Token Economics 23: Plug and Play Enterprise


A central concern of economics is the question of how do people work together within some form of enterprise and then redistribute the value created by that collective effort, in a way that is optimal for the entire organization.

An enterprise is a structured project or organization designed to achieve valued ends.

What defines a business, enterprise or company is a business model. For something to be considered a business there must be some coherent business model which defines how the organization creates value, exchanges it and generates revenue and thus achieve its objectives.

A business model can emerge wherever there is the opportunity to create, exchange and capture value. If we discover a new source of mineral under the around that people need, then we can build a business model on top of it by extracting it, exchanging it and capturing some revenue from that value stream.

This business model is realized through the construction of a business or enterprise. Enterprises then operate on top of some value stream, intercepting, transforming, exchanging and retaining value.

These enterprises enable the specialization and division of labor within economy and thus the production of complex products and services.

Previously we found that we have to typically be inside of one of these formal structured organizations to be able to be productive in this way. But the proliferation of connectivity and reduction of transaction costs taking place bring about a deep structure transformational in the economy from closed organization defined by their boundaries to open networks defined by their protocols. And this offers new ways to really unlock and harness the assets and creative potential of people around the world within new larger and more complex networked organizations.


With the rise of the internet has come a new way for structuring the division of labor within the economy through on-demand, networks or what have come to be called platforms.

Platforms are information networks that enable two-sided markets, for producers and consumer to connects and exchange value. These web platforms like Alibaba, Amazon, Google or Facebook have today already risen to the top of market capitalization within the space of just a decade or so to replace the corporations of industrial capitalism.

These platforms differ from the traditional organization as they are designed to be dynamic and event-driven. Where providers and consumers can couple or decouple from the network on-demand instead of having fixed roles, like Uber drivers, or Airbnb hosts.

They are modular, tasks and service provisioning are broken down into small modules that can be easily produced and consumed, like on-demand videos on YouTube or blog posts.

They are scalable, a seller on Alibaba can easily and rapidly go from a few hundred dollars in sales to a few million.

They are based around interactions and the exchange of value in real-time instead of fixed structures and procedures. Much of the platform’s operations are automated through software running on centralized servers.

The advent of blockchain technology will overtime extend these previous trends into the world of fully automated and autonomous networked platforms. On a more technical level, this will create a new architecture for our enterprises and entire economies. This new design paradigm is best captured in the term service-oriented architecture.


Service Oriented Architecture (SOA) is an approach to distributed systems architecture that employs:

  • loosely coupled services
  • standard interfaces
  • and protocols
  • to deliver seamless cross-platform integration

It is used to integrate widely divergent components by providing them with a common interface and set of protocols through which they can communicate within what is called a service bus.

Over the past few decades, service-oriented architecture has arisen as a new systems architecture paradigm within I.T. as a response to having to build software systems adapted to distributed and heterogeneous environments that the Internet has made more prevalent.

There are many definitions for SOA, but essentially it is an architectural approach to creating systems built from autonomous services that are aggregated through a network.

SOA supports the integration of various services through defined protocols and procedures to enable the construction of composite functions that draw from many different components to achieve their goals. It requires the unbundling of monolithic systems and the conversion of the individual components into services that are then made available to be reconfigured for different applications.

Over the course of the latter half of the 20th-century enterprises consolidated their IT infrastructure within Enterprise Resource Planning systems (ERP) behind firewalls.

Over the past decade or so those IT systems have started to migrate to the cloud, but now they will be moving increasingly to this distributed cloud of these next-generation blockchain networks.

As today’s enterprises face new challenges of having to collaborate across large networks, foster innovation within their organizations and as information technology is greatly accelerating the pace of change, reducing the barriers to entry, shorter and shorter product life cycles are the norm.

These enterprises have to respond to fast-changing environments by becoming more agile and the most advanced and forward-looking of these enterprises are already moving towards a platform model to achieve this.


The enterprise of tomorrow will unlikely be based on the static structures of today. But instead will be event-driven networks as we go from a push model of industrial production to the pull model of the services economy.

Service-oriented blockchain based networks will use advanced analytics to pull together resources when and where needed on demand.

The enterprise of tomorrow will be more like an ever-evolving swarm rather than a structured machine, with value being created in micro-interactions dynamically within networks of peers; some large, some small.

Enabling this rapid coupling and decoupling from blockchain networks – of people, resources, and technology – when and where needed will require plug-n-play, API like interfaces.

With the confluence of the services economy, blockchain, and analytics for the first time, we can actually identify what people are contributing to an enterprise, what economic value they are creating, and begin to reward people in real-time.

The enterprise will need to be inherently designed to be able to plug in any capacity to the network as required. The most successful of these networks will be those that are able to harness the efforts of the many, along multiple dimensions, in a frictionless automated fashion. When we start to combine these capabilities we start to see a new and very different architecture to the enterprise and economies.

Game Theory 3: Elements of Games


Games in game theory involve a number of central elements which we can identify as players, strategies, and payoffs. In this chapter we are going to zoom in to better understand each of these different elements to a game, talking first about the players and rationality, then strategies and payoffs.


As we touched upon in a previous videos agents are abstract models of individuals or organizations which have agency. Agency means the capacity of actors to make choices and to act independently on those choices to affect the state of their environment and they do this in order to improve their state within that environment.

In order to act and make choices, agents need a value system and need some set of rules under which to make their choices so as to improve their state with respect to their value system.

A big idea here is that of rationality, and we have to be careful how we defined this idea of rationality. A dictionary definition of rationality would read something like this “based on or in accordance with reason or logic”. Rationality simply means acting according to a consistent set of rules, that are based upon some value system that provides the reason for acting.

To act rationally is to have some value system and to act in accordance with that value system.

When a for-profit business tries to sell more products, it is acting in a rational fashion, because it is acting under a set of rules to generate more of what it values.

When a person who values their community does community work, they are acting rationally. Because their actions are in accordance with their value system and thus they have a reason for acting in that fashion.

Standard game theory makes a number of quite strong assumptions about the agents involved in games. A central assumption of classical game theory is that players act according to a limited form of rationality, what is sometimes call hyperrationality.

A player is rational in this sense if it consistently acts to improve its payoff without the possibility of making mistakes, has full knowledge of other players’ interactions and the actions available to them, and has an infinite capacity to calculate a priori all possible refinements in an attempt to find the “best one.” If a game involves only rational agents, each of whom believes all other agents to be rational, then theoretical results offer accurate predictions of the games outcomes.

Agents have a single conception of value, i.e. all value is reduced to a single homogeneous form called utility. Preferences and value are well defined.

Rational agents have unlimited rationality, the idea of omnipotence, i.e. they know all relevant information when making a choice, they can compute this information and all of its consequences. Within this model, agents have perfect information, and any uncertainty can be reduced to some probability distribution. The agent’s behavior is then seen to be an optimization algorithm over their set of possibilities.

Game theory is a young field of study—less than a century old. In that time, it has made remarkable advances, but it remains far from complete.

