A few thoughts regarding the pandemic (2020)

First, the world has become more susceptible to pandemics due to globalisation. Globalisation has made it easier to move goods, services, capital and, notably, people across borders. Equally important, globalisation has increased the variety and intensity of international interconnectedness. Geographic interconnectedness and interconnectedness between various subsystems (e.g. transport, economy, health) creates significant vulnerabilities. It also creates complexity (Page 2007). Interconnectedness, of course, also creates benefits and opportunities. What is incontrovertible is that de-globalization would cause significant economic costs. A more sensible approach to avoiding or managing future global public health crises would be to put in place improved national risk management systems and intensify international cooperation, in addition to creating national-level buffers capable of softening future shocks (Goldin & Mariathasan 2012). 

Second, human intuition finds it difficult to deal with non-linearities. Epidemics exhibit exponential growth. Exponential growth remains a phenomenon that is difficult for humans to grasp intuitively. (Mathematics can help.) Therefore, dealing with phenomena involving exponential growth and non-linear behaviour often leads to major mistakes in decision making (Perrow 1984). This is why policymakers are so often behind the proverbial curve in the face of phenomena such as epidemics. It is also worth recalling that epidemics, like many other phenomena, can be described by a logistic function, which means that growth accelerate and then decelerates before ultimately coming to a halt, whether or not measures were taken to slow it down (Kuchnarski 2020, Smil 2019). Obviously it will be worth bearing this in mind when evaluating the relative success/ failure of policies aimed at fighting the epidemic – and even more so when it comes to debating their relative success/ failure in the (non-scientific) political realm.

Third, there is risk and there is the way risk is perceived. Misperceiving risk often leads people and individuals to make avoidable mistakes. Dread risk, for example, can lead people to make decisions that lead to even greater harm than the original risk/ event that triggered the decision. Second-round effects of catastrophic events can be more devastating than the original events themselves (Gigerenzer 2004). Traffic-related deaths due to increased driving increased sharply following 9/11. Suicides by US veterans allegedly exceed total US combat deaths in Afghanistan and Iraq. Cognitively, the recency effect and availability heuristics drive irrational decisions. It is therefore worth estimating the second-round consequences of the wholesale shutdown of the economy in response to the epidemic relative to more nuanced approach, including risk stratification (Katz 2020). Good decision-making needs to take account of both first- and second-round effects (even if the focus is on health outcomes only). 

Fourth, the Knightian distinction between risk and uncertainty is crucial – epistemologically and psychologically. Uncertainty can be ontological and/ or psychological. Risk is a way to quantify the unknown. Traditional and Bayesian statistics are key to understanding what is going on and to informing policy decisions. The seasonal flu in the US is estimated to affect up to ¼ of the population and lead to up to 50,000 deaths very year. The current policy response seeks to (1) limit the absolute number of infections, (2) limit the rapid increase of total infections in order to prevent the healthcare system from being overwhelmed (and thus failing to save people who could have been saved with adequate healthcare) and (3) delay their spread in the hope that a drug or vaccination becomes available (before a second and third wave come around). The goal is to limit the absolute number of fatalities. The policy is informed by estimated numbers of (1) infections, (2) required hospitalisations and (3) fatalities. While data (though not always very comparable) is available for (2) and (3), this is of limited use as long as policymakers do not know what the number of total infections has been. It is of limited use because the effective fatality (fatalities/ total infections) rate might be higher (and thus justify more aggressive measures) or the effective fatality rate might be lower (and so the present measures may (may) in retrospect look quite severe). (In the face of reliable estimates and in light of the spike of hospitalisations, it is difficult to oppose policies such as shelter-in-place.) Bayesian and classical statistics are essential to help generate more accurate estimates. Initial uncertainty is gradually transformed into quantifiable risk, as both reliability and validity increase and estimates become more accurate. As an aside, uncertainty is experienced as more stressful by individuals (and financial markets) than significant, but quantifiable risk (de Breker et al. 2016). This is one more reason to generate reliable estimates as quickly as possible.

