Unless you live off the grid, you use apps that capture, and likely resell, your personal data (like your contact information, interests and preferences). Even if you don’t use apps, your network provider and your phone operating system collect your data. Companies benefit from this data in two ways: by using it to optimize their services to better appeal to you, and to re-sell it to other companies.
How can we put people back in control of their data? The issue is that some services genuinely require your data to serve you. For example, it would be difficult to get health insurance without sharing health information with your insurer, or to get a loan without disclosing your credit score.
What if there was a way to show that you were in the healthy range on all metrics without sharing your actual health information, or to prove that your credit score was good enough, without disclosing the actual credit score?
Zero-Knowledge Proofs (ZKPs) allow data to be verified without revealing that data. They therefore have the potential to revolutionize the way data is collected, used and transacted with.
Each transaction has a ‘verifier’ and a ‘prover’. In a transaction using ZKPs, the prover attempts to prove something to the verifier without telling the verifier anything else about that thing.
By providing the final output, the prover proves that they are able to compute something without revealing the input or the computational process. Meanwhile, the verifier only learns about the output.
A true ZKP needs to prove 3 criteria:
1. Completeness: it should convince the verifier that the prover knows what they say they know
2. Soundness: if the information is false, it cannot convince the verifier that the prover’s information is true
3. Zero-knowledge-ness: it should reveal nothing else to the verifier
Example: Where’s Waldo
In a talk given last year Elad Verbin explained zero-knowledge proofs very well with an example using “Where’s Waldo”.
In the “Where’s Waldo” kids’ books, the reader is asked to find Waldo (wearing glasses, red-and-white sweater, blue jeans and a beanie cap) in an illustrated crowd of people doing various things.
Assume that I (the writer) am the prover and you (the reader) are the verifier. I claim to have an algorithm that can find Waldo easily, but I’ll only let you use in exchange for a fee. You want the algorithm, but don’t want to pay before I can prove that it works.
So, like many transactions, we want to collaborate, but we don’t fully trust each other.
To prove that I have a working algorithm, I put an illustration on the floor showing a large crowd of people. After asking you to cover your eyes, I cover the illustration with a large, flat piece of black cardboard (which covers far more area than the illustration itself) with a tiny cutout in it. The tiny cutout allows us to see Waldo, but where he is located in the image or where the puzzle begins and ends. Then, I ask you to close your eyes again, and I take the board off the Where’s Waldo puzzle.
I have proven that I can find Waldo in the puzzle quickly, without telling you where Waldo is in that image, how I found him so fast or anything else about that illustration. The more times we repeat this exercise, the more probable it is that I have an effective, fast algorithm.
Interactive and Non-Interactive ZKPs
There are two types of ZKPs: interactive and non-interactive.
Interactive: The Where’s Waldo example above is an interactive proof since I, the prover, performed a series of actions to convince you, the verifier, of a certain fact. The problem with interactive proofs is their limited transferability: to prove my ability to find Waldo to someone else, or to the verifier several times, I will have to repeat the entire process.
Non-interactive: In a non-interactive proof, I can deliver a proof that anyone can verify for themselves. This relies on the verifier picking a random challenge for the prover to solve. Cryptographers Fiat and Shamir found that an interactive protocol can be converted into a non-interactive one using a hash function to pick the challenge (without any interaction with the verifier). Repeated interaction between the prover and verifier becomes unnecessary, since the proof exists in a single message sent from prover to verifier.
Zero-Knowledge Succinct Non-interactive ARguments of Knowledge (Zk-SNARKs, a type of non-interactive ZKP) are Zero-Knowledge because they don’t reveal any knowledge to the verifier, succinct because the proof can be verified quickly, non interactive because repeated interaction is not required between prover and verifier and arguments of knowledge because they present sound proof
Use Cases of ZKPs
ZKPs can be used to preserve data privacy in areas such as health care, communications, finance and civic tech.
An interesting use case in finance is a proposal from ING to prove that a number is within a specific range without revealing that number. So, an applicant for a loan could prove that their salary is within a certain range to qualify for a loan, without giving away the exact amount of their salary.
The most prominent use of ZKPs thus far is Z-Cash, a cryptocurrency that allows for private transactions.
The AdEx Network allows for decentralized, ZKP advertisement auctions in which a user can bid for the price of placing an ad without revealing what that price is to other users.
Zero-Knowledge Proofs have immense potential to put people back in control of their data, by allowing others to verify certain attributes of that data without revealing the data itself. This will have enormous impact in finance, health care and other industries, by enabling transactions while safeguarding data privacy.
Bitcoin’s blockchain suffers from a scalability problem.
While Visa handles 2000 transactions per second on average, Bitcoin can only handle only 7 transactions per second with a block capacity of 1 MB.
Bitcoin’s Lightning Network is an effort to tackle this scalability problem.
A Layer on top of the Bitcoin Blockchain
The idea behind the Lightning Network is that smaller, everyday transactions need not be stored on the main Bitcoin blockchain.
The Lightning Network is a second layer built on top of the main blockchain. It enables faster micro-transactions using ‘off-chain payment channels’. Using this off-chain approach, transactions deemed less important or peripheral are conducted off of the main chain.
Off-chain transactions using the Lightning Network only involve 2 entries on the main Bitcoin blockchain — one to open a private ‘payment channel’ between two parties, and the other to close it out. A payment channel is a private channel between two users which allows them to transact with each other off-chain. Since the latest balance sheet signed by both parties is used to close out the private channel through a main chain transaction, the integrity of payments is maintained.
Lightning Network Example — Two Parties
To better understand the Lightning Network, let’s assume you purchase lunch from the same cafeteria at work every day.
These routine daily transactions between you and the cafeteria (two parties that trust each other) need not be recorded on the main Bitcoin blockchain. Instead, you and the cafeteria deposit a certain amount of Bitcoin into a ‘Multi-Signature Account’ (aka ‘Multi-Sig Account’).
You — 1 BTC
Cafeteria — 0 BTC
This transaction is recorded on the main Bitcoin blockchain. However, now you have a private off-chain channel to transact with the cafeteria. Transactions using funds from the Multi-Sig account can only occur when both parties agree.
The next day, you order lunch for 0.01 BTC
Both you and the cafeteria sign this transaction with your private keys. This transaction is recorded on the private channel and the Multi-Sig account now shows the following balance:
You — 0.99 BTC
Cafeteria 0.01 BTC
The off-chain payment channel can be closed at any time by either you or the cafeteria. All you have to do is present the latest balance sheet signed by both parties and broadcast it to the main Bitcoin network. The private channel is closed out through a transaction that distributes funds your respective accounts on the main Bitcoin blockchain
Network Effects with Multiple Parties
In addition to allowing off-chain transactions between two trusted parties, the Lightning Network enables network effects. Using the previous example, let’s say you have two separate private off-chain channels open: one between you and the cafeteria, and another between you and a close co-worker.
One day, the co-worker accompanies you to the cafeteria for lunch. She wants to buy lunch from the cafeteria as well. Rather than open her own off-chain private channel, she can transfer money to the cafeteria by using your Multi-Sig account as a go-between between her and the cafeteria (with all the appropriate balance sheets becoming updated along the way).
If there are multiple people with Multi-Sig accounts between her and the cafeteria, the payment tries to get from your co-worker (the origin) to the cafeteria (the recipient) using the path with the fewest intermediaries and the least fees (as long as the intermediates have enough money in their individual accounts). A transaction can thus jump through connected payment channels
Scalability has been a major problem facing leading public blockchains. Facing competition from EOS, Qtum, Ethereum and others, the Lightning Network is Bitcoin’s effort to tackle scalability by conducting large numbers of transactions off chain, thereby reducing the load on the main Bitcoin blockchain. The Lightning Network is a much-needed initiative to tackle scalability on the oldest, most established and best known public blockchain.
In traditional software development, code developed in one computing environment often runs with bugs and errors when deployed in another environment.
Software developers solve this problem by running software in ‘containers’ in the cloud.
How Containers Work
Containerization involves bundling an application together with all of its related configuration files, libraries and dependencies required for it to run in an efficient and bug-free way across different computing environments.
The most popular containerization ecosystems are Docker and Kubernetes.
Containers versus Virtual Machines
Containers are often compared to Virtual Machines (VMs), since both of them allow multiple types of software to be run in contained environments.
Containers are an abstraction of the application layer (meaning that each container simulates a different software application). Though each container runs isolated processes, multiple containers share a common Operating System.
VMs are an abstraction of the hardware layer (meaning that each VM simulates a physical machine that can run software). VM technology can use one physical server to run the equivalent of many servers (each of which is called a VM). So, while multiple VMs run on one physical machine, each VM has its own copy of an Operating System, applications and their related files, libraries and dependencies.
Running software in containerized environments generally uses less space and memory than running software within different VMs, since the latter requires a separate copy of the Operating System to run on each VM.
IBM Bets on Containers by Acquiring RedHat
Red Hat’s OpenShift platform helps manage containers in popular ecosystems such as Docker and Kubernetes.
In October 2018, IBM announced its acquisition of Red Hat for $34 billion — the largest sum ever paid for a software acquisition. The deal and its valuation show that major technology companies believe that containerization in the cloud is the future of software.
