CRISPR stands for Clusters of Regularly Interspaced Short Palindromic Repeats. These are short stretches of nucleotide sequences crucial for the immune systems of bacteria and archaea.
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It’s being touted as a cheap, efficient tool for gene editing. 

In the case of bacteria, when primarily a virus body (aka bacteriophage) attacks a bacterium, it injects its genetic material which consists of single-stranded RNA (ssRNA) into the cell. The viral genome then takes over or hijacks the bacterial machinery to make numerous copies of itself.
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The CRISPR region has numerous repeats of CRISPRs, where bacteria incorporate small nucleotide sequences specific to the virus invading them known as ‘spacers.’ So the CRISPRs have these spacers between them and each viral invasion adds a spacer into the region.

The spacers are specific small sequences of the viral RNA that the bacteria keep as memories, just in case the same virus attacks again. So when the virus attacks again, the spacers are used as templates to make a CRISPR-RNA or crRNA that has complementarity to the specific sequence on the viral RNA. The crRNA goes and binds to the foreign viral RNA, and the Cas9 (CRISPR-associated) protein cleaves in a particular position, leaving the viral genome ineffective to make its copies or do anything at all.

The Cas9 protein typically binds to two RNA molecules: crRNA and another called trans-activating crRNA or tracrRNA. These two RNA molecules guide Cas9 to the target site to make its final cut. This target sequence is complementary to a 20-nucleotide long stretch of the crRNA.

It has been researched and found that the protein makes a double-stranded break, i.e. it cuts both strands of a DNA’s double helix. Now, if you’re wondering why the Cas9 doesn’t attract the bacteria’s own DNA, it is because there is another mechanism to ensure this. Short DNA sequences called protospacer adjacent motifs or PAMs, are sort of tags that sit adjacent to the target DNA sequence. And if PAM is not present, it doesn’t make a cut. This is possibly why the protein doesn’t ever attack the CRISPR region in the bacteria. 

Now that we’ve understood the natural phenomenon and imagined it in action, what exactly is the purpose of it? Gene editing isn’t anything new but, at the same time, isn't old enough for us to be sure of what we’re doing. It takes years and years of research to publish one result.
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In fact, the first time we got to see what CRISPR looks like in action was only two years ago in 2017! A team of researchers led by Mikihiro Shibata of Kanazawa University and Hiroshi Nishimasu of the University of Tokyo made this possible. Here’s the Breathtaking New GIF Shows CRISPR Chewing Up DNA. 

Genetic modification isn’t too new as we’ve been cultivating crops by selective breeding practices, to increase the quality of produce, for centuries. But the production of the first genetically modified food to be granted a license for human consumption only goes back to 1992. Researchers genetically modified tomatoes to remain firm and ripe for their short-shelf life and named them Flavr Savr. They didn’t selectively breed the tomatoes, but modified the genes specific to the ripening and firmness of tomatoes and reproduced them.
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When we refer to gene-modification or editing, we mean doing so by removing a particular nucleotide (or more) from the sequence, substituting it with another, or adding a new nucleotide to the sequence.

Any changes in the gene sequence - called mutations - affect the proteins to be synthesized by them, which are responsible for the characters and features of the organism. For example, sickle-cell anemia is caused due to a point mutation, which is a change in one nucleotide of a gene sequence. As a result of the mutation, the red blood cells become sickle-shaped, and a lot of problems like general body pain, a reduced ability to fight infections, and vision issues arise. DNA profiling at the embryonic stage can tell if a baby could be born with a genetic disorder. This can then be genetically modified to reverse the mutation, and have the baby be born with no abnormality. (A side-fact: sickle-cell anemia provides a genetic resistance to malaria.)

