TechnologyINSIGHTS | GLOBAL

Today we find ourselves, as a society, at a unique point in history.

For the first time, technological opportunity is sufficient for our

innovation appetite. The reason: Specific changes on our horizon are of

such great proportion that regulatory, social and economic conventions

need to catch up with technology advances before these systems

can become market standard. Many organizations, corporations and

governments are attempting to prepare themselves for the future by

trying to predict outcomes. However, I think we can do better by focusing

on key friction areas — the barriers to getting there.

This paper identifies four friction areas that must be addressed along

our journey towards an autonomous economy. Using understandable

analogies, I write about the progression from “smart” economies

to “autonomous” economic systems by describing both the systems

themselves and the paths taken in developing them. I explain innovation

as an entropic force and that, despite those frictions, the autonomous

economy is on its way. And it will be here sooner than we may think.

Autonomous Economy: Coming Sooner Than You Think

Yoav Intrator, Ph.D., Managing Director, J.P. Morgan Head of Israel Innovation

and Technology Center,

Corporate & Investment Bank

Autonomous Economy: Coming Sooner Than You Think | 2

Imagine — a U.S.-based health food chain that signs a contract with a Chinese tofu manufacturer, but this time the orchestration of the design-bid-manufacture-deliver process is fully automated and executed by an autonomous contract. This autonomous contract (utilizing AI and blockchain technologies, among others) engages all relevant parties and is used to orchestrate and monitor its execution end-to-end.

Earlier processes might have included integration with other autonomous subcontracts. The intelligent orchestration and monitoring components start fairly early in the bidding process and continue into the manufacturing process, the packaging of the tofu, and eventually its delivery to a health food warehouse. These processes are likely to include financing, insurance, payments, order management, bill of lading, product identification, product verification, quality assurance, and even exceptions handling.

The health food chain brands itself as a store that guarantees product quality and freshness. In this case, the autonomous contract includes a series of service level agreements (SLAs) that specify the quality expected, temperature range during shipping, target delivery dates, etc. Regardless of the level

of automation, exceptions will always arise, such as delivery delays caused by inclement weather, but even these are managed intelligently. Figure 1 illustrates such a case: Because of bad weather, the autonomous freight vehicle broke down and the temperature as measured by the IoT device and recorded in the blockchain exceeded the contract’s SLA. At the time of the breakdown, and shortly after, the product is still fresh; however, because of the potential delay in delivering the product to the U.S., the product won’t be considered fresh by the health food chain’s requirements or the FDA regulations.

At this point the contract is considered void and an intelligent exception agent is triggered. The produce is now introduced, with all of its history, automatically to the local Chinese marketplace for bidding. The initial proposed pricing offer was derived from technology that uses machine learning that monitors historical market pricing. A new autonomous contract is instituted automatically binding the new bid winner with the manufacturer, and is now digitally chained to the previous contract in the blockchain. The new contract is now intelligently monitored between the local manufacturer and the local food store in China.

• Autonomous contract orchestrating E2E processes across all parties

• Tracking SLAs

U.S.

Time

Temperature

$

A. CONTRACT U.S. > China

Figure 1: An example of autonomous exchange for a Chinese tofu manufacturer Serial autonomous contracts interact to reconfigure the supply chain under adverse conditions

Autonomous Economy: Coming Sooner Than You Think | 3

Source: Intrator, 2018

• SLA failure due to adverse conditions

• Void contract

• Reproduce alternative contract

U.S.

Time

Temperature

$

A. CONTRACT U.S. > China

• Goods put up for bid on the local market

• Players now bound by new contact

U.S.

Time

Temperature

$

A. CONTRACT U.S. > China

China

Time

Temperature

¥

A. CONTRACT China > China

Autonomous Economy: Coming Sooner Than You Think | 4

This is an example of how an entire supply chain is being optimized and autonomously executed using currently available technologies (IoT, blockchain, machine learning, AI, etc.). While this scenario represents a logical set of events and solutions, there are still significant challenges to reaching this state.

In a previous article, I discussed and defined the term “smart economy” and I hinted that the forthcoming innovative economic phase will be the autonomous economy (Intrator, 2017). This paper is not debating whether an autonomous economy will eventuate or not. I believe it is a matter of “when” rather than “if.” But here too (not just conveniently),

I will avoid any prediction but suggest that it is likely to happen sooner than we think. In addition, given the many media and academic articles discussing the impact or the consequences of such a state on our society, be they positive or negative, I do not believe I can add much more to such discussions. Instead, in this article I will attempt to define the term “autonomous economy” and then attempt to focus on identifying the “last miles” or final barriers to reaching this reality, and the implications of these obstacles. Whether it is IT, investment, product, organization change management or all of the above, understanding what the “last mile” comprises can help organizations define relevant strategies for the path forward.

Challenges in forecasting penetration of disruptive technologies

Auke Hoekstra, who heads the Spark City research program with a focus on energy transition, depicts in Figure 2 how a very credible organization such as the International Energy Agency (IEA) failed to predict in its last 13 World Energy Outlook (WEO) reports the growth in installed photovoltaic

(PV) energy capacity — in simple terms, the output from solar cells. Humans are poor in predicting when an innovation will have its biggest impact. In its last 13 projections (over a 15-year period), the IEA had to revise its projections not only in terms of solar capacity growth but also in price projections.