Application containerization is a positive development for distributed applications and micro-services because each container operates independently of the others. Because each container operates independently of others it helps prevent interdependencies and also safeguards from a single point of failure. Each application or micro-service communicates with the others through their APIs. The container virtualization layer is also extremely flexible and can scale up micro-services to meet rising demand for an application component and distribute the load
Containerization presents two security challenges. First, since containerized applications share a common Operating System, security threats to the Operating System can affect the whole system. Second, and related, security scanners in containerized environments generally protect the Operating System but not the application containers, leaving the latter vulnerable.
At the same time, containerization also bolsters security since it isolates application containers to a significant degree
Containerization is a major trend in software development and is its adoption will likely grow in both magnitude and speed. Large players like Google and IBM are making big bets on containerization. Additionally, an enormous startup ecosystem is forming to enable containerization.
Containerization’s proponents believe that it allows developers to create and deploy applications faster and more securely than traditional methods. While containerization is expensive, its costs are expected to fall significantly as containerization environments develop and mature. Containerization is thus likely to become the new norm for software development.
It is well known that deoxyribonucleic acid (DNA) stores our genetic information. However, an increasing number of scientists and futurists are recognizing the potential of DNA to store non-genetic information.
DNA is found in almost every cell in the human body. It stores biological information, such as eye color, hair color and skin tone. The genetic data contained in DNA serves as a blueprint for each cell to perform its functions. So, DNA essentially ‘programs’ the human body.
DNA is made up of four base components: Adenine, Guanine, Cytosine and Thymine (known as AGCT). From these four bases, DNA forms groups of three nucleotides (known as codons). A codon is the unit that gives our cells instructions on protein formation.
How to Store Non-Genetic Information in DNA
Our information technology infrastructure is based on the storage of information in bits (which are made up of two digits: 0s and 1s), whereas DNA information is stored in strings of four potential base units. For example:
DNA Sequence (AGTCATGAC)
So, to store non-genetic information in DNA, we must first translate binary data from bits to the four unit (AGCT) structure of DNA data.
While this is not difficult theoretically, it presents some complications in practice.
Since DNA uses organic matter, DNA data storage will be far more efficient than our current data storage mechanisms. Data stored in molecular form will use only the bare minimum number of atoms necessary for storage.
Scientists have successfully stored data in synthetic DNA. Synthetic DNA is like real DNA, but is created from scratch by scientists. The data stored on synthetic DNA is kept in test tubes, and not attached to any living organisms
Benefits of Synthetic DNA Data Storage
There are several benefits of synthetic DNA data storage. DNA lasts for thousands of years, whereas data in traditional hard drives can get corrupted or damaged within 30 years.
Due to the efficiency of DNA storage, the storage capacity of DNA is massive: a single gram of synthetic DNA can store over 215 petabytes of data!
Additionally, DNA can be copied endlessly for free.
Drawbacks of Synthetic DNA Data Storage
The major drawbacks of synthetic DNA storage include prohibitive costs and access time. While it currently costs a lot to store data in DNA form, this cost can be expected to drop precipitously as the technology evolves. It currently takes hours to input and retrieve data from DNA, rendering it impractical for most real-time applications. Scientists are working on reducing this access time.
Multiple efforts are underway to explore the potential of DNA to store cryptographic keys and other private information. One idea is to bury sensitive information in the DNA, so that it is sufficiently well hidden that it need not be encrypted. This method is known as ‘DNA Steganography’. The startup Carverr is pursuing one implementation of this idea by attempting to store Bitcoin passwords (known as private keys) in DNA.
The financial and engineering barriers to viable storage of non-genetic data in DNA are formidable and this technology is in its infancy. Overcoming these barriers would bring about a revolution in data storage and security, allowing massive amounts of data to be stored securely in just a gram of matter. It would also open up futuristic, new organic computing use cases, including Brain-Computer Interfaces.
Siri and Alexa are pretty good at answering your questions. Google often shows you products you are actually interested in buying. But how do these technologies work?
Apple, Amazon, and Google, the leading technology companies of our time, have heavily invested in Siri, Alexa, and AdSense, respectively. Each of these technologies is powered by Artificial Intelligence (AI).
I have previously written about AI and how it evolves from machine learning algorithms. In this article, I will focus more on the history, categories, and applications of AI.
To recap briefly: AI is the phenomenon of computers simulating human intelligence, for example by comprehending and solving a complex problem, and correcting course as necessary. A computer that can solve a problem generally considered to require human reasoning or skill (for example, learning, planning, reasoning, perceiving, solving problems, moving, or manipulating objects) is using AI.
History of AI
During the Second World War, noted British computer scientist Alan Turing worked to crack the ‘Enigma’ code which was used by German forces to send messages securely. Alan Turing and his team created the Bombe machine that was used to decipher Enigma’s messages. The Enigma and Bombe Machines laid the foundations for Machine Learning. According to Turing, a machine that could converse with humans without the humans knowing that it is a machine would win the “imitation game” and could be said to be “intelligent”.
In 1956, American computer scientist John McCarthy organised the Dartmouth Conference, at which the term ‘Artificial Intelligence’ was first adopted. Research centres popped up across the United States to explore the potential of AI. were developed in America to expertise the new technology. Researchers Allen Newell and Herbert Simon were instrumental in promoting AI as a field of computer science that could transform the world.
Getting Serious About AI Research
In 1951, an machine known as Ferranti Mark 1 successfully used an algorithm to master checkers. Subsequently, Newell and Simon developed General Problem Solver algorithm to solve mathematical problems. Also in the 50s John McCarthy, often known as the father of AI, developed the LISP programming language which became important in machine learning.
In the 1960s, researchers emphasized developing algorithms to solve mathematical problems and geometrical theorems. In the late 1960s, computer scientists worked on Machine Vision Learning and developing machine learning in robots. WABOT-1, the first ‘intelligent’ humanoid robot, was built in Japan in 1972.
However, despite this well-funded global effort over several decades, computer scientists found it incredibly difficult to create intelligence in machines. To be successful, AI applications (such as vision learning) required the processing of enormous amount of data. Computers were not well-developed enough to process such a large magnitude of data. Governments and corporations were losing faith in AI.
Therefore, from the mid 1970s to the mid 1990s, computer scientists dealt with an acute shortage of funding for AI research. These years became known as the ‘AI Winters’.
New Millennium, New Opportunities
In the late 1990s, American corporations once again became interested in AI. The Japanese government unveiled plans to develop a fifth generation computer to advance of machine learning. AI enthusiasts believed that soon computers would be able to carry on conversations, translate languages, interpret pictures, and reason like people. In 1997, IBM’s Deep Blue defeated became the first computer to beat a reigning world chess champion, Garry Kasparov.
Some AI funding dried up when the dotcom bubble burst in the early 2000s. Yet machine learning continued its march, largely thanks to improvements in computer hardware. Corporations and governments successfully used machine learning methods in narrow domains.
Exponential gains in computer processing power and storage ability allowed companies to store vast, and crunch, vast quantities of data for the first time. In the past 15 years, Amazon, Google, Baidu, and others leveraged machine learning to their huge commercial advantage. Other than processing user data to understand consumer behaviour, these companies have continued to work on computer vision, natural language processing, and a whole host of other AI applications. Machine learning is now embedded in many of the online services we use. As a result, today, the technology sector drives the American stock market.
Four Types of AI
As I mentioned in my previous article, there are many ways to classify different kinds of AI algorithms. Here, I will first categorize them in terms of how advanced they are, and then discuss their applications.
Reactive machines are basic in that they do not store ‘memories’ or use past experiences to determine future actions. They simply perceive the world and react to it. IBM’s Deep Blue, which defeated chess grandmaster Kasporov, is a reactive machine that sees the pieces on a chess board and reacts to them. It cannot refer to any of its prior experiences, and cannot improve with practice.
Limited Memory machines can retain data for a short period of time. While they can use this data for a specific period of time, they cannot add it to a library of their experiences. Many self-driving cars use Limited Memory technology: they store data such as the recent speed of nearby cars, the distance of such cars, the speed limit, and other information that can help them navigate roads.
Theory of Mind
Psychology tells us that people have thoughts, emotions, memories, and mental models that drive their behavior. Theory of Mind researchers hope to build computers that imitate our mental models, by forming representations about the world, and about other agents and entities in it. One goal of these researchers is to build computers that relate to humans and perceive human intelligence and how people’s emotions are impacted by events and the environment. While plenty of computers use models, a computer with a ‘mind’ does not yet exist.
Self-aware machines are the stuff of science fiction, though many AI enthusiasts believe them to be the ultimate goal of AI development. Even if a machine can operate as a person does, for example by preserving itself, predicting its own needs and demands, and relating to others as an equal, the question of whether a machines can become truly self-aware, or ‘conscious’, is best left for philosophers.
Functions of AI
Though I briefly discussed these earlier, the phased development of AI over past six decades has unearthed various applications. Here are the most common ones:
Industry has often sought to leverage technology to drive productivity. So, to reduce production costs, industries have automated many repetitive activities and processes to reduce the amount of human intervention required. Machines and computers use automation to perform repetitive tasks and adapt to changes in circumstances. Automation has been widely adopted in both blue-collar and white-collar workplaces.
