were limited by being mathematical rather than computational.

To conclude, a field of Socio-Cognitive Systems would consider the cognitive and the social in an integrated fashion – understanding them together. We suggest that computational representation or implementation might be necessary to provide concrete reference between the various disciplines that are needed to understand them. We want to encourage research that considers the cognitive and the social in a truly integrated fashion. If by labelling a new field does this it will have achieved its purpose. However, there is the possibility that completely new classes of theory and complexity may be out there to be discovered – phenomena that are denied if either the cognitive or the social are not taken together – a new world of a socio-cognitive systems.

Notes

[1] Some economic models claim to bridge between individual behaviour and macro outcomes, however this is traditionally notional. Many economists admit that their primary cognitive models (varieties of economic rationality) are not valid for individuals but are what people on average do – i.e. this is a macro-level model. In other economic models whole populations are formalised using a single representative agent. Recently, there are some agent-based economic models emerging, but often limited to agree with traditional models.

Acknowledgements

Bruce Edmonds is supported as part of the ESRC-funded, UK part of the “ToRealSim” project, grant number ES/S015159/1.

References

Carley, K., & Newell, A. (1994). The nature of the social agent. Journal of mathematical sociology, 19(4): 221-262. DOI: 10.1080/0022250X.1994.9990145

Conte R., Andrighetto G. and Campennì M. (eds) (2013) Minding Norms – Mechanisms and dynamics of social order in agent societies. Oxford University Press, Oxford.

Dignum, F. (ed.) (2021) Social Simulation for a Crisis; Results and Lessons from Simulating the COVID-19 Crisis. Springer.

Herrmann E., Call J, Hernández-Lloreda MV, Hare B, Tomasello M (2007) Humans have evolved specialized skills of social cognition: The cultural intelligence hypothesis. Science 317(5843): 1360-1366. DOI: 10.1126/science.1146282

Hofstede, G.J, Frantz, C., Hoey, J., Scholz, G. and Schröder, T. (2021) Artificial Sociality Manifesto. Review of Artificial Societies and Social Simulation, 8th Apr 2021. https://rofasss.org/2021/04/08/artsocmanif/

Gabbay, D. M. (1996). Fibred Semantics and the Weaving of Logics Part 1: Modal and Intuitionistic Logics. The Journal of Symbolic Logic, 61(4), 1057–1120.

Ghidini, C., & Giunchiglia, F. (2001). Local models semantics, or contextual reasoning= locality+ compatibility. Artificial intelligence, 127(2), 221-259. DOI: 10.1016/S0004-3702(01)00064-9

Granovetter, M. (1985) Economic action and social structure: The problem of embeddedness. American Journal of Sociology 91(3): 481-510. DOI: 10.1086/228311

Kuhn, T,S, (1962) The structure of scientific revolutions. University of Chicago Press, Chicago

Lafuerza L.F., Dyson L., Edmonds B., McKane A.J. (2016) Staged Models for Interdisciplinary Research. PLoS ONE 11(6): e0157261, DOI: 10.1371/journal.pone.0157261

Miller, J. H., Page, S. E., & Page, S. (2009). Complex adaptive systems. Princeton university press.

Newell A, Simon H.A. (1972) Human problem solving. Prentice Hall, Englewood Cliffs, NJ

Simon, H.A. (1948) Administrative behaviour: A study of the decision making processes in administrative organisation. Macmillan, New York

Simon, H.A. (1957) Models of Man: Social and rational. John Wiley, New York

Dignum, F., Edmonds, B. and Carpentras, D. (2022) Socio-Cognitive Systems – A Position Statement. Review of Artificial Societies and Social Simulation, 2nd Apr 2022. https://rofasss.org/2022/04/02/scs

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)

Content

What more is needed for Democratically Accountable Modelling?

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By Bruce Edmonds

(A contribution to the: JASSS-Covid19-Thread)

In the context of the Covid19 outbreak, the (Squazzoni et al 2020) paper argued for the importance of making complex simulation models open (a call reiterated in Barton et al 2020) and that relevant data needs to be made available to modellers. These are important steps but, I argue, more is needed.