1990 2000 2010 2020

1,200

1,000

800

600

400

200

02030

PV History2017 New Policies (NPS)

2015 NPS2013 NPS2012 NPS2011 NPS2010 NPS2009 REF2008 REF2006 REF2004 REF2002 REF

2016 NPS

Figure 2: Cumulative PV capacity: historic data vs. IEA WEO predictions (GW of total installed capacity)

Source: International Energy Agency, World Energy Outlook.

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In 2006, the IEA predicted that under an ambitious scenario the global installed PV capacity could reach 50 gigawatts by 2030 (within 24 years). In 2017 (after 11 years), the figure already stood at 300 GW. A similar phenomenon was exhibited concerning projection of the required investment per kW of output from a PV facility. In 2008, the IEA predicted that by 2030 (within 22 years) the investment cost for a PV system would fall to $2,600 per kW, a target that was met just four years later in 2012.

Many, including Auke, pondered why we fail to predict the impact of innovation more accurately. He identified several hypotheses, each grounded in human nature: linear thinking, cognitive bias, tribal pressures, bad assumptions, and others. Reviewing much of the rationale, I sense that one critical aspect is missing. There is a lack of recognition that innovation is not just a process, but possibly also a force. Innovation should be treated as a force that is influenced by collections of innovations, which constitute other forces.

The only way to understand the overall impact of any innovation is to understand the amalgamation of these forces.

No single innovation operates in a vacuum; indeed, the impact of each innovation must be evaluated by considering the impact other innovations have on one another. A powerful parallel is the way one weather storm can affect other storms in the same area, and that the direction of two storms can either negate or enhance each other.

In the PV case, for example, one must consider the impact of wind energy, energy storage, geothermal energy, government policies, social trends, electric vehicles, etc. The success of these complementary or substitutable technologies or trends is (and will likely continue) affecting PV adoption and growth. Given the compound complexities, I will propose that we should let the technology do the talking and, from there, derive our predictions.

Innovation as a force

In my previous paper “Analyzing and Redefining Smart Economy,” I redefined the term “smart economy” and hinted about the next economic innovative phase, which is the autonomous economy. I highlighted the implications innovation has on our economy including a discussion about how adoption of these technologies will cause commodity costs to decline and agricultural and manufacturing yields to rise.

An interesting corollary is the adoption of robots. 3D printing, for example, marginalizes economies that rely on providing low-cost human manual production services (e.g., India and China). China grasped the potential impact of such technology and is now developing a throng of robots to redefine its

economy. As the price of robots drops, the economic value of their adoption increases. The rate of adoption and their 24-hour non-stop operation will eventually drive the cost of their yield down towards zero. One of the world’s largest mining companies, has deployed robots to replace human miners, making iron mining more profitable.

Additionally, in relationship to this, Bloomberg’s Michael Liebreich calls this period and the next few years An Age

of Plenty on Steroids, which will force prices down as supply begins to match or even exceed demand.

As mentioned in my previous paper, there are four main “laws” or observations that drive the pace of change (see Figure 3).

No single innovation operates in a vacuum; indeed, the impact

of each innovation must be evaluated by considering the

impact other innovations have on one another.

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Figure 3: Four laws that drive the contemporary nature of technological change

Speed Capacity Throughput Reach

Moore 2X/2y

Computational SpeedPrice Performance

Storage CapacityPrice Performance

Rate =>ThroughputPrice Performance

ReachPrice per n+1

Kryder 2X/18m Buttler 2X/9m Metcalfe N2 UnstoppableRate ofChange

Everything that will be economically viable and can be automated will be automated

Source: Intrator, 2018

Our economic system works more thermodynamically than it does mechanically.* For example, the second law of thermodynamics claims that entropy always increases with time. Entropy (also known as the “arrow of time”) in its simplest sense is conceived of statistically as the “measure of the disorder” within a system which we can view as the “rate of disruption.” The law states, “The evolution of our universe has been characterized by an ongoing transformation from a simple, restricted highly condensed homogeneous state to an increasingly complex widely dispersed, dynamic, multi-potent, and granular diversity” (Donaldson, 2011). To fully understand the impact of IT innovation on our economic universe, I suggest we use entropy and entropic force as an analogy. An entropic force describes a system that increases its entropy, i.e., disorder and not necessarily in a negative sense; much like physics, an entropic force does not judge.

The underlying themes of the second thermodynamic law resemble the impact of technology, and how it disrupted our simple agricultural economy, producing an industrialized society, and more recently an economy that is highly dependent upon information technology (IT).

Innovation is an irreversible process, one that increases the entropy of our universe (Van der Steen, 1999). All complex natural processes are irreversible, and because entropy is a state function, the change in entropy of the system is the same, whether the process is reversible or irreversible. Similar to the way a cracked egg can’t be re-formed to its original state, we cannot go back in time and undo innovation. Even if it were possible, how far would we have to go? Maybe we would have to undo the invention of the wheel?