Machine learning is a revolutionary idea: feed a machine a large amount of data, and it will use the experience gained from the data to improve its own algorithm and process data better in the future. The most significant arm of machine learning is Neural Networks. Neural Networks are interconnected networks of nodes called neurons or perceptrons. These are loosely modeled on the way the human brain processes information.
Neural Networks store data, learn from it, and improve their abilities to sort new data. For example, a Neural Network tasked with identifying dogs can be fed various images of dogs tagged with the type of dog. Over time, it will learn what kind of image corresponds to what kind of dog. The machine therefore learns from experience and improves itself.
Deep Learning is a subset of Machine Learning. In Deep Learning, Neural Networks are arranged into sprawling networks with a large number of layers that are trained using massive amounts of data. It is different from most other kinds of Machine Learning, which generally stress training on labeled data (for example, a picture of a dog with a tag identifying the name of the dog, and some instructions on how to process each of these). In Deep Learning, the sprawling artificial Neural Network is fed unlabeled data and not given any instructions. It determines the important characteristics and purpose of the data itself, while storing it as experience. Returning to our dog example: when images of a dog are fed to a Deep Learning Neural Network, the machine itself determines the important characteristics of each breed of dog from the images, and can then use these to identify a given dog’s breed.
Machine Vision seeks to allow computers to see. A computer captures images from a mounted camera and converts them from analog to digital (the latter can be easily analyzed). Machine Vision methods often seek to simulate the human eye. Machine Vision has various potential uses, such as signature identification and medical image analysis.
Natural Language Processing (NLP)
NLP techniques (including voice recognition, text translation, and sentiment analysis) allow computers to comprehend human language and speech. While Siri and Alexa are examples of commercially available products using NLP algorithms, the major technology companies have developed far more advanced NLP techniques than the ones Siri and Alexa use.
Enterprise Applications of AI
Below, I list just a few applications of AI in each industry. These are merely examples – they do not come anywhere close to being exhaustive.
In healthcare, AI can help improve patient outcomes and reduce costs. Machine Vision can already help diagnose issues in X-Rays and other such images far better than human doctors can. AI can also be used to create medical chatbots and other applications that provide medical answers on the internet, or to more easily schedule doctor appointments.
In the corporate world, consumer preferences are constantly shifting. AI, after digesting enough information about consumer preferences, can help understand or even project these trends. It can also be used in virtual customer service agents or chatbots.
By observing students, AI can determine how they best learn. It could also provide personalized virtual tutors tailored to the student’s skill level and personality.
From trading securities and commodities to powering customer-facing robot investment advisers, AI has many uses on Wall Street and in the financial services industry.
The outcomes of potential or real legal cases depends on rules established in previous such cases, known as precedents. Machine Learning alone is not enough to process precedents and derive rules, because the reasoning in precedents is very fact-heavy. However, if AI truly understands the words written in legal judgments, it could have a transformative impact on the practice of law.
AI-powered robots are replacing segments of the human workforce. This cuts both ways for humanity: it could reduce the number of low-skilled jobs available, but also make products cheaper for all customers. AI could also help tailor creative solutions to global problems, ranging from care for aging populations, to combating extreme weather.
AI’s march has not been slow and steady. Rather, it has been characterized by decades of investment and hype, followed by periods of disappointment and lack of investment. AI has made great progress in the past decade. Yet today’s most prominent AI method, Deep Learning, is reaching the boundaries of its capabilities.
A new AI paradigm will soon emerge. Companies and governments are currently investing heavily in AI. Competition among American, Japanese, Chinese and other governments will bolster AI algorithms.
As for conversational AI, Siri and Alexa are passable but not great conversational partners. My guess is that by 2030, we will have conversational machines that are indistinguishable from humans, and that can therefore win Turing’s imitation game.
Virtual Economies are an emergent phenomenon. Companies have a lot to gain by that successfully creating virtual economies around their platforms. Data and experiences from virtual economies may also help economists, social scientists and policy makers improve our real-world economies.
This post is split into two sections:
Part 1 describes virtual economies, businesses that can benefit from virtual economies, relevant technologies and key economic principles required to understand how to create a virtual economy.
Part 2 presents a comprehensive process for creating a virtual economy. This includes defining how value is created and exchanged, economic planning and governance mechanisms, user strategies, mapping relationships and best practices regarding architecture, design and features.
The word “User” throughout this post refers to either of the following:
· Video game players
· Blockchain platform participants
· Social, E-commerce and Sharing Economy platform participants
Whenever I use the term ‘Games’ I specifically mean MMOs & MMORPGs
· MMOs: massively multiplayer online games
· MMORPGs: massively multiplayer online role-playing games
I’ve written this post with action-oriented people in mind. It’s meant to be a quick guide to get you started. Throughout this post I have tried to keep my explanations brief and crisp.
Part 1: Prerequisite Knowledge
1. Virtual Economies
2. Relevant Technologies
3. Businesses that can Benefit from Virtual Economies
4. Economic Principles
Part 2: Building a Virtual Economy
5. User Profiles
6. The User Value Grid
7. Defining How Value is Exchanged
8. Mapping Relationships & Interactions
9. Economic Planning
10. Creating User Benefits & Incentives
11. Best Practices
PART I: PREREQUISITE KNOWLEDGE
1. Virtual Economies
What is a Virtual Economy?
A Virtual Economy is an economy that exists in a virtual world where users can exchange virtual or real assets, products and services in the context of a game or platform environment. Users can participate in virtual economies for entertainment or for real economic benefit.
Virtual economies originally emerged in MUD (Multi-User Dungeon) games as early as the late 1970s, but exist on other non-gaming platforms as well. Today the largest virtual economies exist on MMORPGs (massively multi player online role-playing games) such as World of Warcraft & Guild Wars.
User engagement and moderation on some social networking platforms have evolved into forms of social currency. Virtual economies have inadvertently developed on these platforms. A virtual economy can exist on any platform on which real money can be spent on user created digital assets, products, services and interactions.
How Virtual & Real Economies Interact
There is a growing overlap between virtual and real economies. Assets that exist in virtual economies are often traded in the real world using real money. These transactions are usually conducted on online auction sites and are referred to as ‘Real Money Transactions’ (RMTs).
Many platforms actively promote the idea of linking virtual goods to real world money. Some gaming platforms, however, discourage and even prohibit the exchange of real-world money for virtual goods, as it is believed to be detrimental to gameplay.
Gold farming is a practice where users play online games with the objective of acquiring in-game currency and then selling it to others for real money. Gold farming takes advantage of economic inequality as most gold farmers are from developing nations, and they sell their tediously earned in-game currency for real money to wealthier players from developed countries.
What Virtual Economies Mean for Businesses
A lot of successful companies own platforms in which virtual economies exist. By creating virtual economies in a game-like environment for their users to interact and collaborate in, company platforms can experience rapid growth in their primary business activity.
There are several benefits for companies that create a virtual economy for their consumers to participate in.
Earning Opportunities for Users
Virtual economies are becoming increasingly popular because they create earning opportunities for their users. Users are able to interact in new ways, create value and earn real money on these platforms.
User Engagement & Platform Growth
Platforms which are able to gamify their interactions have higher rates of engagement and user retention. Applications with virtual economies can experience a lot of organic growth because their users actively spread the word and encourage more people to join.
Some platforms allow third party advertisers and business service providers to participate in their environments. Businesses and service providers in virtual environments often develop collaborative rather than adversarial relationships with users.
2. Relevant Technologies
This section outlines some of the technologies and concepts which can be used in virtual economy creation.
A blockchain is an immutable digital ledger in which data and transactions are recorded chronologically. Blockchains hold batches of valid transactions in ‘blocks’. Each block is linked to the blocks before and after it using cryptographic hashes.
Blockchains are tamper-proof. A number of security mechanisms such as ‘Merkle Trees’ make it very difficult to tamper with data saved in previous blocks. Integrity of data is one of the key features of this technology.
Decentralization is at the heart of public blockchains. Every user of a public blockchain can participate by downloading the entire blockchain as well as the associated software. Decentralized data storage enables each user to have the exact same copy of the evolving blockchain ledger.
A range of complex use cases are possible with this technology. Blockchains are easily auditable and can be private or public, permissioned or permission-less. Blockchains create an environment where users can interact and transact without having to trust each other.
Cryptocurrencies are digital assets which were primarily designed to be mediums of exchange. Cryptocurrencies are powered by blockchain technology, and are therefore decentralized in nature. Cryptocurrencies use extremely powerful cryptographic security mechanisms to secure financial transactions.
Developers can assign a set of attributes and rules to a cryptocurrency which they have created, such as the total supply, the process of creating new units and how the transfer of value will be verified.
You can think of cryptocurrencies as programmable money. A smart contract can have cryptocurrency units programmed into it, which are only released to someone who fulfils the conditions or work described by the creator of the smart contract.
A token represents a unit of value of an asset or utility issued by a private entity. Tokens are digital and usually reside on top of a blockchain platform. Tokens are usually fungible, meaning that a token issued by a company holds the same value as all the other tokens that it has issued.
Utility tokens represent a unit of value and can be redeemed for a good or service provided by the issuing company.
Security tokens are tradeable financial assets issued by a private company. Security tokens represent either debt, equity or derivatives.
