Are We on the Edge of Chaos? Part II — Government
“Real generosity toward the future lies in giving all to the present,” — Albert Camus
Earlier this year I wrote a Medium article, “Are We On The Edge Of Chaos?”. I concluded the article:
“In my opinion, the edge of chaos signals a phase transition to a world where we recognize the urgency of the environmental problems and prioritize natural systems accordingly. W. Brian Arthur, the noted complexity economist, believes that technologies appear to solve the then-current problems. Perhaps the technology paradigm built around AI has appeared because we humans need more tools to address the environmental and wealth inequality problems. I believe we have the tools to address these challenges and the time to do it. I am increasingly worried about whether we have the will.”[1]
Continuing to ponder this situation, I have become increasingly concerned. I am particularly concerned about the role of government in the current period. I am beginning to think we need to redefine democratic government’s role in order for western society to adapt at the speed required. Let me explain.
Santa Fe Institute Professor Tim Koehler researches complexity and complex systems. His research shows:
“that sociopolitical development is dominated first by growth in polity scale, then by improvements in information processing and economic systems, and then by further increases in scale. We thus define a Scale Threshold for societies, beyond which growth in information processing becomes paramount, and an Information Threshold, which once crossed facilitates additional growth in scale.”[2]
Koehler’s first point, “growth in polity scale” shows us that communities evolved into various forms of “polity” or government. This consolidation of interests took place in part because a centralized government could process information more efficiently than individuals. This logic is the same logic that Ronald Coase developed in his Nobel Prize-winning work to explain why corporations emerged. When a network (community) is small and disorganized, a dominant node appears because it can process information more efficiently. Koehler also shows us that systems have a capacity to process information, which limits the scale of processing inputs to produce the desired outputs. As we advance the information processing, we are then able to expand the system. The Computer Age that began in the 1960s would be a proof point. The emerging New Paradigm — artificial intelligence (AI), cloud computing and Internet of Things (IOT) — will again allow us the ability to expand the scale of the system. To keep the math simple, let’s say that the time between the Computer Age and the New Paradigm was sixty years (1960–2020). Now imagine that we have a new technology paradigm every ten years. What would this be like? Imagine going from the horse to supersonic aircraft in ten years. Or, imagine 2020 every ten years. Michael Simmons describes 2020 and a future of new technology paradigms every ten years well:
“We saw once-in-a-generation events in nearly every sphere of life. Each of these events rippled throughout society leading to unpredictable second-order effects which upended our long-held beliefs about media, democracy, business, and citizenship to name a few. Our emotions went from positive to negative extremes as we faced unprecedented opportunities and challenges. We had to fundamentally rethink our lives, relationships, and work.”[3]
If you think this scenario is farfetched, Ray Kurzweil, noted futurist and Google exec, describes the future:
“My models show that we are doubling the paradigm-shift rate every decade.”
What does that mean? It means that our linear, cause-effect model for managing future uncertainty has become outdated by the rate of advances in technology. If we examine the historical linear model, in sixty years we would have one new paradigm. In Kurzweil’s model — in sixty (6x10) years — we have a 64 fold paradigm effect or 2⁶. The advances in technology dictate the rate of change and that change is much faster than we can imagine. The Wikipedia graph below makes the point visually, with the exponential graph in green and the linear graph in red.
Evolution did not prepare us for change at an exponential rate. I have followed Kurzweil’s writings since 2010 when I worked on a project at the MIT Media Lab where he was held in high regard. However, a series of recent developments made me realize that the paradigms are coming faster and at a greater scale. Those developments include:
· In 2019 cloud computing had annual revenues of $227 billion, achieved in only thirteen years[4]
· Using AI and their cloud-based research-as-a-service model, Moderna developed their COVID vaccine in only two months
· GPT3 is an AI text modeler that turns out creative literature comparable or better than most humans
· Data Robot has raised $750 million for the development of their “no code” AI which greatly simplifies the application of AI
As daunting as this exponential change may be, I am more worried about the government’s ability to adapt. I am afraid that government in its current federalist structure will interfere with the natural, self-empowered, adaptive resilience of the citizens. To paraphrase economist Bryan Arthur and many of his colleagues at the complexity research institute Santa Fe Institute, economics has largely ignored the role of energy and information in economic theory development. Whether you agree or not, this framework provides an interesting way to evaluate government. How does government deal with energy and information, the fundamental components of reality. To deal with energy, we might say that government regulates the “tragedy of the commons” for natural and shared manmade resources. Current pollution levels for any traditional energy source would find government remiss. In the case of information, understanding information is more challenging. As Koehler pointed out, a primary function of government is to regulate information. Beginning in the 1700s, with books and newspapers becoming more available, government had a natural advantage to establish economies of scale as the owner (libraries) and source of information (through publications and newspapers). These economies of scale continued until the 1980–1990s period when online information services (AOL, CompuServe) appeared, the Internet gained popularity and Google search changed the world. At that point the citizenry had equal or greater computing power, modern telecommunications networks and worldwide access to information. The government’s advantage in economies of scale in information were gone. Even if you think they were holding on, the government advantage definitely disappeared with the popularity of smartphones and real-time information. The population expected a world running in real-time and not a reaction time that includes analysis, draft legislation, debate and a compromised outcome.