Traditional game theory assumes that the players of games are hyperrational — that they act in best accordance with their own desires given their knowledge and beliefs. This assumption does not always appear to be a reasonable one. In certain situations, the predictions of game theory and the observed behavior of real people differ dramatically.

People in the real world operate according to a multiplicity of motives, some of the time people are in a situation where they are simply trying to optimize a single metric, but more often they are not. They are embedded within a context where they are trying to optimize according to a number of different metrics.

The fact that people aren’t always optimizing according to a single metric is illustrated in the many games where people don’t choose actions that give them the greatest payoff within that single value system.

The best empirical examples of this are taken from the dictator game. The dictator game is a very simple game, where one person is given a sum of money, say 100 dollars, this person plays the role of “the dictator,” and is then told that they must offer some amount of that money to the second participant, even if that amount is zero. Whatever amount the dictator offers to the second participant must be accepted. The second participant, should they find the amount unsatisfactory, cannot then punish the dictator in any way.

Standard economic theory assumes that all individuals act solely out of self-interest. Under this assumption, the predicted result of the dictator game is that the “dictator should keep 100% of the cake, and give nothing to the other player.” This effectively assigns the value of what the dictator shares with the second player to zero.

The actual results of this game, however, differ sharply from the predicted results. With a “standard” dictator game setup, “only 40% of the experimental subjects playing the role of dictator keep the whole sum.” In research by Robert Forsythe, et al, they found the average amount given, under these standard conditions, to be around 20% of the allocated money.

In any case, in the majority of these game trials, the dictator assigns the second player a non-zero amount.

The obvious reason for this is that the dictator is not simply trying to optimize according to a single monetary value – that a strict conception of rationality would posit – but is acting rationally to optimize according to a number of different value systems.

They want the money, yes, but they are also optimizing according to cultural and social capital that motivates them to act in accordance with some conception of fairness and it is out of the interaction of these different value systems that we get the empirical results.

What agents value can be simple or it can be complex.

A financial algorithm is a form of agent that acts according to some set of rules designed to create a financial profit; this is an example of a very simple value system.

In contrast, what a human being value is typically many things. People value social capital, that is to say, their relationships with other people and their roles within social groups. They care about cultural capital, how they perceive themselves and how others perceive them. They care about financial capital and natural capital. They often care about their natural environment to a greater or lesser extent.

Likewise, the set of instructions or rules can be based on some simple linear cause and effect model – what may be called an algorithm – or they may be much more complex models – what may be called a schema.

Thus when we say that someone is acting rationally and maximizing their value payoff, this can be in many different contexts. A person helps an old lady onto the bus, not because they are going to get paid for this, but what they do get from this is some sense of being a decent person and they gain some payoff in that sense.

Thus it is not the concept of rationality or that people try to optimize their payoff that needs to be revised. It is the narrow definition of rationality as optimizing according to a single metric that needs to be expanded within many contexts that involve social interaction.

The classical conception of strict rationality based upon a single metric will apply in certain circumstances. It will be relevant to many games in ecology, where creatures have a simple conception of value maximization.

Likewise, it will often be relevant to computer algorithms and software systems and sometimes relevant for socioeconomic interactions, or at least partially relevant.

As the influential biologist Maynard Smith, in the preface to the book Evolution and the Theory of Games, “paradoxically, it has turned out that game theory is more readily applied to biology than to the field of economic behavior for which it was originally designed.”

If we want an empirically accurate theory of games between more complex agents it will need to be expansive in its conception of value and rationality to include the more complex set of value systems and reasoning processes that are engendered in such games. We have spent quite a bit of time talking about this idea of rationality as it is a major unresolved flaw within standard game theory, one that is important to be aware of.


Strategy is the choice of one’s actions.

In game theory, player’s strategy is any of the options they can choose in a setting where the outcome depends on the action of others. A strategy, in the practical sense, is then, a complete algorithm for playing the game, telling a player what to do for every possible situation throughout the game.

For example, the game might be a business entering a new market and trying to gain market share against other players. This will not just happen overnight but they will have to take a series of actions that are all coordinated towards their desired end result. They might first have to organize production processes and logistics, then advertising, then pricing etc. Each of these actions we would call a move in the game, and the overall strategy consists of a set of moves.

A player’s strategy set defines what strategies are available for them to play. For instance, in a single game of rock-paper-scissors, each player has the finite strategy set of rock, paper, scissors.

Likewise, a player’s strategy set can be infinite, for example in choosing how much to pay when making an offer to purchase an item in a process of bartering, this could be potentially infinite, it could be any increment.


In some games, there will not be one primary strategy that an agent will always choose but in many circumstances, they may have a number of options and choose between them with some given probability. This will often be the case when they don’t want the other player to know in advance which move they will take.

For example, in smuggling goods across the Vietnam-Chinese border, the smugglers have many different points of entry available to them and the police have many different points that they could secure. In such a case neither side wants always to choose the same location, they want some degree of randomness in the strategy that they choose.

This gives us a distinction in games between those with strategies that one will always play and those that one will play only with a given probability. This distinction is captured in the terms mixed and pure strategy.

Pure strategies are ones which do not involve randomness and tell us what to do in every situation. A pure strategy provides a complete definition of how a player will play a game. In particular, it determines the move a player will make for any situation they may face.

Strategies that are not pure—that depend on an element of chance—are called “mixed strategies.” In mixed strategies, you have a number of different options and you ascribe a probability to the likelihood of playing them. As such we can think about a mixed strategy as a probability distribution over the actions players have available to them.


For every strategy taken within a game, there is a payoff associated with that strategy.

A player’s payoff defines how much they like the outcome of the game.

The payoffs for a particular player reflect what that player cares about, not what another player thinks they should care about. Payoffs must reflect the actual preferences of the players, not preferences anyone else ascribes to them.

Game theorists often describe payoffs in terms of utility — the general happiness a player gets from a given outcome. Payoffs can represent any type of value, but only the factors that are incorporated into the model. Thus we have to be careful in asking what do the agents really value.

Payoffs are then essentially numbers which represent the motivations of players. In general, the payoffs for different players cannot be directly compared, because they are to a certain extent subjective.

Payoffs may have numerical values associated with them or they may simply be a set of ranking preferences. If the payoff scale is only a ranking, the payoffs are called “ordinal payoffs.” For example, we might say that Kate likes apples more than oranges and oranges more than grapes.

However if the scale measures how much a player prefers one option to another, the payoffs are called “cardinal payoffs.” So if the game was simply one for money then we could ascribe a value to each payoff, that would be the quantity of money gained.

In many games all that matters is the ordinal payoffs, all we need to know is which options they prefer without actually knowing how much they prefer them. This is useful because in reality people don’t really go around ascribing specific values to how much they like things, but they do think about whether they prefer one thing or another. Kate may know that she likes apples more than oranges but she would probably laugh if you asked her to put values on how much more she likes them.

In the next section, we start to play some games, looking at how to solve games, how we find the best strategies and talk about the important idea of equilibrium.

Trusted Tokenization

This is a a short introduction to Tokenization, and how it is used by Sweetbridge. More specifically, it’s a broad overview of tokenization of assets and tokenization of rights.