The Central Dilemma

The crux of the dilemma is as follows. Complex and urgent situations (such as the Covid19 pandemic) are beyond the human mind to encompass – there are just too many possible interactions and complexities. For this reason one needs complex models, to leverage some understanding of the situation as a guide for what to do. We can not directly understand the situation, but we can understand some of what a complex model tells us about the situation. The difficulty is that such models are, themselves, complex and difficult to understand. It is easy to deceive oneself using such a model. Professional modellers only just manage to get some understanding of such models (and then, usually, only with help and critique from many other modellers and having worked on it for some time: Edmonds 2020) – politicians and the public have no chance of doing so. Given this situation, any decision-makers or policy actors are in an invidious position – whether to trust what the expert modellers say if it contradicts their own judgement. They will be criticised either way if, in hindsight, that decision appears to have been wrong. Even if the advice supports their judgement there is the danger of giving false confidence.

What options does such a policy maker have? In authoritarian or secretive states there is no problem (for the policy makers) – they can listen to who they like (hiring or firing advisers until they get advice they are satisfied with), and then either claim credit if it turned out to be right or blame the advisers if it was not. However, such decisions are very often not value-free technocratic decisions, but ones that involve complex trade-offs that affect people’s lives. In these cases the democratic process is important for getting good (or at least accountable) decisions. However, democratic debate and scientific rigour often do not mix well [note 1].

A Cautionary Tale

As discussed in (Adoha & Edmonds 2019) Scientific modelling can make things worse, as in the case of the North Atlantic Cod Fisheries Collapse. In this case, the modellers became enmeshed within the standards and wishes of those managing the situation and ended up confirming their wishful thinking. An effect of technocratising the decision-making about how much it is safe to catch had the effect of narrowing down the debate to particular measurement and modelling processes (which turned out to be gravely mistaken). In doing so the modellers contributed to the collapse of the industry, with severe social and ecological consequences.

What to do?

How to best interface between scientific and policy processes is not clear, however some directions are becoming apparent.

  • That the process of developing and giving advice to policy actors should become more transparent, including who is giving advice and on what basis. In particular, any reservations or caveats that the experts add should be open to scrutiny so the line between advice (by the experts) and decision-making (by the politicians) is clearer.
  • That such experts are careful not to over-state or hype their own results. For example, implying that their model can predict (or forecast) the future of complex situations and so anticipate the effects of policy before implementation (de Matos Fernandes and Keijzer 2020). Often a reliable assessment of results only occurs after a period of academic scrutiny and debate.
  • Policy actors need to learn a little bit about modelling, in particular when and how modelling can be reliably used. This is discussed in (Government Office for Science 2018, Calder et al. 2018) which also includes a very useful checklist for policy actors who deal with modellers.
  • That the public learn some maturity about the uncertainties in scientific debate and conclusions. Preliminary results and critiques tend to be jumped on too early to support one side within polarised debate or models rejected simply on the grounds they are not 100% certain. We need to collectively develop ways of facing and living with uncertainty.
  • That the decision-making process is kept as open to input as possible. That the modelling (and its limitations) should not be used as an excuse to limit what the voices that are heard, or the debate to a purely technical one, excluding values (Aodha & Edmonds 2017).
  • That public funding bodies and journals should insist on researchers making their full code and documentation available to others for scrutiny, checking and further development (readers can help by signing the Open Modelling Foundation’s open letter and the campaign for Democratically Accountable Modelling’s manifesto).

Some Relevant Resources

  • CoMSeS.net — a collection of resources for computational model-based science, including a platform for publicly sharing simulation model code and documentation and forums for discussion of relevant issues (including one for covid19 models)
  • The Open Modelling Foundation — an international open science community that works to enable the next generation modelling of human and natural systems, including its standards and methodology.
  • The European Social Simulation Association — which is planning to launch some initiatives to encourage better modelling standards and facilitate access to data.
  • The Campaign for Democratic Modelling — which campaigns concerning the issues described in this article.

Notes

note1: As an example of this see accounts of the relationship between the UK scientific advisory committees and the Government in the Financial Times and BuzzFeed.