Tokens can be programmed to expire at a certain time or when particular conditions are met. These tokens may or may not have economic value or certain rights attached to them.
Limited Use Tokens
The use of tokens can be limited by the issuing authority. Tokens can be programmed so they can only be spent in certain places or when certain conditions are met. The limited use feature can help set the economic value of the tokens.
Non-fungible tokens (NFTs) are a special type of cryptographic token that represents something unique. Each non-fungible token is different from other tokens, not directly interchangeable with them and valued differently.
Non-Fungible Token Enabled Asset Ownership
The ownership of virtual and real world assets can be embedded into non-fungible tokens. For example, ownership of the Mona Lisa can be embedded into a non-fungible token. This token can then be traded digitally and whoever holds it can claim ownership of the Mona Lisa.
On the Ethereum blockchain non-fungible tokens can currently be created using the ERC-721 token standard. Another non-fungible token standard known as the ERC-1190 has been proposed on the Ethereum Network. Explainer video here.
Scarcity is what makes a good valuable. Digital media has been easily shareable and replicable, with or without the consent of the owner of the media’s intellectual property.
Non-fungible cryptographic tokens have finally enabled digital scarcity to exist. A non-fungible token cannot be replicated. An image held in a non-fungible token could be copied, but the ownership of the original image can only be held in that one token.
Digital scarcity is becoming an increasingly important topic in the fields of entertainment and Digital Rights Management.
3. Business Types
Virtual economies suit certain business types more than others. Blockchain companies, gaming companies and platform based businesses stand to gain the most from a well designed virtual economy.
Blockchain platforms are decentralized peer to peer networks. These networks are cryptographically secured and use consensus mechanisms to prevent modification of data. Blockchain platforms enable a range of different user interactions and unique features. Blockchain use cases include smart contracts, tracking and optimizing logistics, identity management, distributed storage, secured voting, managing healthcare records and interactions, digital rights and media, energy tracking and trading, fintech and banking, real estate registry.
Blockchain companies offer a platform where users can interact with one another, exchange value and collaborate. Cryptocurrencies are built using of blockchain technology, and units of cryptocurrency can be exchanged automatically when certain conditions are met in a smart contract. Complex blockchain platforms create virtual economies where scarce digital assets can be created, utilized and traded by users. Several different categories of users can exist depending on the functions and complexity a blockchain platform has.
Blockchain platforms with an intelligently designed environment will enable every user type to gain some value by being a part of their environment.
Gaming Companies: MMOs & MMORPGs
MMOs: massively multiplayer online games
MMORPGs: massively multiplayer online role-playing games
Multiplayer online games inevitably create huge virtual economies. Persistently open online worlds are continuously evolving with thousands of regular players creating and exchanging value with one another. Users can interact, collaborate, organize themselves and compete with each other on a large scale. Games already have virtual economies that enable players to create in-game assets and objects and then trade them with each other, sometimes for real money.
Games which are currently popular provide social interactions, roleplaying, have unique themes and progress in a somewhat defined manner. A distinct culture usually develops around these games.
In-game inflation has been an economic issue that several gaming platforms have had to address. Several gaming companies have hired economists to help optimize their in-game virtual economies.
A platform’s purpose is to match users and facilitate the exchange of social currency, goods and services. Many types of platforms exist. Social media platforms include Instagram and Facebook. Social ride sharing is well known, due to Uber and Lyft. Matchmaking platforms such as Tinder and Match.com help people meet one another. Platforms such as Upwork & Fiverr help match people in the gig economy. Ecommerce platforms such as E-bay & Amazon help people buy and sell. Successful platform companies spend a lot of time thinking about their core interaction, their participants and the value they are creating for their users.
Platform companies are a lot more rigid in their interactions compared to blockchain and gaming companies. However, interactions between users on microwork and sharing platforms are becoming more game-like to maintain motivation and user engagement. A lot of value may be created by allowing users to serendipitously develop new functions and interactions. Enabling virtual economies to develop around more traditional platforms could increase interaction and enable growth.
4. Economic Principles
This section presents a few economic principles which need to be understood to properly create and manage virtual economies.
Micro economics is the social science that studies the behavior of individuals and firms to better understand their decision-making mechanisms. It analyzes market mechanisms that establish relative prices among goods and services and how the decisions made affect the utilization and distribution of finite resources.
Users interact with each other in the goods market. Producers make up the supply side, and consumers buying their products and services make up the demand side. A market could be competitive and open or monopolized by a handful of users. A proper analysis must take the structure of the goods market into account to create an accurate model.
Macro economics studies how the entire economy behaves. It analyzes interrelations among the different sectors of an economy to better understand how the whole functions.
There are two primary areas of research in Macro economics:
1. The Business Cycle: understanding the causes and consequences of short-term fluctuations in national income
2. Increasing National Income: understanding what factors decisively affect long term economic growth
Macro economics focuses on the way the economy performs as a whole and analyzes factors like output, consumption, savings, GDP and inflation, among others. A governing body uses these factors to develop its economic policies.
Inflation is the rate at which the prices for goods and services is rising, and therefore, the purchasing power (or intrinsic value) of a currency is falling.
Inflation happens when the money supply grows faster than the rate of economic growth. Main causes of inflation are demand growing faster than supply and price rises due to higher costs of production or raw materials. Most economists today favor a low and steady rate of inflation.
A high inflation rate is regarded as harmful to an economy because it adds inefficiencies to the markets, makes it difficult to budget or plan long-term and uncertainty about the future purchasing power of money discourages investment and saving.
Deflation is a decrease in the general price level of goods and services.
Deflation happens when excess production occurs, consumption decreases, or when the money supply decreases. Deflation happens naturally over time when the money supply of an economy is fixed. Cryptocurrencies that have a fixed supply will experience deflation.
Deflation can cause an increase in unemployment. As firms make less money they may lay more people off in order to cut costs.
A deflationary spiral is where decreases in price lead to lower production, which in turn leads to lower wages and demand, which leads to further decreases in price.
A currency is money in circulation that is used as a medium of exchange. A currency is common within a nation. Cryptocurrencies are common in enclosed digital environments. Cryptocurrency exchanges enable users to change their holdings from one currency to another without having to switch to fiat in between.
A set of rules and mechanisms are needed to govern any economic system. Governance is the way in which rules and actions are structured, sustained regulated and held accountable. How formal a governance system should be depends on the level of complexity of the environment being governed.
Fiscal policy is the policy a government follows to collect money and then spend to influence the economy. Revenue collection is primarily through taxes, and expenditure can be done in several ways including through direct investment and by providing subsidies for certain sectors. Fiscal policy is used to stabilize the economy over the course of a business cycle.
A central bank is an institution that manage a state’s currency, money supply and interest rates. A central bank holds a monopoly on increasing the monetary base in a state.
Monetary policy is the process by which a central bank controls the cost of short term borrowing or the monetary base, often targeting an inflation rate or interest rate to ensure price stability and trust in the currency. It is set by the central bank.
A currency board is a monetary authority which maintains a fixed exchange rate with a foreign currency. In a virtual economy a currency board is important if interoperability with another virtual economy is enabled.
National Income Identity
The national income or product identity describes the way in which Gross Domestic Product (GDP) is measured. The formula for GDP is Consumption + Investment + Government Spending + (Exports — Imports). In short, this is GDP = C + I + G + (X — M).
PART II: BUILDING A VIRTUAL ECONOMY
5. User Profiles
In a virtual economy, value is created by users, so the logical place to start the design process is with the user.
The goal of this section is to describe a detailed user profile document that defines Users, their functions and their permissions.
Define Types of Users
Start out by defining the types of users your platform has.
Instagram and Twitter users create personal accounts, accounts for their pets, or accounts for their companies, but the user type for these accounts is still the same.
There is no need for this step to be complex. Simply list the users that your platform has in a vertical column.
Functions are the actions that users can take on a platform.
Different categories or subgroups of users may use certain functions more than others. On Instagram and Twitter, companies use the promote function to boost their posts more than individual users, but these functions are all accessed through the same single type of user account.
In the following column, next to each user type, list of all the functions that the user can take on your platform.
Assign Permissions & Constraints
Not all functions are accessible to all users. A number of permissions and constraints are put in place by the creators of an application, and usually individual users are able change some permissions and constraints can be set by editing their settings.
Certain functions are possible but discouraged. For example Uber drivers are dinged when they reject rides that they’ve been matched to, and riders are dinged when they cancel a ride that has arrived and been waiting for a while.
In more complex applications and games, certain functions and permissions are unlocked only once the user either submits required information, makes a payment or earns their way to a certain level. These are conditional permissions, and they should also be mapped and defined in this document.
In the third column next to each individual function, write down a detailed description of the associated permissions and constraints. The description should include details such as:
6. The User Value Grid
The objective of this step is to specify how users create value. I’ve include a sample User Value Grid at the end of this step. You should create your User Value Grid once you have defined each sub-section discussed below.
Users create value by engaging in a set of actions on a platform. The specific actions that each user can take are defined in the User Profiles document that you created in the previous section. Multiple user types will engage in divergent actions and therefore create different forms of value.
Define Types of Value
Value will be closely related to the functions available and how they are used. Begin by mapping out the functions.
There are two categories of value that emerge on a platform or virtual environment; planned value which developers have created by design, and unforeseen value that is created by users acting unexpectedly.
