Game—DISASTER: Blockchain-Enabled Token Trading Game for Supply Chain Management

1. Introduction

The blockchain-enabled token trading game for supply chain management addresses aspects of supply chain management and blockchain technology that are both fundamental and timely. The global economy has gone through one of the most severe supply chain disruptions in decades, driven by the Covid-19 pandemic, resulting in shortages, panic buying, and extreme price fluctuations across industries. Having experienced supply chain shocks, students are eager to understand what caused the mismatches between supply and demand and what can be done to reduce them. The game demonstrates how markets can provide a solution. Whereas there are active markets for commodities, currently, markets do not exist for most goods that flow through supply chains. This is primarily because most goods are not standardized and would not offer high enough trade volumes to justify the high market setup and operating costs for financial intermediaries. Blockchain technology has the potential to lower those costs and enable the creation of virtual markets in supply chains for at least some goods. In our companion paper, see Wendt et al. (2023), we discuss how virtual markets in supply chains work and analyze the market dynamics in various settings.

To allow students to experience firsthand the benefits and challenges of virtual markets in supply chains and to learn about blockchain technology, we created this educational game. At a high level, the learning objectives the game helps to achieve are the following:

1. Students will be able to discuss the sources of randomness in supply chains, evaluate the costs of the mismatch between supply and demand, and explain the importance of strategic interactions among firms.

2. Students will be able to explain how virtual markets work, evaluate markets’ potential to improve supply chain performance, and be able to create trading strategies to take advantage of that potential.

3. Students will be able to analyze the strengths and weaknesses of blockchain applications in supply chains, evaluate the technical feasibility of blockchain-enabled virtual markets, and program elements of the virtual markets firsthand.

Objectives 1 and 2 are the most salient for supply chain management courses, and objectives 2 and 3 are the most salient for blockchain technology courses. Table 1 presents a breakdown of these learning objectives for each course according to Bloom’s taxonomy (Bloom 1956, Krathwohl 2002). Depending on the course level (bachelor, master, or PhD) and participants’ prior knowledge, different pedagogical objectives can be emphasized (e.g., creating smart contracts may not be suitable for a bachelor course but well-suited for a technology-focused PhD course).

Table 1. Pedagogical Objectives

In this game, the supply chain is a distribution system consisting of one supplier (played by the system) and multiple retailers (managed by the participants). Retailers have varying valuations for goods and buy claims (tokens recorded on a blockchain) on the supplier’s capacity before the random demand is realized. Once the demand is realized, retailers can trade with each other by submitting buy and sell trading orders on a virtual market. The players’ objective in the game is to maximize their profit. One conceivable example of applying such markets in supply chains could be for trading goods currently loaded on a vessel delivering to an overseas market. When in transit, companies can trade these goods among each other in response to received demand signals. This trading approach makes sure that the company with the greatest need by the time goods arrive at the port owns the goods, which supports efficient operations because it avoids extra transportation time and costs. For additional examples see Wendt et al. (2023).

The blockchain-enabled token trading game for supply chain management is hosted on the web-based DISASTER¹ platform (Hellwig et al. 2022), does not require any installation, and is customizable (instructors can modify the number of rounds, duration per round, supply chain parameters, and trading mechanism). The game can be played in 30 minutes, and with discussion, the entire learning experience lasts between 60 and 90 minutes. The game can be conducted in person, online, or in hybrid mode. The game allows between 3 and 60 participants and can be managed by an instructor with no additional support. The user interface is intuitive, and beyond instructions, no prior knowledge of blockchain technology, trading, or inventory management is required to participate. A teaching note containing a step-by-step instructor guide, test questions, and player instructions as well as a ready-to-use PowerPoint deck for deploying this game are available for download. The game, including all accompanying documents, is freely available for academic use. The data generated by playing the game is not used outside a particular classroom without permission from game participants.

2. Literature Review

In the following, we first provide select references covering the theoretical concepts (based on the discussion in Wendt et al. 2023) that form the basis of this game and subsequently review the literature related to simulation games and experimental learning.

Two mitigation approaches are widely suggested for improving the allocation of supply among multiple demand sources—namely, inventory pooling and transshipments. Inventory pooling satisfies different demand sources from a single location and reduces cost, increases efficiency, and reduces demand variability compared with decentralized approaches (Eppen 1979, Lee 2004). However, inventory pooling is typically managed by a central supply chain entity, which impedes its practical implementation, especially for firms in competition.