A classic example of treating a genetic disorder by gene modification is Adenosine Deaminase or ADA Deficiency. Children born with ADA deficiency have virtually no immunity to microorganisms and are diagnosed with severe combined immunodeficiency (SCID). (These babies are kept inside bubbles free of any microorganisms to keep them alive, and are therefore called bubble babies.) Most of these babies don’t survive past the age of 2. The deficiency can be treated by enzyme replacement therapy or ERT in which they are given (through injections) the adenosine deaminase enzyme for the development and functioning of the immune system. But the problem with ERT is that the enzyme has to be introduced into the body time and again. Because of the nature of the disorder, it becomes a potential candidate for gene therapy. 

Gene therapy is the mechanism of introducing a gene in the body of an organism. Reproductive T-cells from ADA sufferers are taken out of the body, and modified to carry the corrected gene which can produce ADA. These are injected back into the body, which can then reproduce to make normal immune cells.

But what is the drawback? Gene therapy wasn’t so effective before CRISPR, given that anything could go wrong at any step. Incorporating the change at the right position is crucial for the gene’s function, but is also very challenging. Other gene-editing tools also come with many challenges and are time-consuming and expensive too. CRISPR on the other hand, is cheaper, more efficient and much more flexible and is consequently gaining a lot of traction.

Two 2012 research papers were pivotal in the study of using CRISPR. Published in journals Science and PNAS, the papers helped transform the bacterial defence mechanism into an efficient, programmable gene-editing tool.

Thanks to the studies, we know that Cas9 can be directed to cut any region of DNA. We can simply change the crRNA nucleotide sequence to bind to the complementary DNA target. Martin Jinek and his colleagues simplified the system further by fusing crRNA and tracrRNA, to create a single ‘guide-RNA’. And so the genome editing with CRISPR only requires the two components, guide-RNA and Cas9.

Moreover, designing a stretch of 20 nucleotide base pairs (hydrogen-bonded nucleotide pairs that form the two strands of a double-stranded DNA) matching a gene we want to edit, is achievable. The RNA with these 20 base pairs that are only found in the target gene and ‘nowhere else in the genome’ is vital.
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With CRISPR cuts at very specific positions can be made. It doesn’t care about the sequence of the crRNA. We can make our own crRNA complementary to the gene we want to make changes to. Our cells have their own machinery and mechanisms to be able to join back the cut ends. The cell may join them back as it is, which may introduce mutations. However we can also give our own sequences having ends acting as templates to join the cut blunt ends and thus ‘repair’ the cut - and voila, the gene has successfully been edited, theoretically speaking. 

Zinc Finger Nucleases and Transcription Activator-Like Effector Nucleases (TALENs) dominated the scene before CRISPR was heralded as the gene-editing tool. These tools can each cut DNA like CRISPR, but making and using them is difficult. However, they have their own applications and advantages:

ZFNs have an easier delivery process to the target gene. TALENs seem to have a higher precision rate than CRISPR but may cause less off-target mutations or unintended consequences. Research using these tools is still going on.
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Biotech company Cellectis uses TALEN gene-editing technology to make CAR-T therapies for leukemia, and Sangamo BioSciences makes ZFNs that can disable a gene known to be key in the HIV infections. 

​CRISPR-Cpf1 has several advantages over the CRISPR-Cas9 technique, with significant implications for research and therapeutics.

Cpf1 is similar to Cas9 in function, i.e. it cuts the target DNA.

• While Cas9 complexes with two RNAs, the Cpf1 only requires a single RNA to find the target.
• Cpf1 is smaller, so it's easier to be delivered into cells and tissues
• Perhaps most significantly, Cas9 cuts both strands of DNA at the same position creating blunt ends, which often undergo mutations while joining; whereas Cpf1 cuts both strands at different positions, leaving short overhangs or sticky ends that can be joined more precisely.
• Cpf1 cuts far away from the target site so that even if the targeted gene has already been mutated, it can be cut again to introduce more mutations.
• Cpf1 also provides more flexibility in choosing target sites. Cas9 recognizes naturally occurring PAM sequences to make its cut, while Cpf1 recognizes different PAM sequences, leaving more flexibility in distinguishing target genomes. 

​CRISPR can very well be used in producing crops and animals that are healthier and environmentally resilient, for example BT crops. 