Source: New York Times

Fifth, governments play a pivotal role in addressing public health crises. Competent political decision-makers, a competent bureaucracy and state capacity are crucial in efficiently addressing crises. Transparency and data-driven decision-making can help engender public trust and facilitate the implementation of important policies, especially where their success depends on voluntary compliance. Similarly, a lack of transparency may not only complicate decision-making (e.g. withholding of information by lower-level officials), but public suspicion over official transparency may undermine the effectiveness of policy decisions. A highly centralized political regime has advantages and disadvantages. If it pursues the right policies, it will be more effective than more fragmented regimes (e.g. China vs US). At the same, centralised and uniform magnify policy mistakes, limit opportunities to oppose mistaken policies and limit learning. While democratic regimes may, on average, be more transparent, they do not necessarily have a more competent and capable of bureaucracy (cf. Singapore, Korea, China, US). Similarly, in case of a severe crisis, autocratic regimes may be thought to have an advantage in terms of their ability to take extreme measures. However, most democratic regimes have an extensive arsenal of powers and measures they can resort in the event of a severe national crisis. Admittedly, much of this is speculation. What is needed is a thorough empirical examination of how various regimes types (democracy, centralization, bureaucratic competence/ state capacity) fare in the face of (public health) crises.

Sixth, the rally-around-flag effect and the yearning for a strong leader are very common features of a national crises. This is related to the psychological need for certainty and an abhorrence for uncertainty (see above). It is well-understood that uncertainty tends to lead to anxiety, a feeling of helplessness and even passivity and in people. It is therefore not surprising that people have a psychological need for leadership and authority, especially in moments of heightened uncertainty, and therefore rally around the flag. Intriguingly, even in the case of non-crisis-related epistemic uncertainty, people will tend to look leaders and authority for guidance or, rather, they look to individuals/ institutions that come across as authoritative. Unfortunately, this is also why so many people are prone to fall for the advice of well-dressed investment bankers, eloquent professors and “hedgehogs” appearing on primetime TV, while they really should be listening to the more thoughtful (if complex) arguments and less deterministic/ more probabilistic predictions of “foxes” (Tetlock 2005, Tetlock 2016). A desire for authoritative leadership in situations of uncertainty and increased risk may be hardwired in humans. If it is, it is arguably more likely to stem from humanity’s more recent agricultural rather than our (more egalitarian) hunter-gatherer past (Jathe & Ryan 2010). Perhaps not surprisingly, people are quite willing to accept government guidance in crisis situations. In part, this may be explained by the fact that such guidance can help overcome coordination and collective action problems (Sheppard et al. 2006). Speaking of which.

Seventh, externalities – costs or benefits related an action affecting others – is a key concept in understanding several aspects of the epidemic. For example, young people, less at risk from the epidemic, generate negative externalities for the rest of society by not complying with restrictions on public gatherings. Governments, national or state, buying up healthcare supplies reduce their availability to and/ or increase the price for others. Externalities are also closely related to the problem of collective action and successful cooperation (Olson 1965). In the present situation, coordination and cooperation among individuals or countries would lead to improved aggregate social welfare outcomes but are often undermined by “free-riding”. Furthermore, people who quarantine themselves do so for legitimate reasons. But as long as the fatality risk remains unchanged and a certain share of the population will get affected no matter what (absent effective drugs or vaccines), they are playing a zero-sum game that generates obvious externalities. Effective coordination could involve government-guided risk stratification policies that would help generate better aggregate welfare and health outcomes. If, for example, the herd immunity threshold can be reached, while helping vulnerable groups avoid infection, lives could be saved and aggregate welfare (a horrible economic term in this context) improved.