Sweetbridge describes itself as “a blockchain-based economy that connects anyone with underutilized resources – assets, skills, intellectual property and trust networks, to businesses desiring to improve performance, thereby enabling all participants – organizations and individuals – to provide outcomes that solve disruptive problems, improve asset liquidity, and create mutually shared value.”

Game Theory 2: Types of Games


As we talked about in the previous module, a game within game theory is any situation involving interdependency between adaptive agents.

Games are fundamentally different from decisions made in a context with only one adaptive agent. To illustrate this point, think of the difference between the decisions of a bricklayer and those of a business person. When the bricklayer decides how he might go about building a house he does not expect the bricks and mortar to fight back. We could call this a neutral environment.

But when the business person tries to enter a new market, they must take note of the actions and of the other actors in that market in order to best understand the viable options available to them.

A situation that depends only on the actions of one actor is best understood as one of decision theory not so much game theory.

Like the business person, all players engaged in a game must recognize their interaction with other intelligent and purposeful agents. Their own choices must allow both for conflict and for possibilities for cooperation. So a game really tries to capture this dynamic where autonomous agents that have their own goals are interdependent in effecting some joint outcome.

A game has three major components: players, strategies, and payoffs.

  • A player is a decision maker in a game.
  • A strategy is a specification of a decision for each possible situation in which a player may find themselves.
  • A payoff is a reward or loss that players experience when all the players follow their respective strategies.


Games are represented in either a matrix form or as a tree graph.

  • The matrix form models a game without time involved where players must choose their strategies simultaneously.
  • A tree graph model involves time as an element allowing for choices to be made in a sequential process over a course of time, thus forming a tree-like representation that captures the choices made by agents at each stage in the game.

The matrix model is the most common method for representing a game and is called in game theory normal-form representation. The normal-form representation to a game associates the players with the axes to the matrix, with each column or row along the axis corresponding to one unique strategy for the player.

Where the players’ different strategies interact in the matrix, a value is placed to represent the associated payoffs for each player if those given strategies are played.

In simultaneous games, the players don’t have to move at the same time. The only restriction is that no players can know the other players’ decisions when they make their own choice.

The normal form is a condensed form of the game, stripped of all features but the possible options of each player and their payoffs during one iteration of the game. This makes it more convenient to analyze.

A game where choices are made sequentially over time is represented as a decision tree graph that branches out with each iteration of the game as time goes forward and players have to make choices. An example of this extensive form of game would be chess where players move in a sequential process with each move of one player creating a multiplicity for possible new moves of the other as they branch out into the future.

Players engaged in a sequential game then have to look ahead and reason back as each player tries to figure out how the other players will respond to his current move, how he will respond in turn, and so on. The player anticipates where his initial decisions will ultimately lead and uses this information to calculate his current best choice.


Agents within a game are making their choices based on the information available to them. Thus we can identify information as a second important factor in the makeup of the game.

In any given game agents can have complete information meaning each player has knowledge of the payoffs and possible strategies of other players, or incomplete information referring to situations in which the payoffs and strategies of other players are not completely known.

An example of a game of perfect information would be one that is called the ultimatum game where one player receives a sum of money and proposes how to divide the sum with the other player. The second player chooses to either accept or reject this proposal. If the second player accepts, the money is split according to the proposal. If the second player rejects, neither player receives any money. In this game, all information is available to all players.

In contrast many real world games involve imperfect information. For example, prisoner dilemma games only make sense if given imperfect information where you are choosing without knowing how the other has chosen.

Information plays an important role in real-world games and it can work as an advantage or disadvantage to the players. When one player knows something that others do not, sometimes the player will wish to conceal this. For example in playing poker, and at other times they will want to reveal it, for example, companies offering guarantees for their products is a display of the information that they have that their product is not going to break down soon, and they want customers to know this information.


This reveals also how games can be asymmetrical. Meaning the payoffs to individuals for the different possible actions may not be the same. If the identities of the players can be changed without changing the payoff to the strategies, then a game is symmetric.

Many of the commonly studied 2 × 2 games are symmetric.

Games of coordination are typically symmetrical. Take for example the case of people choosing the sides of the road upon which to drive. In a simplified example, assume that two drivers meet on a narrow dirt road. Both have to swerve in order to avoid a head-on collision. If both execute the same swerving maneuver they will manage to pass each other, but if they choose differing maneuvers they will collide. In the payoff matrix successful passing is represented by a payoff of 10, and a collision by a payoff of 0 and we can see how the payoff to each player are symmetrical.


Games are played over some mutually desired resource, what we are defining as value within that game. For example, countries go to war over territory, businesses compete for market share, creatures for the resources within an ecosystem, political parties for decision making power, athletes for prizes and prestige etc.

In all of these situations, there is some shared conception of what agents value and some interdependence in how that value is distributed out depending on the actions of the agents.

But the question is whether the total value distributed out to all agents remains constant irrespective of their actions or can it grow or decrease depending on their capacity to cooperate.

Constant-sum games are games in which the sum of the players’ payoffs sum to the same number. These games are games of pure competition of the type “my gain is your loss”.

Zero-sum games are a special case of constant-sum games, in which choices by players can neither increase nor decrease the available resources. In zero-sum games, the total benefit to all players in the game, for every combination of strategies, always adds to zero.

One can see this in the game paper, rock, scissors or in most sporting events.

In zero-sum games, the relationship between the agents’ payoffs are negatively correlated, which is called negative interdependence, meaning individuals can only achieve their goal via the failure of another agent and this creates an attractor towards competition, there is no incentive to cooperate and thus these games are called strictly competitive as competition is always the best strategy.

Non-constant games or non-zero sum games are those in which the total value to be distributed can increase or decrease depending on the degree of cooperation between actors.

For example, through the members of a business working together they can create more value than working separately, thus the whole payoff gets bigger. Equally, the total payoff may get smaller through conflict, like in an arms race between two gangs in a city.

In non-zero sum games, the outcome for agents is positively correlated, if one gets more the other will too if one gets less the other will too. With non-zero sum games, we can get positive interdependence between the agents, meaning members of a group come to share common goals and perceive that working together is individually and collectively beneficial, and success depends on the participation of all the members leading to cooperation.


A cooperative game is one in which there can be cooperation between the players and they have the same cost.

So cooperative games are an example of non-zero sum games. This is because in cooperative games, either every player wins or loses.

Cooperation may be achieved through a number of different possibilities. It may be built into the dynamics of the game as would be the case with a positive-sum game where payoffs are positively correlated. In such a case the innate structure of the game creates an attractor towards cooperation because it is both in the interest of the individuals and the whole organization.

A good example of this are the mutually beneficial gains from trade in goods and services between nations. If businesses or countries can find terms of trade in which both parties benefit then specialization and trade can lead to an overall improvement in the economic welfare of both countries, with both sides seeing it as in their interest to cooperate in this organization, because of the extra value that is being generated.

Equally, cooperation may be achieved by external enforcement by some authoritative third party such as governments and contract law. Where we cooperate in a transaction because the third party is ensuring that it is in our interests to do so by creating punishments or rewards.

