5 suggested common themes for an Economics that takes its subject matter seriously
from Bruce Edmonds
This is another contribution under the theme of Geoff Davies’ essay “The Nature of the Beast”, which considers the question of how do we build a narrative linking the various heterodoxies. 5 possible uniting themes are suggested.
1. Economic phenomena are social phenomena
Economics involve all sorts of intelligent, social and adaptive behaviour including: social norms, fashions, identity, context-dependency, trust, friendship etc. Economic exchange can involve all these things, is embedded in our social and cultural life and can often only be fully understood in its social context. The deliberate exclusion of “non economically rational” elements of sociology and psychology is simply not supported by evidence. Economic phenomena is just that part of social phenomena which involves the exchange or transfer of items of value. Social behaviour undoubtably came before economic behaviour in the development of humankind, it is the more fundamental category.
2. Evidence should be paramount
Although all science involves theorising and confronting theory with evidence, evidence should be the ultimate winner of any dissagreement between them. The balance in the physical sciences is more towards the evidential end of the representional spectrum, with many researchers making careers in collecting evidence and in inventing new ways of collecting evidence. A shift back towards evidence and away from theory needs to occur in Economics. It should be a fundamental principle that evidence is never ignored without a very good reason for doing so. This means various kinds of evidence should be taken seriously, including the narrative evidence collected in the “softer” of the social sciences (e.g. ethnography).
3. Accepting the complexity of Economic phenomena
Social phenomena are very complex. Whilst there are sometimes identifiable broad trends, this should not be mistaken for understanding. Many complex systems exhibit broad trends in overall behaviour but can still surprise us when the circumstances change – we should expect economic systems (e.g. real markets) to be the same. A change of expectations to the phenomena we seek to understand is in order – away from looking for a “clever” simple model/mechanism that will explain a wide class of phenomena towards dealing with the contingent, context-dependent, complex, various phenomena we observed. Economics might turn out to be more like zoology than theoretical physics. This is indeed a dissapointment, but simply ignoring the difficulty of our subject matter will not facilitate real progress. A mature science seeks to limit claims beyond its capacity, even in policy advice and grant proposals.
4. Rejecting analytic formalism where this distorts good analysis
(i.e. using appropriate tools)
Following from the above, there is no reason (apart from blind optimism) to hope that formal models that are adequate to our modelling goals and the phenomena we face will be at all simple, and certainly not analytically tractable. Previously there was no choice if one wished for a formal model, but we now have the option of simulation models – there is now simply no need to distort our phenomena to make it fit the tool of analytic mathematics. Analytic models that require assumptions that are implausibly strong or (worse) there is evidence against should simply be binned. (This does not mean there is not role for analytic maths, for example it can be used to check and analyse the properties of complex simulation models). Precise models that can be replicated and asnalysed are important to science, but there is simply no longer any need to use the wrong tool.
5. Recognising the need for clusters of related models of many kinds and levels
Following on from the last point, we are faced with a dilemma – complex models that relate more directly to what is observed but are hard to understand and analyse (i.e. relevance); or simpler models that dont relate to observations (at best to our ideas about what we observe) but that can Social phenomena are not only complex but that can be thoroughly understood (i.e. rigour). The truth is we need both rigour and relevance, which means we will not achieve this using one model or one technique. Rather we will have to make do with “clusters” of related models capturing the phenomena – different aspects, at different granularities, and at different levels of abstration. So, for example, we might acquire a series of representations of evidence from many different sources (ethnographic, statistical, social network, interviews, observations, lab experiments etc.), theser might be related to complex “data integration” simulation models that are consistent with as many of these as possible. Then this complex simulation might be a safe target for simplification and abstraction in other, simulation and alaytic models, since these can be adequately tested for relevance against the simulation model they are about.
Complex, relatively rich, but precise representations (records of evidence and data-integration simulation models, etc.) might be the closest thing to a common language for relating the multifarious stands of a wider economics that takes its subject matter seriously.