Ending on a note of a cautious optimism, research suggests that people overestimate the severity and duration of significant events in psychological terms. Crises related to wars and terrorist attacks as well as personal trauma are often experienced as very severe. Empirical research, however, demonstrates that people overestimate the longer-term psychological impact of such events (Brockman 2013). One is tempted to link this empirical fact to the concept of complex adaptive systems. To the extent (sic!) that the international economy, nation-states, societies and cities are complex adaptive systems, they have a tendency to exhibit occasional instability followed by adaptation and survival. Following major instability, they continue to perform many of the same functions, albeit in potentially different ways, as they did before. This time will be no different (Dower 2000, Schivelbusch 2000).

Understanding vs explaining in social research (2020)

The purpose of theory in the social sciences is (1) description, (2) explanation and (3) prediction – or some combination thereof (Singer 1961). Milton Friedman (1953) argues that a good theory is a theory that generates good predictions, that is, predictions that are better than those generated by rival theories. In this view, the purpose of economics and economic theory is prediction, not explanation. In some deeper sense, prediction is explanation. There is nothing over and above explanation than the degree of accurate prediction. Prediction is explaining from such a positivist point of view. Most historians, by contrast, seek to generate descriptions and explanations, but rarely if ever predictions. But what exactly constitutes an explanation?

Traditionally both philosophers of social science and social scientists have distinguished between Verstehen (understanding) and Erklären (explaining) (Hollis & Smith 1991; Hollis 1994). This is a distinction that goes back Wilhelm Dilthey and Heinrich Rickert and was later picked up by Max Weber. At risk of oversimplification, understanding involves the interpretation of human action, while explaining is generally focused on objective or objectifiable social facts. Understanding often makes use of counterfactual theories of causation rather than a deterministic (or statistical) regularity view of causation, as does explanation. Understanding typically involves the meaning of an action from the agent’s point of view and focuses on the subject and subjective meaning. This requires what Weber calls ‘interpretive understanding’ of the world on the part of the analyst. In cultural anthropology and verstehende Soziologie, for example, this approach is associated with empathic understanding (for explanatory purposes) and participatory observation (for descriptive purposes). 

Erklären is generally less comfortable with subjectivity how agents view the world and seeks to make use of objective or quantifiable social facts or objectifiable social phenomena. Understanding looks for reasons, explaining for causes. Understanding is more closely associated with individualism, explaining generally with holism. Understanding frequently uses 'thick description' instead of abstract, controlled quantification. It tends to generate idiographic rather than nomothetic knowledge. Understanding typically features prominently in historical writing, especially the history of unique, exceptional events. Explaining is geared towards regularity and a certain degree of replicability. It is also associated with social science. Historians lean lean towards understanding and social scientists, and especially behaviorists of the Skinnerian persuasion, lean towards explaining. Understanding is associated with Geisteswissenschaft (humanities) and explaining with Naturwissenschaft (natural science). Explaining is tied to positivism and understanding to hermeneutics. Karl Popper (1957) was famously critical of applying natural scientific methods in the realm of human affairs, criticising its as 'the poverty of historicism'.

The symbolic anthropology of Clifford Geertz (1973) would represent one end of the spectrum. Large-N quantitative studies relying on highly aggregated variables the other. Again history as an intellectual pursuit leans towards thick description, meaning and ideas (Carr 1961, Collingwood 1946, Evans 2001, Winch 1958), tough there are notable exceptions such as quantitative history and the Écoles des Annales, not to mention postmodernists. Understanding is often associated with intentionality. According to Weber, for example, intentional actions can be (1) zweckrational, (2) wertrational, (3) affective or (4) traditional. To make sense of individual and social behaviour, an analysts needs to make sense of a agent's actions and how an agent interprets the world. In Weber's view, this allows for ‘explanatory understanding’ and even ‘causal interpretation’ (for a thoughtful discussion, see Nusser 1986). Positivists would take exceptions to assigning causal power how an agent attributes meaning to the world. Even in Weber's verstehender Soziologie, the analysts needs to understand agents' action in light of how they see the world.

