A large literature, beginning with Coase and extended by Williamson, has explored the determinants of firm boundaries. Since the late 1980s, the dominant expectation has been that successive waves of information technology, which reduce transaction costs, would systematically dissolve the firm in favour of market coordination through a series of bilateral contracts. As with previous generations of IT, AI offers scope for further reductions in transactions costs. Here we argue that AI also generates powerful countervailing forces so that we can meaningfully speak of a Coasean reversal. AI’s cognitive generality creates irreducible complexity of production at the technological frontier. This exceeds the coordinative capacity of external agglomeration, giving firms incentives to internalise Marshallian externalities that were previously accessed through clusters. In addition, the rapid expansion of the adjacent possible destabilises external coordination as contractual counterparties rationally move toward newly accessible opportunities, even for activities that are not themselves inherently complex. We synthesise four foundational contributions—Marshall on agglomeration externalities, Coase on transaction costs, Aoki on the computational theory of the firm, and Kauffman on structured capability spaces and the adjacent possible—to show that the firm, the industrial cluster, and the open market are three expressions of a single ‘economic technology’: coordination for innovative recombination. Their relative effectiveness is governed by the computational complexity of production and the topology of the innovation space. We illustrate the framework with four structural empirical archetypes—agglomeration exploitation, compensatory integration, portfolio balancing, and algorithmic internalisation—that exemplify the distinct strategic pathways the theory predicts.

The literature on the fall of civilizations spans from the archaeology of early state societies to the history of the 20th century. Explanations for the fall of civilizations abound, from general extrinsic causes (drought, warfare) to general intrinsic causes (intergroup competition, socioeconomic inequality, collapse of trade networks) and combinations of these, to case-specific explanations for the specific demise of early state societies. Here, we focus on ancient civilizations, which archaeologists typically define by a set of characteristics including hierarchical organization, standardization of specialized knowledge, occupation and technologies, and hierarchical exchange networks and settlements. We take a general approach, with a model suggesting that state societies arise and dissolve through the same processes of innovation. Drawing on the field of cumulative cultural evolution, we demonstrate a model that replicates the essence of a civilization's rise and fall, in which agents at various scales-individuals, households, specialist communities, polities-copy each other in an unbiased manner but with varying degrees of institutional memory, invention rate, and propensity to copy locally versus globally. The results, which produce an increasingly extreme hierarchy of success among agents, suggest that civilizations become increasingly vulnerable to even small increases in propensity to copy locally.

Economic AffairsVolume 41, Issue 3 p. 506-508 BOOK REVIEW FULLY GROWN: WHY A STAGNANT ECONOMY IS A SIGN OF SUCCESS, by Vollrath, Dietrich University of Chicago Press (2020), 296 pp. ISBN: 978-0226666006 (hb, £20.00); 978-0226666143 (e-book, £10.09) Paul Ormerod, Corresponding Author Paul Ormerod pormerod@volterra.co.uk Volterra Partners and Department of Computer Science, University College London, UK Correspondence Email:pormerod@volterra.co.ukSearch for more papers by this author Paul Ormerod, Corresponding Author Paul Ormerod pormerod@volterra.co.uk Volterra Partners and Department of Computer Science, University College London, UK Correspondence Email:pormerod@volterra.co.ukSearch for more papers by this author First published: 28 October 2021 https://doi.org/10.1111/ecaf.12499Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume41, Issue3October 2021Pages 506-508 RelatedInformation

Urban housing markets, along with markets of other assets, universally exhibit periods of strong price increases followed by sharp corrections. The mechanisms generating such non-linearities are not yet well understood. We develop an agent-based model populated by a large number of heterogeneous households. The agents' behavior is compatible with economic rationality, with the trend-following behavior found to be essential in replicating market dynamics. The model is calibrated using several large and distributed datasets of the Greater Sydney region (demographic, economic and financial) across three specific and diverse periods since 2006. The model is not only capable of explaining price dynamics during these periods, but also reproduces the novel behavior actually observed immediately prior to the market peak in 2017, namely a sharp increase in the variability of prices. This novel behavior is related to a combination of trend-following aptitude of the household agents (rational herding) and their propensity to borrow.