Unforeseen value is extremely interesting as it is created when users or groups find creative new ways in which to create value through the platform. Often these uses take the founding teams by surprise.
Each function usually has quantified known value and in many cases unforeseen value. For example, having a million followers on Instagram will give validation to the account owner that they are posting popular and in-demand content, the unforeseen value may be that the account owner can now charge others for posting promotional content on their account, or sell their account altogether for real money.
Define How Each User Type Can Create Value
Value can be created on every platform. It is your responsibility as the platform developer to clearly define how value can be created in the environment you are building.
Value flows directly from the functions that you have made available for the users, and in many cases, combinations of different valuable activities create their own niches and cult fan bases.
Sticking with the social media example for a moment, a twitter account with 10,000 users has a certain value associated with it, while a twitter account with far fewer followers but with a blue tick for a verified profile has another type of value associated with it. If these two types of values were combined the combined account (ie 10,000 users + a verified blue tick) would have a far higher value than either of the two accounts by themselves.
When you design your user value grid create leave some space by each function to discuss all planned and possible unseen value that is created. You could also create a new document dedicated to exploring the combinations of value that may be created in your ecosystem. Also explore the value that is created by multiple user types using different combinations of functions.
Define How Each User Type Can Buy Value
Value can be earned and value can be bought. As I mentioned earlier, value is often traded on unsanctioned auction sites that the platform creators aren’t even aware of.
Several platforms and games allow their users to purchase value within their own ecosystem itself.
For example, a user who posts engaging content on a social platform and builds up a fan following of 10 million other users can then sell their user profile to someone for a significant amount of money.
Define how users can buy value because that will help you properly define how to create an exchange and keep a pulse on the economy of your platform, sanctioned and unsanctioned
Define How Each User Unlocks / Wins Rewards
Now that you have had a chance to think about the users, functions and types of value it makes sense to start defining and mapping out rewards. Rewards are an extremely important strategic component of virtual economies as they serve two critical functions:
1. Rewards can strategically be added or removed from a virtual environment to curb inflation or tackle deflation
2. Rewards can be used to encourage or discourage types of economic activity on the platform
Some rewards can be dependent on other factors in the environment, such as time spent on the platform, rank or hierarchy, while others can be open to all participants.
Users who are willing to spend more on the platform and high loyalty to the ecosystem should benefit by receiving certain types of rewards. Other rewards should encourage and incentivize idle users or conservative players to ramp up their participation and involvement in the system.
Think about all of the rewards that will exist in your virtual economy and then map them out for each type of user.
7. How Value is Exchanged
Once value has been created it will be traded. If your users can create and exchange value easily on your platform this will really help your platform grow. Happy users that gain from this virtual economy will spread the word and encourage others to join your platform.
Let’s spend some time thinking about how we foresee value being exchanged and how we would like users to trade value in an ideal scenario. Will your platform support the exchange mechanisms as a free benefit for users, or will it charge a percentage or flat fee for certain transactions?
We can make in-app environments and tools available to our users in order to enable the productive exchange of value. The tools and strategies in this section will include: digital & fiat currencies, in app stores, shops & auctions.
Value is most commonly exchanged in return for currency. You should give your users different currency options to choose from. It could be regular dollars, a cryptocurrency or even an in game or in app currency. In-game and in-app currencies are a great option as long as there is a convenient place where people can easily trade this currency for dollars. Perhaps a dollar backed in-game token or stablecoin is also something that you could experiment with when thinking about in game currencies. The great thing about digital money is that it can be programmed to be moved, released, traded or rewarded when certain milestones or conditions are met. This makes it perfect for gig-economy platforms, competitive gameplay and other gamified environments.
Consider including a store somewhere on your platform which is enticing and dynamic. Your store could include things that you as the app developer are selling or it could be a place of barter where other users can also list items or rewards which they have earned within the environment. The latter would be a marketplace or an auction area rather than a traditional store.
Will your store be available all the time, or will it be open only for certain times in a particular location or level? Will there be a single general store or multiple specialized stores? Are you only going to allow offers in shop or can users engage in trade with each other anywhere in the platform?
Timing & Exclusivity
A well-designed offering will include timing and exclusivity based on each user’s loyalty, time on platform, spending habits, level, experience and other characteristics.
Not all items should be available to all users. Goods should be available based on properly thought out metrics & hierarchy depending on type of business or application you are looking to build.
I suspect there will still be some exchange of value offline regardless of how optimized the in-app exchange mechanisms are, and that’s okay. It would be of benefit if you did some research now and then to see where the offline transactions where happening and what the nature of these transactions are. Once you have more data to work with you can think about how to try and bring these transactions back on-line or somehow position your virtual economy to strategically gain from these offline transactions as well.
Well Informed Users
The key to having a robust economic system on your platform is well informed users. Find ways to inform your users how they can exchange value on your platform. Whether you do this through messages within the app, emails to your users, or intelligently designed pop-ups during gameplay is up to you. But make sure that your users know:
8. Mapping Relationships & Interactions
You can try and build a map of the relationships that you think will form while you’re building your platform but you should regularly revisit this document and update it once your platform is up and running with multiple users. Things will change and evolve fast and it will help if you have a clear map outlining the intricate relationships on your platform.
The Virtual Economy Triangle
Most successful virtual economy platforms will connect two or more types of users and service providers. There are three types of individuals or organizations involved here:
You will gain significant insight if you map the permutations and combinations of transactions, communication and interaction that could occur between these three parties.
Guilds & Specialized Groups
Guilds can fulfill the role of a market or a resource allocation mechanism in the economy: given a set of resources (armor, healing, guiding newer players, and so on). produced, the guild structure and rules determine how they are distributed among members and possibly outsiders too. Guilds can sometimes even replace the market itself — which is the usual resource allocation mechanism. Even though a marketplace can exist, in many cases players may choose to use an alternative mechanism — the guild.
Guilds and specialized groups are usually formed by influential players and groups through methods of organization that may or may not be enabled by your platform. On large platforms guilds and specialized groups are formed pretty fast. For example, cryptocurrency miners have formed groups on various networks and they have organized themselves into large mining pools. Pet lovers have organized themselves in groups and communities on Facebook.
Groups and users can find many mutually beneficial reasons to collaborate. Collaboration will occur between a diverse set of groups and users and it will give you greater insight into your platforms ecosystem were you to map it out and monitor it. Collaborations are most commonly seen in large MMORPG games. Where specialized groups with certain sets of skills work with other groups or individuals for a common aim even though their personal interests or incentives may vary.
Allowing Serendipitous Relationships
Serendipitous relationships are those which can occur by chance in a beneficial way. Relationships such as these are often the most valuable aspects of a platform. A select group that may have discovered a unique way to create and capture massive value purely by chance will be highly engaged with your platform and spread the word far and wide for others to join.
Patterns and trends will emerge once your virtual economy platform is up and running. Closely monitoring user generated value and having the design flexibility necessary for moving quickly and optimizing the platform to allow for serendipitous relationships to flourish should prove to be beneficial to your virtual economy.
9. Economic Planning
Having comprehensive economic planning and governance mechanisms in place is essential. Platform economies may experience inflationary or deflationary pressures and are often prone to abuse by different types of users. Having a well-defined arsenal of tools and strategies defined for each scenario is important. The economic and governance systems you set in place will depend on the nature of the platform you are building.
Structure & Governance Mechanisms
If there are rules and complex issues that the entire platform’s community needs to agree upon every now and then, you should create a governance mechanism on your platform. A lot of blockchain platforms have robust governance mechanisms where the community partakes in discussions before key decisions are made votes are cast regarding the direction that the platform will take. Governance decisions should be different from economic decisions, because lumping both together often results in abuse by well organized groups of users.
Economic decisions to keep inflation at bay, monitor and encourage competitiveness of the platform and incentivize users to engage with the platform in positive ways should be made by the designers of the platform. Highly evolved and complex platforms should consider hiring an economist. Several tools can be used to monitor and regulate the economic health of your platform including forms of tax, incentives, releasing new dimensions of your platform as well as balances and sinks.
Balances & Sinks
Creating the right balances and sinks mechanisms will be important for any platform environment. A balance is a place where users earn money, rewards or value on your platform. Sinks are places where users can spend value, money or rewards on your platform.
These are key tools that will help you regulate the inflationary or deflationary pressures on your platform.
Real Money & Virtual Currencies
As previously mentioned, you can monitor virtual currencies or in-app points, but real currencies will inevitably be used to exchange value on your platform. People will start trading value from your platform offline for real money. It’s better to be aware of it, collect all the data you can and then plan your next economic moves on the platform.
10. User Benefits & Incentives
Incentivizing your users for higher engagement and rewarding them for being on your platform are two important growth strategies for your platform. If you can do this in unique and interesting ways, the users will be your greatest cheerleaders. This section has a few best practices and tips on user benefits and incentives.
Start with a Positive Balance
When a new user is starting off, they should do so with a positive balance. Games have perfected this: a new player starts off with a set of lives or a number of points or tools that they can use in game play while they learn the basics and try to get the hang of the game. It completely changes the mindset of the user if they are rewarded with a positive balance right at the start. Things don’t seem so gloomy when you have some resources to play around with when you begin. You can also do this by offering a grace period of a few weeks where new users don’t pay fees or taxes on their transactions or earnings. The magnitude and nature of the positive balance you choose will depend on the nature of the virtual economy you are building.