Likewise, cooperation may be achieved through peer-to-peer interaction and feedback mechanisms as will talk about in future videos.

A non-cooperative game is one where an element of competition exists and there are limited mechanisms for creating institutions for cooperation. This may be because of the inherent nature of the game we are playing. That is to say, it is a zero-sum game which is strictly competitive and thus cooperation will add no value.

Noncooperation may be a function of isolation, lack of communication and interaction with which to build up the trust that enables cooperation.

We see this within modern societies, as these societies have grown in size they have transited from communal cooperative systems based on the frequent interaction of members to requiring formal third parties to ensure cooperation because of the anonymity and lack of interaction between members of large societies.

Lastly, there may simply be a lack of formal institutions to support cooperation between members. An example of this might be what we call a failed state where the government’s authority is insufficiently strong to impose sanctions and thus can not work as the supporting institutional framework for cooperation.


In this video we have looked at some of the basic features to games, we talked about the two basic forms for representation, that of the normal form in a matrix model and that of the extensive form as a tree graph that unfolds over time.

We talked about the important role of information, where games may be defined as having imperfect or perfect information and how agents may use information to their advantage.

We talked about symmetrical and asymmetrical payoffs in games. We briefly looked at zero sum games and non-zero sum games where the payoffs can get larger given cooperation.

Finally we talked about the distinction between a cooperative and non-cooperative game and some of the factors that create these different types of games which we will be discussing further throughout the course.

Game Theory 1: Game Theory Overview


We live in a world that exhibits extraordinary levels of order and organization on all levels from the smallest molecules, to human social organizations to the entire universe. We might say that it is the job of the enterprise of science to try and understand this extraordinary order and organization that we see in the world around us. And in many ways, we have been very successful in the past few hundred years in making progress in this project. We understand the workings of the atom, the structure of DNA, we understand the origins of the universe, how galaxies form and the precise elliptical orbit of the Earth around the Sun.

But what all these systems that we have been so good at describing and predicting the behavior of have in common is that they are inert. That is, they do not have any degree of autonomous adaptive capacity.

Here we can make a fundamental distinction between those systems that are composed of inert elements and those that are composed of adaptive elements.

Because these inert systems that are studied in physics and chemistry do not have adaptive capacity we can describe them through a single global rule. We can write equations about how elements will react when combined or how the solar system will change over time according to a set of differential equations in a deterministic fashion.

Unfortunately, this approach does not work when dealing with systems that are composed of adaptive elements that are non-deterministic in their behavior.

Adaptation gives the elements in the system the capacity to respond in different ways depending on the local information they receive. And the overall organization that forms is in fact not a product of a global rule, like we might have for a chemical reaction. Instead, the result is a product of how these adaptive agents respond to each other.

With these adaptive systems, the overall makeup of the organization is not necessarily defined by a top-down rule, but may emerge out of how the elements adapt and respond to each other locally.

There is no algebraic or differential equation to describe how international politics works, why families fall apart. or the success of a business within a market. The overall workings of these adaptive systems is an emergent phenomenon of local rules and interdependencies.


And it is these systems composed of adaptive agents that are interdependent that game theory tries to understand and model.

A game is a system wherein adaptive agents are interdependent in affecting each other and the overall outcome.

Game theory is the mathematical modeling of such systems.

These adaptive systems are pervasive in our world, from cities and traffic to economies, financial markets, social networks, ecosystems, politics, and business.

The central ingredients of these systems is that of agents and interdependency. Without either of these elements, we don’t have a game.

If the elements did not have agency and the capacity for adaptation they would have no choices and we would have a deterministic system.

Likewise, if they were not interdependent then they would not form some combined organization and we would then study them in isolation in which case likewise we would not have a game.


Games are formed out of the interdependencies between adaptive agents.

So what is an adaptive agent? An agent is any entity that has what we call agency. Agency is the capacity to make choices based upon information and act upon those choices autonomously to affect the state of their environment.

Examples of agents include social agents, such as individual human beings, businesses, governments, etc. They may be biological agents such as bacteria, plants, or mammals. They may also be technologies such as robots or algorithms of various kind.

All adaptive systems regulate some process and they are designed to maintain and develop their structure and functioning. For example, plants process light and other nutrients and their adaptive capacity enables them to alter their state so as to intercept more of those resources. The same is true for bacteria and animals, as well as for a basketball team or a business. They all have some conception of value that represents whatever is the resource that they require, whether that is sunlight, fuel, food, money etc.

This creates what we can call a value system. That is to say, whatever structure or process they are trying to develop forms the basis for their conception of value and they use their agency to act and make choices in the world to improve their status with respect to whatever it is they value.

As we can see this concept of value is highly abstract. And as we will discuss in a future module this value system can be very simple or very complex but it forms the foundations to what we are dealing with when talking about adaptive agents and games.

You can’t model a game without understanding what the agents value and the better you understand what they really value and incorporate it into the model the better the model will be.

Thus agents can also be defined by what we call goal oriented behavior. They have some model as to what they value and they take actions to affect their environment in order to achieve more of whatever is defined as value.


In game theory, a game is any context within which adaptive agents interact and in so doing become interdependent.

Interdependence means that the values associated with some property of one element become correlated with those of another. In this context, it means that the goal attainment of one agent becomes correlated with the others.

The value or payoff to one agent in the interaction is associated with that of the others.

This gives us a game. Wherein agents have a value system, they can make choices and take actions that affect others and the outcome of those interactions will have a certain payoff for all the agents involved.

A game then being a very abstract model can be applied to many circumstances of interest to researchers. And it has become a mainstream tool within the social sciences of economics, political science and sociology but also in biology and computer science.

The trade negotiations between two nations can be modeled as a game. The interaction of businesses within a market is a game. The different strategies adopted by creatures in an ecosystem can be seen as a game. The interaction between a seller and buyer as they haggle over the price of an item is a form of game. The provision of public goods and the formation of organizations can be seen as games. Likewise, the routing of internet traffic and the interaction between financial algorithms are games.

To quickly take a simple concrete example of a game, let’s think about the current situation with respect to international politics and climate change. In this game, we have all of the world’s countries and all countries will benefit from a stable climate. But it requires them to cooperate and all pay the price of reducing emissions in order to achieve this.

Although this cooperative outcome would be best for all, it is in fact in the interest of any nation state to defect on their commitments as then they would get the benefit of others reducing their pollution without having to pay the cost of reducing their own emissions.

Because in this game it is in the private interests of each to defect, in the absence of some overall coordination mechanism the best strategy for an agent to adopt given only their own cost-benefit analysis, is to defect and thus all will defect and we get the worst outcome for the overall system.


This game is called the prisoner’s dilemma and it is the classical example given of a game, because it captures in very simplified terms the core dynamic, between cooperation and competition, that is at the heart of almost all situations of interdependence between adaptive agents.

In the interdependency between agents there comes to form two different levels to the system: the macro-level, wherein they are all combined and have to work cooperatively to achieve an overall successful outcome, and the micro-level, wherein we have individual agents pursuing their own agendas according to their own cost-benefit analysis.

