The average number of interactions of games between different colors and human players in the experimental setup. The number of interactions between humans and agents is different due to the different agent concentration of color groups. The number represents all starting interaction (ie, sending requests). In the purely human setup A and the purely agen t-based setup D, the number of interactions started is the same. However, a different group configuration in setup B and C affects the number of starting interactions from the viewpoint of human vs and agents. The performance of each group in the game has not been able to form a large cluster, and other groups have begun more interaction to them to maximize their payoffs, and as a result. It should be noted that there is a situation in which they can coordinate their actions for cooperative interactions. Such a support for autonomous agents caused a smaller cluster size as a result of the tendency of coordinating interactions to break the cluster to promote other players' movement. In total, hybrid setups had more interactions on average on average than pure agents and human setups (A and D are both 61) (B is 90, C is 86).

Fig. 4 < SPAN> Average score in each round of different sets. The games using the setup A reached the maximum number of points in all four games, but the hybrid setup B, C, and setup D did not reach up to 15 rounds. Of the four setups, hybrid setup B was the worst. However, since the individual players belonging to one of the setup B color groups achieved the maximum rewards by the fifth game (that is, 27 points in total), the difference between hybrid setups is small. be. The error bar represents a standard error.

Figure 3

Discussion

The average number of games between different colors and human players in the experimental setup. The number of interactions between humans and agents is different due to the different agent concentration of color groups. The number represents all starting interaction (ie, sending requests). In the purely human setup A and the purely agen t-based setup D, the number of interactions started is the same. However, a different group configuration in setup B and C affects the number of starting interactions from the viewpoint of human vs and agents. The performance of each group in the game has not been able to form a large cluster, and other groups have begun more interaction to them to maximize their payoffs, and as a result. It should be noted that there is a situation in which they can coordinate their actions for cooperative interactions. Such a support for autonomous agents caused a smaller cluster size as a result of the tendency of coordinated interactions to break the cluster to promote other players' movement. In total, hybrid setups had more interactions on average on average than pure agents and human setups (A and D are both 61) (B is 90, C is 86).

Fig. 4 Average scoring in each round of different setups. Games using the setup A reached the maximum number of points in all four games, but the hybrid setup B, C, and setup D did not reach within 15 rounds. Of the four setups, hybrid setup B was the worst. However, since the individual players belonging to one of the setup B color groups achieved the maximum rewards by the fifth game (that is, 27 points in total), the difference between hybrid setups is small. be. The error bar represents a standard error.

Figure 3

The average number of interactions of games between different colors and human players in the experimental setup. The number of interactions between humans and agents is different due to the different agent concentration of color groups. The number represents all starting interaction (ie, sending requests). In the purely human setup A and the purely agen t-based setup D, the number of interactions started is the same. However, a different group configuration in setup B and C affects the number of starting interactions from the viewpoint of human vs and agents. The performance of each group in the game has not been able to form a large cluster, and other groups have begun more interaction to them to maximize their payoffs, and as a result. It should be noted that there is a situation in which they can coordinate their actions for cooperative interactions. Such a support for autonomous agents caused a smaller cluster size as a result of the tendency of coordinating interactions to break the cluster to promote other players' movement. In total, hybrid setups had more interactions on average on average than pure agents and human setups (A and D are both 61) (B is 90, C is 86).

