## 5 Simulation Model with Human Behavior
### Simulation Model
#### 5.2.3 Human Behavior Decision Parameters  
The results of the experiment and strategy analysis were used to determine the values of the parameters described above that define the behavior of the agents. The determined parameters are shown in Table 4. On average, players made transaction action decisions every 60.6 s and service action decisions every 34.8 s. The time for the transaction action \(t_{transactionAction}\) and the time for the service action \(t_{serviceAction}\) were defined in order for agents to have a similar frequency of decision-making with respect to transactions and services as the players in the experiment. To ensure asynchronous actions, agents select a value for each action between the set minimum and maximum values. In the transaction action process, a recognized strategy during the experiment was considered in which players waited until the last few seconds before the new transaction was confirmed to place their transaction on the pending list, thereby reducing the chance of their transaction being overtaken by other players. In the case of an agent, when the time for the transaction action elapses, it first checks how much time is left until the new transaction will be confirmed and waits to make a decision until the last 3 s before the confirmation. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline \(t_{transactionAction}\)_Min_ & 40 s \\ \(t_{transactionAction}\)_Max_ & 80 s \\ \(t_{series}\)_ActionMin_ & 30 s \\ \(t_{service}\)_ActionMax_ & 40 s \\ \(t_{transaction}\)_Pending_ & 600 s \\ \(w_{stake}\) (4 groups) & 0.1, 0.07, 0.04 and 0.037 \\ \(w_{unstake}\) & 0.5 \\ \(w_{price}\) & 1.3 \\ \(t_{offerPending}\) & 50 s \\ \(stake_{min}\) & 0.02 \\ \(stake_{max}\) & 0.04 \\ \(unstake_{min}\) & 0.18 \\ \(unstake_{max}\) & 0.22 \\ \(k_{ferDeleted}\) & 1.02 \\ \(k_{feDeletedEmpty}\) & 0.99 \\ \(k_{few}\) & 0.95 \\ \(k_{fewNew}\) & 1.05 \\ \(k_{fewNewDeleted}\) & 0.95 \\ \(k_{fewNDeletedEmpty}\) & 0.965 \\ \(k_{existOffer}\) & 1.07 \\ \(k_{perviousOffer}\) & 0.15 \\ \(k_{emptyOffer}\) & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Selected agent model setting parameters. Several of these parameters were set using trial and error, while others were defined using the findings obtained from the experimental results; \(k_{feNew}\) is equal to the value of the coefficient for setting the transaction fee \(\overline{k_{f}}\), which was calculated in the strategy analysis. The same is true for \(k_{feNewEmpty}\), \(k_{feNewDeletedEmpty}\) and \(k_{feDeletedEmpty}\), which means that in the case of the transaction not being deleted or the list of pending transactions being empty, the agent sets a lower transaction fee from the previous or current highest price. This kind of behavior was seen in the experiment, especially in the first half of the game when the blockchain network had not yet become congested. In contrast, \(k_{feDeleted}\) and \(k_{feNewDeleted}\) are set to a value greater than 1, which means that in the case of transactions being deleted because the transaction fee is set too low or because they have waited too long for confirmation, agents start to raise their transaction fees. This increases the transaction fee when the blockchain network is congested. The values of the staking weight \(w_{stake}\) are divided into four groups. The first three values are for a group of agents that follow staking strategy 1. In the experiment, not all players within this group had the same tendency to stake in the blockchain network. The last value represents \(w_{stake}\) for the group of agents following staking strategy 2, who do not want to change their stake significantly. The value of the coefficient for setting the service price \(k_{previousOffer}\) is close to the value for the coefficient of the setting price \(\overline{k_{p_{empty}}}\) obtained in the strategy analysis. The set value means that if the supply in the market is lower, the agent increases the price of its service (as happened in the first half of the experiment).