Why Would Cryptocurrencies Survive and Prosper?

X-Order have been investing in research work in Singapore.

One of our research initiatives is the IEEE International Data Mining Conference (ICDM) from 17–20 November 2018. Our founder Tony, and Data Scientist Zeng Li spoke on why cryptocurrencies would survive and prosper.

Using the Agent Based Model, we simulate this multibillion phenomenon from its early stage, and detect the key factors and thresholds that affect the evolution of Bitcoin. Then, through the lenses of complexity theory, we created a demand network model, which we simulate cryptocurrencies’ expansion into multiple domains. In this network model, the expansion from a demand node to another can be influenced by surrounding nodes in a complex way. This research pilots the possibility of using a systematic and quantitative way to explain the diffusion of a technology with an ever-growing commodity attribute.


10,000 BTC. That was the price of two pizzas in one of the first ever transactions done in Bitcoin. That was back in 2010. [3] Bitcoin’s growth has been a shock-story, from $0.0025 USD/ Bitcoin to its peak of nearly $20,000 USD/ Bitcoin. [2] In 2017, within less than 1 year, Bitcoin had spiked 1034% compared to other financial assets which had less than 600% growth (in ten years!).

Bitcoin aside, a vibrant cryptocurrency market has also emerged. As of 15th November 2018, 2095 cryptocurrencies were circulating on the secondary market with a total market cap of $205 billion USD. [1] This is equivalent to a mid-size country’s annual GDP!

Is Bitcoin an Asset Bubble?

With such a large market, liquidity and speculation come hand in hand. However, even after Bitcoin lost half of its peak value early this year; active transaction addresses, which signifies potential active users, have seen a steady recovery.

Rise on the User’s Side

The Metcalf Theory states that an increased number of end users would exponentially grow the potential connections among a network system, which suggests an increase in the intrinsic value of the network.

But why? Why would Bitcoin survive?

There are other alternative technologies such as Bitgold, which did not last for long — but why would we then believe that Bitcoin will survive?

Bitcoin Alternatives

Basic Theoretical Framework

There are three networks of actors with interactions among each other.

  1. Agent Network: Participants in the market are represented by agents. Each has a different set of demands, which can be fulfilled using corresponding technologies.
  2. Demand Network: It is a dynamic network of demands, representative by all agents.
  3. Technology Network: Technology is defined as a way to fulfill one’s demand. By this definition, apple can be a technology when it is used to fulfill the demand for eating.
Three Layers Framework

There are two ways to expand the demand network.

  1. Demand and technology are considered twin system because they need each other to evolve. They intertwine with each other, creating new demand.
  2. Agents’ demands are dynamic, they change based on new information received, therefore creating new demands.

This can be understood from the Economics’ perspective:

· Technological advancement lowers the barrier of being a manufacturer, and cost of production, resulting in an increase in supply (from S1 to S2).

· This leads to a movement along the demand curve (from E1 to E2).

Supply Curve Shifts Right

Evolution of Cryptocurrency

To illustrate the evolution of our theoretic framework:

  1. During the 1st Stage: A technology is introduced and survive in a specific demand domain. Different colours represent different technologies — the blue technologies emerge and gradually get a stabilized share of the market.
  2. During the 2nd Stage: From this domain, the technology starts to mature and gets introduced to other domains, creating more use cases through the demand network.

To gain a more realistic perspective, we start with defining the potential demands of cryptocurrency. It all starts from the first and most important domain, which we think it is when the Bitcoin has became one of the popular payment methods of the Darknet. This creates stable user channels, and liquidity for the technology, which laid out the foundation for the technology’s further evolution.

Starting from the first demand domain, and perhaps the most critical one, we proposed an Agent-Based Model (ABM) to illustrate how Bitcoin, as a technology, is introduced and gained a stable market share.

A Demand Chain Reaction

ABM Simulation

Bitcoin is identified as a representative of emerging technologies, which from the beginning to its present survival stage is a complex process. With characteristics of its own coupled with market development factors, the ABM method can be utilized to reflect the process.

