By Amna Silim
The financial collapse of 2007/08 and the subsequent deep recession and sluggish recovery have left huge scars on the global economy. In the UK, the government is grappling with an unprecedented budget deficit and unemployment is over 1 million higher than it was before the recession. This is a crisis for the real economy and for economic policymakers, but it should also be seen as a crisis for the economics profession and for economic theory. Not only did mainstream neoclassical economics – which has been the overwhelmingly dominant strand in economic thinking for over a century – fail to predict the collapse and recession, its models do not even concede that such events could happen. In the future, there is bound to be more interest in economic theories that offer a better explanation of recent events; and this is where heterodox economics comes in.
The failure to predict or explain the financial collapse and recession has put neoclassical economic thinking in the dock, but such an interrogation is long overdue. Sharp fluctuations in economic growth are just one of the real-world phenomena that traditional economics is poor at understanding. From actual human behaviour through to constant innovation, there is much that traditional economic thinking struggles to explain.
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Neoclassical economic theories describe a world in which rational agents act as optimal decision-makers. Guided by possession of a full set of information, self-interested agents maximise utility while firms maximise profits. As a result, the economy is said to behave in a static and linear manner and the system tends towards a state of equilibrium: supply equals demand and an optimal price is set. Macroeconomic patterns are simply the sum of microeconomic properties (Blanchard 2010).
In this model, economies are not necessarily always in equilibrium; exogenous shocks, such as the development of a new technology, can disrupt them. But these disruptions will be temporary and market mechanisms will work to push the economy back to equilibrium. From a neoclassical perspective, economic development occurs through cyclical patterns of equilibrium, shocks, destabilisation and restabilisation. In each cycle the content of the economy such as the goods and services it offers might change, but its very nature essentially remains the same.
This conventional model can be challenged on four fundamental fronts: the tendency to equilibrium, exogenous shocks, individual rationality and systemic consistency. In the real world, economies are not static and geared towards equilibrium; they are dynamic and in constant flux. This dynamism is endogenous; it originates within the system, not from exogenous shocks. Consumer preferences are not formed by individuals acting solely on their own but are the result of a complex process that includes observing and interacting with other consumers. Economic agents do not have a fixed set of preferences based on rational assessment; they are subject to whims and to mimicking the behaviour of other agents. As a result, the nature of the economic system transforms over time.
In reality, the economy is a complex ecology rather than a complicated machine. It does not respond in predictable ways. It is path-dependent, with each phase building on the previous one. A greater appreciation of this reality has led to the emergence of new schools of thought that are challenging the neoclassical world view and attempting to provide a more realistic understanding of the way economies develop and change.
Complexity, evolutionary and behavioural economics
Various schools of economic thought outside the neoclassical mainstream are often placed together under the banner heading of ‘heterodox economics’. This term is used to describe any innovative way of thinking about the economy, from those that represent complete breaks from the neoclassical approach to others seeking to undermine only some of its main ideas.
In this piece, three strands of heterodox economics are discussed in some detail: complexity, evolutionary and behavioural economics. Each offers different insights into economic analysis by seeking a more accurate representation of the economy, and in so doing opens up new possibilities for policymakers. This essay summarises their basic tenets – and discusses what they might mean for public policy.
Complexity economics challenges fundamental orthodox assumptions and seeks to move beyond market transactions, static equilibrium analysis and homo economicus (the perfectly rational, self interested individuals defined in orthodox economic models). Brian Arthur, Steven Durlauf and David Lane (1997) suggest complexity has six defining characteristics.
- Dispersed interaction: Developments in the economy result from the interaction of heterogeneous agents, whose actions are determined by their environment and by the predicted actions of other agents.
- The absence of a global controller: The economy is characterised by competition and coordination between decision-makers and no single agent is able to exploit all opportunities in the economy.
- A cross-cutting hierarchical organisation: The economy is comprised of many levels of organisation and there are many intertwined interactions that span across all levels.
- Continual adaptation: Decision-makers or agents are continually learning and adapting to their environment, emergent patterns and interactions.
- Perpetual novelty in the system: New niches continually emerge out of new markets, new technologies, new behaviours and new institutions.
- Out-of-equilibrium dynamics: The economy is typically operating far from any equilibrium or optimal output and there is constant improvement.