Experiments on mice that share more than 90% of human genes have shown that CRISPR can knock-off a defective gene associated with Duchenne Muscular Dystrophy (DMD), eliminate the HIV infection and inhibit the formation of deadly proteins involved in Huntington’s disease.
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Chinese scientists in 2015 created two ‘super muscular’ beagles by disabling a gene that directs normal muscle development.

Other CRISPR animal studies have ranged from genetically modifying long-haired goats for higher production of cashmere, and breeding hornless cows to eliminate the pain of shearing horns off. 

​Human research mostly moves the slowest due to ethical and regulatory issues. And will continue to remain slow due to the permanent nature of altering the human genome. 

This is probably where the most important ends meet, the future of medicine can be rewritten with CRISPR. The current drug discovery process is too long, given the need to ensure patient safety and gain a thorough understanding of side effects. One drug can take more than a decade to make its way to shelves, and then most likely eventually be banned because of side effects and complications. CRISPR can bring more customized therapy to the market more quickly, speeding up the traditional drug discovery process.
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CRISPR allows accurate and fast identification of potential gene targets for efficient pre-clinical testing. And since it can knock-off particular genes, CRISPR gives researchers a faster and more affordable way to study more genes, in order to know which ones are affected by a disease. It can also provide more ways to treat patients and to design more efficient antibiotics.

CRISPR is also a more efficient method of gene therapy to treat single-gene disorders such as ADA deficiency, beta-thalassemia and sickle-cell anemia.

CRISPR can also be used to successfully combat the growing problem of antibiotic-resistance, in which bacterial strains become resistant to existing antibiotics, rendering the infection untreatable. 

In the 2000s, when the ins and outs of CRISPR were still unclear, scientists at yogurt company Danisco used an early version of CRISPR, to combat a key bacterium found in milk and yogurt cultures that kept getting infected by viruses.
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Now, when climate change hinders the production of food and agriculture, CRISPR will be needed in cultivation processes. For example, cacao is becoming increasingly more difficult to grow as farming regions are becoming hotter and drier. Environmental changes will also accompany the growth of new pathogens and microorganisms that are non-existent today.
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Gene editing can make farming more efficient, and curb global food shortages for crops like potatoes and tomatoes. Crops can also be made resilient and resistant to droughts and pathogens.

Another interesting area is the production of learner livestock. In October 2017, researchers at the Chinese Academy of Sciences in Beijing used CRISPR to genetically engineer pig meat to have 24% less body fat. 

​CRISPR can be used to re-engineer microbes and create new materials. We can alter microbes to increase diversity, make more efficient biofuels and create new bio-based, environmentally friendly materials. 

​CRISPR’s potential benefits don’t end here, the list isn’t even fully defined yet; however they don’t come without their limitations. Regulatory bodies are holding CRISPR back, and slowing down research because we still don’t understand the long-term consequence of editing genes and genomes.

When CRISPR is used for human gene therapies, safety will be the biggest concern. It is a brand new tool, and may have a wide range of side effects that we may have no knowledge of. The main concern here is the off-target activity. While, theoretically, a single-gene edit reverses a mutation that causes a disease, it can also cause an unintended activity elsewhere in the genome. Similar to side effects that happen with drugs we take for medicinal purposes. A plausible consequence is also an abnormal growth of tissue leading to cancer.
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Another issue is that a mosaic generation can be formed. CRISPR can lead to a person having both edited and unedited cells - a mosaic, which can give them mixed characteristics such as having two complexions.

Moreover, immune system complications can also arise, which means that interventions and therapies may trigger an undesired response from a patient’s immune system. 

​Gene editing can also lead to biological activities due to a lack of precision, as with Cas9 protein that leaves blunt ends. While this can be combated by using Cpf1 instead of Cas9, other limitations may still remain. 

​A fantastic idea to make real-life museums - edit the genome of the embryo of the closest living relative of the extinct animal and bring them back to life. These initiatives are already being pursued by different scientific groups and organizations. But should we bring back what’s already gone? We don’t know what effects this may have on the human population and other species as we are gradually evolving to live without extinct organisms. 

