Muddling Through Does Not Work for Pandemics: COVID-19 Letter Published in The Financial Times

In 1959, Charles Lindblom wrote The Science of “Muddling Through”, advocating an incremental approach to public policy and management. “Muddling through” does not work for pandemics. The science of pandemics is dominated by epidemiology, not behavioral science. The delay between policy decisions (or indecisions) and the resultant high UK death rate needs tracing back from the prime minister and his advisers to Cobra, the chief scientific adviser, and the Scientific Advisory Group for Emergencies. However, this is one form of contact tracing that can be delayed until the inevitable public inquiry.

Dr Duncan Robertson
Fellow in Management Studies,
St Catherine’s College,
University of Oxford, UK

Charley Says Social Distancing

Charley was a character in UK Public Information Films in the 1970s. Here he is in ‘Charley Says Social Distancing’


“Treat everyone else as if they’re about to catch fire and you’re a lit match.”


Charley: Miaoweeaawwwoa [I can’t believe I’m writing this]

Narrator: This is you. You’re a match. And these are your friends. You catch the virus and you’re on fire. You’re too close – now all your friends are alight – you’ve infected them with the virus.

Tony, the Boy: Charley says that if ever you see a box of matches lying around, tell mummy because they can hurt you.

Narrator: We’d like the virus to spread slowly so to match the capacity of the health service. It takes a few days before your friends get ill, so you can’t see it spreading.

The virus is spreading rapidly so we need to slow it down. If you’re older or have an underlying medical condition, you need to stay apart.

That’s better. Keep apart. Treat everyone else as if they’re about to catch fire and you’re a lit match.

Charley: Awoooarwah.

SOCIAL DISTANCING. DON’T GET TOO CLOSE

For advice on COVID-19, see:HM Government NHS (UK National Health Service) WHO (World Health Organization) US CDC (Centers for Disease Control and Prevention) US NIH (National Institutes of Health)

YouTube Link: https://www.youtube.com/watch?v=4YmN0iV3f0I

Twitter Link: https://twitter.com/Dr_D_Robertson/status/1241307225626423296

I am concerned about the UK Government’s approach to social distancing

The real problem with Coronavirus Covid-19 is that when the health service becomes overloaded, the death rate goes up significantly. So, it is imperative that we keep the number of cases at any one time below or as near as possible to NHS capacity.

The Government’s model relies on shifting the peak. I am not clear that we are doing this fast enough. We are an outlier compared to other countries in our social distancing policy response.

There is a tension between epidemiologists – those who study how diseases spread (a very established science started around the time of the Spanish Flu 100 years ago), and behavioural scientists who study how people behave and react.

Epidemiologists have models validated against past pandemics. Behavioral scientists do not.

There are economic and social costs of social distancing. But if you delay social distancing too much, there are potentially very real human costs in increased mortality. Doctors will need to make very difficult moral decisions on who to treat and who not to treat.

I do not understand why, if the intention is to create herd immunity, why we are not isolating our vulnerable population, especially those in care homes.

Behavioral insights are great when you are trying to get people to pay their tax bills on time. And if people don’t, it doesn’t really matter. With a pandemic, if you get the timing wrong, more people die unnecessarily. Then you look back from your computer and say, yes, we got that behavioral model wrong while doctors and nurses on the front line are exposed to extra cases that could put their own lives at risk.

Here’s a video to show what we should be trying to do:

Social Distancing using Play-Doh and Matches

There has been much talk about using social distancing as a response to the Coronavirus outbreak. Here’s a video of how social distancing can be used:

  • Close Contact, where the population is infected very rapidly. This is not A Good Thing – the national health service is overwhelmed, and the death rate goes up as a result
  • Extreme Distancing, where the viral spread takes too long. This is not A Good Thing – the social and economic costs are huge
  • Optimal Distancing, where the spread is controlled and the human, social, and economic costs are optimized. This is A Good Thing. It is also very tricky to get right.

Coronavirus: The First Big Test of Behavioral Science

The United Kingdom is at a crossroads, an ideological battle between natural science and behavioral science. Let’s hope for all our sakes we get this one right.

Boris Johnson, the UK Prime Minister, is facing a dilemma. When do we go from the so-called containment phase for controlling Covid-19 Coronavirus, to the delay phase.

In the medical / natural science corner, is the Chief Medical Officer, Professor Chris Whitty, who has presented himself calmly, reassuringly, as completely on top of his brief. He is a physician and an epidemiologist (as well as a lawyer, and an MBA). His evidence at the newly formed House of Commons Coronavirus Committee was calm, frank, precise. He is exactly the sort of advisor that any government would be proud to have. Flatten the peak. Delay the virus spread. Keep the height of the peak low. Save lives.