References

Barton et al. (2020) Call for transparency of COVID-19 models. Science, Vol. 368(6490), 482-483. doi:10.1126/science.abb8637

Aodha, L.Edmonds, B. (2017) Some pitfalls to beware when applying models to issues of policy relevance. In Edmonds, B. & Meyer, R. (eds.) Simulating Social Complexity – a handbook, 2nd edition. Springer, 801-822. (see also http://cfpm.org/discussionpapers/236)

Calder, M., Craig, C., Culley, D., de Cani, R., Donnelly, C.A., Douglas, R., Edmonds, B., Gascoigne, J., Gilbert, N. Hargrove, C., Hinds, D., Lane, D.C., Mitchell, D., Pavey, G., Robertson, D., Rosewell, B., Sherwin, S., Walport, M. & Wilson, A. (2018) Computational modelling for decision-making: where, why, what, who and how. Royal Society Open Science, DOI: 10.1098/rsos.172096.

Edmonds, B. (2020) Good Modelling Takes a Lot of Time and Many Eyes. Review of Artificial Societies and Social Simulation, 13th April 2020. https://rofasss.org/2020/04/13/a-lot-of-time-and-many-eyes/

de Matos Fernandes, C. A. and Keijzer, M. A. (2020) No one can predict the future: More than a semantic dispute. Review of Artificial Societies and Social Simulation, 15th April 2020. https://rofasss.org/2020/04/15/no-one-can-predict-the-future/

Government Office for Science (2018) Computational Modelling: Technological Futures. https://www.gov.uk/government/publications/computational-modelling-blackett-review

Squazzoni, F., Polhill, J. G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., Chappin, É., Borit, M., Verhagen, H., Giardini, F. and Gilbert, N. (2020) Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action. Journal of Artificial Societies and Social Simulation, 23(2):10. <http://jasss.soc.surrey.ac.uk/23/2/10.html>. doi: 10.18564/jasss.4298

Edmonds, B. (2020) What more is needed for truly democratically accountable modelling? Review of Artificial Societies and Social Simulation, 2nd May 2020. https://rofasss.org/2020/05/02/democratically-accountable-modelling/

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)

Content

Predicting Social Systems – a Challenge

4 Comments

By Bruce Edmonds, Gary Polhill and David Hales

(Part of the Prediction-Thread)

There is a lot of pressure on social scientists to predict. Not only is an ability to predict implicit in all requests to assess or optimise policy options before they are tried, but prediction is also the “gold standard” of science. However, there is a debate among modellers of complex social systems about whether this is possible to any meaningful extent. In this context, the aim of this paper is to issue the following challenge:

Are there any documented examples of models that predict useful aspects of complex social systems?

To do this the paper will:

  1. define prediction in a way that corresponds to what a wider audience might expect of it
  2. give some illustrative examples of prediction and non-prediction
  3. request examples where the successful prediction of social systems is claimed
  4. and outline the aspects on which these examples will be analysed

About Prediction

We start by defining prediction, taken from (Edmonds et al. 2019). This is a pragmatic definition designed to encapsulate common sense usage – what a wider public (e.g. policy makers or grant givers) might reasonably expect from “a prediction”.

By ‘prediction’, we mean the ability to reliably anticipate well-defined aspects of data that is not currently known to a useful degree of accuracy via computations using the model.

Let us clarify the language in this.

  • It has to be reliable. That is, one can rely upon its prediction as one makes this – a model that predicts erratically and only occasionally predicts is no help, since one does not whether to believe any particular prediction. This usually means that (a) it has made successful predictions for several independent cases and (b) the conditions under which it works is (roughly) known.
  • What is predicted has to be unknown at the time of prediction. That is, the prediction has to be made before the prediction is verified. Predicting known data (as when a model is checked on out-of-sample data) is not sufficient [1]. Nor is the practice of looking for phenomena that is consistent with the results of a model, after they have been generated (due to ignoring all the phenomena that is not consistent with the model in this process).
  • What is being predicted is well defined. That is, How to use the model to make a prediction about observed data is clear. An abstract model that is very suggestive – appears to predict phenomena but in a vague and undefined manner but where one has to invent the mapping between model and data to make this work – may be useful as a way of thinking about phenomena, but this is different from empirical prediction.
  • Which aspects of data about being predicted is open. As Watts (2014) points out, this is not restricted to point numerical predictions of some measurable value but could be a wider pattern. Examples of this include: a probabilistic prediction, a range of values, a negative prediction (this will not happen), or a second-order characteristic (such as the shape of a distribution or a correlation between variables). What is important is that (a) this is a useful characteristic to predict and (b) that this can be checked by an independent actor. Thus, for example, when predicting a value, the accuracy of that prediction depends on its use.
  • The prediction has to use the model in an essential manner. Claiming to predict something obviously inevitable which does not use the model is insufficient – the model has to distinguish which of the possible outcomes is being predicted at the time.

