How to create a simple smart contract to manage auctions ?

Paulina Lewandowska

07 Dec 2022
How to create a simple smart contract to manage auctions ?

In the previous tutorial you learned how to create a smart contract, which is a wallet. In this tutorial we will try to consolidate the knowledge from the previous tutorial, and expand it with new knowledge. 

So let's get to work. All the things you may not understand will be explained to you later in the text.

Defining variables

Let's start by defining the variables we'll need to make the smart contract work. We certainly need :

  • address of the person to whom the highest bid will be sent,
  • time when the auction in question will end,
  • address of the person who placed the highest bid,
  • why wei is the highest bid,
  • a table of addresses and the amount of money they transferred to the smart contract so that those who did not win the auction can withdraw their money,
  • variable whether the auction is completed or not.
pragma solidity 0.8.11;

contract Auction {

    address payable public beneficiary;

    uint public auctionEndTime;

    address public highestBidder;

    uint public highestBid;

    mapping(address => uint) pendingReturns;

    bool ended;

}

What can you not understand from the above code?

Mapping in solidity is a key-value array, equivalent to dictionary in other languages.

If you don't know why the auctionEndTime variable is given in plain uinta, it's because the time when the auction ends will be given in Unix time.

Let's create a constructor that will take the address to which the highest bid will be sent and how long the auction will last.

constructor(

        uint biddingTime,

        address payable beneficiaryAddress

    ) {

        beneficiary = beneficiaryAddress;

        auctionEndTime = block.timestamp + biddingTime;

    }

What is worth paying attention to ?

  • block.timestamp is a variable that simply means what time it is now given as Unix time of course ;)))

Function for making an offer

Now let's create a function that will be used to submit your bid. The function will return an error if :

  • The auction has ended
  • Our bid will be lower than the highest bid

At the end of the function execution, the function will emit an event that the highest bid has changed. The frontend application can listen to the emitted events on the smartcontest, so that as soon as the highest bid has changed, it can update it in the user interface.

event HighestBidIncreased(address bidder, uint amount);

    event AuctionEnded(address winner, uint amount);

    error AuctionAlreadyEnded();

    error BidNotHighEnough(uint highestBid);

    error AuctionNotYetEnded();

    error AuctionEndAlreadyCalled();

    function bid() external payable {

        if (block.timestamp > auctionEndTime)

            revert AuctionAlreadyEnded();

        if (msg.value <= highestBid)

            revert BidNotHighEnough(highestBid);

        if (highestBid != 0) {

            pendingReturns[highestBidder] += highestBid;

        }

        highestBidder = msg.sender;

        highestBid = msg.value;

        emit HighestBidIncreased(msg.sender, msg.value);

    }

As you can see in the code above, we define ourselves events with parameters that we can emit on the blockchain. To emit an event in solidity we type 

emit + event name and parameters

We have defined our own errors, which if we want to call them we type in revert + the name of our errror and parameters.

You have probably already guessed that instead of ifs and custom errors we could have used requier.

This function checks if the auction has already ended, if msg.value is higher than the highest bid, if so we update the mapping pendingReturns so that the person who placed the highest bid earlier can get his money back. We assign the highest bid to msg.value and the highestBidder to msg.sender, at the end of the function execution we emit an event that informs that the highest bid has been increased.

Function to end the auction, and transfer the highest bid to the benefactor

Now let's create a function so that after the auction ends, the beneficiary's address can send money to his wallet.

This function should:

  • Return an error if the auction has not yet ended,
  • return an error if this function has already been called,
  • change the ended variable to true,
  • emit an event indicating that the auction has ended,
  • transfer an amount of Ethereum equivalent to the highest bid to the benefactor.
function auctionEnd() external {

        if (block.timestamp < auctionEndTime)

            revert AuctionNotYetEnded();

        if (ended)

            revert AuctionEndAlreadyCalled();

        ended = true;

        emit AuctionEnded(highestBidder, highestBid);

        beneficiary.transfer(highestBid);

    }

Function for people who did not win the auction and want their money back

Now, the last function we need to make our contract ready! It will be for addresses that did not win the auction and want to get their money back. Let's consider what such a function should have in it :

  • it should check how much Ethereum you owe in the mapping pendingReturns and assign this value to a variable,
  • it should change how much you owe to 0,
  • it should send as much Ethereum as you have stored in the variable.