Mitigation efforts like social distancing help reduce the disease caseload on any given date, and can keep the healthcare system from becoming overwhelmed.
Image: New York Times adapted from CDC/Economist https://www.nytimes.com/2020/03/11/science/coronavirus-curve-mitigation-infection.html

In the behavioral science corner is, well, I am not sure who. Maybe it’s the Chief Scientific Advisor, who highlighted the need to take account of behavioral science. Yes, please do. It’s a wicked problem, and please include more complex social modelling.

But what we are now seeing is what the Director General of the World Health Organization (up until now also criticised for its seemingly political response to the issue) could be referring to as ‘alarming levels of inaction’.

I do hope, however, that Boris Johnson is being guided by the science, both behavioral and epidemiological, and not by advisors who profess to be superforecasters. You don’t have to be a superforecaster to forecast that if we get this wrong, many will die unnecessarily.

Contagion: How to Model It and What R-nought (R0) Actually Means

The 2011 film Contagion, starring the spectacularly ill-fated Gwyneth Paltrow, is a dramatization of a viral pandemic starting in pretty analagous circumstances to the current Wuhan Coronavirus (2019-nCoV) outbreak. It’s a good film, and is a great introduction to the work of Centers for Disease Control (CDCs) that monitor the spread – the epidemiology – of the disease. There are two scenes where R-nought, or R0, are described:

Despite the blogger character in the clip describing the spread, using a R0 of 2, as being a problem you can do on a napkin, it takes a little more thinking about. He also seems a bit confused about R0, talking about growth from 2 to 4 to 16, to 256, to 65,536 each day. That’s not what R0 is – it is not a rate, and actually if the rate was 2, this would mean 2 to 4 to 8 to 16 to 32 etc., each time doubling the number. It is possible that he is thinking that there are two generations each per day, but that’s not whatR0 is.

So, on to the professionals:

The CDC epidemiologist in the clip is more on point (despite having sloppy notation with no subscripts). This is better – it shows the reproduction number for the infection – note again, this is not a rate – no time dimension is involved – it basically shows the number of cases on average each case generates.

This population modelling – so called SIR (Susceptible, Infected, Recovered) system dynamics modelling – is just one of several approaches that can be used to model contagion across a population. My recent paper ‘Spatial Transmission Models: A Taxonomy and Framework’ sets out a review of what they are and the advantages and disadvantages of each. In brief, we can model the population numbers, the individual agents that carry the virus, the network of contacts between infected individuals, or the regions or cells in which individuals are located (city districts, for example). The paper is available to read by clicking on the link here.

Techincal Note: Calculating Hamming Distances Between Two Binary Strings in Excel

I haven’t seen anywhere that does this, so here’s how to calculate the Hamming distance between two binary strings in excel.

3-bit binary cube Hamming distance examples
Credit: Wikipedia / en:User:Cburnett

Say you have two binary strings, say 001001 and 100100. How do you calculate their Hamming distance? It turns out it isn’t that easy in Excel, but is possible.

How do we calculate the Hamming distance between 100 and 011 in the cube above, shown as the shortest (red) line joining these two points?

To cut to the chase, the formula for calculating the Hamming distance between strings in cell A1 and B1 is:

=LEN(B1) - LEN(SUBSTITUTE(DEC2BIN(BITXOR(BIN2DEC(B1), BIN2DEC(A1)), LEN(B1)), "1", ""))

BITXOR(A1, B1) sums the difference in bits between the strings in A1 and B1 according to Exclusive OR XOR logic:

INPUTSOUTPUT
ABA XOR B
000
011
101
110

However, Excel needs these numbers as decimals, and the BIN2DEC function converts these binary strings to decimals.

And finally, a trick for counting the occurances of a character in a string:

LEN(A1)-LEN(SUBSTITUTE(A1,”1″,””)) counts the number of 1’s in the string.

As I say, quite a technical note, and I hope useful to people looking at Hamming distances.

Agent-Based Strategizing: New Book Published at Cambridge University Press

My new book, Agent-Based Strategizing, has been published at Cambridge University Press. It is available to download for free until 31 July 2019 at the link below. The book is an overview of how agent-based modelling has been (and can be) used in strategic management.

https://www.cambridge.org/core/elements/agentbased-strategizing/4AD9D0D7416DE46AEB7F1A5478772ACF

Abstract: Strategic management is a system of continual disequilibrium, with firms in a continual struggle for competitive advantage and relative fitness. Models that are dynamic in nature are required if we are to really understand the complex notion of sustainable competitive advantage. New tools are required to tackle challenges of how firms should compete in environments characterized by both exogeneous shocks and intense endogenous competition. Agent-based modelling of firms’ strategies offers an alternative analytical approach, where individual firm or component parts of a firm are modelled, each with their own strategy. Where traditional models can assume homogeneity of actors, agent-based models simulate each firm individually. This allows experimentation of strategic moves, which is particularly important where reactions to strategic moves are non-trivial. This Element introduces agent-based models and their use within management, reviews the influential NK suite of models, and offers an agenda for the development of agent-based models in strategic management.