SECTION 3 :::::::::::

Herbert Simon. 'Organizations and Markets'

His paper has been used to demonstrate that, for all the rhetoric about free markets, the capitalist system is primarily made up of organizations that plan and allocate goods internally, in ways not directly driven by market forces.

But carried out with greater realism, Simon’s model demonstrates something else: it shows how neoliberalism has opened up the possibility of postcapitalism. Let’s add some detail:

The turnover of each green blob (the organization) determines its size; the money involved in each transaction determines the thickness of the red lines between them.

The blue lines, which show the internal hierarchy of a firm, have to end in dots as well – the workers: baristas, computer programmers, aircraft engineers, shirt factory employees. Simon didn’t feel the need to model workers separately, but we do. Let’s make them blue dots.

To be realistic, each blue dot is also at the centre of a web of thin red lines – connecting each wage earner, as a consumer, to retailers, banks and service companies.

Already, the globe looks a lot redder than Simon originally described it. There are trillions of thin red lines.  

Now let’s add the dimension of time: what happens during a typical twenty-four-hour cycle? If this is a normal capitalist economy we notice the blue dots (the workforce) oscillate in and out of the organizations once a day. As they leave work they start putting out red lines – spending their wages; when they go into the workplace they tend not to – this is a capitalist economy in 1991, remember.

Finally, let’s run the model forward in time, from 1991 to now. What happens to the picture?

First, a lot more tiny red lines appear. A young woman leaves her farm in Bangladesh to work in a factory – her wages generate a new red line; she pays a local nanny to look after her kids, generating a new market transaction: a new red line. Her manager earns enough to start buying health insurance, paying interest to a bank, obtaining a loan to send his son to college. Globalization and free markets generate more red lines.

Secondly, the green blobs split, forming smaller green blobs as firms and states outsource non-core operations. Some of the blue dots turn green – i.e. workers become self-employed. In the USA, 20 per cent of the workforce are now self-employed ‘proprietors’. They too generate more red lines.

Third, the red lines become longer, reaching out across the globe. And they don’t stop when people go to work: buying and selling is now happening digitally, both inside and outside the working day.

Finally, the yellow lines appear.

‘Whoa!’ says the Martian fleet commander. ‘What yellow lines?’

‘It’s interesting,’ says the ship’s economist. ‘We have spotted a whole new phenomenon. The yellow lines seem to show people exchanging goods, labour and services but not through the market and not within typical organizations. A lot of what they are doing seems to be done for free, so we have no idea how thick these lines should be.’

Evgeny Preobrazhensky, the murdered Soviet economist, predicted that as market forces began to disappear, economics would become a discipline for designing the future, not just analysing the past. ‘This is quite a different science,’ he said, ‘this is social technology.’2

There’s a chilling quality to that phrase, conjuring the dangers of treating society like a machine. But Preobrazhensky’s description of the tools ‘social technology’ would use were prescient and subtle. He called for an ‘extremely complex and ramified nervous system of social foresight and planned guidance’. Note the terms: foresight and guidance, not command and control. And note the simile: a nervous system, not a hierarchy. All the Soviets had was command, control and the bureaucratic hierarchy, but we have the network. When it comes to organizing change, the network can function better than a hierarchy, but only if we respect the complexity and fragility that comes with it.

Given that we are decades into the info-tech era, it is startling that – as Oxford maths professor J. Doyne Farmer points out – there are no models that capture economic complexity in the way computers are used to simulate weather, population, epidemics or traffic flows.7

In addition, capitalist planning and modelling are typically unaccountable: by the time a major infrastructure project starts delivering results, ten or twenty years after its impact was first predicted, there is no person or organization still around to draw conclusions. Thus, most economic modelling under market capitalism is actually close to speculation.

So one of the most radical – and necessary – measures we could take is to create a global institute or network for simulating the long-term transition beyond capitalism.

It would start by attempting to construct an accurate simulation of economies as they exist today. Its work would be Open Source: anybody could use it, anybody could suggest improvements and the outputs would be available to all. It would most likely have to use a method called ‘agent-based modelling’ – that is, using computers to create millions of virtual workers, households and firms, and letting them interact spontaneously, within realistic boundaries. Even today such a model would be able to draw on realtime data. Weather sensors, city transport monitors, energy grids, postcode demographic data and the supply chain management tools of global supermarket groups are all giving off relevant macro-economic data in realtime. But the prize – once every object on earth is addressable, smart and feeding back information – is an economic model that does not just simulate reality but actually represents it. The agents modelled virtually are eventually substituted by granular data from reality, just as happens with weather computers.

Once we are able to capture economic reality in this manner, then planning major changes in an accountable way becomes possible. Just as aircraft engineers model millions of different stress loads on the tail-fin of a jet, it would be possible to model millions of variations of what happens if you reduce the price of Nike trainers to a point between their present $190 and their production price, which is likely to be lower than $20.

 In postcapitalism, the state has to act more like the staff of Wikipedia: to nurture the new economic forms to the point where they take off and operate organically.

For twenty-five years, the public sector has been forced to outsource and break itself into pieces; now would come the turn of monopolies such as Apple and Google. Where it’s dysfunctional to break up a monopoly – as for example with an aircraft manufacturer or a water company – the solution advocated by Rudolf Hilferding 100 years ago would suffice: public ownership.

But in the postcapitalist project, the purpose of the basic income is radical: it is (a) to formalize the separation of work and wages and (b) to subsidize the transition to a shorter working week, or day, or life. The effect would be to socialize the costs of automation.

Why pay people just to exist? Because we need to radically accelerate technological progress. If as the Oxford Martin School study suggested, 47 per cent of all jobs in an advanced economy will be redundant due to automation, then the result under neoliberalism is going to be an enormously expanded precariat.

In a modern car factory there is a production line, and there are still workers with spanners and drills. But the production line is intelligently managing what the workers do; a computer screen tells them which spanner to use, a sensor warns them if they pick up the wrong one, and the action is recorded somewhere on a server.

We need to be unashamed utopians. The most effective entrepreneurs of early capitalism were exactly that, and so were all the pioneers of human liberation.

One specific problem is how to record the experience of failure into persistent data that allows us to retrace our steps, amend them and roll out the lessons across the whole economy. Networks are bad at memory; they are designed so that memory and activity sit in two different parts of the machine. Hierarchies were good at remembering – so working out how to retain and process lessons will be critical. The solution may be as simple as adding a recording and storing function to all activities, from the coffee shop to the state. Neoliberalism, with its love of creative destruction, was happy to dispense with the memory function – from Tony Blair’s ‘sofa’ decision-making to the tearing up of old corporate structures, nobody wanted to leave a paper trail.

What happens to the state? It probably gets less powerful over time – and in the end its functions are assumed by society. I’ve tried to make this a project usable both by people who see states as useful and those who don’t; you could model an anarchist version and a statist version and try them out. There is probably even a conservative version of postcapitalism, and good luck to it.