The increasing failure of neo-classical economic models
Neo-classical economics focus on what we can measure. Reasonable to a large extent, as we of course wants to do our best to understand progress and predict consequences of actions in order to be able to respond in time to prevent unpleasant developments. The has always been so but the Financial collapse of 2008 has significantly increased the focus and attention on trying to predict and prevent economic problems.
However, the desire to be able to quantify and predict make the econometric models heavily biased and misleading. They and GDP (Gross Domestic Product) do NOT measure value produced and are as thus extremely dangerous to guide policies. What happens is that the models adapt policies to the flawed model assumptions. This facilitate a serious bias towards system preservation and away from policies facilitating true progress and growth.
However unsatisfactory from a position of Economic Management, we truly need to focus more on the complex causality and less on the numbers emerging from biased and increasingly misleading econometric models.
- The main problem - they do not measure value
Economic models that assume GDP express value created in society are utterly wrong.
Not only because some processes are omitted (e.g. voluntary such as friends helping each-other, social such as household or black market selling e.g. homemade products or even shady services such as prostitutions or even criminal activities) or economic elements included wrongly (e.g. the cost of externalities in case of pollution or non-recycling of non-renewable resources)
The real problem is that the very outcome of value chains are grossly and increasingly worse misrepresented by the market price. They simply ignore the Citizen Profit despite this is the fastest growing and accumulating proportion of the economy.
When you work with models that do not understand value, how can you say anything meaningful about quality of investments and Public Choice?
- The Innovation problem
Beyond the value problem is an even bigger problem when talking about predictions.
Innovation and growth is about CHANGE - we only improve by more rational processes, better sustainable resources and better adaption to individual needs. But this means that growth is essentially about change and doing things in new ways!
How can you predict this without simply modelling the assumptions of the claims turning the process of answering and calculating consequences of questions into assuming themselves without anything that resemble science and testable prediction?
- Finally - both these problems are growing in the digitalization phase where change is biggest and individual value (should be) exploding.
The Digital transformation represent a radical departure from previous market processes in many ways.
First and foremost the individual part of any product or service is rapidly increasing and thus so does Consumer Profit. The fight to control individuals though Digital Infrastructure at the expense of Citizen profit is key to the problems of our time.
Second is that change occur on a most faster and continuous basis in many directions at the same time. Digital Value chains reorient and reconfigure themselves much faster, dynamic and in complex ways compared to earlier. This means that not only prices, but the actual structure and dynamics of a market change constantly making models assuming Status Quo increasingly useless to describe and especially predict economic change, Worse the models are often the source of problems.
As a simple example - the financial crisis of 2008 what the result of a massive increase in lending that fueled unsustainable "growth" according to the models. This was the consequence of a double failure - first a political as government globally pushed for growth and pumped liquidity and relaxed restrictions on lending everywhere. Second in the private sector where short-term profit without due consideration as to the massive bubble effects created accumulated a snowball that did with devastating effect.
But fact is that neither governments or market seems to have learned from the mistake. Bubbles are back and e.g. US government debt is exploding. Again model economists try to create "condifence" that this time they have it under control - they haven´t as they simply do not understand what is happening.
We need a Digital Renaissance on economics
The models simply don´t work for their usage and assuming they work is one of our biggest problems creating mistakes from doing more of what created the problems in the first place. Because the models claim that growth is just spending instead of focusing on sustainable value creation.
The root problem is the desire to control. Just like the safe of a Bible defining the World made the Vatican Priests fight so hard with the Inquisition to maintain order in the Universe so does model economic and bureaucrats to force society into their models, however flawed and damaging this process may be.
The outcome is not much different from Communist Chinas Big Leap ending up in hunger and disaster as they simply had no idea as to what the complexity of that they were trying to control. Just like China didn´t blossom until the Bureaucrats released control and European economy grew rapidly after the Reformation, the world today is calling for a release of assumed economic control based on flawed and biased models. (But in all honesty, China also represent the best example against anarchistic laissez-faire, e.g. on environment issues as markets suffer from serious negative externalities unless careful attention to frameworks is politically ensured)
We shouldn´t stop trying to understand and model, but we need to understand that adapting policies to what we know is flawed is worse than releasing controls and focusing on the frameworks of empowered citizens directing value chains to produce Citizen Profit.
In some ways the second Digital Renaissance is a revolt to the first Natural Sciences Renaissance as it has created and environment in which social processes are assumed to be run like a Scientific model - the very failure that former Eastern Europe and many others taught us is very dangerous.
We need to question what we can/cannot model while in parallel focus on the digital frameworks.