Throughout the COVID-19 crisis, governments have relied heavily on the advice of epidemiologists and health professionals, while economists maintained a low profile in policymaking. This paper illustrates how economic insights could have helped and would be essential in future pandemics. Its focus is on microeconomics and on the fact that when the set of incentives which someone faces changes, individuals are likely to change their behaviour. Governments have failed to appreciate the importance of incentives throughout the pandemic. For example, test and trace has failed due to a lack of understanding of the incentives in a system of self-isolating. Likewise, to maximise the vaccine uptake, governments could pay everyone who gets vaccinated. Governments have also relied too heavily on opinion polls on lockdown. These have consistently shown strong support for restrictions. But economists prefer to rely on preferences revealed by actions, and not on those stated in surveys. The former show less enthusiasm for lockdown, with regulations being widely evaded. A key part of the economists’ policy tool kit is cost-benefit analysis. Studies published using this, by distinguished economists, uniformly suggest that the costs of lockdown exceed its benefits.

Nyman and Ormerod (2017) show that the machine learning technique of random forests has the potential to give early warning of recessions. Applying the approach to a small set of financial variables and replicating as far as possible a genuine ex ante forecasting situation, over the period since 1990 the accuracy of the four-step ahead predictions is distinctly superior to those actually made by the professional forecasters. Here we extend the analysis by examining the contributions made to the Great Recession of the late 2000s by each of the explanatory variables. We disaggregate private sector debt into its household and non-financial corporate components. We find that both household and non-financial corporate debt were key determinants of the Great Recession. We find a considerable degree of non-linearity in the explanatory models. In contrast, the public sector debt to GDP ratio appears to have made very little contribution. It did rise sharply during the Great Recession, but this was as a consequence of the sharp fall in economic activity rather than it being a cause. We obtain similar results for both the United States and the United Kingdom.

SummaryWe compare the trajectory of deaths (both in hospitals and care homes) on a daily basis in Sweden and England and Wales (which constitute 90 per cent of the UK population) from 11 March to 7 August 2020, the latest date at which the relevant data is available for England and Wales.Deaths in both Sweden and England and Wales peaked on 8 April. The build up to the peak was very similar in both. Given the time lag between infection and death, the lockdown would have had little effect on the peak number of deaths.By the first week of August, the deaths are very similar in both. However, from early May the decline in England and Wales has been much sharper.We estimate that to 7 August, lockdown saved 17,700 lives in England and Wales, or just under 20,000 extrapolating to a UK level.

The Economic Policy Uncertainty index had gained considerable traction with both academics and policy practitioners. Here, we analyse news feed data to construct a simple, general measure of uncertainty in the United States using a highly cited machine learning methodology. Over the period January 1996 through May 2020, we show that the series unequivocally Granger-causes the EPU and there is no Granger-causality in the reverse direction

Economists are showing increasing interest in the use of text as an input to economic research. Here, we analyse online text to construct a real time metric of welfare. For purposes of description, we call it the Feel Good Factor (FGF). The particular example used to illustrate the concept is confined to data from the London area, but the methodology is readily generalisable to other geographical areas. The FGF illustrates the use of online data to create a measure of welfare which is not based, as GDP is, on value added in a market-oriented economy. There is already a large literature which measures wellbeing/happiness. But this relies on conventional survey approaches, and hence on the stated preferences of respondents. In unstructured online media text, users reveal their emotions in ways analogous to the principle of revealed preference in consumer demand theory. The analysis of online media offers further advantages over conventional survey-based measures of sentiment or well-being. It can be carried out in real time rather than with the lags which are involved in survey approaches. In addition, it is very much cheaper.