An alternative definition is based on the observed tendency of the economy to produce dynamic outcomes. Richard Day (1994) argues, for example, that ‘[an] economic system is dynamically complex if its deterministic endogenous processes do not lead it asymptotically to a fixed point, a limit cycle, or an explosion’. In other words, complex systems are non-linear, dynamic and involve continuous adaptation to patterns the economic system itself creates. As a result, these systems are, in contrast to the linear systems described by neoclassical economics, unlikely to rest at a given equilibrium point.
Complexity economics considers the economy to be a ‘complex adaptive system’ in which constant interaction plays a significant role. A complex adaptive system allows for a wide set of interactions between individuals and recognises that an economic actor’s preferences are diverse (Beinhocker 2007). Agents do not just respond to market signals, such as price; they also interact with other agents and this influences their subsequent choices and actions (Arthur 1999). The system is adaptive because agents learn from experience, and from the experience of others, and so gain knowledge they would otherwise have lacked. (In contrast, in traditional economic theory, the economy is populated by ‘representative agents’ or identical decision-makers operating in isolation.) If we accept the existence of these complex and overlapping interactions, this requires us to rethink the equilibrium outcomes that are at the centre of neoclassical assumptions.
In complexity economics, it is accepted that interactions between different actors at the micro level will lead to particular macroeconomic outcomes. Unlike in traditional economics however, the complexity view is that micro- and macroeconomics are not separate fields and macro patterns are not the simple aggregation of the micro decisions of uniform decision-makers (Fontanta 2008). Micro level interactions mean macro patterns cannot be reduced to individual level behaviour; these patterns can only be seen as a whole (Durlauf 2011). Thus, economic growth, for example, cannot be reduced to its individual properties or elements; rather it is a result of various interactions at the micro level (Metcalfe et al 2002).
Furthermore, once a macro pattern has been established, there is nonstop adaptation that leads to a generation of new patterns – emergent phenomena – arising from within the system. This process is referred to as endogenous evolution.
In a complex system, these interactions not only influence macro patterns but also create increasingly complex networks. Economic transactions take place across a range of networks, unlike in traditional models, which assume agents interact only through auctions or oneto-one negotiation (Beinhocker 2007). If agents have the ability to learn and adapt their behaviour accordingly, and alter their preferences and decision-making in an unpredictable manner, they can no longer be seen as rational entities operating with perfect information. In this respect, complexity economics has much in common with behavioural economics, while learning and adapting is central to evolutionary economics.
Evolutionary economics is closely related to complexity economics and, as its name suggests, sees the process of evolution as central to economic developments. Evolution involves endogenous change – a process of selection, adaptation and multiplication (Metcalfe et al 2002). As a result of experience and adaptation, some economic strategies and decisions work and some fail. Those that succeed are scaled up or multiplied; those that fail are cast aside. This process of continuous knowledge gathering and adaptation is driven by feedback mechanisms and the interactions between agents and their environment (Nelson and Winter 1982).
Innovation is central to evolutionary economics and is considered a marker of the capitalist economic system (Lent and Lockwood 2010). Indeed, innovation implies experimentation with new forms of physical technology, social technology and business techniques which – as history tells us – are core drivers of increases in efficiency and productivity, economic growth and the generation of wealth (Beinhocker 2007). This process of selection, adaptation and multiplication also takes place at the firm-level, where there is continual generation and selection of new products and services. The lack of narrative around innovation is one of conventional economic theory’s greatest flaws: indeed, by assuming that economies and firms are in or close to equilibrium, neoclassical models simply overlook the role of innovation in modern capitalism.
Like complex systems theory, evolutionary economics emphasises the crucial role of history in shaping the future. Past interactions and decisions have major impacts on the economy – a characteristic known as path dependence – and any initial small changes in an economy can produce drastic downstream effects, partially driven by networks and cross-cutting hierarchical organisation. Economic outcomes are determined not only by current conditions but also by previous decisions and initial conditions (Durlauf 1997).