One might argue that organizations like FEMA in the U.S. can respond in real-time. I would ask — how far away from your home is the nearest FEMA warehouse, is the warehouse stocked properly for the next black swan event, who is managing event misinformation on social media? As much as we are challenged by Kurzweil's concept of exponential effects over time, the federal government of the U.S. (except the military and law enforcement) is more challenged to operate in real-time. If we look for government to operate in real-time, we have to opt for a greater reliance on cities and their governments. Cities are locally knowledgeable, resources are close by and the benefits of the community provide a form of operating leverage for the local government. This city model is also consistent with network theory, which says that large, well-connected networks do not have large centralized, hierarchical node structures and tend toward networks of equal size nodes that frequently self-organize. In simpler terms, the Internet makes a shift of power to cities the natural evolution predicted by network theory. The recent U.S. government strategy for AI by the National Security Commission on Artificial Intelligence, The Final Report, anticipates such a shift in importance toward the cities.
A shift in decision making to the cities alone is not sufficient. We must also change the way governments work. Government, whether democratic or dictatorship, has operated on the principle to “not make mistakes”. This “focus on the problem and not the opportunity” increases the likelihood of the current regime staying in power. Problems are a greater risk than an opportunity missed. Therefore, a government does not opt for the most common model in nature — the explore-exploit model. This model is universal in nature and manmade systems and explains, for example, scientific and industrial revolutions and entrepreneurship. The model says that one hypothesizes, tests the hypothesis and keeps proposing new hypotheses until one works well. Such an approach avoids the deadly status quo bias. The method of such interim, iterative efforts by a government apparently does not enhance reelection or fiscal responsibility. However, it does foster new science, technology and entrepreneurship. Interesting contradiction.
One of the great advantages of this iterative model of exploration is that one can begin testing before the solution is completely developed. In this world where McKinsey finds information doubling every eighteen months, information and certainty will never be finalized. To process the magnitude of new information, to process information in real time and to test new solutions before a consensus is formed will require a new philosophy of management in government. This change in thinking may be a more daunting challenge than wealth inequality or the environment.
If change is coming at Kurzweil’s pace, do we want government to stand still and wait until the risk of error is lowest? Or, have we reached the point where we change the 18th century model of government and adopt a model with an iterative process for problem management? How would we structure such a government? I think the answer lies in two changes in city government. First, city governments have to really serve their citizens, but in the 21st century that means collecting and providing data, predictive analytics and information analysis of value to the citizens. This data would inform real-time decision-making by the citizens. Second, each city needs to build a modern IT infrastructure that incorporates the functionality of AI, cloud computing and edge computing through IOT. This infrastructure would be designed to be additive. When a new citizen service is needed, it is just a new module bolted onto the city’s platform infrastructure in the cloud with access to all the data stored there. The diagram below from venture capital firm Andreessen Horowitz gives you an idea of how the city IT infrastructure in the cloud could be organized.
City-states were one of the earliest forms of government to consolidate economic, political and cultural management in a single location. (This model still survives today in Singapore, Vatican City and Monaco.) The advantage of this early model of government was that it formalized the operation of the community to more fairly share community resources and efficiently produce the results desired by the greatest number of citizens. The United Nations predicts that sixty-eight percent of the global population will live in cities by 2050 and eighty-two percent of the population in North America already lives in cities.[5] The city model is time-tested, popular and increasing in popularity as a place to live. Cities offer the benefits of operating in real-time and may even be proven to be more effective than more disbursed populations in managing pollution and environmental risk.
City government has been slow to modernize its IT infrastructure, according to McKinsey, where “half of the respondents are still not using artificial intelligence (AI) anywhere within their organizations.”[6] If cities accelerated this type of infrastructure improvement to a model combining AI, cloud computing and IOT, I believe it would accelerate the shift in power away from the federal government to the cities. As history has shown, the city is a time-tested model. Hopefully it is better positioned to serve the citizens in this “real-time”, exponential rate of change world which we are not yet prepared to live in.
This article benefited from Michael Simmons article, “Google Director Of Engineering: This is how fast the world will change in ten years”.
The views expressed herein are the author’s personal views and do not reflect the views of any organization that employs the author or with whom he is affiliated.
[1] https://roberthhacker.medium.com/are-we-on-the-edge-of-chaos-6ba67987add
[2] https://www.nature.com/articles/s41467-020-16035-9?fbclid=IwAR3IRJcRhSpprYf00TwNepqjZrIXIL_Al-DdHwVci72LhbSf5NkHzt0P2cA#:~:text=We%20find%20that%20sociopolitical%20development,by%20further%20increases%20in%20scale.&text=Polities%20diverge%20in%20socio%2Dpolitical,Threshold%2C%20but%20reconverge%20beyond%20it.
[3] https://medium.com/accelerated-intelligence/google-director-of-engineering-this-is-how-fast-the-world-will-change-in-ten-years-6f1e653b5374
[4] https://www.gartner.com/en/newsroom/press-releases/2019-11-13-gartner-forecasts-worldwide-public-cloud-revenue-to-grow-17-percent-in-2020
[5] https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html
[6] https://www.mckinsey.com/industries/public-and-social-sector/our-insights/accelerating-data-and-analytics-transformations-in-the-public-sector?cid=other-eml-alt-mip-mck&hdpid=4c080e08-fd21-4067-b033-4b575b74e3d7&hctky=10393242&hlkid=67722e8be713480ca021accce59578c1