Overlaying the understanding/ explaining distinction with the issue of causation complicates the picture further. This is not the place to get into debates about contingency/ necessity and counterfactual/ regularity/ process theories of causation or retrace the debate initiated by Hume, Kant and Descartes. Suffice it to observe that it can be somewhat unsatisfying to identify agency, and especially individual agency, as a cause of a historical or social event (Kincaid 1997). What caused WWII? Adolf Hitler. On the other hand, what 'caused' the building to burn down? An arsonist (as opposed to an electrical short-circuit). Often, and especially in the case of singular, unique and important historical events, one often expects to be given an answer to a why question. An answer to a why question often involves intentionality. In the case of understanding, a reason (motive) can offer a satisfactory answer. In the case of explaining, a cause can offer a satisfactory answer, that is, do without interpretation, meaning and intentionality. Explaining often involves subsuming a case under an observed or established or statistical regularity or general law. (As an aside, this creates a reference problem. True, if a large number of cases is analysed, a multi-factorial regression analysis may provide a satisfactory answer. But if one wishes to explain/ predict a specific event, the outcome is necessarily binary and the probability is either zero or one. An analyst making use of a detailed understanding of a specific case may (may!) be better positioned to make a prediction, even if the epistemological foundation of such a prediction appears weak. Again, this suggests that explaining pursues different epistemic goals than understanding.)

The regularity and the counterfactual theory of causation do different things. Finding regularities and using regularities to make predictions often involve statistical statements. Counterfactual approaches to causation, more compatible with understanding, are capable of dealing with individual factors, events, often individual action as the 'cause' of another event. (Admittedly, this distinction is perhaps somewhat too stark, but not indefensible.) Another related important difference is that the regularity view generates a probabilistic forecast, but does not necessarily identify an underlying causal mechanism (sometimes it does). In case of a counterfactual analysis of causation, an underlying causal mechanism is posited or at least implied. With respect to causal analysis, understanding and explaining approaches tend to differ, too.

Starting from the assumption of purposeful, rational intentions and actions, whatever the ends of the purposed actions (e.g. security gains, economic gains, affective gains), is an approach widely used in policy analysis. Policy analysis is closer to an understanding than an explaining approach. They do however often include, at least implicitly, insights from an explaining approach in terms of regularity. In much of International Political Economy, it is not possible to explain and predict individual policy decision without at least implicit assumptions about the distributional-economic consequences of such decision. Often knowledge about the distributional consequences is nomothetic and is derived from models or statistical analysis (or both). Thanks to game theory, an approach combining understanding and explaining allows for the explanation and prediction of agents' behaviour in an interactive context. If interactive behavior is involved, one needs to determine what type of game the various players play. Do the seek absolute gains or do they seek relative gains, that is, do they engage in a zero- or non-zero-sum game? Microeconomic analysis is a variation of this approach. Where it deals with perfectly competitive markets that lead to the elimination of agents in case of incompatible behaviour, it has clear associations with Darwin's theory of evolution, Waltzian neo-realism and perhaps even more so Gilpinian realism (Gilpin 1981, Waltz 1979, see Wolforth 2011). Bueno de Mesquita (2010) claims that a game-theoretical framework has significant predictive power. One only needs to know the following: Who are key actors? How influential are they? How committed are they to the issue at hand? How resolved are they?  It is often the only sensible approach to take. Simply subsuming a case under a (arbitrary) reference class will generally be of listed usefulness given that each individual case has important idiosyncratic elements that make it difficult to feel comfortable about reference class choice.