Thus, prediction is different from other kinds of scientific/empirical uses, such as description and explanation (Edmonds et al. 2019). Some modellers use “prediction” to mean any output from a model, regardless of its relationship to any observation of what is being modelled [2]. Others use “prediction” for any empirical fitting of data, regardless of whether that data is known before hand. However here we wish to be clearer and avoid any “post-truth” softening of the meaning of the word for two reasons (a) distinguishing different kinds of model use is crucial in matters of model checking or validation and (b) these “softer” kinds of empirical purpose will simply confuse the wider public when if talk to themabout “prediction”. One suspects that modellers have accepted these other meanings because it then allows them to claim they can predict (Edmonds 2017).

Some Examples

Nate Silver and his team aim to predict future social phenomena, such as the results of elections and the outcome of sports competitions. He correctly predicted the outcomes of all 50 electoral colleges in Obama’s election before it happened. This is a data-hungry approach, which involves the long-term development of simulations that carefully see what can be inferred from the available data, with repeated trial and error. The forecasts are probabilistic and repeated many times. As well as making predictions, his unit tries to establish the level of uncertainty in those predictions – being honest about the probability of those predictions coming about given the likely levels of error and bias in the data. These models are not agent-based in nature but tend to be of a mostly statistical nature, thus it is debatable whether these are treated as complex systems – it certainly does not use any theory from complexity science. His book (Silver 2012) describes his approach. Post hoc analysis of predictions – explaining why it worked or not – is kept distinct from the predictive models themselves – this analysis may inform changes to the predictive model but is not then incorporated into the model. The analysis is thus kept independent of the predictive model so it can be an effective check.

Many models in economics and ecology claim to “predict” but on inspection, this only means there is a fit to some empirical data. For example, (Meese & Rogoff 1983) looked at 40 econometric models where they claimed they were predicting some time-series. However, 37 out of 40 models failed completely when tested on newly available data from the same time series that they claimed to predict. Clearly, although presented as being predictive models, they could not predict unknown data. Although we do not know for sure, presumably what happened was that these models had been (explicitly or implicitly) fitted to the out-of-sample data, because the out-of-sample data was already known to the modeller. That is, if the model failed to fit the out-of-sample data when the model was tested, it was then adjusted until it did work, or alternatively, only those models that fitted the out-of-sample data were published.

The Challenge

The challenge is envisioned as happening like this.

  1. We publicise this paper requesting that people send us example of prediction or near-prediction on complex social systems with pointers to the appropriate documentation.
  2. We collect these and analyse them according to the characteristics and questions described below.
  3. We will post some interim results in January 2020 [3], in order to prompt more examples and to stimulate discussion. The final deadline for examples is the end of March 2020.
  4. We will publish the list of all the examples sent to us on the web, and present our summary and conclusions at Social Simulation 2020 in Milan and have a discussion there about the nature and prospects for the prediction of complex social systems. Anyone who contributed an example will be invited to be a co-author if they wish to be so-named.

How suggestions will be judged

For each suggestion, a number of answers will be sought – namely to the following questions:

  • What are the papers or documents that describe the model?
  • Is there an explicit claim that the model can predict (as opposed to might in the future)?
  • What kind of characteristics are being predicted (number, probabilistic, range…)?
  • Is there evidence of a prediction being made before the prediction was verified?
  • Is there evidence of the model being used for a series of independent predictions?
  • Were any of the predictions verified by a team that is independent of the one that made the prediction?
  • Is there evidence of the same team or similar models making failed predictions?
  • To what extent did the model need extensive calibration/adjustment before the prediction?
  • What role does theory play (if any) in the model?
  • Are the conditions under which predictive ability claimed described?