It is precisely because the rules and dynamics that govern the whole and those that govern the parts are not aligned that we get this core constraint between cooperation and competition.

This is what is called the social dilemma and it can be stated very simply as what is rational for the individual is irrational for the whole.

If you do what is rational according to the rules of the macro-level to achieve cooperation then you will be operating in a way that is irrational to the rules of the micro-level and vice versa.

If either of these dimensions to the system was removed then we would not have this core constraint. If the agents were not interdependent within the whole organization then there would be no macro-level dynamic and the set of parts would be simply governed by the rules of the agents locally.

Equally, if each agent always acted in the interests of the whole without interest for their own cost-benefit analysis, then again we could do away with the rules governing the micro-level and we would simply have one set of rules governing the whole thus there would be no core dynamic of interest, things would be very simple and straightforward. The complexity arises out of the interaction between these two different rule sets and trying to resolve it by aligning the interests of the individuals with those of the whole.


So we have outlined what game theory is, talking about it as the study of situations of interdependence between adaptive agents and how these interdependencies create the core dynamic of cooperation and competition that is of central interest to many. In the coming videos in this section, we will talk about the different elements involved in games and the different types of games we might encounter.

Game Theory Course Introduction


As we watch the news each day, many of us ask ourselves why people can’t cooperate, work together for economic prosperity and security for all, against war? Why can’t we come together against the degradation of our environment?

But in strong contrast to this, the central question in the study of human evolution is why humans are so extraordinary cooperative as compared with many other creatures. In most primate groups, competition is the norm, but humans form vast complex systems of cooperation.

Humans live out their lives in societies, and the outcomes of those social systems and our individual lives is largely a function of the nature of our interaction with others. A central question of interest across the social sciences, economics, and management is this question of how people interact with each other and the structures of cooperation and conflict that emerge out of these.

Of course, social interaction is a very complex phenomenon. We see people form friendships, trading partners, romantic partnerships, business compete in markets, countries go to war, the list of types of interaction between actors is almost endless.

For thousands of years, we have searched for the answers to why humans cooperate or enter into conflict by looking at the nature of the individuals themselves. But there is another way of posing this question, where we look at the structure of the system wherein agents interact, and ask how does the innate structure of that system create the emergent outcomes?

The study of these systems is called game theory. Game theory is the formal study of situations of interdependence between adaptive agents and the dynamics of cooperation and competition that emerge out of this. These agents may be individual people, groups, social organizations, but they may also be biological creatures, they may be technologies.

The concepts of game theory provide a language to formulate, structure, analyze, and understand strategic interactions between agents of all kind.

Since its advent during the mid 20th-century, game theory has become a mainstream tool for researchers in many areas most notably, economics, management studies, psychology, political science, anthropology, computer science and biology. However, the limitations of classical game theory that developed during the mid 20th century are today well known.

Thus, in this course, we will introduce you to the basics of classical game theory while making explicit the limitations of such models. We will build upon this basic understanding by then introducing you to new developments within the field, such as evolutionary game theory and network game theory that try to expand this core framework.

In the first section, we will take an overview of Game Theory. We will introduce you to the models for representing games, the different elements involved in a game and the various factors that affect the nature and structure of a game being played.

In the second section, we look at Non-cooperative Games. Here you will be introduced to the classical tools of game theory used for studying competitive strategic interaction based around the idea of Nash equilibrium.

We will illustrate the dynamics of such interactions and various formal rules for solving non-cooperative games.

In the third section, we turn our attention to the theme of Cooperation.

We start out with a general discourse on the nature of social cooperation before going on to explore these ideas within a number of popular models, such as the social dilemma, tragedy of the commons, and public goods games. Finally, talking about ways for solving social dilemmas through enabling cooperative structures.

The last section of the course deals with how games play out over time as we look at Evolutionary Game Theory. Here we talk about how game theory has been generalized to whole populations of agents interacting over time through an evolutionary process, to create a constantly changing dynamic as structures of cooperation rise and fall.

Finally, in this section we will talk about the new area of Network Game Theory, that helps to model how games take place within some context that can be understood as a network of interdependencies.

This book is a gentle introduction to game theory and it should be accessible to all. Unlike a more traditional course in game theory, the aim of this book will not be on the formalities of classical game theory and solving for Nash equilibrium, but instead using this modeling framework as a tool for reasoning about the real world dynamics of cooperation and competition.

What is Machine Learning?

Artificial Intelligence (AI) is a catchall term that refers to the science of computers with human-like capabilities

Machine learning is a sub-category of AI, or a way of doing AI.

Machine learning is not the same as programming. it’s a way of teaching computers what to do by way of example.

You give the computer a bunch of examples of what you want it to do and it figures out how to do it by itself.

This video provides a description of how machine learning could be used to individually identify ducks and geese in a barn without teaching them all about the details of ducks and geese.

Token Economics 22: Trust & Transparency


In a Facebook survey done in 2016 asking millennials if they trust banks, 92% of them said they do not trust banks.

In contrast to this, the blockchain is creating a new form of native digital trust that is significantly absent in existing institutions today.

This loss of trust in centralized institutions is one of the hallmarks of many post-industrial societies today. In a world of trusted centralized institutions, few would take interest in a distributed system that requires a paradigm shift in thinking.

These token economies are going to gain the trust that is lost from our existing institutions by being more transparent and the fact that they are auto-enforced by code.

Blockchains are a technology of transparency. Public ledger systems let us see all the interactions in the whole system – even if those interactions are anonymous – and this is very different to the world we live in today.


The closed nature and misalignment of interests within centralized institutions of today reduces their capacity for transparency.

Facebook does not tell you that they are making a profit out of you, with your data and the advertisements they deliver to you because there is a subtle misalignment of interests there and they don’t want that to be transparent. Likewise, their algorithms are black boxes, they don’t want others to know about them.

Centralized systems create many boundaries that block the flow of information across the whole network and increase its overall opacity.

Gavin Wood a co-founder of Ethereum describes well the kind of economy that we have created with centralization when he says, “the world is much like a set of walled gardens, within the garden you’re free to play, you are taken in if you accept the authority of the household that actually owns the garden. But it’s very difficult to get between the gardens in reality. This boils down to banks and various financial institutions making it very difficult and timely reconciling transactions that go between them. But the more important thing is that as individuals and small business owners it’s very difficult for us to interact with each other if we don’t yet know or trust each other. Instead we have to go to these guardians of society, these intermediaries, these trusted authorities the middlemen in order to interact.”

When you remove the centralized component in these networks you also remove the wall around them that they create, which can work to greatly increase transparency across whole networks. By switching to a peer-to-peer model, you switch to a model based upon direct feedback loops between peers. To get that dynamic real-time information feedback loop you need transparency. The information has to actually flow directly instead of being mediated.

By aligning the interests of the network, you can make transparency possible as people have less of their misaligned incentives to hide from each other. When things are on the blockchain then everyone can go and audit what has happened. This is like finding bugs in open source software where “many eyes make all bugs shallow.”