Individuals (often) respond to structural incentives and system-level pressures influence behaviour. An agent-centred sociology, as is often used in policy analysis, focuses on the costs and benefits of various courses of actions under given constraints. Rationality and interest maximization offer an explanation, while behaviour incompatible with systemic constraints will prove unsuccessful and disappear. In the case of atomistic, perfectly competitive markets, the constraints are a given. In a less structured context (e.g. oligopolies), the systemic constraints can potentially be manipulated by the agents. The age-old question whether it is the agent or the structure that determines outcomes is akin to the economic question whether supply creates demand or demand creates supply. Anthony Giddens (1984) has proposed the term ‘structuration’. A firm operating in perfectly competitive markets will experience the market structure as given and forces the firm into adaptive behaviour. A firm operating in oligopolistic or monopolistic markets confronts a situation where it can potentially manipulate the systemic constraints it faces (game theory often comes in handy in this situation, analytically speaking). A firm operating in a monopolistic market has even fewer constraints. Systemic constraints can be more or less binding and in some cases they can be manipulated, but not in others. An understanding of an agent's intention and whether an agent acts in zweckrational or wertrational way requires understanding - at least in a particular historical situation. Alternatively, agent preferences can simply be postulated, behaviour measured and then compared whether it matches the model's (explaining theory) prediction.

Much (but not all) said and done. Using an understanding approach to account for individual decisions, idiosyncratic phenomena or unique events often leads to the charge of a 'just-so explanation' at best and a highly subjective, arbitrary, solipsistic story at worst. However, small, random events can and do cause (sic!) large, important events in deterministic (non-linear) systems. And such large-scale outcomes are unpredictable in principle even if the processes that lead from the small, random event to the significant outcome are completely deterministic (Strogartz 2014). Moreover, to the extent that unique events are the effects of a cause at all (something the regularity view of causation/ explanation would deny), such events may also be accounted for by causal complexity. Metaphysically speaking, there is no reason to believe that different combinations of independent variables (or background conditions) cannot have the same effects on a selected dependent variable (Pearl 2018, Ragin 2000). Causal complexity may be difficult to model and prove, but that does not mean that it can be dismissed out of hand. Social scientists have made use of conjunctural  causation for ages (e.g. Barrington Moore, Theda Skocpol and Charles Tilly). The point is that causation is a more difficult concept than positivist and the explainers assume. In short, the explaining camp overreaches when it dismisses the understanding epistemic framework as subjective and non-causal in principle. At the same time, it is far from obvious that an explaining approach relying on quantification and correlation rests on a sounder epistemic foundation given well-known issues related to statistics, correlation and causation.

Blaise Pascal’s suggestion that the history of the world would have been different had it not been for Cleopatra’s nose (Marc Anthony and Caesar falling for her and all that) appears perfectly sensible in light of our most sophisticated scientific models. After all, complex systems and chaos theory do point to the importance of small random events in terms of large outcomes (Miller & Page 2007). Then again, if replicability and regularity are seen as the hallmarks of science, Pascal's insight will not find much resonance with the 'explaining camp'. There is room for understanding and explaining approaches once one reminds oneself that they tend to have different epistemic objectives. Ultimately, they do form a symbiotic relationship as meaning and fact, individual and system, idiosyncratic and nomothetic knowledge are often invariably interconnected. Yet an awareness of how and where they differ undoubtedly makes one a better, more thoughtful and self-aware analyst.

Disease agents, cognitive biases, complex systems & world history (2020)

Complex systems theory and cognitive biases are heuristically useful tools for thinking about the present coronavirus epidemic. Complex systems are commonly characterised by (1) sudden transitions and non-linearities, (2) limited predictability or fundamental uncertainty, (3) large events, (4) evolutionary dynamics (5) self-organisation and (6) emergence. In such systems, small, insignificantly seeming events can cause large events [(1) and (3)]. This is popularly known as the butterfly effect, whereby a small local change (in a deterministic, but non-linear) system can lead to large (non-linear) differences at a later stage (the butterfly flapping its wings in Brazil causes a hurricane in Canada – or something to that extent).