To study the key factors that allow Bitcoin to emerge and survive, we will embody Complexity Theory’s characteristics in the model on the basis of the theory of increasing returns. The main characteristics of complexity theory are decentralized intersection, absence of global controller, continuous adaption and infinite innovation.

Model Outlook

· Agent: People (using technology)

· Network: More applicable for social network model — scaling-free network (the Barabasi-Albert preferential attachment model) — decentralized intersection and absence of global controller

· Interaction: Only agents within one degree of connection are considered — decentralized intersection

· Decision: Uses personal preferences and increasing return functions to make comprehensive decisions — continuous adaptation and infinite innovation

· Mutation: Mutation mechanism of subject preference and the imitation mechanism of decision-making is put in place to increase the randomness of the model realistically — continuous adaptation

Within a Scaling-free Network
Decision Making & Mutation

Theory of Increasing Returns

W. Brian Arthur’s (1983) original paper was turned down by 4 top journals over a 6 years period, probably because increasing returns seemed rare and obscure. The paper was finally published in the Economics Journal.

Even then, the theory only became prominent when it was identified that high-tech markets operated under increasing return. If a technology has a network of users and flourishes, this enlarges its network and boosts its advantage, allowing it to go on to capture a large portion of the market. By the 1990s, the theory has become a staple for tech markets, with Silicon Valley embracing increasing returns. Eric Schmidt, then CTO of Sun Microsystems, was quoted saying “we launched Java based on Arthur’s ideas”. [4]

Mathematics Equation & Further Explanation of Theory

Simulation Results

Graphical Representation of the Technology Coefficient

The results of this model are mainly based on the fact that the technology gain function is a solution-free linear function. k is the technology gain coefficient, which represents the average speed of technological development.

It can be seen from the top and middle image respectively that when k<1 and k>1, there are different steady states. This indicates that society’s average technology gain coefficients are crucial for the emergence and survival of emerging technologies. The bottom image shows an increase in the survival probability of Bitcoin in the presence of people who have a strong belief in it. The current results have not been smoothed.

Further, it can be seen that Bitcoin’s emergence and survival are not only related to its own advantages and speed of development but also closely related to the average speed of technology development on the larger scale. There are two main market equilibrium results that stem from several technologies co-existing in the market as the technologies progress further. The first result is that all technologies capture equivalent market share, and the second is to have only one technology monopoly. These two kinds of equilibrium are obtained under the condition that the speed of technology development is different.


For the ease of demonstration, we simulate the market share movement and progress using two technologies in a scale-free network of 150 subjects. In the actual simulation experiment, 10,000 subjects were used across a 200-weeks’ timeline.

Simulation Illustration

Despite the robustness of the model, there are three main shortcomings:

  1. It currently uses a static network
  2. The current technology gain function is a linear unbounded function
  3. The way technology development is explained here is only related to the number of participants who used the technologies

Moving forward, here are our next steps as we continue to build on the model:

  1. Research utilizing dynamic scale-free networks
  2. Portray the development of technology as an S-shape growth, which multiple technologies have complex processes linkages
  3. Expand the existing list of technical attributes and portray the development of technology in other ways


[1] Coinmarketcap.com. (n.d.). Cryptocurrency Market Capitalizations | CoinMarketCap. [online] Available at: https://coinmarketcap.com/ [Accessed 4 Dec. 2018].

[2] Mauldin, J. (2014). Is Bitcoin the Future?. Retrieved from https://www.forbes.com/sites/johnmauldin/2014/12/01/is-bitcoin-the-future/#3ed12d2a2ceb

[3] Price, R. (2017). Someone in 2010 bought 2 pizzas with 10,000 bitcoins — which today would be worth $100 million. Retrieved from https://www.businessinsider.sg/bitcoin-pizza-10000-100-million-2017-11/?r=UK&IR=T

[4] W, B. (2018). Increasing Returns. [online] Tuvalu.santafe.edu. Available at: http://tuvalu.santafe.edu/~wbarthur/increasingreturns.htm [Accessed 4 Dec. 2018].

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