Transshipments exchange inventory between two locations that each have a shortage and excess, respectively. However, transshipment strategies are typically restricted by terms established prior to the realization of random shocks, rendering this approach unfit to address ad hoc mismatches between demand and supply. Other disadvantages of transshipments include costs of double inventory handling, logistics costs, risks of disclosing competitive information, and longer lead times (Tagaras 1989, Rudi et al. 2001).

Markets can provide an additional solution. To that end, blockchain technology has the potential to lower the setup and operating costs and enable the creation of virtual markets in supply chains for at least some goods (see Hellwig et al. 2020 for key attributes of this technology). The proof of owning an asset (holding a digital token) and any change in this ownership are permanently recorded in the distributed ledger, and thus, double spending is avoided. Moreover, the likelihood of conflicts arising among market participants is reduced, and trust in both the data and the trading procedure is enhanced. This is achieved through the use of prearranged and mutually agreed on smart contracts along with immutable, transparent records of past transactions. Each transaction is automatically produced and verified through digital signatures. Anonymity is guaranteed, ensuring that sensitive information is not disclosed (Babich and Hilary 2020).

Simulation games and experimental learning environments are well-suited and common in learning concepts from management science, operations management, and technology applications (Wright 2015). Successful games are characterized by the ease of implementation, the encouragement of interaction, and the opportunity to generate discussions once completed (Griffin 2007). As such, online games are especially preferable compared with traditional off-line games because of the reduced setup time required for the instructors, the simplicity of reviewing and presenting results, and the reduced coordination requirement of participants (Wood 2007).

In the realm of supply chain management, multiple classroom games are available to convey important concepts. For instance, Robb et al. (2010) introduce a competitive inventory management game that facilitates learning about demand uncertainty, demand estimation, and the costs of inventory. Liu et al. (2013) develop a simulation that allows real-life inventory decision making for participants. Bansal et al. (2022) describe a game that enables participants to actively interact with and solve capacity planning problems.

Several games have been developed recently to facilitate learning about blockchain technology. BlockTrain for example, can be used to teach the fundamentals and key principles about blockchain in a gamified manner, for example, how decentralized databases converge and how blocks are added to the chain (Cortiz et al. 2021). Other games, such as ChainTutor and Bloxxgame, focus on blockchain’s underlying mechanisms and explain what blocks, hashes, keys, signatures, and consensus algorithms are (Liu 2018, Dettling and Schneider 2020). Sunny et al. (2022) introduce the Blockchain-enabled beer game that adopts mechanisms of the traditional beer distribution game (a four-stage serial supply chain in which firms aim to fulfill their customer demand and minimize costs; see Forrester 1958) and applies a decentralized app on the Ethereum blockchain for demonstration.

Whereas most blockchain-related games can be used to teach blockchain’s fundamentals and the underlying mechanisms, only a few allow participants to experience blockchain’s specific potential in real-life supply chain settings.

We expand the offering of educational games by introducing the web-based blockchain-enabled token trading game for supply chain management. The game provides participants with firsthand experience on how blockchain technology can be deployed in the realm of supply chain management, demonstrates the potential of virtual markets to ameliorate the performance of supply chains by reconciling supply and demand, and showcases the challenges of serving uncertain customer demand.

3. Game Description

The supply chain captured in this game consists of one upstream supplier (played by the system) and multiple retailers (played by the participants) as illustrated in Figure 1. Retailers can trade in a market. Two different market sizes can be selected: markets comprising three retailers and comprising all retailers. A detailed explanation of the different market sizes and additional variations of the game are provided in Section 6.2.

The game is played over multiple rounds. The sequence of events within each round is depicted in Figure 2 (in this figure, i indexes a retailer; MCP is the market-clearing price). Particular values are used in the following description for convenience, but all supply chain parameters are easily adjustable by the instructor.

Players’ retail prices are uniformly distributed between $51 and $100, and the demand is uniformly distributed between 0 and 200 units and is independent of the retail price. Whereas demand and prices can be correlated in real-life markets, we removed this feature from the game to simplify the participants’ decision process. The parameters for all retailers are identical, but retail prices and demand realizations are independent across rounds and between retailers. This mimics retailers who act in different or geographically distant markets and have disparate valuations for products, which is frequently apparent across industries (e.g., as demonstrated by the inclination of different U.S. states to pay for medical ventilators subject to the severity of their shortage). Tokens ordered from the supplier or acquired in the market in a given round expire at the end of the round. Thus, there is no inventory carryover between rounds. Similarly, customer backlog is lost at the end of the round, and demand is not carried over between periods.