Well, let's get to work !

  function withdraw() external{

        uint amount = pendingReturns[msg.sender];

        pendingReturns[msg.sender] = 0;

        payable(msg.sender).transfer(amount);

    }

That's the end of today's tutorial ! Our smart contract is ready. In order to practice and consolidate your knowledge, as a task you can try to replace custom errors with requirs. However, if this is not enough for you, you can also improve this contract so that it can be used to conduct several auctions at once.

Tagi

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Nextrope on Economic Forum 2024: Insights from the Event

Kajetan Olas

14 Sep 2024
Nextrope on Economic Forum 2024: Insights from the Event

The 33rd Economic Forum 2024, held in Karpacz, Poland, gathered leaders from across the globe to discuss the pressing economic and technological challenges. This year, the forum had a special focus on Artificial Intelligence (AI and Cybersecurity, bringing together leading experts and policymakers.

Nextrope was proud to participate in the Forum where we showcased our expertise and networked with leading minds in the AI and blockchain fields.

Economic Forum 2024: A Hub for Innovation and Collaboration

The Economic Forum in Karpacz is an annual event often referred to as the "Polish Davos," attracting over 6,000 participants, including heads of state, business leaders, academics, and experts. This year’s edition was held from September 3rd to 5th, 2024.

Key Highlights of the AI Forum and Cybersecurity Forum

The AI Forum and the VI Cybersecurity Forum were integral parts of the event, organized in collaboration with the Ministry of Digital Affairs and leading Polish universities, including:

  • Cracow University of Technology
  • University of Warsaw
  • Wrocław University of Technology
  • AGH University of Science and Technology
  • Poznań University of Technology

Objectives of the AI Forum

  • Promoting Education and Innovation: The forum aimed to foster education and spread knowledge about AI and solutions to enhance digital transformation in Poland and CEE..
  • Strengthening Digital Administration: The event supported the Ministry of Digital Affairs' mission to build and strengthen the digital administration of the Polish State, encouraging interdisciplinary dialogue on decentralized architecture.
  • High-Level Meetings: The forum featured closed meetings of digital ministers from across Europe, including a confirmed appearance by Volker Wissing, the German Minister for Digital Affairs.

Nextrope's Active Participation in the AI Forum

Nextrope's presence at the AI Forum was marked by our active engagement in various activities in the Cracow University of Technology and University of Warsaw zone. One of the discussion panels we enjoyed the most was "AI in education - threats and opportunities".

Our Key Activities

Networking with Leading AI and Cryptography Researchers.

Nextrope presented its contributions in the field of behavioral profilling in DeFi and established relationships with Cryptography Researchers from Cracow University of Technology and the brightest minds on Polish AI scene, coming from institutions such as Wroclaw University of Technology, but also from startups.

Panel Discussions and Workshops

Our team participated in several panel discussions, covering a variety of topics. Here are some of them

  • Polish Startup Scene.
  • State in the Blockchain Network
  • Artificial Intelligence - Threat or Opportunity for Healthcare?
  • Silicon Valley in Poland – Is it Possible?
  • Quantum Computing - How Is It Changing Our Lives?

Broadening Horizons

Besides tuning in to topics that strictly overlap with our professional expertise we decided to broaden our horizons and participated in panels about national security and cross-border cooperation.

Meeting with clients:

We had a pleasure to deepen relationships with our institutional clients and discuss plans for the future.

Networking with Experts in AI and Blockchain

A major highlight of the Economic Forum in Karpacz was the opportunity to network with experts from academia, industry, and government.

Collaborations with Academia:

We engaged with scholars from leading universities such as the Cracow University of Technology and the University of Warsaw. These interactions laid the groundwork for potential research collaborations and joint projects.