If adaptation and innovation are central to the evolutionary economics critique of neoclassical economics, then the psychology of human beings is central to that of the behavioural economists. In short, behavioural science is a combination of psychology and economics that has led to a debunking of the traditional economic assumption of rational, self-interested individuals. This approach explores the limits to human rationality in decision-making. It argues that human agents do not possess the flawless ability to maximise utility or profits by weighing all available alternatives presented to them and that there are flaws and imperfections associated with decision-making (Lambert 2006).
Behavioural economists believe decision-makers exhibit what they call bounded rationality, bounded self-interest and bounded willpower (Jolls et al 1998). Bounded rationality recognises the limitations agents face when it comes to decision-making. Despite any prior intentions to be rational, limited information and other constraints prevent agents from making optimal decisions. In addition, agents are not always selfish, or self-interested: their self-interest is usually bounded by a sense of fairness. And bounded willpower acknowledges that agents at times find it difficult to make decisions that will benefit them in the long term.
Agents and firms rely on decision-making methods that differ from those described in neoclassical economics. Heuristics, framing and loss aversion shape their choices (Thaler and Sunstein 2008). When making decisions, economic agents cut corners. They use rules of thumb (heuristics) rather than gather all the relevant available information (an impossible task anyway); they reach different conclusions depending on how a problem is framed to them; and they avoid taking decisions that might lead to losses (Lambert 2006).
These behaviours characterise the actions of consumers. For example in a study commissioned by the Office of Fair Trading in the UK (2010), price framing was found to heavily influence outcomes. Consumers frequently miscalculated and achieved lower value when purchasing special offers compared to those offered at a simple unit price. They simply assumed that the special offer must be the best deal. Evidence of market inefficiencies like this shows people are not always rational decision-makers in their role as consumers.
Acknowledging the psychology of individuals in decision-making has led to more accurate representations of agents in economic models, thanks in part to behavioural science. These findings are shared by other heterodox economic schools. In models derived from a complexity or an evolutionary economics perspective, therefore, people are not assumed to be rational agents: they factor in the ability of agents to learn and adapt based on past experience and allow for trial and error and flexible behaviour (Nelson and Winter 1982).
To summarise then, complexity, evolutionary and behavioural thinking puts strong emphasis on dynamics, adaptation, psychology, disequilibrium and innovation. Modern economies are complex adaptive systems, rarely tending towards a steady state equilibrium in which supply equals demand and markets clear. Most change occurs endogenously, rather than as a result of exogenous shocks. Economies operate with constant fluctuation and multiple equilibria.
Policymakers operate in a neoclassical framework for the most part. They tend to evaluate various policy interventions by estimating the impact a given policy change might have on the economy and comparing this to what would happen in the absence of that policy being pursued.
Complexity economics on the other hand suggests that since the economy is a complex, adaptive and dynamic system, it is inherently difficult to predict outcomes and responses to particular policy changes (Ormerod 2010a). This presents immediate challenges for policymakers. Predicting future trends is problematic if markets and economies do not return to equilibrium, when agents are not always rational and when uncertainty is in-built into the system.
A deeper understanding of the relationship between macro outcomes and individual decisions is therefore needed for policy formulation. Solutions under complexity tend not to be based on deductive analysis or top-down approaches, but explore interaction and behaviour using a bottom-up approach. This inductive method makes use of empirical analyses such as agent-based modelling (Holt et al 2010) and tends to do away with conventional modelling techniques.
Indeed complexity economists believe emergent phenomena are better understood through computer simulations than through mathematical theorems (Rosser 1999). Computer simulations allow researchers to explore a wide range of possible outcomes (Arthur et al 1997) while agent-based modelling allows us to capture the key features of complex economic systems, in particular the interactions and networks between agents.
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Given the above, Eric Beinhocker (2007) argues that the role of government should start from the premise of seeking to ‘shape the fitness environment’. This would allow free markets to assume their natural role of differentiating, selecting and amplifying successful economic behaviour. But by analysing and monitoring evolutionary processes within the market, policymakers can attempt to influence them so as to better respond to society’s needs. The aim of policymakers should, therefore, be to shape the environment in which plans or projects are more or less likely to succeed or fail according to their ability to meet society’s needs.
