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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”

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)

What can and cannot be feasibly modelled of the Covid-19 Pandemic

By Nick Gotts

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

The place of modelling in informing policy has been highlighted by the Covid-19 pandemic. In the UK, a specific individual-based epidemiological model, that developed by Neil Ferguson of Imperial College London, has been credited with the government’s U-turn from pursuing a policy of building up “herd immunity” by allowing the Sars-CoV-2 virus to spread through the population in order to avoid a possible “second wave” next winter (while trying to limit the speed of spread so as to avoid overwhelming medical facilities, and to shield the most vulnerable), to a “lockdown” imposed in order to minimise the number of people infected. Ferguson’s model reportedly indicated several hundred thousand deaths if the original policy was followed, and this was judged unacceptable.

I do not doubt that the reversal of policy was correct – indeed, that the original policy should never have been considered – one prominent epidemiologist said he thought the report of it was “satire” when he first heard it (Hanage 2020). As Hanage says: “Vulnerable people should not be exposed to Covid-19 right now in the service of a hypothetical future”. But it has also been reported (Reynolds 2020) that Ferguson’s model is a rapid modification of one he built to study possible policy responses to a hypothetical influenza pandemic (Ferguson et al. 2006); and that (Ferguson himself says) this model consists of “thousands of lines of undocumented C”. That major policy decisions should be made on such a basis is both wrong in itself, and threatens to bring scientific modelling into disrepute – indeed, I have already seen the justified questioning of the UK government’s reliance on modelling used by climate change denialists in their ceaseless quest to attack climate science.

What can social simulation contribute in the Covid-19 crisis? I suggest that attempts to model the pandemic as a whole, or even in individual countries, are fundamentally misplaced at this stage: too little is known about the behaviour of the virus, and governments need to take decisions on a timescale that simply does not allow for responsible modelling practice. Where social simulation might be of immediate use is in relation to the local application of policies already decided on. To give one example, supermarkets in the UK (and I assume, elsewhere) are now limiting the number of shoppers in their stores at any one time, in an effort to apply the guidelines on maintaining physical distance between individuals from different households. But how many people should be permitted in a given store? Experience from traffic models suggests there may well be a critical point at which it rather suddenly becomes impossible to maintain distance as the number of shoppers increases – but where does it lie for a particular store? Could the goods on sale be rearranged in ways that allow larger numbers – for example, by distributing items in high demand across two or more aisles? Supermarkets collect a lot of information about what is bought, and which items tend to be bought together – could they shorten individual shoppers’ time in the store by improving their signage? (Under normal circumstances, of course, they are likely to want to retain shoppers as long as possible, and send them down as many aisles as possible, to encourage impulse buys.)

Agents in such a model could be assigned a list of desired purchases, speed of movement and of collecting items from shelves, and constraints on how close they come to other shoppers – probably with some individual variation. I would be interested to learn if any modelling teams have approached supermarket chains (or vice versa) with a proposal for such a model, which should be readily adaptable to different stores. Other possibilities include models of how police should be distributed over an area to best ensure they will see (and be seen by) individuals or groups disregarding constraints on gathering in groups, and of the “contagiousness” of such behaviour – which, unlike actual Covid-19 infection events, is readily observable. Social simulators, in summary, should look for things they can reasonably hope to do quickly and in conjunction with organisations that have or can readily collect the required data, not try to do what is way beyond what is possible in the time available.

References

Ferguson, N. M., Cummings, D. A., Fraser, C., Cajka, J. C., Cooley, P. C., & Burke, D. S. (2006). Strategies for mitigating an influenza pandemic. Nature, 442(7101), 448-452. doi:10.1038/nature04795

Hanage, W. (2020) I’m an epidemiologist. When I heard about Britain’s ‘herd immunity’ coronavirus plan, I thought it was satire. The Guardian, 2020-03-15. https://www.theguardian.com/commentisfree/2020/mar/15/epidemiologist-britain-herd-immunity-coronavirus-covid-19

Reynolds, C. (2020) Big Tech Fights Back: From Pandemic Simulation Code, to Immune Response. Computer Business Review 2020-03-15. https://www.cbronline.com/news/pandemic-simulation-code.


Gotts, N. (2020) What can and cannot be feasibly modelled of the Covid-19 Pandemic. Review of Artificial Societies and Social Simulation, 29th April 2020. https://rofasss.org/2020/04/29/feasibility/


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