Building Strategic Partnerships:

Our team connected with industry leaders, exploring opportunities for partnerships in regard to building the future of education. We met many extremely smart, yet humble people interested in joining advisory board of one of our projects - HackZ.

Exchanging Knowledge with VCs and Policymakers:

We had fruitful discussions with policymakers and very knowledgable representatives of Venture Capital. The discussions revolved around blockchain and AI regulation, futuristic education methods and dillemas regarding digital transformation in companies. These exchanges provided us with very interesting insights as well as new friendships.

Looking Ahead: Nextrope's Future in AI and Blockchain

Nextrope's participation in the Economic Forum Karpacz 2024 has solidified our position as one of the leading, deep-tech software houses in CEE. By fostering connections with academia, industry experts, and policymakers, we are well-positioned to consult our clients on trends and regulatory needs as well as implementing cutting edge DeFi software.

What's Next for Nextrope?

Continuing Innovation:

We remain committed to developing cutting-edge software solutions and designing token economies that leverage the power of incentives and advanced cryptography.

Deepening Academic Collaborations:

The partnerships formed at the forum will help us stay at the forefront of technological advancements, particularly in AI and blockchain.

Expanding Our Global Reach:

The international connections made at the forum enable us to expand our influence both in CEE and outside of Europe. This reinforces Nextrope's status as a global leader in technology innovation.

If you're looking to create a robust blockchain system and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

Monte Carlo Simulations in Tokenomics

Kajetan Olas

01 May 2024
Monte Carlo Simulations in Tokenomics

As the web3 field grows in complexity, traditional analytical tools often fall short in capturing the dynamics of digital markets. This is where Monte Carlo simulations come into play, offering a mathematical technique to model systems fraught with uncertainty.

Monte Carlo simulations employ random sampling to understand probable outcomes in processes that are too complex for straightforward analytic solutions. By simulating thousands, or even millions, of scenarios, Monte Carlo methods can provide insights into the likelihood of different outcomes, helping stakeholders make informed decisions under conditions of uncertainty.

In this article, we will explore the role of Monte Carlo simulations within the context of tokenomics.  illustrating how they are employed to forecast market dynamics, assess risk, and optimize strategies in the volatile realm of cryptocurrencies. By integrating this powerful tool, businesses and investors can enhance their analytical capabilities, paving the way for more resilient and adaptable economic models in the digital age.

Understanding Monte Carlo Simulations

The Monte Carlo method is an approach to solving problems that involve random sampling to understand probable outcomes. This technique was first developed in the 1940s by scientists working on the atomic bomb during the Manhattan Project. The method was designed to simplify the complex simulations of neutron diffusion, but it has since evolved to address a broad spectrum of problems across various fields including finance, engineering, and research.

Random Sampling and Statistical Experimentation

At the heart of Monte Carlo simulations is the concept of random sampling from a probability distribution to compute results. This method does not seek a singular precise answer but rather a probability distribution of possible outcomes. By performing a large number of trials with random variables, these simulations mimic the real-life fluctuations and uncertainties inherent in complex systems.

Role of Randomness and Probability Distributions in Simulations

Monte Carlo simulations leverage the power of probability distributions to model potential scenarios in processes where exact outcomes cannot be determined due to uncertainty. Each simulation iteration uses randomly generated values that follow a specific statistical distribution to model different outcomes. This method allows analysts to quantify and visualize the probability of different scenarios occurring.

The strength of Monte Carlo simulations lies in the insight they offer into potential risks. They allow modelers to see into the probabilistic "what-if" scenarios that more closely mimic real-world conditions.

Monte Carlo Simulations in Tokenomics

Monte Carlo simulations are instrumental tool for token engineers. They're so useful due to their ability to model emergent behaviors. Here are some key areas where these simulations are applied:

Pricing and Valuation of Tokens

Determining the value of a new token can be challenging due to the volatile nature of cryptocurrency markets. Monte Carlo simulations help by modeling various market scenarios and price fluctuations over time, allowing analysts to estimate a token's potential future value under different conditions.