Today, technology is disrupting all aspects of our lives, including our societies and the natural environment we all share (see Figure 4). Forces of IT innovation are continuously generating new tools and new business models. From a social aspect, IT is disruptive with tools such as Twitter, Instagram, Facebook and LinkedIn. It is safe to say that our social life has begun to evolve around IT innovation. Our economic life is increasingly influenced by new business models based on new technologies; the products offered by Airbnb, Uber and Kickstarter are familiar examples. Innovative technologies are used for recycling, energy creation (e.g., wind turbines and PV solar panels), emergency alert systems and so on.

* Thermodynamics defines the statistical behavior of large numbers of entities, whose exact behavior is given by more specific laws. The second law of thermodynamics can be used to determine whether a process is reversible or not.

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Figure 4: The thermodynamic nature of today’s entropic force of IT-based innovation

5G

EASEmergency

Alert System IT Innovation Force ≈ Entropic Force

ProfitabilityAirbnb

Kickstarter

Uber

Zopa

BehavioralFacebook

LinkedIn

Snapchat

Google Plus

Ecological

Key Enabler

IT

Economic

Social

Environment

Source: Intrator, 2018

Defining autonomous economy using the power of analogy

In a search for the best way to define an “autonomous economy” (AE), I resorted to analogies. Analogies are “close enough.” Therefore we must remember that “close only counts in horseshoes and hand grenades”, so where possible I will try to highlight some of the differences between the analogies and this topic.

The most obvious example I like to use is the automotive industry. What started as a mechanical innovation, a car, is now a highly digitally dependent vehicle, better referred to as a “smart car.” Recent innovation in and adoption of

data science (principally machine learning and AI) is rapidly transitioning our smart cars into autonomous cars. The main difference between smart cars and fully autonomous cars is who is sitting in the driver’s seat. In smart cars humans are assisted by IT; in other words, IT is an enabler. In an autonomous car the vehicle is driven by the technology and can thus be classified as a robot. In Figure 5, I use these three terms as an analogy to describe how we transitioned from our parents’ economy into today’s smart economy, and how we are rapidly moving towards an autonomous system of economic exchange.

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What makes cars smart is their foundational digital platform, which continuously manages relevant available signals to deliver a safe and frictionless driving experience.

In the late 1970s we were introduced to the anti-lock braking system (ABS), a mechanical innovation designed to prevent uncontrolled skidding of the vehicle. In the early ’90s we were introduced to autonomous cruise control (ACC) systems, which are sensing systems integrated into the car’s ABS. The ACC allows the driver to maintain a safe distance from the cars ahead.

Both systems were designed to significantly improve safety by taking over driving functions that were previously performed exclusively by humans. Autonomous cars deliver what seems to be the ultimate goal — sustainable transportation.

An effective driving experience is just an interim evolutionary step. Similarly today’s smart economy is merely an interim step indicative of a forthcoming autonomous reality.

The smart car’s evolution, while increasingly digitally dependent, is gradually delegating functional capabilities to technology (e.g., ACC) but still depends on human decision-making. So is the case with smart economy. The platforms that support smart cars or a smart economy comprise semi-digitally integrated technologies that deploy IT to support human decisions. In the case of financial services, for example, more and more functions are being automated, e.g., algo-trading.

In short, if a smart economy is an IT-enabled economy, an autonomous economy is an IT-driven economy.

CarsAnti-lock braking system

Smart CarsAdaptive cruise control

Autonomous CarsArtificial intelligence

EconomyMostly mechanical, human driven economyTraditional banking

Smart EconomyIT enabledHuman decision drivenSemi digitally integrated technologye.g. HFT, Algo trading

Autonomous Economy“IT is now in the driver seat”; IT drivenDeep and broad platform connectivity and opennessPlatform’s continues observation and adaptabilityPlatform’s able to monitor and evaluate it own learning

Human

IT

FunctionalDecision

Level

Figure 5: The parallels between the transition from ordinary cars to autonomous cars and the transition to an autonomous economy

Source: Intrator, 2018

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Autonomous economies are likely to be supported by an amalgamation of what we consider today to be disruptive platforms that, among other inputs, leverage AI. These platforms are likely to have deep and broad connectivity and continuous observation and adaptability capabilities with ongoing evaluation of their own learnings.

Similar to the automobile industry, the transition to a fully autonomous state will be gradual. The US SAE (Society of Automotive Engineers) and the VDA (German Automotive Industry Association) define five levels of automation (see Figure 6).

Human Machine

Transfer of responsibility

PassengerEyes o Mind o Hands o Feet o Driver

AutonomousHighlyautomated

Fullyautomated

Partiallyautomated

AssistanceNo assistance

0 1 2 3 4 5

Figure 6: The five levels of automation

Source: Adapted with permission from SAE International from SAE J3016™ Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles (2018-06-05), https://www.sae.org/standards/content/j3016_201806/.

I doubt if the IT industry will be able to define the transition so formally and what the value is of doing so with respect to the financial industry. Regardless, the transition to a fully autonomous economy will become more and more a reality as many of the regulatory, social, political and technical issues are considered, and, eventually, resolved.

The transition to an autonomous state is reflected in an ongoing delegation of human decision-making to technology.

Somewhere in the transition one might wonder who is assisting whom: a machine assisting a human or a human assisting a machine? This transition is being mimicked now with significant challenges in the automotive industry. Some of these challenges are regulatory and legal; for instance, not all states have consented to autonomous cars roaming their streets, and workers in the transportation industry fear automation will render them jobless. All these variables, and many more, can be seen as “a fear of losing control.”