An example is the use of carbon taxes. One of the main purposes of a carbon tax is to shift the fitness landscape so that projects and technologies with low emissions have a better chance of succeeding. Here the market is still allowed to differentiate, select and amplify successful plans – but the environment in which the market operates is shaped by government. However, while they can be important in influencing behaviour and market outcomes, carbon taxes and other pricing instruments have their limitations. As Jim Watson argues, carbon pricing assumes that consumers and businesses will react rationally to the price signal. Since complexity economics suggests that this will not always be the case, additional measures may be needed to drive forward the low-carbon transition at a sufficient rate – particularly if the carbon price is set too low.
Policy can also draw from evolutionary economics, for example, by focusing on how selection mechanisms create desirable and socially optimal outcomes. Evolutionary economics sheds light on problems of long-term economic growth (Nelson 2005), environmental change (Faber and Frenken 2009) and regional policy (Boschma and Lambooy 1999), as well as new innovations and technologies, and the effects of technological and social change (Lent and Lockwood 2010).
In particular, evolutionary economics argues that the way to thrive in an evolving and changing economy is to innovate. Perhaps because neoclassical models overlook its role, innovation has rarely featured at the centre of economic policymaking in the UK. Historically, innovation has been patchily applied in the UK as part of growth strategies, and businesses and policymakers have been slow to respond to rapid business transformations. The evolutionary approach, however, suggests innovative business activity should be actively encouraged. Indeed, as Adam Lent and Matthew Lockwood (2010) argue, the UK’s growth strategy would greatly benefit from placing innovation at its core.
Methods from evolutionary economics have also been used to inform approaches to international development. Richard Nelson (2005) suggests moving away from the overly rigid neoclassical prescriptions of simply increasing investment in human and physical capital in developing countries and towards greater learning and innovation. This would involve learning how other countries have advanced their economy and gaining the knowledge of how modern technology can be used most effectively in achieving desired economic outcomes (Reinert 2006). In an earlier article with Sydney Winter (1982) Nelson argued that ‘flexibility, experimentation, and ability to change direction as a result of what is learned are placed high on the list of desiderata for proposed institutional regimes’.
Crucially, policymaking from an evolutionary economics perspective recognises that the state is limited by the same factors facing agents: it is not, and cannot be, in possession of a full set of information. Therefore, the state must be willing to learn from experience and adapt its approaches. Policymaking needs to be more flexible and willing to break with organisational routines.
While complexity and evolutionary economics have struggled to get a foothold in policymaking to date, many governments have begun to reflect on the analysis of behavioural economists when exploring policy interventions. In the UK, for example, the government set up in July 2010 a dedicated Behavioural Insights Team (also known as the ‘Nudge Unit’), tasked with assessing potential policy interventions through the lens of behavioural thinking. In particular, it is seeking to use what is referred to as ‘choice architecture’ to evaluate the impact that framing details in different ways can have on how people make decisions. Choice architecture has already been applied and proved to be effective across a number of areas, including savings for pensions. While most people understand that pensions offer substantial rewards in the future for a relatively modest sacrifice made in the present, enrollment in voluntary schemes tends to be at a low level. Changing the rules so that workers must ‘opt out’ rather than ‘opt in’ to pension schemes has been found to significantly increase participation.
As the title of Richard Thaler and Cass Sunstein’s influential book (2008) implies, small changes of this sort can ‘nudge’ people to make better decisions about their health and financial wellbeing. Libertarian paternalism has the potential to create better outcomes, while retaining people’s right to choose. What is more, change can often be brought about at little to no cost; simply paying more attention to framing a particular choice may have a greater chance of achieving the desired outcome. As a result, behavioural concepts are being progressively incorporated into policies in many areas including environmental change, finance, international development, healthcare and competition policy. But, as Paul Ormerod has argued elsewhere (2010b), successful ‘nudges’ must also be grounded in an awareness of the network effects that influence an individual’s choices and behaviour and how this can change over time: without this understanding, nudges may fail in the same way as conventional command and control policies.
The neoclassical economic model is based on a series of simplifying assumptions that result in a poor representation of the real world. New schools of economic thought are emerging built on a more accurate analysis of the way economic agents behave and the way decisions are really made. These heterodox schools of economic thought dismiss notions of rational economic agents and profit-maximising firms in favour of a greater focus on psychology, interactions and history.