Part of the problem with centralized systems is that they are vulnerable to a rich get richer lock-in effect.

The issue with the centralized model is that large organizations get capital easier, greater liquidity and they get to dictate terms because they are seen to be more efficient and stable. This makes it more difficult for new startups to compete.

When the Internet started it was built on open protocols like email or TCP/IP and everyone was able to create. It was easy to discover websites. That’s not true in the internet anymore.

Closed networks like Facebook or Twitter are gated communities that use their user data to gain an advantage.

If you are a startup they also have the potential to shut you down as soon as you compete with them or violate their terms of service.

Once a centralized organization of this kind has grown it is very easy for them to become extractive, because it is difficult for people to change providers. Any system that becomes extractive will not want you to know that it is such and this will again reduce transparency in the system.


One of the major challenges faced by organizations today is rapidly escalating complexity within almost all domains.

As our environments become more complex bureaucratic organizations have responded to that by creating more subsystems – more specialized departments and domains – the result being that things have been broken up into these different silos.

These silos provide the organization with some of the specialized capabilities for it to respond to the increased complexity within its environment. But at the same time have the effect of locking information about what’s going on inside because they don’t want to share that information; because they’re afraid competitors or customers will take advantage.

The more complicated things get the more we basically break things up and the more fractured and siloed the system becomes.

The greater the resistance to the overall flow of information within the system and the greater the overall opacity.

Blockchain networks enable us to collaborate within large networks, connecting horizontally and replace proprietary technology with open source protocols, greatly increasing transparency on the network.

This transparency can be used to reduce risk and uncertainty and thus reduce costs. With the blockchain – because everything is digitally native – we can have the actual information about transactions within the network. And we can, for example, lend against that with minimal risk.

If there is a smart contract that an organization pays you every month then you can use that to get a loan against it with minimal risk and thus minimal cost.

Also because these may be smart contracts you could just adjust those contract so that the capital is automatically routed to the lender as payback. Also no one can run away with the money because it is controlled by the network which reduces risk again.

Likewise the network could control for bad actors routing the finance around them.


Just as the underlying technology is based upon a proof-of-work or proof-of-stake system, so to a true services economy that the blockchain enables should be based on outcomes delivered. Unlike selling products which are all about the promise of a functional system, services can be measured according to the actual functionality delivered; the work delivered instead of simply being given a product that may or may not function well. The proliferation of sensing and big data analytics will enable us to measure and quantify our economies in unimaginable ways and in so doing begin to track the actual functionality delivered, which is at the end of the day what people really want, or are increasingly wanting as the so-called “burden of ownership” of the industrial age product-based system starts to take hold within consumer societies.

An “outcomes” system of this kind is again just one more way that a blockchain based economy could work to better match the information layer of token exchange with the underlying flows of real value.

Token Economics 21: Automated Networks


The term used to describe the new forms of organization created by blockchain networks is “decentralized autonomous organizations.” But one could just as well term them “decentralized automated organizations” as the automation of basic organizational procedures will be a central aspect of this new form of economic organization.

Blockchain protocols build upon the capacities of telecommunication networks to interconnect, and of the capacities of the microprocessor to run complex software systems for coordination. But whereas the previous set of information technologies gave us digital platforms for organizing economic production, the blockchain promises to extend this model to fully automated distributed networks.

The promise of the blockchain since its beginnings has been to challenge centralized, top-down decision-making through, distributed consensus, radical transparency, and auto enforceable code.

Smart contracts on the blockchain disintermediate existing institutions and radically reduce transaction costs thus allowing for new forms of decentralized organizational structures that were not feasible before. More specifically, this business model “automates” the governance to a certain degree. It frees up more time to actually spend on getting work done, although it also requires a much larger leap of faith by all parties involved to trust in an automated “trustless system.”

As one commentator noted, we can call private blockchains training wheels for public blockchains and now public blockchains are in many ways just training wheels for these new autonomous decentralized networks, which just work and everyone can trust them. These are gonna be some of the most powerful networks that we have seen because the code is immutable and many functions are automated. In many ways, they will be unstoppable in the way that Bitcoin is automated and likewise in many ways unstoppable.


An enterprise can be defined by its business model as a system that operates within some environment, intercepting resources and processing those into some output of value, while capturing some of that value and redistributing it within the organization.

People work together to create value and then redistribute that value amongst members, what changes with the blockchain model is that we take out the centralized coordination component and replace it with code in the form of smart contracts.

Smart contracts on the blockchain radically reduce transaction costs and automate basic management operations creating the basis for a peer-to-peer economy; allowing for new forms of organizational structures that were not feasible before.

The enterprise can be converted into an automated plug-n-play model where anyone who can deliver a service can plug into the system and provide that service directly through a smart contract receiving tokens in exchange.

Brendan Blumer CEO of, the makers of the EOS network, describes this evolution in the enterprise when he says “what we’re really moving into is the era of open source companies and the types of innovations that you’re seeing with open source technology, the explosion in development and projects like GitHub… the core of open source allows us to all build on each other’s work. In the future when I wake up I may not even have an employee or employer. I may be able to just work for absolutely any company in the world that I can add value to. Imagine that you wake up and say I have a great idea for Airbnb, you examine the code you start writing something and you put it out there, the public accepts that, forks you into the network, pays you a bounty, now you’ve got a decentralized network a piece of code that has essentially just hired you, that has taken your ideas, that has incorporated them into the organization and you have been paid and they don’t even know who you are.”

When everything is open source and everything is able to be viewed anyone can add value to that business, anyone can connect and say what if we do this, or what if we add that feature. The past decades have shown how open-sourcing software and open-sourcing development can skyrocket the acceleration of technology innovation and service delivery.

Because we’re not reinventing the wheel anymore and anyone can come in and add a good idea and it can be adopted by the greater public. What happens when you do that to a company? When you’re competing with Uber with everybody as your employee? Every bit of your code is auditable, anyone can make suggestions, if those suggestions are good they can be forked right in that’s really what these decentralized autonomous corporations enable.


Blockchain networks will extend the recent development of the on-demand economy and online freelancing platforms that have enabled people to work as freelancers contributing to many different projects without one fix form of employment.

By digitizing everything, automating networks and enabling micro exchanges of value token networks will enable a new mode of production where tasks are modularized and made available for anyone with skills to pick up, perform and receive tokens in exchange. And of course, because token economies are multi-value economies this production process could be of any kind.

The influential blockchain thinker William Mougayar describes this when he says “We are moving from user-generated content that you are familiar with, which is really the cornerstone of social media when you post a picture on Instagram, when you write a few lines on Facebook or Twitter, that is called user-generated content. In the future, we are going to have user-generated work, but this is work that we are going to get paid for by the blockchain by all of these cryptocurrencies that will come into existence.”