From a reverse angle the butterfly effects turns into a butterfly defect (Goldin & Mariathasan 2014). More concretely, globalization can be seen as having created a complex system with various interdependencies and connectivities. As such, the economy, logistics, health etc. susceptible to small events causing outsized events. In the past, a local disease outbreak was more likely to remain localised. Today a local disease outbreak can spread and cascade very quickly through various interconnected and interacting systems. However, it is also a feature that complex systems are self-organising and adapt. Instead of flying to another continent to meet a client, people video-conference. Instead of importing intermediate inputs and components from a abroad, they begin producing it locally. The system adjusts, albeit at a cost in terms of efficiency, but it adjusts nonetheless. If it does not, it is not a self-organising, adaptive system (Miller & Page 2007).

Commercial Air Traffic, 2009

Undoubtedly, the increased interconnectedness of the contemporary globalised world has created significant risks and vulnerabilities as well as opportunities (Keohane & Nye 1977, Bostrom 2008). Strong interconnectedness creates vulnerabilities, especially if there is a lack of redundancy build into the system. Complex systems theory suggests that diversification can help create greater resilience. If global chip production depends on only one or two countries and moreover on just-in-time supply management, the risk of catastrophic failure increases, especially if no "back-up system" is put in place. Moreover, the interconnectedness of the various sub-systems (production, trade, logistics, health, demand, economy, financial markets and politics) means that small events can cascade throughout other sub-systems making such events potentially even more impactful. This added level of “complexity” makes it even more difficult to predict the behaviour of the system or its sub-systems. It is possible to model the spread of a virus under certain assumptions and assign probabilities to various scenarios. But this is of only limited usefulness when it comes to predicting the reaction of economic agents or financial investors. 

The impact of an epidemic also critically depends on agents' perceptions and especially their perception of risk. The coronavirus epidemic may be example of what Gerd Gigerenzer calls a ‘dread risk’, that is, a low-probability and high-impact event (at the individual level). Dread risk leads people to make mistakes. More people die from lightning than from terrorism, yet people take greater precautions vis-a-vis the risk of terrorism than lightning. Dread risk also related to the well-established availability and recency biases. Because more people avoided flying following 9/11, car accident related deaths increased and outstripped the total number of passengers killed on 9/11 (Gigerenzer 2004). Admittedly, given the initial uncertainty about the potential severity of risk, it may be prudent to over-estimate risk and resort to extreme measures (e.g. quarantines) and accept adverse consequences in other sub-systems (e.g. economic activity). However, once a clearer risk picture emerges, more rational behaviour should emerge at both the individual and government levels. There is an additional problem. Bureaucracies are by nature very risk-averse (Downs 1964) and similarly politicians will tend to err on the side of caution, especially in view of media coverage that favours human interest stories over probabilities and trade-offs. (The media targets the very biases in news consumers that are detrimental to dealing with a situation in a rational way.) Cognitively, availability heuristics lead media consumers to overestimate risks.

Bureaucrats and politicians are bound to act in a risk-averse, loss-avoiding manner - even more so in times of uncertainty (another well-established biases established by prospect theory). This complicates a fully rational, purely economic cost-benefit analysis. The complexities and uncertainty involved in imposing various measures and their unintended, indirect and longer-term consequences further complicate decision-making. Framing is often crucial (Tversky & Kahneman 1986). A purely rational cost-benefit analysis would need to (1) quantify the risks related to the epidemic, including the (economic) costs of fatalities and (2) quantify the costs of various counter-measures. Valuing a human life in monetary terms is emotionally uncomfortable. However, it can be done. Individuals could put a monetary value on their own life (bets, expected value) or insurance companies can calculate the monetary value of an individual in terms of discounted future cash flows. It is an emotionally uncomfortable but relevant question to ask: if it were to be established that the present epidemic is 10x more lethal than seasonal influenza, does this justify taking severe measures to combat the epidemic (BBC March 2, 2020)? And if a 10x higher mortality justifies the measures, would next year's seasonal influenza that is 2x as lethal justify taking the same measures again in spite of immediate economic costs and unintended consequences, including fatalities (e.g. people in quarantine not being able to access critical medication)? This a moral and ethical as well as economic question. It should be part and parcel of a rational public debate and government decision-making.

