Why do blockchain projects require simulation?

Reasons required

Blockchain project can be said to be its ultimate goal to build a stable ecosystem. In order to create a stable ecosystem here, it is necessary to ensure fair and reasonable distribution of coins to be paid as compensation among participants. Therefore, if you can check how the coin circulates, you can gauge the health of the ecosystem, or the success of the blockchain project. Simulation is one of the ways in which you can check how a coin circulates within an ecosystem.

Coin is not only a real asset, such as money, but also a distribution logic of the coin is built and is not easy to correct in the event of a problem. It is not technically difficult to modify, but due to the interests of participants, coordination is not easy. A similar case is the fact that hard-forking occurred in 2017 following the upgrading of bitcoin protocols and the hacking of Etherium smart contract, which resulted in different positions between participants. In addition, through simulation, one can check if the intangible ecosystem moves in the direction of the goal, as if the logic of the tangible product is not a problem. We want to first summarize the existing business modeling process that we are familiar with and evaluate blockchain business (project) modeling to improve understanding.

1. Commercial enterprise business modeling

Enterprise business modeling is a process of finding customer value. There are many different approaches, but the main method used by start-ups is LeanStartup. Here I’m just going to illuminate this one way. LeanStartup is about making simple (build) and measuring feedback quickly and focusing on customer value.

It is a strategy that aims to quickly adjust to the trend of the market by taking off from sophisticated strategies and planning after market research that is mostly conducted by large companies. The key is to move the members of the organization, constantly asking questions about why -> what -> how to do with the common goal (vision-strategy-product). So what does simulation mean for business modeling?
Simulation is to check how well the product behaves as intended. Because it is expensive to mass-produce products directly, test whether or not they can produce desired results in a virtual environment. Simulation is particularly advantageous in making dangerous products that are virtual to the product but cannot be tested directly on humans because it creates user experience data.

1.2 Blockchain business model

The blockchain business modeling process that requires coin (compensation) is considered to require the removal of the term “business” personally. Because in business, there is a trait of being smart. Blockchain business takes the form of platform business, but it allocates revenue as it has contributed to all participants, not to monopolize it, so it is an ecosystem building model such as a combination of spiritual attributes. From here on out, I will call it a blockchain project, not a blockchain business.

The blockchain project, as already mentioned, is platform business. Fair distribution of coins to be picked up as rewards is a prerequisite for successful projects. Therefore, you can check the health level of the ecosystem by monitoring the circulation of the coin. Simulation is a simulation that allows a virtual cycle of coin to be performed on a space. Experience data can also be used as predictive data because it can be created through simulation. The ability to obtain user experience data for products made by a company is a huge benefit. The blockchain project is easy to apply simulation because the starting point is virtual space.

2. simulation process

Agents Definition ▶ Establish appropriate relationships between agents ▶ Set environmental and agents’ constraints ▶ Set simulation runs ▶ Reduce side effects among feedback results ▶ Repeat

Among the many tools that can be simulated on an agent-based basis, described by Repast Symphony.

Action to consider when simulating in Repeat
— Define simulation model requirements
— Conceptual modeling (defining objects, means, purposes, etc.)
— Set model initialization
— Setting up simulation scenarios
— Setting up simulation parameters
— Check output variables
— Analyze results

3. Element technology required in simulation

Simulation is often used to assess whether a product is in the process of being built at a low cost before it is actually made. As the number of subjects that can be virtualized increases due to the development of computer technology, not only products but also new road traffic analysis and natural disaster analysis are being applied to many areas. There are a number of patterned technologies that can be applied to the simulation, but here we are only referring to those that can be used directly in the blockchain project.

3.1 ABMS

ABMS (Agent Based Modelling and Simulation) is a modeling paradigm introduced in simulations by self-determination agents. Maintain behavioral rules and individual characteristics in the agents’ decision process. Agent fittings can interact. Agents can also respond to the ever-changing changes in the surrounding environment. So it’s a simulation approach suitable for the network economy model that ties agents together. A network model simply means tying up related agents.

3.2 Mechanism Design

Mechanism design is a simulation modeling method that has been introduced a lot in blockchain projects recently. Game theory, as is already well known, is a theory that studies finding the best strategy in the rules of a given game.
Mechanism Design, as opposed to game theory, is a theory that defines the results that designers want and creates a game that encourages players to move toward the results. When considering token economy in blockchain project, designers assume optimal model and implement it by repeating and optimizing until corresponding results are released. Mechanism design is largely divided into social choice functions that designers can determine and strategies for each agent that designers cannot control.