Of course, negative answers to any of the above about a particular model does not mean that the model cannot predict. What we are assessing is the evidence that a model can predict something meaningful about complex social systems. (Silver 2012) describes the method by which they attempt prediction, but this method might be different from that described in most theory-based academic papers.

Possible Outcomes

This exercise might shed some light of some interesting questions, such as:

  • What kind of prediction of complex social systems has been attempted?
  • Are there any examples where the reliable prediction of complex social systems has been achieved?
  • Are there certain kinds of social phenomena which seem to more amenable to prediction than others?
  • Does aiming to predict with a model entail any difference in method than projects with other aims?
  • Are there any commonalities among the projects that achieve reliable prediction?
  • Is there anything we could (collectively) do that would encourage or document good prediction?

It might well be that whether prediction is achievable might depend on exactly what is meant by the word.

Acknowledgements

This paper resulted from a “lively discussion” after Gary’s (Polhill et al. 2019) talk about prediction at the Social Simulation conference in Mainz. Many thanks to all those who joined in this. Of course, prior to this we have had many discussions about prediction. These have included Gary’s previous attempt at a prediction competition (Polhill 2018) and Scott Moss’s arguments about prediction in economics (which has many parallels with the debate here).

Notes

[1] This is sufficient for other empirical purposes, such as explanation (Edmonds et al. 2019)

[2] Confusingly they sometimes the word “forecasting” for what we mean by predict here.

[3] Assuming we have any submitted examples to talk about

References

Edmonds, B. & Adoha, L. (2019) Using agent-based simulation to inform policy – what could possibly go wrong? In Davidson, P. & Verhargen, H. (Eds.) (2019). Multi-Agent-Based Simulation XIX, 19th International Workshop, MABS 2018, Stockholm, Sweden, July 14, 2018, Revised Selected Papers. Lecture Notes in AI, 11463, Springer, pp. 1-16. DOI: 10.1007/978-3-030-22270-3_1 (see also http://cfpm.org/discussionpapers/236)

Edmonds, B. (2017) The post-truth drift in social simulation. Social Simulation Conference (SSC2017), Dublin, Ireland. (http://cfpm.org/discussionpapers/195)

Edmonds, B., le Page, C., Bithell, M., Chattoe-Brown, E., Grimm, V., Meyer, R., Montañola-Sales, C., Ormerod, P., Root H. & Squazzoni. F. (2019) Different Modelling Purposes. Journal of Artificial Societies and Social Simulation, 22(3):6. http://jasss.soc.surrey.ac.uk/22/3/6.html.

Grimm V, Revilla E, Berger U, Jeltsch F, Mooij WM, Railsback SF, Thulke H-H, Weiner J, Wiegand T, DeAngelis DL (2005) Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310: 987-991.

Meese, R.A. & Rogoff, K. (1983) Empirical Exchange Rate models of the Seventies – do they fit out of sample? Journal of International Economics, 14:3-24.

Polhill, G. (2018) Why the social simulation community should tackle prediction, Review of Artificial Societies and Social Simulation, 6th August 2018. https://rofasss.org/2018/08/06/gp/

Polhill, G., Hare, H., Anzola, D., Bauermann, T., French, T., Post, H. and Salt, D. (2019) Using ABMs for prediction: Two thought experiments and a workshop. Social Simulation 2019, Mainz.

Silver, N. (2012). The signal and the noise: the art and science of prediction. Penguin UK.

Thorngate, W. & Edmonds, B. (2013) Measuring simulation-observation fit: An introduction to ordinal pattern analysis. Journal of Artificial Societies and Social Simulation, 16(2):14. http://jasss.soc.surrey.ac.uk/16/2/4.html

Watts, D. J. (2014). Common Sense and Sociological Explanations. American Journal of Sociology, 120(2), 313-351.

Edmonds, B., Polhill, G and Hales, D. (2019) Predicting Social Systems – a Challenge. Review of Artificial Societies and Social Simulation, 4th June 2019. https://rofasss.org/2018/11/04/predicting-social-systems-a-challenge

© The authors under the Creative Commons’ Attribution-NoDerivs (CC BY-ND) Licence (v4.0)