A good illustration of this is initial bounty offerings (IBO) which are a more recent development to ICOs. IBOs are “a way to crowdsource human resources, business development, marketing and user acquisition for blockchain technology ecosystems, by offering network tokens in exchange for contributions to the ecosystem.” They represent a limited-time process by which a new cryptocurrency is made public and distributed to people who invest their skills and time to earn rewards in the new cryptocurrency. Unlike an Initial Coin Offering where the coins are sold, an IBO requires an exchange of skills and greater commitment by community members in the development of the technology.

UCash is one project using this method, you can earn UCASH tokens for doing tasks like, writing an article, blog post or producing a video about UCASH or translating the UCASH white paper into different languages.

The technologist Vince Meens talks about the potential at the intersection of virtual reality (VR) and blockchain for enabling these new on-demand token networks. Where anyone could put a bounty on something that they want to see done, whether that is having the lawn mowed in the park or feeding homeless people. With the use of VR goggles, one could walk around and see the digital currency bounties left all around us available for earning by performing valued tasks.

Indeed bounty hunting is a surprisingly general and powerful model which could be used to incentivise people to find and remove any unwanted phenomena. We could have bounty hunters that are going after rewards for finding bad transactions on the blockchain, for finding bad data on the internet, for removing spam messages or for finding violations of some law etc. We just simply post rewards for finding anything that we don’t want and it is a decentralized system anyone can go after the reward. Once again this is the power of being able to now design incentive systems.


Likewise, these smart contract networks will automate the provisioning of services. Entrepreneurs will be able to create an application and release it into the “wild” ready to be employed by anyone and everyone who needs that functionality. The entrepreneur in turn simply observes micro-payments accumulating in their wallet. A designer could release their design into the “wild” and end users could download that design to their 3D printer and have the product almost immediately, paying automatically with their download.

Likewise, music services will follow suit. Currently, music licensing relies heavily on paperwork and trust in a music industry dominated by centralized organizations that take the majority of profits at the expense of producers. These intermediaries between the producer and listener of the music can easily take 80% of the price of the good. Musicians hope and trust that sales of their music and merchandise are properly calculated and reported to them but have no way of really verifying. As streaming and digital downloads eliminate physical sales of media containing songs, the music would appear to be a great candidate for tokenization. If music ownership was represented on a blockchain, the many participants in creating the music could have their shares set electronically. The vision would be to have every listener of their music require “unlocking” the file and paying, with payment then being distributed to the appropriate holders.

This model could though, be generalized to the whole of the economy. Once a product has been turned into a service the terms of that service can be encoded in a smart contract, the contract is put on the blockchain and made publicly accessible through APIs. Tokens are then automatically streamed to a wallet in exchange for the usage of the service. That is a generic model that would apply to any economic good once it has been servitized.


These automated blockchain token networks hold out the possibility to radically improve the efficiency across the supply networks that run our globalized economy. The founder of the Sweetbridge project describes well the role of supply chains in the global economy when he notes, “most people don’t know what supply chains are, but everything you eat everything you wear almost everything you own and everything we use on a day to day basis was processed by, moved, stored or created in a supply chain. Supply chains manage 2/3 of global trade, so that’s about 54 trillion dollars worth of global GDP. Supply chain is the science of managing the creation of something and the construction of it through value chains that have many, many parties involved in them, so the blockchain has an ability to affect the supply chain far more than I think most people recognize.”

Token networks will enable automated coordination and the flow of goods along whole supply chains. Supply chains that currently involve massive amounts of friction, in terms of verification, regulation, financing and various forms of information exchanges. These supply chains may work to a certain extent in developed economies, but 40% of exchanges are now between emerging markets. Take for example a rice farmer who wants to sell rice from Vietnam to Nigeria, this involves an exchange between Vietnamese dong and Nigerian pounds. Just to go from one of those currencies into the dollar – the international exchange currency – and then back into the other currency it may costs up to 20% of the transaction value.

Binkabi, is one blockchain startup that tries to replace this model with a direct peer-to-peer network for agricultural products, which automatically identifies the trades coming from the different countries in different directions and tries to match those of similar size so that the companies can exchange currencies directly between them. This can work to take out the centralized component and remove massive amounts of redundancy in the network.

But going forward we will start to tokenize whole supply chains. As we begin to understand supply chains not in terms of products and companies but instead as service networks or value networks that deliver a service and build token economies around that process of value delivery. Here again whole supply chains, just like enterprises and whole economies, will evolve into service-oriented networks where tokens reflect the service delivered and individuals and organization can plug in to deliver modular capabilities to the network receiving tokens in return.


The important thing to always remember in this respect is that much of the greatest potential of blockchain systems is only possible given the effective interaction between the token network and the physical world. Having highly efficient automated token networks that then bump into very slow, manual, physical procedures would be like driving a super fast Ferrari in rush hour traffic.

Blockchains are protocols for networks, they can only deal with what is inside the network. But for those networks to become the dominant mode for organizing society and economy, they have to interact with the real world of people, organizations, things, and physical environments.

At present virtually all of our newly formed networked systems are dependent upon traditional centralized systems of organization to support their existence in the physical world. The only way that these networks are going to gain their full autonomy is by interacting directly with physical technology and real-world environments. This is now made possible by the Internet of Things and advanced data analytics.

The blockchain and token economies exist within the context of this next generation of web technologies and they have to all be working synergistically.

If the linkage between IoT, big data, and the blockchain is not made then these new systems will remain – like the networks of web 2.0 – dependent upon industrial age institutions and the potential will be lost.

We will end up in the same situation as previously where networks like Twitter and Facebook gave people the tools to connect and start the protests of the Arab spring, but not the physical means to realize that change.

Both the Internet of Things and complex analytics are massive technological changes. If you simply focus on token economies and the blockchain without thinking about those other elements, you are missing the bigger picture. The platforms that manage to use all three effectively and synergistically will likely, for better or worse, dominate the world of tomorrow.

Token Economics 20: Token Service Networks


In just the past few decades our world has been radically changed by the development of almost invisible layers of information networks that now wrap around the planet connecting ever more people into common exchanges.

Telecommunications has connected us. Online platforms have provided the coordination mechanisms for organizing more and more spheres of our lives.

But now a new dimension is being added to this as blockchains enable us to securely record and exchange value automatically and with low friction.

It is when we put all these components together that we get the infrastructure for truly rethinking and redesigning economic and enterprise structures based upon open dynamic networks.

Information technology, telecommunication networks, online platforms and blockchains are enabling us to create ever larger systems of organization for economic production and exchange. Enabling the switch from closed organizations competing to open networks with these networks being organized via market mechanisms.

The blockchain, through smart contracts, lowers the information costs and transaction costs associated with many interorganizational contractual arrangements. And so expands the scale and scope of economic activity that can be undertaken.

It allows markets to operate where before only large firms could operate. And it allows businesses and markets to operate where before only government could operate.

Previously institutional structures and technologies worked to strengthened coordination and cooperation within organizations leading to the formation of ever larger centralized operations.

Large-scale differentiation of labor was a key innovation in the enterprise that greatly expanded during the industrial revolution. With mechanized automation individuals could focus on repeatedly performing the same operation rapidly with those diverse activities being coordinated through production processes. Meaning that it was now not any one individual that produced things, but instead the whole organization.