The seminal work on epidemiological models was carried out in the late 1920s and early 1930s. The models have developed substantially since, but their key drivers are those discovered nearly a century ago. Epidemiological models have real scientific value. However, any forecast made with them must rely on assumptions about human behaviour. A crucial one is the extent, to which people who are susceptible to infectious diseases mix socially with people who are already infected. The greater the mixing, the more people will catch the disease. Epidemiology is not about understanding how behaviour might be changed, such that it is different in the future to the past. Economists have expertise in analysing how people change behaviour when either incentives or the set of information they possess changes. Economists have been conspicuously absent from the policy debate over the easing and ultimate ending of lockdown. Yet whether a second wave of COVID-19 occurs depends crucially on assumptions that are made about how people will behave. Economists should become much more active in this area.

Geography makes little use of the concept of equilibrium. Unlike economics, geographical inquiry is based on the recognition of differences and asymmetries among regions and civilisations. In this it does not refer to general mechanisms that would be equivalent to the market for fixing prices and equilibrating supply and demand. Early geographers searched for explanations to the great variety of landscapes and ways of life that were observed all over the planet. Modern geographers study both the ‘vertical’ interactions between societies and their local milieu and the ‘horizontal’ interactions between cities and regions. This involves two opposing causes of territorial inequalities, spatial diffusion of innovation and urban transition. Whereas diffusion of innovation alone might result in homogeneity, combined with the dynamics of city formation the result is increasing heterogeneity and inequality. The phenomenon of increasing returns with city size is explained by higher population densities and connections multiplying the probability of productive interactions, as well as by adaptive valuation of accumulated assets. While there may be great wealth, in some large urban agglomerations large informal settlements of slums and shanties are still expanding. Global societal evolution is an open process with no fixed asymptotic point in the future: there is no final equilibrium state to reach for the world. Open evolution may hamper the quality of predictions that can be made about the future, but geographical knowledge of past dynamics may help to make forecasts more certain. Powerful analytical tools have been developed in the last five or six decades that greatly improve the quality of geographical work and its ability to provide stakeholders and decision makers with clearer insights for exploring possible territorial futures. Geographical Information Systems are now universally used in all kind of administrations dealing with localised services. Detailed geographical information from many data sources enables a shift from a macro-static view to a micro-macro dynamical view that is necessary for management and planning policies in a non-linear world. As a science geography remains deliberately far from equilibrium. D. Pumain (!) Université Paris I, 13 rue du Four, 75006 Paris, France e-mail: pumain@parisgeo.cnrs.fr © The Author(s) 2017 J. Johnson et al. (eds.), Non-Equilibrium Social Science and Policy, Understanding Complex Systems, DOI 10.1007/978-3-319-42424-8_5 71

How one builds, checks, validates and interprets a model depends on its 'purpose'. This is true even if the same model code is used for different purposes. This means that a model built for one purpose but then used for another needs to be re-justified for the new purpose and this will probably mean it also has to be re-checked, re-validated and maybe even re-built in a different way. Here we review some of the different purposes fora simulation model of complex social phenomena, focusing on seven in particular: prediction, explanation, description, theoretical exploration, illustration, analogy, and social interaction. The paper looks at some of the implications in terms of the ways in which the intended purpose might fail. This analysis motivates some of the ways in which these 'dangers' might be avoided or mitigated. It also looks at the ways that a confusion of modelling purposes can fatally weaken modelling projects, whilst giving a false sense of their quality. These distinctions clarify some previous debates as to the best modelling strategy (e.g. KISS and KIDS). The paper ends with a plea for modellers to be clear concerning which purpose they are justifying their model against.

How one builds, checks, validates and interprets a model depends on its ‘purpose’. This is true even if the samemodel code is used for di erent purposes. This means that a model built for one purpose but then used for another needs to be re-justified for the new purpose and this will probably mean it also has to be rechecked, re-validatedandmaybeeven re-built in adi erentway. Herewe reviewsomeof thedi erentpurposes for a simulationmodel of complex social phenomena, focusing on seven in particular: prediction, explanation, description, theoretical exploration, illustration, analogy, and social interaction. The paper looks at some of the implications in terms of the ways in which the intended purpose might fail. This analysis motivates some of the ways in which these ‘dangers’ might be avoided or mitigated. It also looks at the ways that a confusion of modelling purposes can fatally weakenmodelling projects, whilst giving a false sense of their quality. These distinctions clarify some previous debates as to the best modelling strategy (e.g. KISS and KIDS). The paper ends with a plea for modellers to be clear concerning which purpose they are justifying their model against.

