The Future of Economics: From Complexity to Commons

Paul B. Hartzog, Futurist

This article looks at three crucial insights for the future of economics: Complex adaptive systems; how technologies of cooperation enable commons-based peer-to-peer networks; and why we need complex adaptive systems to understand new economies


The Edge of Chaos

Complex adaptive systems has enjoyed considerable attention in recent decades. Chaos theory reveals that out of turbulence and nonlinear dynamics, complex systems emerge: order from chaos.

We learned that complex systems are poised on the “edge of chaos” and generate “order for free” (Stuart Kauffman). They are composed of many parts connected into a flexible network. As matter and energy flow through, they spontaneously self-organize into increasingly complex structures. These systems, continuously in flux, operate “far from equilibrium” (Ilya Prigogine). Beyond critical thresholds, differences in degree become differences in kind. “More is different.” (Phil Anderson)

Complexity science reveals the difference between prediction and attraction. We can know that a marble in a bowl will reach the bottom even though we cannot predict its exact path because of sensitivity to initial conditions. Deterministic chaos means path dependence, where future states are highly influenced by small changes in previous states. A typical economic example is the lock-in of the now-standard “QWERTY” keyboard.


We see network effects: adding another node to a network increases the value of all other nodes exponentially, because many new connections are possible, economically “increasing returns to scale” (Brian Arthur). Reed’s Law goes even farther, because new groups can be formed, exhibiting a much greater geometric growth. We know about “small-world,” or “scale-free,” networks, so called because there is no statistic at any scale that is representative of the network as a whole, e.g. no bell-curve average, but instead a “long tail,” mathematically a logarithmic “power law.” Some networks are robust to random failures but vulnerable to selective damage, i.e. network attacks that target nodes with a higher centrality. Furthermore, “centrality” means different things inside different network topologies. Network structure affects the frequency and magnitude of cascades. Like avalanches in sand piles, power laws create “self-organized criticality” (Per Bak).

Information Landscapes

Complex systems constitute “fitness landscapes,” exhibit cycles of growth and decline, are punctuated by explosions of diversity and periods of stasis, and show waves of ebb and flow, seen in traffic patterns. On fitness landscapes, algorithms that pursue merely maximization, without the ability to observe remote information from the landscape, freeze in local optima. Without system diversity, there is no improvement. Swarms escape because they not only read information from the landscape but also write to it, creating shared information environments.

Landscapes and occupants impart selection pressures on each other. Good employees and good jobs both outperform bad ones. Agents and strategies evolve. Adaptation can become maladaptation when selection pressures change.

Dynamics and Time

When we study the spread of disease through a forest we see a slow progression of infected trees. However, when we study the spread of fire, we see the same pattern enacted much faster.

Complex systems and their dynamics are not new. What is new is that human systems have accelerated to the point where political, economic, and social changes now occur rapidly enough to appear within the threshold of human perception. We change from slow social movement to an era of “smart mobs.” Consequently, while it may be true that we did not need the tools of complex systems in the past, because economic change was slow and did not require a dynamical viewpoint, the current speed of economic change demands this new lens.


A crucial global economic phenomenon is the rise of commons-based peer-to-peer networks. “Technologies of cooperation” (Howard Rheingold) enable people to self-organize in productive ways. Open-source software was one first clue to powerful new ways of organizing labor and capital. “Commons-based peer-production” is radically cost-effective (Yochai Benkler). By “governing the commons” (Elinor Ostrom), shared resources managed by communities with polycentric horizontal rules, without reliance on either the state or the market, escape the “tragedy of the commons.” Our thinking about production, property, and even the state, must evolve to reflect the growing participatory economy of global stewardship and collectively-driven “platform cooperatives” (Michel Bauwens). New commons include food, energy, “making,” health, education, news, and even currency.

The rise of 3D printing and the Internet of Things combined with participatory practices yields new forms of value production, paralleling new forms of value accounting and exchange. We witness a “Cambrian explosion” of new currency species, like BitCoin, and innovative trust technologies to support them: the blockchain and distributed ledgers. Just as 20th century electrical infrastructure remained fragmented until standards enabled a connected network (Thomas Hughes), new infrastructure matures when separate solutions merge and the parts reinforce the stability of the whole.


Economics as a discipline can only remain relevant as long as it can provide deep engagement with contemporary reality. Overly-simplified models and problematic axioms cannot guide us forward. The world is an interwoven, heterogeneous, adaptive “panarchy.”

Harnessing complexity requires understanding the frequency, intensity, and “sync” of global connectivity. Analyzing many futures demands better tools. To analyze “big data,” first we need data. Complexity science utilizes multi-agent simulations to investigate many outcomes, sweep parameters, and identify thresholds, attractors, and system dynamics. Complexity methods provide unique metrics and representations, animated visuals rather than static graphs.

This is not just big data; it’s dynamic data. With distributed systems, it becomes peer-to-peer data: shared infrastructure. Just as ants leave trails for others, shared infrastructure bolsters interoperability through a knowledge commons. Restricting connectivity and innovation, e.g. with intellectual property rights, carries extreme costs now. Fitness impedes uncooperative agents and strategies. Fortunately new commons have novel “copyleft” licenses already, promoting fairness and equity.