Pregnant couples can be told by their doctor whether their child has the possibility of having a genetic disorder, for example Down Syndrome, which is very common. Whether the couple decides to abort the child or not is their personal choice. An estimated 92 percent of women who undergo prenatal diagnosis of Down Syndrome choose to have an abortion. Is there a way you can save the baby from having the syndrome? Yes. Gene editing. And CRISPR allows a much easier and cheaper way of doing so. This can be done for multiple genetic disorders that affect humankind.

If you’ve read or heard about the Chinese scientists editing genes of an embryo, you would also have heard about the global outcry they have received for doing so. These scientists had wanted to make the baby resistant to HIV, smallpox and cholera. However in the scientific community, the use of CRISPR or any other gene-editing tool to edit human babies is considered highly unethical, and is not even legal. This is because when genetic modification is done to a germ (reproductive) cell, the change is permanent, and will follow in generations to come unless it’s modified again, but the state of natural normalcy will never be achieved.
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You may want to ensure that your baby is resistant to a disease that they could get through the use of genetic modification. Others may want to ensure that their child has a specific eye color, or height and so on. It is from this concept that the term designer babies originates. There is growing concern that there will be no end to what people may choose to genetically modify in their children, and what this may mean for the future. To choose to bring a change in your baby would mean that you’re deciding the fate of a human being that hasn’t even been born yet.

CRISPR is a breakthrough technology which will ultimately change the world, who we are, how we live and possibly even bring extinct species back to life. It will change our eating habits as well the food that we eat. It will help us optimize modern medicines so we can fight infections, diseases and genetic disorders more efficiently. CRISPR allows us to edit genes and work with biology in a way which was never before possible. 


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Shaan Ray

Quadratic Voting is a method of collective decision-making in which a participant votes not just for or against an issue, but also expresses how strongly they feel about it. It can help protect the interests of small groups of voters that care deeply about particular issues. Quadratic Voting can be used in democratic institutions, in corporate governance, and blockchain-enabled collective decision-making.

In Quadratic Voting, each participant is given a number of credits that can be used to vote for an issue. However, the cost of casting more than one vote for an issue is quadratic, not linear. So, the marginal cost of each additional vote is far higher than of the previous vote.
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Here is the Quadratic Voting formula: Cost to the voter = (Number of votes) to the power of 2.

Imagine that a vote generally costs $1 to put toward an issue, and you have $100 of voting credits. You want to cast your vote toward protecting endangered species. Casting one vote will cost you $1. However, casting two votes for the same issue will cost you $4, casting three votes for the same issue will cost you $9 and casting 10 votes for the same issue will cost you your entire $100 of credits.

So, while you are increasing the chances of victory for your issue with each additional vote, the quadratic nature of the voting ensures that only those who care deeply about issues will cast additional votes for them.

After Democrats won Colorado’s Governorship and both of the state’s houses in 2018, they used Quadratic Voting to decide which appropriations bills to fund first. Since legislators were likely to sponsor their own bills and vote for them, the Democratic caucus sought a method to gauge which bills had everyone’s support.
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Initially, the Colorado Democrats assigned 15 tokens for each legislator to use on their preferred 15 bills. After this didn’t work well, they talked to Microsoft economist Glen Weyl, who explained how Quadratic Voting could provide a solution.

Weyl saw Quadratic Voting as a solution to the ‘tyranny of the majority’ issue. Regular voting assumes that everyone cares for an issue equally, which is rarely the case. The reality is that some legislators do not care about certain issues, care moderately about others and care deeply about a few.

So, instead, each legislator was given 100 tokens. If a legislator cast one vote each for several issues, it would cost them one token each. However, a legislator could cast more than one vote for an issue, at the following cost in tokens:

​Colorado’s experiment with Quadratic Voting was largely successful.