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Revisiting the term ‘autonomous’

Let’s dive into the characteristics of autonomous economies. The Oxford English Dictionary defines autonomous as “freedom to act independently.” Synonyms for autonomous include self-governing, self-ruling, self-determining, independent, free and unmonitored. In the automotive industry autonomous cars are expected to demonstrate intelligence, continuous learning capabilities (feedback), dynamic assertiveness, self-governance, self-awareness and self-navigation, and be goal driven and goal bound.

Professor Amnon Shashua from Intel’s Mobileye speaks of the need for autonomous cars to exhibit assertiveness. According to Shashua, Mobileye pilots have demonstrated their capability to drive aggressively, which is the norm on Jerusalem’s roads. “In Israel, if you are not assertive on the roads you may as well stay at home,” he said (Tomer, The Calcalist, May 25, 2018). Despite their programmers’ top priorities of safety and caution, autonomous cars cannot just drive defensively. If they did, they would likely be very ineffective, and might even cause accidents.

Just as autonomous cars need to exhibit assertiveness and take some calculated risks, economic policies and strategies must have the capability to adjust more dynamically to

changing environments. For example, an autonomous investment agent must determine when to be bullish or bearish, and an autonomous governance agent ought to be capable of determining precisely when and how to adjust taxes, tariffs or subsidies. I have noted that the autonomous agent must “have the capability” since the decision on when to be assertive is complex and might not always be predictable. Nevertheless, it must conform to a set of well-defined rules including those of a moral nature, such as not increasing bias. Drawing on this example, we realize a key insight into an autonomous system’s level of human input.

Although autonomous systems are by definition orchestrated to operate independently given that their conceptualization, construction and installation were all performed by humans, the machines will naturally have at least some distant element of human bias. However, much like an autonomous car must know when to sacrifice defensive driving for assertive driving, an autonomous financial or economic network must have an embedded ability to “rewrite its wrongs” and challenge even its most primary foundations in its quest for optimality. Next, I discuss the need for AI platforms that will avoid or at least minimize unintentional biases.

The four interconnected stages of autonomy

Returning to the example of the autonomous car, there are four interconnected stages: sensing, mapping, planning and acting. I will use the same analogy to represent the autonomous economy and attempt to note the differences.

Sensing: The sensing capabilities in an autonomous car are used to identify landmarks, obstacles, signals, lanes, cars or pedestrians. To ensure high accuracy for safety and resiliency, the car manufacturers are likely to use different redundant and, in some cases, complementary technologies. For example, the AV’s vision system may deploy its cameras and radars to perform the same function so that in bad weather conditions the radars can compensate for poor signals emitted from the cameras.

Similarly, sensing in the autonomous economy is to read market signals based on micro or macroeconomic, public or private, local or global activities. Autonomous economic agents must continuously and accurately identify market signals, existing or new actors, actors’ financial behaviors and so forth; these may include interest rates, market sentiments and public signals from competitors such as M&As, purchases, cash flow, declaration of dividends and more. Dissimilar to autonomous cars, which “forget” other passed vehicles, autonomous economic agents need a longer-term “memory” and remember the decisions of other economic agents — their behavior, dilemmas, choices and strategies.

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Mapping: Autonomous cars use mapping for navigation, pinpointing locations to identify alternative routes, for example. The autonomous economy will use it to identify opportunities, threats and alternative routes of commerce. These include opportunity costs, barriers to trade, and game-theoretical equilibriums of collaboration versus competition.

Planning: In this stage, the autonomous car weighs alternative navigation paths against a set of predefined goals, policies and driving rules. Rules might include “do not cause accidents,” whilst policies might include quickest route, best scenery, cheapest route and fewest emissions. Within the autonomous economy, it really depends on which economy we are modeling. If we are conceiving of a corporate economic system, then the planning stage can be associated with rules such as conforming to local and federal regulations, whilst policies might be long term versus short term, profit targets and delivering shareholder value. If the economy is modeled from a public-sector perspective, then its rules might include laws at state and national levels; its policies might be to reduce poverty, social biases, spending, etc.

Acting: In an autonomous car, acting manifests itself in driving style. In other words, the actual dynamic decisions that the robot makes by considering real-time road conditions, weather, passenger goals, etc., are reflected externally as its current driving style, either assertive or defensive. Similarly, in the autonomous economy, actions that the agent takes again depends upon what it represents — a city, a state, a corporation, a conglomerate, etc. Economic agents performing the function of a traditional investment firm must determine its current posture — if and when it is going to execute a bullish versus a bearish strategy. Unlike an autonomous vehicle, an autonomous economic agent deployed by a large corporation or public regulatory body must continuously measure the impact that its actions (investments made or regulations developed) are having on the market. Autonomous economic modules require a continuous feedback loop to review prior market activity in order to determine what, if any, next move should be made. After all, no action is also an investment strategy.

The economics behind autonomous economic agents

Over the past decade, we have recognized that economic models are invariably theoretical, and only seldom practical. Some have even conceived of economic models as more of an art than a science. None other than John Maynard Keynes, widely considered the father of macroeconomics, was quoted as saying, “Economics is the science of thinking in terms of models joined to the art of using models” (Keynes, 1938).