The gameplay is split into the following three phases. Players move through phases synchronously, and phases have timers.

3.1. Ordering Phase

In the first phase, participants observe their retail price and have to decide how many tokens to order from the supplier, anticipating uncertain demand. The screenshot is given in Figure 3. The player enters the order quantity and clicks on the blue tick mark to submit it to the system. To support players’ decisions, an order decision support calculator is provided. In this calculator, participants can enter their guess for demand and preferred order quantity to calculate the corresponding profit (without trading). Once the ordering phase is over, all participants simultaneously enter the trading phase.

3.2. Trading Phase

In this phase, players can observe the following information in the “preorder conditions” columns (see Figure 4): the supply pretrade, which equals the initial order quantity; the demand; the sales, which is the minimum between the supply and the demand; and the sales profit. Thus, they can determine if they have excess inventory or a shortage. Participants can perform trades with other retailers by specifying whether to buy or sell tokens and entering the quantity and the price. The trading process is designed so it could be implemented with blockchain technology in practice. For instance, trade orders are submitted anonymously, and trust in the data and the trading process is ensured by constantly sharing historical (previous) transactions. These features ensure that strategic information, such as order quantities, which could suggest the shortage severity, is not disclosed (Hellwig et al. 2022). Once the trading phase is over, the market clears following a single-price, batch auction. Figure 5 illustrates the clearing process.

Sell orders are ranked in price from lowest to highest and buy orders in price from highest to lowest. Units are matched in buy and sell orders whenever the buy price is greater than the sell price of the matched units. The average of the lowest buy price and the highest sell price at which the last match happens is the market-clearing price: all buy orders pay this price, and all sell orders receive this price. The game moves to the evaluation phase.

3.3. Evaluation Phase

Participants observe the outcome of the trading phase (Figure 6). The “posttrade outcomes” columns show the supply posttrade, the sales (the minimum between demand and the quantity held), the sales profit, and the trade cash flow for which a positive number indicates tokens have been sold, whereas a negative number indicates tokens have been bought. The “final outcome” columns show the profit for this round (customer sales profit + trade cash flow), the cumulative total profit of all rounds, and the respective rank. In the box below, the performance of each trade order is shown with three different statuses. “No trade” indicates that the order has not been executed, “partial” indicates some tokens of this trade order were successfully traded, and “executed” indicates that the entire trade order has been successfully traded. At the very bottom, the market-clearing price is shown. The end of this phase marks the end of the round. All subsequent rounds follow the same sequence.

4. Learning with the Game

The game is hosted on the DISASTER platform² and is open to use by anyone, including researchers, instructors, and practitioners. The game can be played in person, remotely, or in hybrid settings. All the instructor and participants need is a stable internet connection and a web browser.

In the following, we outline the end-to-end process for deploying the blockchain-enabled token trading game for supply chain management in a teaching environment, including how the learning goals are achieved. See the accompanying teaching note for further details, including answers to the discussion questions omitted from this article for pedagogical reasons.

4.1. Prior to the Session

Before class, the instructor should set up the game and send instructions to the participants. We recommend conducting the preparations one to three days before the session to allow sufficient preparation time for participants. After the instructor clicks on the provided URL, the instructor is guided to the game creation mask. Here, the game settings can be adjusted (i.e., game name, number of rounds, number of participants).

The game setup also allows for the flexible creation of pregame and postgame questionnaires and surveys in a simple spreadsheet environment. This is used to test participants’ understanding of the game and elicit behavioral characteristics of participants (e.g., their risk profile) through proven elicitation methods³ and to ask participants about their trading strategies and reasoning to encourage reflection on the experience.

4.2. During Class

In the following, we outline the three stages for using the game in class: the kickoff, the gameplay, and the debrief and describe how the learning goals are achieved. Some of the discussion is specific to only supply chain management or blockchain technology courses and is marked as such.

