## 5 Simulation Model with Human Behavior
### Strategy Analysis  
An approach in which the results from the experiment were used as a reference was selected, in contrast to an approach in which agents are modeled without prior understanding of people's behavior [61]. A simulation model of the agent was built based on major decisions, which were mostly made by the players in the experiment. Therefore, to be able to model the agent, the strategies of the players in the experiment were analyzed first, observing four key decision processes in the game:  
* Deciding on the price of the offered service;
* Deciding on which service to purchase regarding the price;
* Deciding on the amount of the transaction fee for the transaction;
* Deciding on the amount of funds allocated to the stake. Four different statistical parameters that describe the decision-making of players in the above-mentioned decision processes were determined. For each player's decision, a parameter was calculated and the distribution of parameter values was plotted (Figures 8 and 9). Figure 7: Frequency of service provision over time. Due to different decision frequencies for different parameters, all the decision frequencies were normalized. The coefficient of buying (\(k_{b}\)) tells us whether a player purchased a service (\(selected\)_Price_) at a higher or a lower price than the current average price (\(price\)) for that type of service on the market (Equation (3)). In the user interface, the services offered were displayed with a histogram, in which the services were ranked from the lowest price to the highest to provide players with a sense of the average price. On average, players bought services during the game that were cheaper than the average price (\(\overline{k_{b}}=0.88\)). The distribution of the parameter is shown in Figure 8a, with the distribution appearing to be normal; thus, it can be concluded that no other strategies emerged for this process. \[k_{b}=\frac{selected\text{Price}}{price} \tag{3}\]  
The coefficient of setting the price (\(k_{p}\)) defines whether the set price (\(set\)_Price_) is higher or lower than the current average price (\(\overline{price}\)) of services of the same type (Equation (4)). Players in the experiment set the price (\(\overline{k_{p}}=0.91\)) lower than the current average price (Figure 8b). In the experiment, there was a situation in which players did not have references from other providers of the same type of service. Therefore, \(k_{p}\) was calculated in the case for which there was no offer of the same type of services (\(k_{P_{empty}}\)). The average price was calculated from the current average service price for the other two services. While the distribution of the coefficient value (Figure 8c) is similar to the normal distribution in  
Figure 8: Strategy coefficient distributions: (**a**) coefficient of buying, (**b**) coefficient of setting price, (**c**) coefficient of setting price when market is empty, (**d**) coefficient of setting transaction fee. Figure 9: Strategy coefficient distribution. this case, it has greater variance than in the case of the service purchasing coefficient. Players in the experiment set the price (\(\bar{k}_{p_{empty}}=1.08\)) higher than the current average price. Based on these results, the players had only one predominant strategy for setting the service price. \[k_{p}=\frac{setPrice}{price} \tag{4}\]  
The coefficient of setting the transaction fee (\(k_{f}\)) characterizes whether the set transaction fee (\(setTxFee\)) for the new transaction is higher or lower than the transaction fee (\(topPendingTxFee\)) of the pending transaction with the highest transaction fee in the list of pending transactions (Equation (5)). On average, players set the transaction fee 5% lower than the currently highest queue fee (\(\overline{k_{f}}=0.95\)). The distribution of the value of the parameter \(k_{f}\) in the experiment (Figure 8d) is again similar to the normal distribution; however, the distribution has long tails.