In 2013, the Nobel Memorial Prize in Economic Sciences was awarded to two professors with 180-degree opposing theories. Professor Eugene Fama espouses the efficient market hypothesis, which states that asset prices reflect all available information, and therefore it is impossible to consistently “beat the market” on a risk-adjusted basis since market prices should only react only to new information. Given the efficiency of the market, as Fama sees it, government intervention should be restricted only to situations where the

proposed intervention is efficient in a Pareto sense (Fama, 2013). In the same year that Fama was awarded this most prestigious honor, so too was behavioral economist Professor Robert Shiller. Shiller holds financial markets to not be efficient, that the market does not always reflect all available information and that rather than the market always getting prices right save for minor aberrations, these aberrations are part of mainstream market inefficiency. Shiller proposes the government be a force for provision and caution rather than full-scale disassociation or intervention (Shiller, 2013).

One of the key reasons for the inability to settle debates such as those over the efficiency/inefficiency of markets, and in general ease the elevation of theory over practice, is the technological limitations in the number of signals that need to be measured, coupled with limited computational and storage capabilities. As time passes, some of these obstacles, chiefly

Autonomous Economy: Coming Sooner Than You Think | 12

those described in my previous paper, are being resolved. We are witnessing efforts to build analytical models that use real market data from the bottom up, rather than top-down theoretical models. The ambiguity of conflicting economic models that are contextually subjective can, to some extent, be addressed by technological applications of the like being developed and conceived of today. Programs that could well form the basis for future autonomous economic agents are increasingly capable of spitting out definitive conditional equations over subjective conclusions. Whilst the answer to many questions asked of economists today is that “it depends,” agents in the future might explain that “it depends on x under condition a and y under condition b.”

Agent-based computational economic modeling (or agent-based modeling, ABM) views economic constructs as dynamic systems of interacting agents (Salgado and Gilbert, 2013). In ABM, agents can be individuals, corporations, universities, governments or nations and they represent “computational objects modelled as interacting according to rules” over time and space. ABM has evolved from a simple simulations tool into an intelligent agent-based model.

The initial objective was to “test theoretical findings against real-world data (market research, trading prices, etc.) in ways that permit empirically supported theories to cumulate over time, with each researcher’s work building appropriately on the work that has gone before” (Tesfatsion, 2003; Judd, 2006). This approach can be applied to empirical cases such as asset pricing, competition versus collaboration (where simulations are rather game-theoretical) and many other important subjects.

Initially the rules were formulated to model behavior and social interactions based on incentives and information (Tesfatsion, 2003). These rules may be the result of

optimization, achieved through AI methodologies such as machine learning and other reinforcement learning techniques (Sutton and Barto, 1998). Agent-based modeling assumes agents with bounded rationality adapting to market forces, rather than an assumption of perfect rationality where optimization definitively occurs when agents are in equilibrium (Holland and Miller, 1998). Whilst ABM traditionally starts from initial conditions that are specified by the modeler, these can now be based on real market state conditions. Similarly, how agents act thereafter, and how they interact, can continue to mirror behavior from the real world.

ABM, as its name gives away, is “agent-centric.” In other words, while it provides the initial framework of a computational economic system comprising multiple interacting agents, it then steps back to observe the agent over time, without further intervention. Markov decision processes (MDPs), on the other hand, observe each action an agent takes within its environment, accounting for the changed state in the environment as a result of its action plus the individual reward or punishment invoked as a result of that action. As Guillem et al. put it, compared to ABM, MDP is a “more systematic and principled decision-making approach, based on casting the simulation environment as an MDP” (Guillem et al, 2014). To this extent, MDPs account for the discount factors of each possible action, what economists term “opportunity cost,” and this is the basis for the reinforcement learning that takes place over time. In addition, the edge that MDPs hold over ABM lies in its “computationally tractable solution to the issue of parameter sensitivity analysis, robustness analysis and could also be used for empirical validation and estimation” (Tesfatsion, 2005). These features are particularly important in systemic applications such as economics, where a range of different variables is being assessed against, and simultaneously independent of, one another.

It is no surprise that, compared with ABM results, MDP solutions to macroeconomic problems like reducing/increasing a tax, or microeconomic problems like the behavioral economic puzzle of how likely a consumer is to purchase a certain product, are more definitive. While ABM is based on Herbert Simon’s concept of “bounded rationality” (Simon, 1955) involving human limitations of information, capacity and time, certain derivatives of an MDP are partially hidden (see Figure 7).

...compared to ABM, MDP is a “more

systematic and principled decision-

making approach, based on casting the

simulation environment as an MDP”.

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Figure 7: Partially hidden derivatives of Markov decision processes

Environment

Reward r t+1

State s t+1

Action a tIntelligent Agent

Source: Astrom, 1965

MDPs and other similar processes of reinforcement learning combine the use of traditional agent-based modeling with the use of data signals from the environment after each action is taken. It is highly likely that we will see more of these bottom-up approaches that leverage emerging technologies to create intelligent economic agents. These agents will be interacting with other types of agents (environment, weather, social, agriculture, transportation, industrial, etc.). The agents will be listening and collecting data, as a vast number of different signals are widely available across multiple domains.