4.2.1. Kickoff (15 Minutes of Class Time).

The goal of this stage is to introduce both the business context and the game itself. For the business context, the challenge of ordering the right number of units in case of uncertain demand is the key question to discuss. To that end, the drawbacks of ordering too few (e.g., lost sales, production halts) as well as ordering too many units (e.g., high inventory cost, write-offs) are highlighted. In practice, two alternatives are presented for better allocation of supply among multiple demand sources, namely, inventory pooling and transshipments. However, neither is ideal. Inventory pooling is usually managed by a central planner in a supply chain, making it difficult to deploy across multiple parties. Transshipments are typically restricted by terms established prior to the realization of random shocks. Other disadvantages of transshipments include costs of double inventory handling, logistics costs, carbon emissions, risks of disclosing competitive information, and longer lead times.

Subsequently, the instructor outlines the use of virtual markets to trade tokens on the supplier’s capacity among retailers to smooth supply and demand. For blockchain technology courses only, the fundamental concepts of blockchain technology that enable the creation of virtual markets are presented and discussed. Namely, (i) the distributed ledger; (ii) the decentralized consensus mechanism, which indemnifies that temporarily dissenting versions of the database converge; (iii) cryptographic security measures; and (iv) smart contracts, which are preprogrammed protocols to enable automated processes and facilitate the creation of digital tokens, which can represent claims on supply chain assets such as suppliers’ capacity (Babich and Hilary 2020, Hellwig et al. 2020). Furthermore, the resulting technical attributes, such as the assurance of the proof of ownership of tokens and the avoidance of double spending, are discussed.

4.2.2. Gameplay (30 Minutes of Class Time).

The game can be started and will run smoothly even if not all players are online (or have connectivity issues) because of preprogrammed bots that will engage on their behalf. The bots only take over the initial ordering decision and will not participate in the trading process. By default, the bot orders a predetermined quantity of tokens. Each ordering decision is recorded whether it was made by a subject or the bot to enable data analyses ex post. The instructor can pause the game at any moment to interject additional information, facilitate a discussion, or review interim results. Real-time results are automatically populated in the instructor game interface; see, for example, Figure 7. As soon as the last round is over, the participants may be asked to complete a postgame survey that is integrated into the interface.

4.2.3. Debrief (15–45 Minutes of Class Time).

The instructor may want to present the players’ performance to provide participants with a sense of how well their strategies worked. As such, a leaderboard similar to Figure 8 is presented, showing each player’s total profit achieved and rank relative to others.

Afterward, the instructor discusses students’ experiences and summarizes key takeaways. Therefore, in the following, we describe how the learning goals are achieved in supply chain management courses and blockchain courses, respectively.⁴ Here, we provide the discussion questions for illustration but do not give detailed answers or insights to achieve the learning goals. This information and additional materials are provided in the teaching note.

4.2.4. Supply Chain Management Course.

4.2.4.1. Randomness and Uncertainty in Supply Chains (Five Minutes).

The challenge of serving uncertain demand for industry players is highlighted during the kickoff. In addition, participants experience it themselves during the order phase in each round. The instructor asks participants the following: How successful were you anticipating the (random) demand? What are the root causes of random demand? Have you experienced it in real life? What are other sources of randomness and uncertainty in supply chains (e.g., delivery from suppliers, manufacturing/process time variation, transportation lead times)?

4.2.4.2. Markets Improve Supply Chain Performance (Three to Five Minutes).

With the possibility of trading in markets, retailers are significantly better off in terms of inventory levels and achieved profits. The instructor presents game results that look similar to Figure 8, which presents the average player’s pretrade and posttrade and asks participants the following: To what extent did you find it beneficial to trade on the markets? Why could you achieve higher profits with markets compared with no markets? Is this the proper way of evaluating the benefits of the market? Why not?

4.2.4.3. Strategies for Choosing Initial Orders and Trading (5–10 Minutes).

Participants have different strategies for placing orders. The instructor can present a histogram of orders from the game, similar to Figure 9, and ask participants to describe their ordering and trading strategies: How did you determine your initial order quantity to the supplier? What was your reasoning? Why did you or some players place extremely high (above maximum customer demand) or low orders (e.g., zero units)? What were the consequences? Would anyone who has not yet followed such a strategy try it if we played again? If yes, why?