Focus on User Earnings
A happy user is your greatest asset. If a user is gaining significant value from being on your platform they will tell everyone they know to get on it. A great strategy is to help ‘Create Platform Millionaires’. Ebay and Facebook are examples of platforms where regular users are able to build million dollar stores or large publishing businesses. A user making money or receiving perceived value will drag others onto the platform.
Focus on User Fame & Promotion
A certain type of user places a lot of value on fame. Creating ‘Platform Superstars’ is another strategy you could experiment with. A user gaining recognition or a following on a platform will promote it. Influencers and superstars will help you build a critical mass of users, especially in the early days. YouTube and Twitch have used this strategy to great effect.
Incentivize Each User Type to Participate
Social platforms so far have incentivized their platform stars well but they have largely ignored their passive users who don’t really create much content but who spend lots of time consuming content on these platforms. A few blockchain enabled publishing platforms such as Flixxo and LBRY are doing a great job of incentivizing all user types to engage with the platform. This will most likely be a big trend going forward.
11. Best Practices
In this section I’ll outline some helpful points and best practices to keep in mind as you think about creating a virtual economy for your platform.
Build Around Your Core Interaction
The key to creating a virtual economy is keeping your core platform interactions at the heart of your design and then building everything else around it.
Core platform interactions serve as the anchors of the application. Uber’s core interaction is connecting drivers and riders. Instagram’s core interaction is photo and video sharing. In the game World of Warcraft the core interaction is enabling users to explore the landscape, fight various monsters, complete quests and interact with players and non-player characters within the game.
Platforms are usually built according to certain design principles. The core business interactions of an application dictate what functions it should enable.
For readers who are trying to build virtual economies on top of their existing platforms it is important to keep your platform’s design principles in mind while working through the steps defined in this post.
For the readers who are in the process of conceptualizing their platforms while reading this, you have the freedom to design your applications after you’ve completed the steps defined in this post. However, it may be beneficial to create a few basic platform design principles now and adhere to them while going through the steps. These self-imposed design principles will give you a frame of reference while thinking through each step.
Virtual economy design and management is an iterative process. The first time you go through the steps you will have a blueprint to create a virtual economy which suits your platform and business model. There may be some loose ends and unresolved design and architecture issues your first time around. As you create a virtual economy and experiment with it for a few days, you’ll generate a lot of data and get a lot of feedback. You should refer to this data as you work toward optimizing your virtual economy in consecutive iterations.
To manage a virtual economy and ensure that it is buzzing along at a healthy pace you will need to closely track a few key metrics. Identifying which metrics to track and defining an acceptable range for each metric should be done early on.
Interoperability is a trend that is really emerging in the world of blockchain platforms. In a sense, interoperability exists between every platform when we use dollars to exchange value on a platform, but platform designers can purposefully build in interoperability on their platform with other worlds. It’s a double-edged sword of course: you want to regulate your virtual economy in a way that it is safe from inflation or deflation and complete interoperability between other platforms could possibly destabilize your environment. Controlled or closely monitored interoperability would be a prudent strategy. Where limited points of interoperability are defined and monitored. These points can be specific exchanges or marketplaces.
Making your platform accessible to everyone reduces the friction of onboarding new users. Some people prefer using their laptops while other prefer their phones or tablets. Designing your platform so that it can be accessed by all devices and still provide a consistent experience is key. Millions of users who haven’t been on the internet before will be active users in the near future. With the advent of 5G, data consumption and streaming is set to grow across the world, even in geographies that have historically low digital consumption.
UI & Design
Again, your platform’s UI & UX will completely depend on the nature of what you’re building. Simplicity in design is a timeless feature, the importance of which cannot be overstated.
Additionally, Augmented Reality and Virtual Reality is already a reality and is set to grow exponentially. This will have significant implications for advertising, retail and media. It wouldn’t hurt platforms to experiment with AR & VR features.
The Freemium strategy may or may not work for your platform. Freemium is when its free to enjoy for all users but for access to extra features and levels they have to pay. Games often have these features where a lot of gameplay is free but then special levels or armor must be purchased in game. Businesses and publishers looking to gamify some of their offerings could experiment with this feature as well.
Design for Value Exchange Mechanisms
Value exchange mechanisms are the different ways in which users can exchange value. It could be through messaging or it could be by following another user. It could even be micro-transactions or a larger economic exchange of value. The more regular the exchange of value, the higher user engagement will be. Implement and embed value exchange mechanisms seamlessly in your environment.
In order to succeed in building a robust virtual economy you must:
A virtual economy is a live thing and needs to be maintained daily.
I hope you found the information and ideas presented here to be valuable and actionable. The points covered in this post are just fundamentals to get you started. There is no recipe for really complicated and dynamic situations, but if you gather data, use the right metrics and think strategically about every decision, you will create a vibrant virtual economy.
I’m passionate about creating a future in which everyone can meaningfully participate in an economy. I believe that data and experiences from virtual economies will enable social scientists, economists and policy makers to improve our real-world economies and possibly advance universal basic income research.
Blockchains are one form of distributed ledger technology. Not all distributed ledgers employ a chain of blocks to provide a secure and valid distributed consensus.
A blockchain is distributed across and managed by peer-to-peer networks. Since it is a distributed ledger, it can exist without a centralized authority or server managing it, and its data quality can be maintained by database replication and computational trust.
However, the structure of the blockchain makes it distinct from other kinds of distributed ledgers. Data on a blockchain is grouped together and organized in blocks. The blocks are then linked to one another and secured using cryptography.
A blockchain is essentially a continuously growing list of records. Its append-only structure only allows addition of data to the database: altering or deleting previously entered data on earlier blocks is impossible. Blockchain technology is therefore well-suited for recording events, managing records, processing transactions, tracing assets, and voting.
Cryptocurrencies, such as Bitcoin, pioneered blockchain technology. Bitcoin’s big rally in late 2017, and the ensuing media frenzy, brought cryptocurrencies into the mainstream public imagination. Governments, businesses, economists and enthusiasts are now considering ways to apply blockchain technology to other uses.
Hashgraphs are also a form of distributed ledger technology.
A hashgraph is a patented algorithm that promises the benefits of the blockchain (decentralization, distribution, and security through the use of hashing) without the drawback of low transaction speed. It was created by Leemon Baird and is the intellectual property of the Swirlds Corporation, which Baird founded.
While Bitcoin allows for approximately 5 transactions per second and Ethereum allows for approximately 15 transactions per second, a hashgraph can process thousands of transactions per second.
The hashgraph algorithm operates through two techniques: Gossip about Gossip, and Virtual Voting.
To understand Gossip about Gossip, imagine five members: A, B, C, D, and E. Each member starts with a transaction, which results in an ‘event’. Then, each member calls another randomly selected member and the two share their transaction history. For example, D calls B and shares D’s transaction history with B. This type of call happens repeatedly, with each member randomly calling another member and sharing its transaction history. So, B now randomly selects another member (let’s say C), and shares its transaction history, which includes D’s transaction history. Simultaneously, E may have called A, and so on. Each call results in an event, and each event holds the hashes of all previous blocks.So, once a member learns about a new piece of information, this information quickly spreads until everyone knows of it.
Virtual voting aims to reach a consensus on the order of transactions. Here’s how it works: first, the events are divided into rounds. The hashgraph algorithm has a definite mathematical answer for when a round is created. Here, for the sake of simplicity, imagine that a round has approximately ten events. Now, each member votes to determine which event should qualify as a ‘famous witness’. To understand how this happens, imagine that each of the members with an event in the next round looks backwards to each event in the current round to see if it can trace its lineage back to the current round’s event. If it can trace its lineage back to an event, it votes yes for that event, and if not, it votes no. The current round event with the most votes is crowned the famous witness for the current round, and provides the definitive order of transactions.
Private & Public
Both hashgraphs and blockchains can exist in public form or in permissioned private forms for enterprise use. Anyone can participate in the public open versions of these technologies. While several public blockchains such as Ethereum exist, the only public version of a hashgraph is called Hedera Hashgraph.
Open Source vs. Patented
Blockchain technology is mostly open source and has a huge community that builds and contributes to various blockchain efforts, from cryptocurrencies to utility tokens. Additionally, blockchain enthusiasts have generally doubted the trustworthiness of traditional institutions, and played up the decentralized nature of blockchains as their defining quality.
On the other hand, hashgraphs are based on a patented algorithm that is owned by Swirlds, and therefore any new hashgraph initiative will rely on Swirlds.
Blockchains and hashgraphs are two implementations of distributed ledger technology. Blockchains employ a single chain of blocks to provide a validated, secure, and distributed consensus. This technology underlies Bitcoin and cryptocurrencies, but also a range of use cases including payments, supply chain, and identity management. Meanwhile, the hashgraph is a patented algorithm that uses the Gossip about Gossip and Virtual Voting techniques across several, parallel lines to achieve fast and secure ledgers. Blockchains are more mainstream and more likely to be public. The Hadera Hasgraph is the only public implementation of the hashgraph algorithm.