For example, Facebook, Twitter and Google emit social signals; Waze, Google Maps, Uber, Lyft and IT-enabled cars emit transportation signals; Amazon, PayPal and Venmo emit commercial signals; and smart lighting, smart waste management, biometric security cameras and the like have recently begun emitting signals concerning public safety and welfare.

Each one of us emits several of these types of signals every minute. All of the above agents will be cross-correlated and feed different economic agents. The economic agents could initiate action in near real time for clients, including

governments. Governments could apply dynamic policies such as variable taxation and measure impact fairly quickly.

I envision the world’s cities competing with one another on the

basis of each city’s economic agents (perhaps like a real-word

Sims game), for example, soliciting citizens based on financial

agents (more citizens equals more taxes).

The infusion of ABM and AI is still an evolving field and it is reflected among others in the field of computational economics known as “agent-based computational economics” (ACE) (Tesfatsion, 2006), or applying deep learning and a multi-layered neural network to agent-based models (with a focus on complex dynamic problems that traditional conventional models will not be able to scale to or address (Van der Hoog, 2016).

This fusion is likely to have a significant impact on how future economies will be operating. As Tesfatsion accurately summarizes, “Economics has not yet benefited from these developments and therefore we believe that now is the right time to apply Deep Learning and multi-layered neural networks to agent-based models in economics.”

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Considerations with respect to an autonomous economy

As mentioned, this paper is not about whether autonomous economies will happen; the entropic force of innovation has taken us far enough down this road to a point of irreversibility. Rather, it is about the last frictions to consider before economic exchange becomes predominantly autonomous.

In the final section of this paper, I identify four areas that are critical to consider in identifying the point where the innovative tsunami sweeps through our economies, soaking them in autonomy. The key frictions to an economy being declared autonomous are trust (including transparency and the reversal of control), supply chain, platform and regulation. While it may seem like I dive into each in detail, with no exception all of them require further discussion.

GOVERNMENT

AUTOMOTIVE

TRANSPORTFINANCIAL

HEALTHCARE

FOOD

FRIC

TIO

N

Trus

tSu

pply

cha

inPl

atfo

rms

......

Regu

lati

on

VR/AR

3D Printing

Cloud IoT5G

Blockchain

CRISPR

Quantum

ML/AI

1 TRUST

2 SUPPLY CHAIN

3 PLATFORMS

4 REGULATION

5 ......

Figure 8: The four key friction areas

Source: Intrator, 2018

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I. Trust

It seems as though no day passes without our hearing about leaders (be it government or corporate) either abusing the system or being accused of doing so. It is like an epidemic where a citizen is the patient powerless to a cancer, with limited tools to fight it quickly and effectively. A paradox exists whereby technology has a major role in exposing corruption

and simultaneously facilitating the generation of “fake news.” Traditionally, when trust is lost, governments or corporations add regulation, process and other levels of bureaucracy. This chart (Figure 9) measures the decline in public trust in the U.S. federal government from 1958 to 2016 against growth in its level of expenditure.

1958

1960

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

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1990

1992

1994

1996

1998

2000

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2006

2008

2010

2012

2014

2016

$8,000

$7,900

$7,800

$7,700

$7,600

$7,500

$7,400 0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

U.S. Gov Spending Public trust on U.S. Gov

Figure 9: U.S. government total spending ($bn) vs. public trust in U.S. government (%)

Sources: Pew Research Centre: U.S. Politics & Policy; BEA – U.S. Bureau of Economic Analysis

At first glance, the two variables seem negatively correlated; however perhaps this is a good time to remember the mantra ingrained in every first-year statistics student: Correlation does not (necessarily) imply causation. The keyword here is ironically that in parentheses, which is too often ignored. While causation cannot be conclusively affirmed, it is reasonable to conclude that levels of U.S. government spending are at best independent of public trust in the government, and at worst the two variables are indeed

negatively correlated. There is also the question of which areas experienced the most significant increases in U.S. government spending and in which areas the public lost the most trust in government. One area of spending, technological R&D, can be credited with the loss of trust and, at the same time, debited in bringing trust back. There are a few requirements for an autonomous economy to survive. I have identified at least two — increased transparency and reversal of control back to the citizens or customers.

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i. Transparency Economists view trust as the most basic currency necessary for commercial exchange (Intrator, Judah & Mercuri et al, 2013). As such, the discussion of this friction is protracted relative to the other three. To survive, governments and corporations must make a concerted and meaningful effort to rebuild and maintain trust. This can be accomplished by adopting emerging solutions that will promote transparency and maintain privacy.

But it is critical to recognize that providing transparency is not a technological challenge, but rather a sociopolitical one. Many of the corruption cases suggest that a lack of transparency is being leveraged as a tool to benefit a few individuals or parties and the success of fighting them is very much in the hands of society; how much social pressure is placed on the institutions to address them?

Organizations or individuals who seek to maintain opacity can only gain time but, similar to Ponzi schemes, time is also its enemy. Organizations or governments that seek to deliver greater transparency as a vehicle to gain greater trust now have an additional powerful tool in their toolbox—- the blockchain also known as distributed ledger technology (DLT).