First-Past-the-Post: In the first-past-the-post system used in most democracies, a candidate can win without getting the votes of a majority of people. Let’s say Candidate A gets 35% of votes, B, 30%, C, 24%, and D, 11%. A wins, but we know that a majority of people voted for someone other than A.

Proportional Voting: To address this, some jurisdictions have adopted proportional voting systems. Here, if 35% of the electorate votes for a given party, then 35% of seats in the legislature given to that party, and so on. Though these systems can be seen as an ‘evolved’ version of the first-past-the-post system, they do not work when a binary (yes or no) decision has to be made.

Ranked Choice Voting: In Ranked Choice Voting (which is used by several jurisdictions in California), each voter ranks their favorite candidates. The candidate with the lowest number of votes is eliminated in each round, and that candidate’s votes are redistributed to the candidates next in each vote’s preference order ahead of the next round. Though Ranked Choice Voting has its strengths, it is a complex and time-consuming system.
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Quadratic Voting: Though Quadratic Voting is also complex, it arguably better protects the interests of small groups of voters that care deeply about particular issues. By increasing the cost of each additional vote, it disincentivizes voters that don’t care about issues from casting several votes for them. It also allows voters to show the intensity of their support for a given issue by casting several votes for it – at the expense of their ability to vote on other issues.

Modern democracies have generally used one person, one vote in their elections and legislative processes. Corporations have often adopted more sophisticated voting mechanisms (for example, allowing a shareholder to designate someone else to vote on their behalf). Complicated but arguably more democratic voting systems, such as Proportional Voting and Ranked Choice Voting, have not found widespread acceptance due to their complexity.

Now that blockchain-enabled collective decision making allows votes to be tracked in a transparent, public way, more complicated voting systems can be adopted. By allowing voters to express not just their preferences but also the intensity of these preferences, Quadratic Voting protects the interests of small groups of voters that care deeply about certain issues.


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Shaan Ray

​The Internet of Things (IoT) is a network of physical objects, including vehicles, medical devices and home appliances, that use sensors and APIs to connect to one another and exchange data over the internet.

Cheap processors, sensors and wireless networks have made it possible to turn anything from a pill to a plane into an internet-connected ‘thing’ that is more useful to its customer. For example, the Orenda coffee maker
can monitor when you wake up and heat coffee accordingly.

Such ‘smart’ devices communicate directly with one another without human intermediaries (engaging in Machine to Machine communication, or M2M). This digital intelligence attempts to merge the digital and the physical.
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The term ‘IoT device’ generally refers to objects that previously were not expected to connect to the internet. So, computers, tablets and smartphones are not considered IoT devices.

IoT is rapidly becoming a reality, as manufacturing companies adopt private 5G networks and major phone carriers roll out 5G wireless coverage.
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Tiny sensors in IoT devices send data to one another and to the cloud using wireless internet networks (Wi-Fi or 5G) and can increasingly compute and store data locally (edge computing). The principle behind an IoT-enabled smart home full of devices engaging in M2M communication can be extended to smart campuses and smart cities.

1 Smart Homes
Smart homes are the first major consumer application of IoT. For example, Google’s Nest Hub can control cameras, doorbells and thermostats around the house. AlertMe, Haier, Philips and Belkin are also market leaders in this space. The global smart home market is already valued at over $80 billion and this figure is expected to rise to $150 billion by 2024.

2 Smart Agriculture
IoT-enabled smart agriculture seeks to use sensors to track light, humidity, temperature, soil moisture and other relevant characteristics to better manage agricultural systems remotely. Such smart farming is significantly more efficient than conventional farming, since it optimizes agricultural inputs (including through irrigation) and outputs (including through targeted harvesting).

3 Livestock Monitoring
Farm use of IoT devices also extends to better monitoring of the location, health and size of livestock. It can also help prevent the spread of disease by enabling the quick identification of sick animals, so they can be removed from the group.