Soon, we will begin to see more novel implementations of distributed ledger technology, beyond the blockchain and the hashgraph.
This year’s election in Italy was shocking. The populist, anti-establishment Five Star Movement party emerged from obscurity to become the largest party in government. Its central campaign plank was to pay a Universal Basic Income (UBI), locally referred to as the “citizen’s income” and “citizen’s wage” to the poor. Italy has long suffered from mass unemployment. The new government is fulfilling its campaign promise: it has introduced a UBI of €780 per person, every month, in its draft 2019 budget
Technology is Destroying Jobs
Economists have often wrongly predicted that technology will destroy jobs. Today, a widely held view is that technological advances will make up for the jobs they destroy by creating new ones, as they have in the past. But this time could be different.
AI could optimize workflows to the extent that minimal human contribution is required, thereby destroying more jobs than it creates. Take the example of taxi drivers. Uber and Lyft have reduced their wages, but increased the demand for their services. When cars become fully self-driving, these drivers will have to seek work elsewhere. A similar example is truck drivers. They will first transition into the passenger seat, supervising self-driving trucks. In the next phase, they will no longer be necessary.
In a couple of decades, the interaction of AI, robotics and hive robotics, digital automation and the Internet of Things could make most jobs unnecessary — just as the population of the world hits an all-time high.
Unemployment, Underemployment and Wage Stagnation are Rampant
Unemployment is already a major issue. Despite the booming US economy and high employment figures, wages are stagnating, a large percentage of people are underemployed and there has been a long decline in the labor participation rate (more people have been staying home and not looking for work).
Even when the global economy is healthy, unemployment is a major problem in much of the world. After the next economic downturn, the world will witness mass unemployment and an even more volatile social environment.
To address unemployment, underemployment and wage stagnation, populist politicians in Italy and elsewhere have turned to UBI as a campaign plank.
What is UBI?
Universal Basic Income (UBI), sometimes referred to as a ‘Basic Income Guarantee’ or ‘Universal Demogrant’, is a proposed welfare program in which every citizen of a country receives an unconditional, regular, and livable sum of money from the government.
Since UBI is unconditional, it is received by the employed and the unemployed alike, and the rich and the poor alike.
UBI’s critics sometimes liken it to socialism or even communism. This is wrongheaded. History has shown communism to be a failed ideology that has led to colossal economic mismanagement and waste, violent unrest, and even genocide and mass starvation. Communist and socialist regimes have often consolidated power in a dangerous manner, enabling government officials to create state-sponsored economic fiefdoms at the expense of the citizens they purport to govern. UBI is an economic program that can co-exist with our tried and tested, modern, capitalist economic system. UBI’s aims are limited and simple: to support the basic needs of every citizen and to eradicate extreme poverty.
Policy Makers should Grapple with Technology’s Effects
Policy makers are presiding over a political and economic system designed over 70 years ago and tweaked a few times since then. It has given us a period of economic growth, technological innovation, and increased personal freedom.
It will have to be tweaked again to head off technology-sponsored mass unemployment.
Already, we are experiencing a backlash against big tech companies in the United States and around the world. Companies that were benign startups in the 1990s have emerged as market-leading conglomerates, and can deploy massive capital, data, and algorithms, not to mention the best tech developers and thinkers that money can buy. The power they have aggregated has increasingly engendered fear and suspicion.
These feelings will only become stronger if technology continues to destroy jobs. The process is far along in many industries, such as agriculture and advanced manufacturing. It is likely that the labor participation rate will continue to drop, with short bursts of job creation masking the overall downward trend.
However, elected officials around the world increasingly find their terms plagued with gridlock, political jockeying, and preparing for the next election. This is hardly the ideal environment for grappling with complex, long-term problems like the threat of mass unemployment in the future. Even the lucky few political leaders who are able to pursue their agendas often attract investment with the hope of creating jobs. Ironically, the sector that attracts the most investment is technology — the same sector which is poised to destroy our jobs in the long term.
This is not to say that policy makers should oppose the march of technology. This approach would be akin to trying to patch a crack in the hull of an oil tanker with a bandage. A more pragmatic approach is to recognize the issue of technology-created unemployment and address the consequences it may have.
The Economic Perspective
Economists often argue that despite UBI’s noble aims, it is too expensive. The amount paid to every citizen must come from somewhere. They are also often concerned by UBI’s effects on incentives to work and be productive.Running perpetual government deficits could be disastrous for the national debt, the money supply is controlled by independent, technocratic, and risk-averse institutions, and all of this exists within a complex, interconnected global economic system. The shockwaves of a poorly-implemented UBI program in one country may be felt by economies across the world. So, how can a government fund a monthly stipend for each of its citizens?
A recent model developed at Wharton (at the University of Pennsylvania) suggested three potential ways to pay for UBI in the United States: by running deficits, by increasing the payroll tax, or by external financing (similar to oil revenues from Alaska providing stipends for all of its citizens).
The model found that UBI would reduce the hours people worked and would reduce the GDP. If UBI is not viable in the United States, it follows that it is even less viable in emerging economies. This explains why most economists oppose UBI.
Power rests with Politicians, not Economists
However, in democracies, economists do not dictate policy — the political process does. In the next economic recession, populist leaders will seize upon unemployment, underemployment, and economic anxiety to promise UBI.
What has happened in Italy could happen elsewhere. Therefore, it is important to think about how UBI and similar programs could be implemented effectively, before a politician promising the moon comes along and clumsily enacts a thoughtless or counterproductive policy.
There have already been many UBI studies and pilots in various parts of the world. Recipients often continued to spend money, were less likely to slip into alcohol or drug abuse, and strived to better their situations. The challenge is to retain these benefits while minimizing unintended costs.
A ‘Economic Stack’ of Proposed Solutions
The ideas below are my contribution to the UBI debate. These ideas should be tested, tweaked, and iterated, with the aim of better modeling a UBI that can enhance the quality of people’s lives without harming the global economic system. Software developers are familiar with the idea of a ‘stack’, which contains a group of technologies that work together, but in which any element can be replaced. I think of the three ideas below as elements of an ‘Economic Stack’: they can be mixed and matched, and they can be tested on top of our current economic system
Proposal 1: Expiring Money
Blockchain technology and cryptocurrencies have introduced money that can be programmed with conditions and variables. I propose adapting this idea to UBI by issuing fiat currency tokens (that represent regular money), which can be programmed to expire at a specific time or when certain conditions are met. A whole array of strategies can be created with this technology. For example, imagine a UBI token (representing $200) which vanishes if not used by the end of this month. This ‘use it or lose it’ feature would ensure that people would spend their UBI income (thereby stimulating the economy), since this would make the UBI generated money impossible to save
Proposal 2: Money Programmed for Certain Goods & Services
Rather than providing money, governments could provide tokens that represent a basket of critical goods and services which each person is entitled to every month. Each person would be free to claim the goods and services each month, or not to. This already exists in many forms (for example, like food stamps in the United States).
Categories of programmable tokens would be innovative because: 1) the funds would be fully digital and could be spent by cell phone (no need for cash or bank transfers, credit cards, or physical stamps that could be lost or destroyed), and 2) the basket would be divided into categories of goods and services (those in each category would be redeemable by corresponding tokens). For example, imagine three categories of coins. The first category is green coins, which can only be spent on rent and associated living expenses. The second category is red coins, which can only be spent on food and beverages. The third category is yellow coins, which can only be spent on medical and other health-related expenses. A government could then decide how many units of each category to dispense to each person each month.
Proposal 3: Tax Benefits for Merchants Accepting Programmed Money
Governments and people have incentives to experiment with new markets, tools, and contracts to help achieve a functional UBI. However, we also need to get providers of goods and services on board: if not properly implemented, either of the above proposals could create black markets for unused currency. It is thus important to ensure that colored money is only spent directly at trusted vendors or at authorized locations (and cannot be transferred to other parties).
Trusted businesses providing these basic goods and services and accepting programmable and/or colored money in return should be given incentives to participate in UBI experiments and pilot programs. These incentives could come in the form of a tax break in a magnitude proportional to how many colored coins are received.
Programmable money can be used by designated cards or even mobile devices.
These are my initial thoughts. I encourage readers to build on these, experiment with them, mix and match, combine them with other ideas, or come up with better, novel ideas
Populist politicians around the world will soon follow Italy’s lead in promising UBI. Public confidence in financial institutions, media companies, governments, healthcare companies, and the judicial system is very low worldwide. The next economic crisis could create a perfect storm for populism.
The populists are tapping into a legitimate grievance. In many ways, the world has never been better:
Given these achievements and positive trends, it is reasonable to ask why so many people in the world live in economic anxiety, and worry about meeting their basic needs.
It is time to proactively explore the impact of a possible UBI on economic incentives and poverty.
I love technology. I am passionate about the infinite possibilities it will help us unlock. But it is a double-edged sword. It could destroy hundreds of millions of jobs in the next few decades. We need to take a proactive approach to plan for this contingency.
I encourage people to take any of the ideas presented in this essay, play around with them, build upon them and work to better understand the UBI puzzle.
Advances in computing require appropriate hardware. Though computers have become smaller and more powerful over time, the power of regular computers (also known as classical computers) is limited.