4.2.4.4. Formation of the Market-Clearing Price (5–15 Minutes).

The market-clearing price exhibits several interesting features that are described in the teaching note. The instructor should show a figure for the game, similar to Figure 10, and ask participants the following: How would you describe the market-clearing price? How did it develop over time? To emphasize the learning goals, the instructor can use figures presented in the teaching note and use them to compare games under different conditions.

4.2.4.5. Beneficiary of Supply Chain Markets (Three to Five Minutes).

Participants already experienced that they (retailers) achieve significantly higher profits after having traded in the market. However, to evaluate the benefits of a supply chain market for all supply chain parties, one needs to compare this setup with a supply chain that does not have a market because retailers’ ordering behavior may change with the prospect of being able to trade in a market. Because this cannot be achieved by running one version of this game, the results of these cross-experiment comparisons are provided in the teaching note. The instructor asks participants the following: Does the supplier also benefit from supply chain markets? What is the reason for this outcome? Does the overall supply chain benefit from supply chain markets?

4.2.5. Blockchain Technology Course.

In blockchain technology courses, the key insights about how blockchain-enabled markets in supply chain work and their potential to improve supply chain performance are presented as well. However, to allow sufficient time to discuss specific learning objectives for blockchain-related content, it is recommended to only present the results without engaging students (yet, if time allows, interactive discussions are encouraged).

4.2.5.1. Markets Improve Supply Chain Performance (Two to Three Minutes).

The instructor presents a figure similar to Figure 8 and highlights that retailers always benefit from having the possibility to trade because they have a second chance to rebalance their inventory after knowing their demand.

4.2.5.2. Initial Order Quantity and Trading Strategies (Two to Three Minutes).

The instructor presents Figure 9 and articulates the different ordering and trading strategies, including their benefits and risks. Again, answers to all questions are described in the teaching note.

4.2.5.3. Formation of the Market-Clearing Price (Two to Three Minutes).

The instructor presents Figure 10 and describes the forces that led to the formation of the market-clearing price.

4.2.5.4. Strengths and Weaknesses of Blockchain Applications in Supply Chains (5–10 Minutes).

Blockchain’s core features that enable the creation of virtual markets in supply chains are highlighted during the kickoff. Now, the instructor facilitates a discussion about the strengths and weaknesses of blockchain technology applications in supply chains. Questions to ask include the following: What are the technical requirements to create virtual markets in supply chains? What are the strengths of blockchain technology in creating such markets? What are the additional advantages of the application of blockchain technology to create markets compared with traditional exchanges? What are the disadvantages and the weaknesses of blockchain-enabled virtual markets in supply chains?

4.2.5.5. Blockchain-Enabled Market Setup (10–15 Minutes).

The next discussion covers the end-to-end process of how a virtual market is enabled on a blockchain. As part of the context setting, the instructor poses the following questions to the participants: What are the technical requirements for retailers and suppliers to participate in a virtual market? What would the end-to-end trading process look like on a blockchain? What kind of smart contracts are required? The instructor presents the process map illustrating the smart contract proof of concept for token-enabled capacity trading (see the teaching note).

4.2.5.6. Smart Contract (10–15 Minutes).

It should be apparent to participants that smart contracts are essential to facilitating a well-functioning market. Therefore, the instructor facilitates a discussion of the smart contract code that can represent a token of the supplier’s capacity. The instructor presents the solidity code snippet (see the teaching note). Depending on the experience of the participants and the time available, participants can also be asked as part of their homework to write the code for the smart contract representing tokens on the supplier’s capacity. Afterward, the instructor can ask two to three participants to present their work and discuss the results.

5. Effectiveness of the Game

To assess the game’s effectiveness, we gathered feedback from participants of a blockchain technology course on how they experienced the simulation game and their learning journey. We conducted a seven-point Likert-scale survey similar to Kuokkanen and Van der Rest (2022). Table 2 presents the responses from 49 participants. A value of seven indicates that the participant strongly agrees with the statement, whereas a value of one indicates that the participant strongly disagrees with the statement. The results show that, after having played the blockchain-enabled token trading game for supply chain management, participants highly supported all statements with an average score higher than six.