4 Connected Cars and Remote Fleet Management
Automated vehicles require a complex network of sensors, software and connections to navigate roads. Eventually, connected cars will use Vehicle-to-Vehicle (V2V) communication to enhance safety and ease congestion. Though driverless cars are a reality, we will likely not see a fully interconnected system of exclusively driverless cars using V2V communications for at least a decade.

IoT is also enabling remote fleet management. For example, a trucking company can remotely view each truck’s speed, acceleration, location, route, fuel, load weight, performance and driver attention.

5 Smart Cities
Smart cities use IoT sensors to measure and optimize water use, energy use, waste management, traffic, air quality and other important city processes. We are only part of the way through a strong urbanization trend. The United Nations expects 68% of the world’s population to live in urban areas by 2050. Smart cities will help mitigate the resulting strain on infrastructure and resources.

6 Health Care and Fitness
By enabling more granular measurements of health indicators, remote monitoring and more timely data analysis, IoT devices can save lives and improve health. Their applications will also benefit physicians, hospitals and insurers, for example by providing more comprehensive health information, reducing health care costs and improving treatment outcomes.

The Apple Watch and other IoT-connected wearables (from LookSee, Myo, Fitbit, Sony, Samsung and others) help track and improve fitness. Soon, we will see smart clothes with embedded IoT sensors.

7 Manufacturing
In manufacturing, IoT processes can improve the productivity of each worker, by automating routine tasks, providing workers with better data, allowing remote monitoring of processes, optimizing supply chains and improving operational efficiency. The automobile industry has already achieved great productivity and efficiency gains through IoT-enabled manufacturing. A potential downside of improved productivity is that fewer workers may be needed.

Security, Privacy and Compliance Concerns
As IoT technology improves, it faces three major challenges. First, interconnectedness may increase vulnerability to bad actors, including in sensitive areas like health and traffic. Second, there are valid concerns that smart homes and smart cities are highly surveilled environments, harming everyone’s privacy. Third, IoT systems will have to comply with a complex patchwork of privacy, security and consumer protection laws that may vary by country, state and city.

The Future of IoT
Globally, there has been significant public and private investment in improving wireless internet speed and reducing latency. Therefore, despite challenges, the IoT applications described above are becoming a reality.

In San Francisco, a new Target store in California sells only IoT devices. In Minneapolis, McKinsey is opening a retail store as a ‘stage’ for technology solutions. Interestingly, the rise of IoT is creating a new emphasis on physical ‘things’ and blurring the line between products and services.
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Shaan Ray

Secure Multi-Party Computation (SMPC) is an important subset of cryptography. It has the potential to enable real data privacy. SMPC seeks to find ways for parties to jointly compute a function using their inputs, while keeping these inputs private.
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To understand SMPC, we must first understand Shamir’s Secret Sharing Scheme. The Scheme’s purpose is to divide and distribute one secret value over several nodes or users, so that no one knows anything about the secret value. To retrieve the secret value, a minimum quorum of users must pool their data together.

​Shamir’s Secret Sharing Scheme can also be used to perform computations on a secret shared value. When we group together the results of each user’s computations on their respective pieces of data, we arrive at the outcome of the computation — without knowing the secret inputs.
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To better understand this concept, let’s use a simple example: a secret number that is larger than 1000, a hundred users within a system, and a quorum of 10 users.

Each user is given a unique number between 100 and 199.

Since the minimum quorum requirement is 10 users, any time a group of 10 or more people come together, their combined numbers, x, will reveal the secret ( x > 1,000 ) without revealing any person’s individual number.

Now, instead of using a number, let’s say the ‘secret’ is a user’s personal data.

SMPC works in much the same way: the personal data is split into several, smaller parts, each of which is masked using cryptographic techniques. Next, each small, encrypted piece of data is sent to a separate, independent server, so that each server only contains a small part of the data.

​An individual or organization looking to discover the ‘secret’ (i.e., uncover the personal data) will need to aggregate the encoded data. Additionally, it will be possible to perform computations based on the personal data, by requiring each server to perform computations on its small part of the data, without disclosing the data.
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People have recently started demanding that governments and companies safeguard their personal information more proactively, especially financial, health, demographic, or other sensitive information. Large organizations are now looking to maintain their customers’ trust by being responsible guardians of their personal data.
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In these circumstances, SMPC’s combination of encryption, distribution, and distributed computation can have a profound impact on data privacy and security.