Quantum computers are a new generation of computers built to solve the problem of exponential scaling (for example, finding the optimal solution to a problem in which there are too many possibilities for a classical computer to analyze).
Background on Classical Computers
Classical computers have many components (including main memory, arithmetic unit, control unit, and others). They represent, process, and control data through these components. Computer chips contain modules, which include logic gates, which in turn include transistors.
A computer module is a collection of electronic circuits on a circuit board. The logic gates are tiny computers within the computer itself. They look at two bits and push one of them out as an output. Their job is to read any input, in order to produce an output. A transistor is a switch that either allows or denies information to pass through it. The combinations of the logic gates form modules that allow for the basic functions of a computer. If we think of the transistor as an electric switch, the electricity is moving from one place to another when the switch is on. If the switch is off, then electrons are blocked.
Computer components are getting smaller. A typical scale for transistors today is 14 nanometers, which is 500 times smaller than a red blood cell. As these transistors decrease in size, electrons can move to the other side of a blocked passage, resulting in it not being blocked at all. This process is called ‘quantum tunneling’, and it is slowing down our technological progress.
Our computers are based on a binary system (also known as a base 2 numeral system), which uses 0 and 1 as bits. A bit, derived from ‘binary unit’, is a unit of information for a computer that holds the values of 0 or 1. For example, a 64-bit computer can work with 64 binary numbers at a time.
Combinations of these bits are used to represent more complex data and operations. The logic gate performs a Boolean function, producing a single binary output. Boolean logic is a division of algebra that is used to create true and false statements. Since classical computers operate in binary, their logic is expressed in Boolean terms. The computer uses operators such as AND, OR and NOT to express the value and return a true or false output. For example, if you have values for x and y and the logical expression states x AND y, the computer would return true if both are true, while if it said x OR y, it would return true if at least one were true.
An easy programming example, minus the programming language would be:
x = 2, y = 4
x AND y are greater than 1: this would return true.
x OR y are greater than 2: this would also return true, as y is greater than 2.
In a classical computer, a true statement could (for example) return a value of 1, while a false statement could return a value of 0. This forms the basis of classical computing: though most calculations require more than one simple true/false statement, classical computers are large combinations of these binary statements. This is what everything from clicking a mouse to opening a browser rests on.
What Are Quantum Computers
For certain problems that a classical computer solves, you may need a very small number of logic gates. However, if you want to find the factors of an extremely large number, it is going to take a large magnitude of logic gates (which that a classical computer will not have).
A quantum bit, known as a qubit, is a computer bit that is able to hold two different states at once, meaning that it can hold a position of 0 and 1 at the same time. A regular bit can only hold one of the two at a specific time.
A qubit can be any two-level quantum system - a spin and a magnetic field, or a single photon (particle representing a quantum of light). This system’s possible states are 0 and 1. Within the quantum realm, the qubit can be in any proportion of both states at once; this phenomenon is called superposition.
Superposition allows quantum computers to analyze far more possibilities than a classical computer. As soon as you test the value of the photon by sending it through a filter, it needs to decide on vertical or horizontal polarization. You cannot predict if it will decide on position 0 or 1. When in superposition, the photon is in some combination of both 0 and 1 simultaneously. However, as soon as you measure its value, it collapses into one of the defined states.
Think of 4 bits that can be on or off. Such a system would have 2^4 (so, 16) possible combinations. In a traditional setting, you can use only one of these. However, qubits could hold all of these 16 combinations at once. This number grows exponentially with every added qubit.
Qubits can hold the property of ‘entanglement’, a close connection between qubits that allows them to react to a change in the other’s state instantly no matter how far apart they are. Therefore, when measuring one entangled qubit, you can directly conclude the properties of its partner without having to test it.
As a traditional logic gate receives a simple set of inputs, it produces one definite output. The quantum gate manipulates an input of superpositions, rotates probabilities, and finally produces a determined state as its output. To break down the steps, a quantum computer:
The essential power of a quantum computer is that you can consider many states simultaneously. In order to make it work, its algorithm must be able to produce an end state that is readable (so, the information that you read out at the end cannot have superpositions). This means that quantum computers require a more complex algorithm design to be useful.
Where Quantum Computers are Effective
Quantum computers will most likely not replace our home computers. However, they can be superior in areas such as data searching. To find something in a database, a classical computer must test every one of its entries. A quantum computer will take the square root of that time to come up with the same answer.
Quantum computers can challenge existing IT security measures. Currently, data is protected by various levels of encryption. In this case, you can give everyone a public key to encode messages only you can decode. While technically this public key can be used to calculate your secret private key by the use of trial and error on a classical computer, it would take far too long to be worth anyone’s time. A quantum computer with exponentially higher speed will be able to do it much faster.
Two conceptual explanations of how quantum computers work
You have a 10 person dinner party. You need to figure out how to seat everyone. There are 10! = 3628800 ways of doing so (where ‘10!’ is pronounced ‘ten factorial’ and represents 10x9x8x…x1. A classical computer will have to go through each of the 3.6 million ways individually and then compare them to figure out the best optimization.
A quantum computer would:
Here we have a maze. Imagine you are in the center of it and want to get out at either of the two exits. You can start exploring each path, one by one. After a lot of tries, you will finally get out of the maze.
Now, imagine you have with you several clones of yourself. Everyone can start exploring the different ways, and one clone will directly find the correct way out. You and your clones were exploring all the different paths at once, meaning that you were all in different places at the same time. You were in a superposition of states, like a qubit. This allows you to find the best solution possible, quickly.
These simplified, conceptual examples help explain why quantum computers (when created) will be immensely helpful in solving large, complex problems.
LIDAR: Laser-Shooting Sensors for Self-Driving Cars
Lidar technology is mainstream again, because of its use in self-driving cars. Lidar has been tremendously useful for decades now, in discovering mining sites, predicting earthquakes, mapping disaster areas before search and rescue operations, and measuring cloud density at airports. This article will dive into where Lidar came from, how it works, and how it is used in autonomous vehicles today
Mapping the Moon — and Mayan Ruins
Lidar (short for ‘Light Detection and Ranging’ technology) is a technology used to detect, range, and map its surroundings. It is similar to radar and sonar, but uses light beams. Light has been used as a measurement tool since at least the 1930s, when light beams were used to measure cloud distances. Lidar was created in the 1960s and entered the public imagination when it was used by the Apollo 15 crew to map the moon’s surface.
A Lidar machine consists of a laser pointing downwards from an aircraft and shooting up to 400,000 pulses or beams per second. The machine then uses active sensors to measure the energy of the reflection it receives. The resulting map of the surface below can be accurate to a within a couple of inches, and is called a ‘point cloud’. Lidar works naturally with GPS, since mapping requires both measurement and positioning. Lidar planes map small segments of land by flying in a pattern over the target area in grids.
Lidar is useful for creating topographical maps. Archaeologists have used it to discover Mayan buildings covered by vegetation in the Central American rainforest. It has also been used to determine ocean depths in shallow areas near land (using two lasers: one for the water’s surface and one for its floor).
Giving Sight to Self-Driving Cars
Luxury car manufacturers have long used Lidar for Cruise Control mode (which allows a car to maintain a certain speed while the driver still pays attention to the road), by mounting a sensor to the front bumper to measure changes in speed and to look out for erratic movements in the cars ahead.
In 2005, German company Sick AG won a DARPA Grand Challenge by mounting five Lidar units on its vehicle. The Grand Challenge included self-driving cars and teams from around the world. The cars were put through a series of tests, like driving in traffic, merging, parking, passing others, negotiating traffic, and performing more complex maneuvers.
Another participant in the Grand Challenge was Dave Hall. Hall had grown bored of running an acoustics company that specialized in subwoofer technology. So, he turned his attention to self-driving cars. In 2007, Hall created a 3D Lidar of his own by packing 64 emitters into a flattened round device on top of his car. The emitters and the on-board computers gave him a precise picture of his surroundings. Hall adapted his prototype for commercial use, and his company released the Velodyne PUCK Lidar sensor (which has since gone through a number of upgrades).
Despite Lidar’s use in obstacle detection and avoidance systems in self-driving cars, it cannot function on its own. (For example, Lidar cannot read traffic signs or comprehend traffic lights.) It is instead used in concert with other sensing systems, including radars and visual cameras. The sensing systems then work with onboard computation to navigate the car.
Thanks to advances in computing power, data storage, and machine learning, Lidar-enabled systems can now differentiate between bicycles and motorcycles, and between children and grown-ups. This allows self-driving cars to understand the flow of traffic and people at a more granular level.
The Rise of Solid-State Lidar
So far, a Lidar system (a laser and a sensor) has had to rotate to scan a surrounding area, making the system either large or expensive.
The recent advent of solid-state Lidar, in which the entire system rests on a silicon chip and does not rotate, has allowed for the twin benefits of more compact systems and more accurate readings. Solid-state Lidar systems are also more durable, which is key for self-driving car manufacturers. So, car manufacturers are paying more attention to emerging solid-state Lidar companies, such as Quanergy and LeddarTech. This year, BMW announced that it will use solid-state Lidar in its self-driving car efforts.
If Lidar technology continues to improve rapidly, companies will be able to offer stationary, compact, and durable Lidar systems for very low prices. Lidar will then become indispensable — not just to self-driving cars, but also to drones and robots.