Historically, catastrophic disease outbreaks have occasionally had massive macro consequences, in addition to the loss of human life. Again the complex systems framework is useful. A small mutation in a relatively harmless virus can have massive, large-scale effects. The Spanish flu, for example, is estimated have killed 10-100 million people in 1918-20. Black death during the middle ages led to a sharp population decline and subsequently led to a rise in labour income and potentially helped reshape the political systems of the early modern era. Malaria may have been an important factor contributing to the fall of the Roman empire (BBC 2017; Harper 2017). Guns, Germs and Steel (Diamond 1999) explains why Western Europe an expansionism was so rapid and how disease agents helped it defeat large empires (see also McMichael 2017). European colonialism killed more indigenous people through diseases than through guns or steel. some studies suggest that disease agents imported by Europeans may have killed 90% of the pre-Columbian population in the Americas or 10% of the world population. It also contributed to global cooling by way of carbon absorption by arable, but uncultivated land following the demographic collapse. This in turn was a factor contributing to subsequent famines and rebellions in Europe and Asia (Koch et al. 2019). By comparison, WWII is estimated to have killed 3% of the global population. (What happened to the Incas might well be what happens to humans once extraterrestrial lifeforms do show up on planet earth.)

The existence of disease agents also helps explain long-term socio-economic processes and outcomes such as economic development. Economists have debated to what extent institutions, culture, natural resource endowment, geography or disease agents have affected countries’ economic development. The existence of certain diseases (e.g. malaria) may have had several detrimental effects. It may have negatively affected local health conditions and the indigenous population. It may also have prevented settler colonialism and favoured extractive forms of colonialism, which, in turn, influenced long-term institutional and economic development through presence/ absence of institutions conducive to economic development (Acemoglu, Johnson & Robinson 2000, Acemoglu, Johnson & Robinson 2001, MacArthurs & Sachs 2001, Sachs 2003). Think New England vs South Carolina, or Bahia vs Rio Grande do Sul, or New Zealand vs DRC). One does not need to take side in this debate in order to appreciate that disease agents can have significant macro-, even world-historical effects even if they, like other explanatory factors, are rarely the only causal factor affecting such outcomes.

The present epidemic is bound to trigger renewed intellectual efforts to understand and deal with future epidemics. Epidemologists and economists should think harder about how to design a close-to-optimal policy responses. How effective are quarantines compared to more voluntary measures to contain the spread of diseases? What are the likely costs and benefits of various measures? Should economic policy be geared towards monetary ro fiscal policy? Should fiscal policy be targeted (and targeting what) or broad? What measures can best help relieve supply chain bottlenecks? Will introducing greater automatic stabilisers help soften the economic downturn? Should or rather will politicians push for broader healthcare coverage in order to reduce the risk of uninsured people by not being able to get medical attention form becoming diseases vectors? How can governments cooperate more effectively at the international level? How can corporates deal more effectively with the risks emanating from inter-connectivity by putting in place more resilient systems and response protocols? Lots of intellectual work to be done.

Global Trade Flows

Last but not least, epidemics cannot be wished away and will continue to occur again and again. While epidemiology is very useful to evaluate the severity of an epidemic (Kucharski 2020) and suggest ways how to deal with it, the relationship between humans and disease agents is a biological arms. In spite of humanity's advanced understanding of micro-biology, the risk of epidemics will persist given the unpredictability of mutations and the lead time that is needed to develop vaccines. And bacteria (and viruses) will not dispels given human's symbiotic relationship with them. Estimates (recently revised) suggest that the human body contains about as many bacteria as human cells (Sender et al. 2016). Bacteria even shape human behaviour in important ways (Enders 2015). In short, once one starts to think of viruses and bacteria, one finds and sees them everywhere. Anthropocentrism is in the end just one particular and not necessarily a heuristically very insightful way of looking at the world - including the social, economic and political world.