Assessing Market Dynamics and Investor Behavior

Cryptocurrency markets are influenced by a myriad of factors including regulatory changes, technological advancements, and shifts in investor sentiment. Monte Carlo methods allow researchers to simulate these variables in an integrated environment to see how they might impact token economics, from overall market cap fluctuations to liquidity concerns.

Assesing Possible Risks

By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks. This is perhaps the most important function of Monte Carlo Process, since these risks can’t be assessed any other way.

Source: How to use Monte Carlo simulation for reliability analysis?

Benefits of Using Monte Carlo Simulations

By generating a range of possible outcomes and their probabilities, Monte Carlo simulations help decision-makers in the cryptocurrency space anticipate potential futures and make informed strategic choices. This capability is invaluable for planning token launches, managing supply mechanisms, and designing marketing strategies to optimize market penetration.

Using Monte Carlo simulations, stakeholders in the tokenomics field can not only understand and mitigate risks but also explore the potential impact of different strategic decisions. This predictive power supports more robust economic models and can lead to more stable and successful token launches. 

Implementing Monte Carlo Simulations

Several tools and software packages can facilitate the implementation of Monte Carlo simulations in tokenomics. One of the most notable is cadCAD, a Python library that provides a flexible and powerful environment for simulating complex systems. 

Overview of cadCAD configuration Components

To better understand how Monte Carlo simulations work in practice, let’s take a look at the cadCAD code snippet:

sim_config = {

    'T': range(200),  # number of timesteps

    'N': 3,           # number of Monte Carlo runs

    'M': params       # model parameters

}

Explanation of Simulation Configuration Components

T: Number of Time Steps

  • Definition: The 'T' parameter in CadCAD configurations specifies the number of time steps the simulation should execute. Each time step represents one iteration of the model, during which the system is updated. That update is based on various rules defined by token engineers in other parts of the code. For example: we might assume that one iteration = one day, and define data-based functions that predict token demand on that day.

N: Number of Monte Carlo Runs

  • Definition: The 'N' parameter sets the number of Monte Carlo runs. Each run represents a complete execution of the simulation from start to finish, using potentially different random seeds for each run. This is essential for capturing variability and understanding the distribution of possible outcomes. For example, we can acknowledge that token’s price will be correlated with the broad cryptocurrency market, which acts somewhat unpredictably.

M: Model Parameters

  • Definition: The 'M' key contains the model parameters, which are variables that influence system's behavior but do not change dynamically with each time step. These parameters can be constants or distributions that are used within the policy and update functions to model the external and internal factors affecting the system.

Importance of These Components

Together, these components define the skeleton of your Monte Carlo simulation in CadCAD. The combination of multiple time steps and Monte Carlo runs allows for a comprehensive exploration of the stochastic nature of the modeled system. By varying the number of timesteps (T) and runs (N), you can adjust the depth and breadth of the exploration, respectively. The parameters (M) provide the necessary context and ensure that each simulation is realistic.

Messy graph representing Monte Carlo simulation, source: Bitcoin Monte Carlo Simulation

Conclusion

Monte Carlo simulations represent a powerful analytical tool in the arsenal of token engineers. By leveraging the principles of statistics, these simulations provide deep insights into the complex dynamics of token-based systems. This method allows for a nuanced understanding of potential future scenarios and helps with making informed decisions.

We encourage all stakeholders in the blockchain and cryptocurrency space to consider implementing Monte Carlo simulations. The insights gained from such analytical techniques can lead to more effective and resilient economic models, paving the way for the sustainable growth and success of digital currencies.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.

FAQ

What is a Monte Carlo simulation in tokenomics context?

  • It's a mathematical method that uses random sampling to predict uncertain outcomes.

What are the benefits of using Monte Carlo simulations in tokenomics?

  • These simulations help foresee potential market scenarios, aiding in strategic planning and risk management for token launches.

Why are Monte Carlo simulations unique in cryptocurrency analysis?

  • They provide probabilistic outcomes rather than fixed predictions, effectively simulating real-world market variability and risk.