I have previously described an applied evolution of DLT being fused with AI into autonomous contracts. In this case the ledger provides the required transparency needed to oversee the contracts, including SLAs between the Chinese tofu manufacturer, the U.S.-based buyer and later, as a result of contract failure, between future bidders in the local marketplace and the next buyer. The fused power of blockchain and AI technology in enabling a functional

autonomous economic system is absolute. However, the fusion is also part of the solution in addressing all the frictions to turning our economies autonomous: Increase trust via transparency and especially the reversal of control back to economic actors rather than regulators, the transformation of the supply chain (the example of the tofu manufacturer), platforms (as will be discussed later) and regulation.

ii. Reversal of controlGiving back control can be accomplished with identity management, which can transpire from corporations to individuals. Recently, the EU’s introduction of the General Data Protection Regulation (GDPR) has given this issue a lot of attention, but inherent and effective implementation is still required.

For example, an issue that arises from automated decision-making under this regulatory context is that GDPR aims to ensure that processing of personal data is not just lawful, but also fair and transparent by granting EU citizens digital rights. Gaining control of personal data is just one way in which the latest technological innovation themes can be conceived as geared towards increasing the individual’s level of control, rather than previous innovations that were perceived to be to the detriment of individual sovereignty.

The blockchain is a transparent way for individuals, firms and regulators to maintain control over the movement and affairs of their digital assets through an accessible platform that, at best, totally protects against human interference, and at worst provides for easy detection of human interference through a detailed public or private ledger.

II. Supply chain

A recent McKinsey study, which surveyed more than a thousand CEOs and CTOs, found that just 2% said the supply chain is at the heart of their digital strategies. The same report found that while 94% of executives agreed that technology will affect supply chains in the future, just 43% had some form of strategy to get there (McKinsey, 2017). Almost all industries have significant portions of their supply chain still driven manually; e.g., RevCycle Intelligence reports that “78% of Hospital Staff Still Face Manual Supply

Chain Management” (LaPointe, 2016). The healthcare system is one of many industries in which robotic process automation (RPA) may be skipped over entirely.

In these industry supply chains, smart process automation (SPA), and even its autonomous variant, are likely to be implemented as a re-engineering of processes rather than a mere optimization of the way in which they function today (Belenzon, Hamdani, Hashai, Kandel et al., 2018).

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The main barriers to supply chain efficiency are amalgamations of many other challenges including dealing with legacy platforms, economically inefficient alternatives, regulatory obstacles, resistance to change, and others. These challenges are actually providing great opportunities for many startups or economies, such as Israel’s, that has turned

innovation into a local currency. Even large corporations are now assessing if the current innovation force with the convergence of several disruptive solutions warrants a redesign of their business processes for optimal cost, efficiency and effectiveness.

III. Platforms

Current platforms are fairly monolithic with minimal intelligence. The majority are centralized, not open and with limited connectivity. AI and machine learning are still evolving. Other new technologies, such as quantum computation capability, are even less mature and are expected to increase the velocity of the innovation entropy significantly. Security challenges are not going away and are likely to become more severe. Platform needs are shifting to more decentralized, intelligent and self-aware paradigms.

This aspect of an autonomous economic platform encompasses an additional element that is not foreign to systems for autonomous transportation and other more “conventional’ robotic functions. Autonomous economic systems must have the ability to self-terminate (not without a circuit breaker that a human might have to apply, as when high-frequency trading caused a crash in the stock market) and to reproduce when adverse conditions obstruct its everyday scheduled sequence of commercial exchange.

This draws parallels to the plant kingdom, particularly autogamy, often known as self-fertilization, which occurs in flora that reproduce hermaphroditically. These organisms will initiate meiosis (the first step in binary fission) when encountering nutritional distress (Berger, 1986). Remember the Chinese tofu manufacturer example outlined earlier and illustrated in Figure 1, where the autonomous contract self-terminates after detecting adverse conditions from which it cannot recover and creates a new autonomous contract that will redistribute the goods to the local market instead of the U.S. market. This resembles an offspring, which is a separate being whose genetic code and ancestry are the building blocks of his/her existence.

The adoption of smart contracts and later autonomous ones will have a significant impact on our future platforms. Today, contractual relationships between businesses are interpreted by each business independently and explicitly in corporate core systems (e.g., ERP, KYC). Smart contracts represent a single shared truth of the business relationship (data models and functionality models) among all related parties (objects) resolving possible ambiguities in interpreting business relationships and providing significant opportunities for optimization, simplification and risk reduction. Those smart contracts act as independent specialized objects that orchestrate business flow among the relevant business entities when and if changes in a contract’s state are detected.

The adoption of smart contracts is likely to simplify the design of future core business systems, but the distributed nature of such relationships — while providing higher levels of trust — will require some new tools to facilitate collaborative design of business relationships, management and monitoring. Smart/autonomous contracts will likely drive new demand for higher level languages that are closer to legal ones, but to avoid ambiguity will still need to be formal languages. The separation between core business objects and the relationship between them as a functional object is more analogous to the way the human brain functions. Our neurons can be seen as smart contracts, with a bridge or functional connector at the synapse level that connects our sensing object (e.g., eyes) with our arms.

The evolution of the smart contract into an autonomous one is likely to involve inclusion of intelligence and learning from humans who today manage not just standard supply chain workflow processes manually but also learn how to manage exceptions.