Complexity economics emphasises the power of networks, feedback mechanisms and the heterogeneity of individuals. Evolutionary economics is centred on the ideas of continuous adaptation and the creation of novelty; it recognises the key roles of innovation, selection and replication in the economy. And behavioural economics seeks to understand how and why individuals behave as they do, rather than assuming that they act like the robotic homo economicus of the neoclassical textbook.
Already these approaches are beginning to help us understand some of the economic anomalies that orthodox economics cannot explain. As they develop in the future and the appetite for new economic thought grows, our understanding of the economy – and our economic policymaking – can only be improved.
Excerpt from Complex New World: Translating new economic thinking into public policy, published by the Institute for Public Policy Research (IPPR).
Arthur B (1999) ‘Complexity and the Economy’, Science, 284: 107–109
Arthur B, Durlauf S and Lane D (eds) (1997) The Economy as an Evolving Complex System II, Redwood: Addison Wesley
Beinhocker E (2007) The Origin of Wealth: Evolution, Complexity and the Radical Remaking of Economics, London: Random House
Blanchard O (2010) Macroeconomics, Harlow: Prentice Hall
Boschma R and Lambooy J (1999) ‘Evolutionary economics and economic geography’, Journal of Evolutionary Economics, 9: 411-429
Day R (1994) Complex Economic Dynamics, Volume I: An Introduction to Dynamical Systems and Market Mechanisms, Cambridge, MA: MIT Press
Dosi G and Nelson R (1994) ‘An Introduction to Evolutionary Theories in Economics’ Journal of Evolutionary Economics, 4: 153–172
Durlauf S (2011) ‘Complexity, Economics, and Public Policy’, working paper, Madison: University of Wisconsin
Durlauf S (1997) ‘What should policymakers know about economic complexity?’ The Washington Quarterly, 21: 157–165
Fontana M (2008) The Complexity Approach to Economics: a Paradigm Shift, Turin: Dipartimento di Economia, University of Turin
Faber A and Frenken K (2009) ‘Models in evolutionary economics and environmental policy: towards an evolutionary environmental economics’, Technological Forecasting and Change, 76: 462–470
Holt J, Rosser B and Colander D (2010) ‘The Complexity Era in Economics’, Middlebury College Economics discussion paper no 10-01, Middlebury: Middlebury College
Jolls C, Sunstein C and Thaler R (1998) ‘A Behavioral Approach to Law and Economics’, Christine Stanford Law Review, 50: 1471
Lambert C (2006) ‘The Marketplace of Perceptions’, Harvard Magazine, March–April 2006
Lent A and Lockwood M (2010) Creative Destruction: Placing Innovation at the Heart of Progressive Economics, London: IPPR
Metcalfe J, Fonseca M and Ramlogan R (2002) ‘Innovation, Competition and Growth: Evolving Complexity or Complexity Evolution’, Revista Brasileira de Inovação, 1: 85–122
Nelson R (2005) ‘Where Are We Now on an Evolutionary Theory of Economic Growth, and Where Should We Be Going?’ CCS working paper no 3, New York: Columbia University
Nelson R and Winter S (1982) An Evolutionary Theory of Economic Change, Cambridge: Harvard University Press
Nelson R and Winter S (2010) ‘Evolutionary Theorizing in Economics’, Journal of Economic Perspectives, 16: 23–46
Office of Fair Trading (2010) The impact of price frames on consumer decision making, London: Office of Fair Trading
Ormerod P (2010a) ‘Economics, management, and complex systems’ in Peter A, Maguire S and McKelvey B (eds), The Sage Handbook of Complexity and Management, London: Sage Publications Ltd
Ormerod P (2010b) ‘N-squared: Public policy and the power of networks’. http://www.thersa.org/ data/assets/pdffile/0003/330258/RSAPamphlet-publicpolicy.pdf
Reinert E (2006) ‘Evolutionary Economics, Classical Development Economics, and the History of Economic Policy: A Plea for Theorizing by Inclusion’, working papers in Technology Governance and Economic Dynamics 01, Tallin: TUT Institute of Public Administration
Rosser J (1999) ‘On the Complexities of Complex Economic Dynamics’, Journal of Economic Perspectives, 13(4): 169–192
Thaler R and Sunstein C (2008) Nudge: Improving Decisions About Health, Wealth, and Happiness, London: Yale University Press
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