Good Mechanism Design Conditions
Efficiency: The mechanism makes a choice that maximizes the sum of the utility of all agents.
Truthfulness: When the balancing strategy of all agents is to report your Type honestly.
Budget Balanced: If the agent’s type changes, the mechanism gets a constant income from the transfer function.
Individual Rationality: If no agent loses anything (average) when it participates in the mechanism.
Tractability: When the results of the mechanism can be calculated within polynomial time

3.3 Causation Theory

Causality is a business strategy model that assumes the best model and narrows the gap in goals while injecting resources. It is one of the predictive business strategies. STP (Segmentation, Targeting, Positioning) is a typical example.

3.4 Effectuation Theory

A model suitable for ecosystem models, such as mechanism design theory. Both are not predictive-based models, but are models based on the constraints of the circumstances and conditions under which participants are autonomous. The difference is that mechanism design leads to the optimal model, and Effectuation Theory opens all exceptions and takes a less damaging approach.

Principle of Effectuation

Centered on means rather than objectives: Strategies focus on the choice of effects in possible means, not on the achievement of prior goals.
- A more tolerable loss center than expected return: Decisions do not depend on pre-calculated ones. The decision maker may not be sure of the outcome of the choice, but proceed as far as it can afford it.
- Focused on Partnership rather than Competitive Element Analysis: Encourage partners with the same goals.
- Focus on using it rather than avoiding contingencies: Maintaining an open mind in a cumbersome situation
- Control rather than predictive: Do not predict an uncertain future, but use the surrounding situation to control the future.

4. An actual case of Effectuation and Causation theory

Paola, an entrepreneur, started a curry restaurant with a causal scenario. Carry out market research using the STP approach. Segment the market and set the initial target. In the next phase, the entrepreneur selects a supplier to provide the necessary means. The final step is to launch the product and start selling it. This approach includes investments in market research (time and money). The products that are released are expected to be profitable.

On the other hand, in an effectuation scenario, Paola starts a restaurant by available means such as a limited amount of money and a preliminary goal. She knows the know-how she needs to run an Indian restaurant. Paola contacts two friends and describes her initial business goals. One of my friends promises to help. She has expertise in delivery services. That’s why Paola now adds new goals. The restaurant offers delivery service with curry. Other friends know potential investors. The investor promises a substantial investment if she provides a more general restaurant menu. Paola now has new business possibilities (purpose). Paola therefore decides to choose a new growth target instead of an initial goal. Include more common menus. In the effectuation scenario, there were more changes than Paola’s earlier ideas. So it’s open to change.

5. Considerations in Simulation Design

5.1 Separating what is measurable from what is not

The data that the blockchain project wants to include in the blockchain will be a coin history and contribution history. The purpose of the contribution data here is to rank and distribute coins by ratio. Therefore, the distribution of the contribution data is only a normal distribution. Therefore, regardless of whether the agents’ activities involved in the simulation are quantitative or qualitative, the final contribution data is a normal distribution, so random creation and measurement of the contribution data has a normal distribution, so there is no problem with comparison analysis.

5.2 Thinking about how ecosystem participants will relate to simulation

SteemIt participants are divided into “writers, readers.” The writer’s relationship with the reader is connected by voting to the story he or she creates. Deeper inside, the reader must meet the writer’s writing on which he or she votes, and how he or she should express the vote and the writing. The existing studied relational models should be studied to see how these participants will be simulated.

6. Future plans

The following article will cover the process of extracting experience data through agent-based simulation that incorporates the theory of effectiveness in the current blockchain project.

Related Articles

1. Agent-Based Simulation of Effectual and Causal Behaviors of Entrepreneurs, Klesti Hoxha, 2012.4.
2. 메커니즘 디자인 최적화로 푸는 토큰 모델 설계, eddy song, Decon.lab, 2018.9.
3. Token Model Simulation #0, 서영교, Decon.lab. 2018.10.
4. Towards understanding startup product development as effectual entrepreneurial behaviors, Anh Nguven Duc, 2016.
5. Repast Statecharts Guide, REPAST DEVELOPMENT TEAM, 2016.9.
6. Repast Java Getting Started, NICK COLLIER, 2016.
7. Agent-based Modelling and Simulation, Charles M. Macal, 2009.
8. 린스타트업 이해와 case study, 이희우, 2015.