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Unfortunately, the process of learning from humans involves adoption of their biases (intentionally or not). I recall that as a student in the late ’70s I realized that those who had operated systems jobs (programs) on the IBM mainframes were intentionally prioritizing their friends over others. An autonomous system that would observe such behavior would do the same, and as such would likely amplify the bias. Where possible, if reinforcement learning is applicable then such a solution provides a great opportunity to avoid or minimize biases. AI is not just about optimization; at minimum, AI will attempt to imitate human behavior and even the natural world. It will have to incorporate features and capabilities that are not there yet — self-awareness, morality and fairness, and, importantly, the ability to explain naturally why they made or plan to make some decisions.

This is one of the key differences: While intelligent beings can explain why they took a certain course of action, at present intelligent systems are limited in their capacity to do so. This is extremely important, particularly at the early adoption of such technology, if we are to outsource governance over property rights — the cornerstone of modern economic

exchange — to autonomous economic agents. The agent should then be able to explain clearly why each change recorded in its ledger took place and demonstrate its legality under society’s laws, which it is designed to serve. Reinforcement learning should ideally occur based upon not only success (today limited to certain domains), but also the agent’s ability to self-learn versus being dependent on machine learning capabilities that are trained by humans and their biases. This is the difference between an intelligence-driven autonomous economic agent and an autonomous economic agent driven by intelligence.

Contracts are likely to be composite, aggregated or chains of contracts. It may be that we will discover more about the nervous system as autonomous solutions develop beyond the AI and machine learning conceived of today. This will be even more real in a quantum computational era. AI should resemble a more neurological structure like our brains, rather than simply being a vehicle for optimization. However, AI’s possibilities, rather than being restricted to human capacity, are bound by the accumulation of all natural forces on our planet, and quite possibly beyond.

IV. Regulation

The fourth friction regards regulatory and governance challenges that account for outdated manual processes. In Israel, for example, bank guarantee processes and their implementations are based on legacy regulatory requirements of storing a physical fireproof card (asset) reflecting significant processes that are slow to adjust to the digital economy. Regulatory bodies, particularly government, recognize the challenges but are unsure how to proceed; they can no longer afford such stagnancy. Regulatory bodies face challenges in reform, data privacy, compliance costs, political uncertainty (think Brexit, Trump, NAFTA), technologies that induce bias, and new risks that are inconceivable. Ignorance is not an option; deciding the best way to utilize them is an option. Regulatory bodies must consider adopting technologies that will allow them to make innovation leaps. The magnitude of such possible leaps draws parallels to the adoption of mobile payments in Africa, which leapfrogged the use of credit cards. The continent witnessed a similar phenomenon with respect to mobile phones instead of landlines. A priority needs to be placed on forward-looking technologies that adopt dynamic policies, settings and

adjustments by continuously monitoring market signals that reflect the impact of new policy. This would transform the consensus from a static to a dynamic policy approach. Regulatory bodies should engage with banks or other highly regulated industries in crafting new modes of setting policy. As the industry adopts more AI, responsibility for defining moral rules will be at the hand of regulatory bodies. These institutions will have a tough task, facing questions with double negatives, like when an autonomous car needs to choose between running over a child or running over a senior citizen, or when an autonomous economic agent deployed by the public health system needs to choose between cutting funding for cancer treatment or cutting funding for open-heart surgeries. Finally, regulatory bodies, such as the Bank of Israel, that have shown openness to readying themselves for these sorts of transformational innovations should not only be lauded, but also be transparent with the benefits they have reaped so as to provide an example to sister institutions in other economies.

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Conclusion

The velocity of the entropic IT-based innovation force with its compound effects of disruption will overcome the four friction areas identified herein. An organization must evaluate continuously different technological solutions that will emerge over time to resolve these friction areas.

In recognizing that autonomy is the next reality, governments and corporations that wish to determine their future strategies should understand that:

• Disruption is a force that is emanating from a fusion of innovative technologies and not from a single trend of innovation.

• Challenges around trust, supply chain, regulation and platform are the key frictions to address before reaching an autonomous state.

• Such frictions are not industry-specific; they cross all industries. This provides an opportunity for startups to develop more generic platform capabilities versus industry-specific ones, and for countries that seek to identify the future of their digital economic direction.

• Governments and corporations that wish to define their future economic strategies and their implications should create four interdependent committees to conduct further research in these areas.

States should not ignore scientific predictions, especially ones that are strongly supported with science and/or historical evidence. For example, a significant number of articles suggest that a large number of jobs will be eliminated while only a few new ones will be created in the looming innovative economic phase. Accordingly, coming generations will have to work several jobs over their lifetime. This can lead to some very interesting challenges such as how to structure the education system of the future workforce so they can be ready for a new era.

Today we are amidst a technologically assisted economy, but the autonomous economy is rapidly creeping up — and it is closer than you think. Intelligence is a critical aspect of autonomy and can be considered the net competitive differentiator. A lot of unknowns remain, and the fear of losing control can be disabling. We need to better understand what we are faced with so that, at a minimum, we can begin having the discussions necessary prior to dealing with what lies ahead.

Acknowledgment

The author gratefully acknowledges the assistance of numerous J.P. Morgan colleagues in reviewing and editing this paper.

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