Table 2. Results of Survey to Test the Game’s Effectiveness

In addition, we asked participants anonymously in the postgame questionnaires to provide written feedback about their experience and comments regarding the game. This provided further insights into the impact of the game on the participants. Participants shared very positive feedback on the intuitive design and easy-to-handle user interface. Furthermore, the illustrative application of blockchain technology in supply chain management is advocated. In particular, the apparent benefits of such virtual markets in addressing the supply–demand mismatch are regularly highlighted by participants and often their first content-related reaction alongside comments covering the high uncertainty of the realized demand. Additional details about the postgame discussions are provided in the teaching note. In the following, we share some appreciation feedback received from participants:

I enjoyed the game because it simulates how we should think in the real world when managing inventory.

Great illustration of how blockchain can help businesses.

I found the game very enjoyable and interesting.

It was a good simulation to play.

It was fun. I would like to play again after trying it for the first time.

The demand was more random than expected.

6. Classroom Experience and Variations

6.1. Classroom Experience

The blockchain-enabled token trading game for supply chain management has been used in leading U.S. and European universities in numerous undergraduate, graduate, MBA, and PhD courses with a total of more than 1,000 students. Regarding placement within a supply chain management course, it is best suited to be conducted after the newsvendor problem is taught. The game also allows playing a newsvendor game, that is, a game in which participants order from the supplier but do not have a market on which to trade. A combination of the newsvendor game and the token trading game allows students to appreciate the benefits of markets firsthand. We recommend taking a break between the newsvendor game and the trading game to avoid cognitive overload for students. In terms of placement within a blockchain course, it is recommended to play the game after basic concepts (e.g., what is a blockchain) are taught; this increases the learning effect of the game.

The instructor experience has been positive throughout, especially given the ease of adjusting game settings as well as pregame and postgame surveys that led to short setup times. Moreover, the high interaction level of all participants that facilitates an engaging learning atmosphere is emphasized. To further increase participants’ engagement and excitement, we provided giveaways or vouchers for the best performing players; if chosen to do so, participants should be informed prior to the game. Because of potential unexpected no-shows or internet connection issues of participants, the programmed bots are particularly valuable because they ensure smooth gameplay and do not influence the experience of other players.

6.2. Variations of the Game

The game experience can be altered by adjusting the following two settings. First is the market size. In the default setting described, the number of retailers equals the number of participants, which can be between 3 and 60. However, the market size can be set to three retailers, leading to all participants being divided into multiple groups of three. Thus, subjects participate in separate, small markets. If this setting is enabled, participants are randomly and anonymously matched with new retailers in every round to avoid reputation-building effects. This setting mimics smaller, less liquid markets. For a detailed discussion on the different outcomes between market sizes and the formation of market-clearing prices, see Wendt et al. (2023).

Second is the market-clearing mechanism. The market-clearing mechanism used in the default setting is a batch uniform clearing process that produces one market-clearing price per market and round. The alternative scenario is a real-time clearing process in which submitted trade orders are executed immediately if a matching order is available. Participants can submit trade orders during the entire trading phase. Whereas the real-time clearing process leads to an even more dynamic and entertaining game experience, market manipulation may happen (and thus, this setting should be used with caution).

7. Conclusion

In this paper, we describe the blockchain-enabled token trading game for supply chain management, which illustrates how virtual markets can operate in supply chains. The game provides participants with firsthand experience on how blockchain technology can be used to improve supply chain performance. Specifically, participants (i) understand the sources of randomness in supply chains, evaluate the costs of the mismatch between supply and demand, and discuss the importance of strategic interactions among firms; (ii) learn how virtual markets work, evaluate their potential to optimize supply chain profits, and create trading strategies to take advantage of that potential; and (iii) understand the strengths and weaknesses of blockchain applications in supply chains and evaluate the technical feasibility of blockchain-enabled virtual markets and program elements of the virtual markets firsthand. The game experience conveys the pedagogical goals in a highly interactive way. In addition, the game is easy to set up and use given the intuitive design, the minimal technical requirements (i.e., an internet connection and a web browser), and the fact that no prior experience in supply chain management or blockchain technology is needed. It can be run with 3 to 60 participants by a single instructor with no additional support and fits into a 90-minute class. This also enables the game to be conveniently conducted in person, online, or in hybrid mode. As such, it has been used in leading U.S. and European universities in undergraduate, graduate, MBA, and PhD courses with a total of more than 1,000 students. Given the high interest of executives who were part of the courses or during conference presentations, we anticipate that the game can also be used for executive education in the future. As a result, we expect that the adoption of such virtual markets in practice will rapidly increase.