From a gene-culture evolutionary perspective, the recent rise in obesity rates around the Developed world is unprecedented; perhaps the most rapid population-scale shift in human phenotype ever to occur. Focusing on the recent rise of obesity and diabetes in the United States, we consider the predictions of human behavioral ecology (HBE) versus the predictions of social learning (SL) of obesity through cultural traditions and/or peer–to–peer influence. To isolate differences that might discriminate these different models, we first explore temporal and geographic trends in the inverse correlation between household income and obesity and diabetes rates in the U.S. Whereas by 2015 these inverse correlations were strong, these correlations were non-existent as recently as 1990. The inverse correlations have evolved steadily over recent decades, and we present equations for their time evolution since 1990. We then explore evidence for a “social multiplier” effect at county scale over a ten-year period, as well as a social diffusion pattern at state scale over a 26–year period. We conclude that these patterns support HBE and SL as factors driving obesity, with HBE explaining ultimate causation. As a specific “ecological” driver for this human behavior, we speculate that refined sugar in processed foods may be a prime driver of increasing obesity and diabetes.

Economic AffairsVolume 38, Issue 2 p. 294-295 Book Review The Tyranny of Metrics by Jerry Z. Muller. Princeton University Press (2018), 240 pp. ISBN: 978-0691174952 (hb, £19.95); 978-1400889433 (Kindle edn, £13.26). Paul Ormerod, Paul Ormerod pormerod@volterra.co.uk Volterra Partners and University College LondonSearch for more papers by this author Paul Ormerod, Paul Ormerod pormerod@volterra.co.uk Volterra Partners and University College LondonSearch for more papers by this author First published: 19 June 2018 https://doi.org/10.1111/ecaf.12293Citations: 1Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume38, Issue2June 2018Pages 294-295 RelatedInformation

In the summer of 2012, I was invited to write a weekly opinion column for City A.M. newspaper. It is a free, business-focused newspaper, launched in 2005, and is distributed at more than 250 commuter hubs across London and the Home Counties, as well as 1,600 offices throughout the City, Canary Wharf and other areas of high business concentration. The general views of the newspaper are broadly supportive of the free-market economy, of capitalism and private enterprise. It is therefore very appropriate that this selection of my columns is being supported and published by the Institute of Economic Affairs. As Allister Heath, editor of City A.M. from 2008 to 2014 and now editor of the Sunday Telegraph, wrote: ‘the IEA is the home of good economic analysis applied to public policy.’ As a description of what I aim to do in my columns, Allister Heath’s statement could hardly be bettered. In the confines of 500 words each week, I try to shed light on a contemporary issue in political economy. I use the phrase ‘political economy’ rather than ‘economics’ deliberately. The great founding figures of the discipline in the late eighteenth and early nineteenth centuries, such as Adam Smith and David Ricardo, regarded themselves as addressing broad questions of public import, rather than being confined to mere technical analysis.

Economic theory has developed a typology of markets which depends upon the number of firms which are present. Much of the literature, however, is set in the context of a given market structure, with the consequences of the structure being explored. Considerably less attention is paid to the process by which any particular structure emerges. In this paper, we examine the process of how different types of market structure emerge in new product markets, and in particular on markets which are primarily web-based. A wide range of outcome is possible. But the uncertainty of outcome of the evolution of market shares in such markets is based, not on the various strategies of the firms. Instead, it is inherent in the behavioral rule of choice used by consumers. We examine the consequences, for the market structure which emerges, of a realistic behavioral rule for consumer choice in new product markets. The rule has been applied in a range of different empirical contexts. It is essentially based on the model of genetic drift pioneered by Sewall Wright in the inter-war period. We identify the parameter ranges in the model in which the Herfindahl-Hirschman Index is likely to fall within the ranges identified by the US Department of Justice: unconcentrated markets; moderately concentrated markets and highly concentrated markets.