Complexity science shows us not only what to do, but also how to do it: build shared infrastructure, improve information flow, enable rapid innovation, encourage participation, support diversity and citizen empowerment.

Useful links

Panarchy 101, or How I Learned to Stop Worrying and Love Global Collapse Paul B. Hartzog

The OECD organised a Workshop on Complexity and Policy, 29-30 September, OECD HQ, Paris, along with the European Commission and INET. Watch the webcast: 29/09 morning29/09 afternoon30/09 morning

Smart networks: coming soon to a home near you

The Webcup 1.0 tells your Internet fridge if the milk’s too cold

Today’s post is from Rudolf Van der Berg of the OECD’s Science, Technology and Industry Directorate.

In 2017 a household with two teenagers will have 25 Internet connected devices. In 2022 this will rise to 50, compared with only 10 today. In households in the OECD alone there will be 14 billion connected devices, up from 1.7 billion today and this doesn’t take into account everything outside the household and outside the OECD. All this leads to the smart world discussed in a new OECD publication, Building Blocks of Smart Networks.

The OECD defines “smart” as: “An application or service able to learn from previous situations and to communicate the results of these situations to other devices and users. These devices and users can then change their behavior to best fit the situation. This means that information about situations needs to be generated transmitted, processed, correlated, interpreted, adapted, displayed in a meaningful manner and acted upon.

Smart networks are the result of three trends coming together (and all being studied by the OECD). Machine to Machine communication means devices connected to the Internet (also known as the Internet of Things). This generates “Big Data” because all those devices will communicate and that data will be processed, stored and analyzed. And to enable the analysis, Cloud Computing will be necessary, because when entire business sectors go from no connectivity to full connectivity within a few years, they will need scalable computing that can accommodate double digit growth. Underlying these trends is the pervasive access to Internet connectivity.

smart network devices

New devices connected to the Internet may be invented, but you’ll see that the table only has everyday objects you may already have, but if you replace it in the coming years, the new version will be connected. (The ever-popular, but never seen in a shop near you, Internet connected fridge doesn’t make the list.) Connected lightbulbs may well be the Trojan horse of the smart home. Some companies estimate that connected lightbulbs will be the same price as normal lightbulbs five years from now. These lights will be able to dim and change color and fit in a regular socket. They can also serve as hubs, extending the communication network in the home to all devices.

Connecting machines and devices to telecommunications networks is nothing new. Even at the dawn of the Internet there were Internet connected coffee pots and coke-machines. It is the scale of the trend that forces us to pay more attention. Dutch company TomTom now has millions of GPS-navigation devices on the road, which have generated 5000 trillion data points. When systems need to be smart, the number of datapoints goes up. A dumb electricity meter can do with one reading per year. A smart meter needs a reading every 15 minutes for the electricity company, while for home automation a sampling frequency of once every 1 to 5 seconds is proposed, which could be a 31 million times increase over traditional datasets.

There are, however, challenges that need to be faced when introducing smart systems.

Human challenges. The way people interact with networks and systems may limit their use. For eHealth, smart systems can allow people to lead a normal life. However, a portable heart monitor that sends alarms every time it loses the signal or measures a false positive can have the opposite effect. Privacy and security concerns of users have prompted the Dutch parliament for example to change the rules for smart meters.

Lifecycle challenges. A car should last for 15 years. A mobile phone works for 2-4 years. Mobile phone networks move to new protocols every 15 years. Energy networks have a 15-50 year lifecycle. When a technology is introduced in a vehicle today, the first cars with that technology may reach the end of their lifecycle in 2028, the last ones in 2038. What’s more, if the lifecycles of two distinct sectors meet, the effect can be even more pronounced. Think of the charge point for electric vehicles. It may have to function for 30 years or more, meaning that all vehicles in the coming 30 years will have to be compatible and that the infrastructure needs to be active for another 15 years. Today’s choices for smart systems will be long-term decisions.

Business Challenges. A previous OECD report concluded that users of M2M systems that make use of mobile technology are locked-in with their mobile networks. They can’t change networks and when the devices go across borders they are locked in with their operators. And according to Norwegian research, as many 30% of devices can be offline for 10 minutes per day. To solve these problems the OECD advises governments to change their numbering policies, so that large scale M2M users can become independent of mobile operators and use multiple networks at the same time.

Another business challenge is that it is unclear who has the lead in the smart networks sector. For smart metering, energy companies, meter manufacturers, ICT-companies and telecom companies have all said they will lead.

Regulatory challenges. Governments will be confronted with difficult policy issues, notably concerning privacy and security. A recent review of industrial control systems of five major manufacturers showed that all five could be hacked and sometimes very easily. If companies that supply multi-million dollar systems cannot get essential elements of security correct, than how can you trust systems bought in a DIY store? Would it be possible for a hacker to turn up the airconditioning or heating in a million homes to bring down the electricity grid?

Other questions governments face are regarding access to data. Who owns the data, is it the company or the consumer? If a government collects a dataset, can it share that data for other uses?

Useful links

OECD work on the Internet economy

OECD work on information and communications policy

OECD work on smart sensors

OECD work on smart grids