Guest post by Corinna Elsenbroich & Gary Polhill
Should we ask people to stay at home during a pandemic?
Or just let the disease run its course?
The COVID-19 crisis forced governments to make difficult decisions at short notice that they then had to justify to their electorate. In many cases, these decisions were informed by computer simulations.
An advanced kind of computer simulation, known as agent-based modelling, proved particularly helpful in evaluating different options where it was used. In agent-based models, there is a virtual representation of an artificial population of human beings, each so-called ‘agent’ going about its simulated daily life, and, critically, affecting, and being affected by, other agents.
So, if one agent becomes “infected”, and spends too long near another agent not yet immune, then the computer simulation can “infect” the other agent. Furthermore, agent-based models can simulate social networks, families, friends, work colleagues, and take into account which people are likely to spend too long near another to transmit infections. Agent-based models can also simulate interactions with wider social environments. If one agent not wearing a mask finds themselves in an area where all the other agents are wearing masks, the simulated agent can decide whether to put their mask on (by allowing themselves to be influenced by the social norm), or remain mask-free (because their identity outweighs the norm, or because they cannot wear a mask for medical reasons).
Each agent has their own ‘story’, and the computer can simulate how these stories intertwine to form the narrative of the artificial population’s interaction with a communicable disease and measures to prevent its spread.
The pandemic was a vivid example of the challenges of governing complex systems. Complex systems are studied by scholars in various disciplines, including mathematics, physics, economics, sociology, computer science, geography, ecology and biology. They are fundamental to life, from the cellular to international relations levels, and as fascinating as they are challenging. The reasons why they are called ‘complex’ are the reasons that make them difficult to govern. Some of these reasons include:
- They are ‘nonlinear’. Using some made-up numbers for the purposes of illustration, nonlinearity means that if a government spends £1Bn to save the first 100,000 lives, they might have to spend £5Bn to save the next 100,000, but only £500M for the 100,000 after that. Nonlinearity is challenging mathematically; a lot of ‘classical’ mathematics (including a 200-year-old algorithm now laughably rebranded as ‘machine learning’) assumes linearity. It is from nonlinearity that we get the concept of a ‘tipping point’: the difference in habitability between 1C and 1.5C of global warming is not the same as the difference between 1.5C and 2C.
- They have ‘fat-tailed’ distributions. A mathematical law called the ‘central limit theorem’ is often used to justify assuming everything has a normal distribution. Because of this, a lot of statistics is focused on working with that distribution. In complex systems, however, the law of large numbers, on which the central limit theorem depends, does not always apply. Distributions can have ‘fat-tails’, meaning that the probabilities of extreme events are higher than if a normal distribution is assumed. Underestimating the probability of an extreme event is risky for a government, and potentially fatal to some of its population.
- They are sensitive to local circumstances. Mathematicians call this ‘non-Markovian’ or non-ergodic, and again, find themselves unable to rely on a large body of work that can be applied very successfully when there is not such sensitivity. The practical outcome is that a policy that works in one place may not work in another.
- They are not at equilibrium. Even now, for some ecologists and economists, the assertion that living systems are not at equilibrium is controversial. Systems apparently remaining in similar (or cycling) states is instead referred to in complex systems language as ‘homeostasis’. The important difference with equilibrium is that homeostasis requires energy, and so by definition is not at equilibrium. For example, your body tries to maintain its blood temperature at the same level (around 36.5C), but has different mechanisms to do this depending on whether the weather is hot or cold, and dry or humid. Mathematically, not being at equilibrium means that calculus becomes a less useful tool. For government, it may mean that after a perturbation, a society will not necessarily return to the way it lived before.
- They are evolutionary. Complex systems can adapt, innovate and learn. This means that a measure that worked historically may not work now. Indeed, even the language used to describe what people do and how they differ can change. In medical circles, we no longer speak of ‘humours’ or ‘miasmas’, but of white blood cells, bacteria and viruses, and their mutations and variants.
Agent-based modelling grew out of studying complex systems as a way of helping scientists understand them better. But that has not led to the community of practitioners being as willing to use their agent-based models to make predictions. Quite the opposite, in fact. Many practitioners, on the basis of their understanding, regard prediction in complex systems as impossible, and point to other important and useful applications of agent-based models.
All these challenges to classical mathematics make prediction in complex systems much harder. Even those who don’t regard prediction as impossible use guarded language like ‘rough forecasting’, or ‘anticipated outcomes’.
However, claiming that prediction is impossible does not help the policy-maker decide what to do about a pandemic, nor to justify the expense and curtailment of liberties to the people. Worse, there is still a significant community of researchers quite willing to ignore complexity altogether, and to apply methods to make predictions and claim them as such that rely on assumptions that are false in complex systems. (In some circumstances, over short time periods, these methods can work because complex systems don’t always behave in complex ways.) Agent-based models have been argued to have an important role in helping people make decisions in complex systems.
It might be that agent-based modellers need to find ways of participating in discussions about governing complex systems, in circles where prediction is part of the narrative, while still being true to their understanding. Rather than remaining a taboo, prediction is something agent-based modellers need to face. In a special issue of the International Journal of Social Research Methodology, we have collected contributions that aim to open up a conversation about prediction with agent-based models. They reflect a diversity of opinion as varied as the backgrounds of people in the community of practitioners.
Our beleaguered global governments, wearily emerging from the pandemic, find themselves facing an escalated war in Europe, polarized societies, economic instability, persistent misinformation spread on social media, a sixth mass-extinction, and ever-more frequent extreme weather events. Each of these issues is complex, multidimensional and multi-scale, and any solution (including doing nothing) has uncertain, unintended, cascading consequences. If agent-based modelling can help with such challenging decision-making, then it should.
The full editorial Agent-based Modelling as a Method for Prediction for Complex Social Systems is freely available International Journal of Social Research Methodology
Corinna Elsenbroich is Reader of Computational Modelling in Social and Public Health Science at University of Glasgow. Follow @CElsenbroich on Twitter and read more research via ORCID
J. Gareth Polhill (known as Gary Polhill) is a Senior Research Scientist in the Information and Computational Sciences Department at The James Hutton Institute. Follow @GaryPolhill on Twitter and read more research via ORCID