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Shaan Ray

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

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.
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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.

There are two types of ZKPs: interactive and non-interactive.
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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.
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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

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.
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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.
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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.


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Shaan Ray

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.

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.

​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’).
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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

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.


--
Shaan Ray

In traditional software development, code developed in one computing environment often runs with bugs and errors when deployed in another environment.
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Software developers solve this problem by running software in ‘containers’ in the cloud.

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 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.

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.


--
Shaan Ray

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.
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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.
​

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:
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Bits (1001100111)

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.
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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

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.
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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.

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.
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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.
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--
Shaan Ray

​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.

Early Days

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.

AI Winters

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.
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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. 
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​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:

Automation

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

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

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

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.

​​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.

Healthcare

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.

Business

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.

Education

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.

Finance

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.

Law

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.

Society

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.
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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.

--
Shaan Ray

​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.

Abbreviations
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
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Brevity
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
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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

What is a Virtual Economy?
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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.

Collaborative Interactions

​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.

This section outlines some of the technologies and concepts which can be used in virtual economy creation.

Blockchains
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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

​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.

Tokens

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

Utility tokens represent a unit of value and can be redeemed for a good or service provided by the issuing company.

Security Tokens

Security tokens are tradeable financial assets issued by a private company. Security tokens represent either debt, equity or derivatives.

Expiring Tokens

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

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.

​Digital Scarcity

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.

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.
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Blockchain Companies
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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
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​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.

​Platform Companies

​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.

​This section presents a few economic principles which need to be understood to properly create and manage virtual economies.

Micro Economics

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

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

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

​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.

Currencies

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.

​​Governance

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.

Central Banks

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.

Currency Boards

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).

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.

Examples:

• Instagram and Twitter both have one type of User
• Uber has 2 types of Users: Drivers and Riders
• Games can have multiple types of Users

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.

Define Functions

Functions are the actions that users can take on a platform.

Examples:

• Twitter’s functions are: tweet, retweet, tweet at users, delete tweet, reply to user tweets, follow users, block users, message users, edit profile, make account private, and more.
• Instagram’s functions are: Upload posts, delete posts, add a location to posts, add descriptions to posts, comment on other pictures, follow other users, like posts, forward posts, message users, block users and more.
• Uber’s functions are: select destination, select pickup location, order a ride, cancel a ride, select number of riders, select which type of car (pool or private), report incident, add tip and so on. From a driver’s perspective, the functions are turn on drive mode, accept ride, message rider, cancel ride, among others.​

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.

Examples:

• Instagram users can edit their settings to allow or limit interactions.
• Uber Drivers and Riders have very well defined, rigid functions with limited ability to set permissions and constraints themselves.
• Games can get extremely complicated with varying levels of skills, permissions and constraints that can be set, earned, bought and lost during gameplay.

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:

• Is the function allowed or not?
• Can the user tweak the settings at all?
• Is it conditional? If yes then to what?
• Does it depend on something else?
• Can permission be bought or earned?
• Are they interacting with someone else through this function or is something interacting with them through this function?

​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.
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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.
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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.
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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.

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.

Currency Exchanges

​​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.

​Store

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.

Offline Transactions

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:

• How they can buy or sell
• What they can buy or sell
• Where they can buy or sell
• How each item they buy or sell can benefit them

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.
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​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:

1. The platform itself
2. Third party service providers on the platform
3. All categories of users on the platform

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.

Collaborations

​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.

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.

​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.

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.

Platform Design

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.

Iteration

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

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.

Accessibility

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.

Freemium
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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:

• Recalibrate your virtual economy often
• Iterate over and over till you optimize your platform
• Communicate with your users regarding changes, features and updates

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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.
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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.

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Shaan Ray