We saw the development of the very large enterprises of the industrial age, such as the corporations that were hired to build the American railroads, with ranks of salary middle managers expanding as fast as the tracks were being laid down.

This industrial model for the generation of value is largely a product of two factors. Firstly, the centralization of production and economies of scale that is inherent to an industrial economy.

And secondly, it is also a product of the relatively high cost of collaboration and communication.

In order to achieve the mass scale that the industrial environment selectively favored, standardization and predictability were a key component. Within this model, there is a strong divide between producers of value and consumers.

On the one side, we have formal well-bounded professional organizations. By aiming to maximize their efficiency, they include only the people who are most productive.

On the other side, we have the consumers who consume the products and services made by the professional organizations. There is a strong divide between producers and consumers, professionals and amateurs, work and play.

Today information technology is changing the very foundation of this dynamic. Blockchains radically reduce the cost of interaction and collaboration between organizations, compared to within them. Thus, the natural size of an organization can be far smaller.

So, once large enterprises have tokenized, then it will also be natural for them to split into smaller and smaller entities, and to reform as needed.

The distinction between the inside of organizations and their external market economy will become increasingly eroded as networked forms of coordination span across traditional boundaries linking inside and outside in a greatly more fluid fashion.

This will have a very profound effect on the overall structure of our economies, as they go from many closed organizations competing within markets to the emergence of large ecosystems of collaboration along whole supply chains and within the provisioning of complex service systems.

Indeed the last few decades with the emergence of the internet has already seen the formation of large business ecosystems.

Eamonn Kelly of Deloitte consulting describes this transformation well when he notes “ecosystems today are doing nothing less than redefining the shape and structure of the economy. They’re increasingly determining business success and business failure. They’re enabling massive and rapid innovation around the world and essentially they’re playing a very, very critical role in shaping the future of our society… Essentially boundaries are blurring everywhere, the boundaries between what large firms and small firms can do. The boundaries between industries and sectors. The boundaries between organizations. The boundaries between technology domains. The boundaries between producers and consumers. Where consumers used to be passive recipients now they’re active participants in the economy… We’re now living in a world where there are more nodes across more networks with more specialized capabilities and above all this extraordinary ability to connect them, to collaborate, to co-create across these systems. That’s the fundamental shift that’s restructuring economies and I think is actually going to fundamentally change our society.”


Recently an important idea has been gaining acceptance within the business community, the idea that businesses of many shapes and sizes can thrive and serve customers better as participants in ecosystems. More diverse and collaborative, more adaptive and agile than traditional industry structures and supply chains.

The term “ecosystem” is a useful metaphor that points to a deep interdependence across players as they Co-evolve and together create and share resources.

Many of these ecosystems are built on top of powerful platforms that facilitate connectivity and invite the active participation of a large number of other players.

Businesses that understand ecosystems and how they work are discovering exhilarating new opportunities to co-create new value streams with multiple players often including customers. They achieve this by harnessing the new coordinating power of advanced technologies to create scale and serve untapped markets, faster than ever before, work with others to meet important human needs and by delivering complex services in ways that would be beyond the capacities of any single organization. They attract and activate passionate communities of talented individuals and organizations and accelerate learning and innovation. To understand the potential of this idea we need just think of one relatively trivial example.

Imagine all of the drug companies having the means and incentives to collaborate on producing a single best drug instead of 90% of their resources being wasted competing while only one gets to patent a new drug.


With the shift towards token economics, our economies will evolve from the traditional model of the industrial age, based around centralized closed organizations competing, to more user-generated systems that both collaborate and compete within large open networks.

The critical change that will come about will be the move towards a service-oriented architecture to whole macro economies and indeed the global economy as a whole.

As the strength of these open trusted networks grows and connectivity proliferates the centralized organization will become unbundled along many dimensions and the product based, push model of competition of the past will evolve into a dynamic, plug and play networked model that works to aggregate modular on-demand services around the needs of end users. Over time those service-oriented blockchain based networks will become increasingly automated through the development of smart contracts.

In a recent article from the RMIT Blockchain Innovation Hub, the authors write “for many industries, the blockchain will radically redefine the boundaries of the firm, allowing individuals to trade their talents and skills in an environment devoid of big business. The eclipse of the large public firm has been predicted before, of course, but this time we believe those predictions will eventuate for many, if not most, industries.”

The organizational paradigm of the token economy will be large service networks. Digital networking technologies enable networks to overcome their historical limits. They can, at the same time, be flexible and adaptive thanks to their capacity to decentralize performance along a network of autonomous components, while still being able to coordinate all this decentralized activity towards a shared purpose.

A huge structural change that is coming about as we move into the information services economy – base on these information networks – is the shift from static structures to dynamic flows of value as the organizational model.

Unlike the industrial economy that was based on fixed structure such as the formal hierarchy or products produced, a service and token economy is one that is fundamentally based on value delivered.

The organization is not based on fixed structures, roles or boundaries, but instead is based more upon value produced and exchange, this value can be defined in terms of services. From this perspective, the organization is a network of value exchange and the members of the organization are those that provide value, the service providers.


Existing centralized companies when they design their products they have to design around the constraints of the existing fiat currency system.

Although not often noticed this, in fact, has a lot of limitations as transfer costs are high. They are slow that’s why we pay employees at the end of the month. It is for this reason that we don’t pay every person, every second. That’s a constraint of the existing financial system and we build our products around those constraints. But this is going to change with the micro-transaction capacities of the blockchain.

When economic activity is moved to a blockchain, tokenized and servitized we can then begin to actually track the real flow of value exchanges and match those with token exchanges. Instead of buying a song you stream it and pay in tokens for what you stream. Instead of paying a flat rate road tax you pay as you drive, or instead of paying a fixed insurance rate you pay your insurance as you drive, etc.


Digital communication networks are the backbone of the network society, as the electrical power networks were the infrastructure on which the industrial society was built.

Furthermore, because the network society is based on networks, and communication networks transcend boundaries, the network economy is global, it is based on global networks.

By reducing the border around centralized organizations blockchain networks morph into ever-larger systems as they provide the underlying infrastructure for the evolution of a new level of economic organization on a global level.

These token economies can be at once local, in that they enable anyone to set up their own micro exchanges of value, but also inherently global. These networks – because they’re living in this global computer network rather than inside of a specific cluster of servers somewhere – have a certain magical property, which is that they’re global by default, they’re everywhere from the day that you release them and the services are universally available. This is quite interesting because it changes delivery at the edges of the network. Currently, we are not very good at delivering services beyond the two billion richest people on earth.

The fact that these networks are inherently global, the fact that all the logic is kind of buried in the payments architecture, the fact that there’s no real recognition or international borders in these systems, because they all operate embedded in the internet, they don’t see the world as a set of countries they just see as an enormous global network, all of those things point to the possibility, currently quite far off, that we are beginning to see global service architectures that run on these systems. Not just the payments which we already have and are being used very successfully in a lot of poorer countries but also the possibility that the services which are built on top of those payments will turn out also to be global by default, which could have a huge democratizing effect on the global economy.