References

1. Figure 4
2. Requests of human players by cluster size in different setups (a) and acceptance (b) activities and autonomous agents in setup D. All setups show similar activities attenuation, but noteworthy that the setup using autonomous agents has shown more relaxed and more relaxed activities than human players alone. This behavior is considered to be the result of the poin t-based reward scheme, the performance of the autonomous agent, and the result of the complete coordination that does not form a large cluster in the hybrid setup. Since the autonomous agent is based on the same model, its activities are also expressed in setup D. (C) Overall activities for each player in the experimental setup. Each marker represents the ratio of the number of times the player performs an action that has an opportunity to accept or accept in a specific setup. The distribution of the marker indicates that no human player requires swaps on all opportunities. It is noteworthy that there were some examples of the player accepting all the demands in Setup A and B.
3. Figure 5
4. Requests (a) and acceptance (b) strategy in experimental setups by cluster size. Each value is the difference between the normalized cluster size (ⓐⓐ) of the neighbors who share the selected neighbors and the selected neighbor's normalization cluster size (ⓐⓐ). The positive value shows a more cooperative decisio n-making in that the cluster size of the particular color is the smallest neighbor, and the negative value is the opposite. If the decision making is completely random, the value is 0. (C) The average strategy of the individual in the game. Each marker represents a player in a specific setting. The autonomous agent is represented by the average value of all agents composed of setup D. The negative value indicates that the average strategy of the player does not require the most useful neighbors, or accepts the demands from neighbors with the smallest cluster size. < SPAN> Human players' requests (a) and acceptance (b) activities for each cluster size in different setups, and autonomous agents in setup D. All setups show similar activities attenuation, but noteworthy that the setup using autonomous agents has shown more relaxed and more relaxed activities than human players alone. This behavior is considered to be the result of the poin t-based reward scheme, the performance of the autonomous agent, and the result of the complete coordination that does not form a large cluster in the hybrid setup. Since the autonomous agent is based on the same model, its activities are also expressed in setup D. (C) Overall activities for each player in the experimental setup. Each marker represents the ratio of the number of times the player performs an action that has an opportunity to accept or accept in a specific setup. The distribution of the marker indicates that no human player requires swaps on all opportunities. It is noteworthy that there were some examples of the player accepting all the demands in Setup A and B.
5. Figure 5
6. Requests (a) and acceptance (b) strategy in experimental setups by cluster size. Each value is the difference between the normalized cluster size (ⓐⓐ) of the neighbors who share the selected neighbors and the selected neighbor's normalization cluster size (ⓐⓐ). The positive value shows a more cooperative decisio n-making in that the cluster size of the particular color is the smallest neighbor, and the negative value is the opposite. If the decision making is completely random, the value is 0. (C) The average strategy of the individual in the game. Each marker represents a player in a specific setting. The autonomous agent is represented by the average value of all agents composed of setup D. The negative value indicates that the average strategy of the player does not require the most useful neighbors, or accepts the demands from neighbors with the smallest cluster size. Requests of human players by cluster size in different setups (a) and acceptance (b) activities and autonomous agents in setup D. All setups show similar activities attenuation, but noteworthy that the setup using autonomous agents has shown more relaxed and more relaxed activities than human players alone. This behavior is considered to be the result of the poin t-based reward scheme, the performance of the autonomous agent, and the result of the complete coordination that does not form a large cluster in the hybrid setup. Since the autonomous agent is based on the same model, its activities are also expressed in setup D. (C) Overall activities for each player in the experimental setup. Each marker represents the ratio of the number of times the player performs an action that has an opportunity to accept or accept in a specific setup. The distribution of the marker indicates that no human player requires swaps on all opportunities. It is noteworthy that there were some examples of the player accepting all the demands in Setup A and B.
7. Figure 5
8. Requests (a) and acceptance (b) strategy in experimental setups by cluster size. Each value is the difference between the normalized cluster size (ⓐⓐ) of the neighbors who share the selected neighbors and the selected neighbor's normalization cluster size (ⓐⓐ). The positive value shows a more cooperative decisio n-making in that the cluster size of the particular color is the smallest neighbor, and the negative value is the opposite. If the decision making is completely random, the value is 0. (C) The average strategy of the individual in the game. Each marker represents a player in a specific setting. The autonomous agent is represented by the average value of all agents composed of setup D. The negative value shows that the average strategy of the player does not require the most useful neighbor or accepts the demands from neighbors with the smallest cluster size.
9. In this research experiment, a total of 13 games were held, twice a session to play a setup A game, twice a session to play 5 sets B games, and twice a session to play 4 sets up C games. The 13 games sent 591 requests from the subjects and received 549 requests. The experiment began with a group of 30 subjects playing a se t-up game four times, and then the subjects were divided into two sets, 15. The two players participated in hybrid setup B and C separately, and added bots to the team. This design allows each setup to maintain the network size to 30 (15 people, 15 bots, 15 bots, see Fig. 1). The agent appeared in the game with an indistingential distinction from the subject, and it was not announced that the players were playing with the agent until the experimental session was over, but this is the bias in the subject's decisio n-making. It was to reduce the potential of 12, 31, not as an intervention, but to evaluate "organic" adaptation. Subjects were effective in reaching the final configuration and the maximum score in each set up A game. < SPAN> In this research experiment, a total of 13 games were performed, two sessions playing four games in a setup A, twice a session to play 5 sets, and twice a session to play 4 setup C games. The 13 games sent 591 requests from the subjects and received 549 requests. The experiment began with a group of 30 subjects playing a se t-up game four times, and then the subjects were divided into two sets, 15. The two players participated in hybrid setup B and C separately, and added bots to the team. This design allows each setup to maintain the network size to 30 (15 people, 15 bots, 15 bots, see Fig. 1). The agent appeared in the game with an indistingential distinction from the subject, and it was not announced that the players were playing with the agent until the experimental session was over, but this is the bias in the subject's decisio n-making. It was to reduce the potential of 12, 31, not as an intervention, but to evaluate "organic" adaptation. Subjects were effective in reaching the final configuration and the maximum score in each set up A game. In this research experiment, a total of 13 games were held, twice a session to play a setup A game, twice a session to play 5 sets B games, and twice a session to play 4 sets up C games. The 13 games sent 591 requests from the subjects and received 549 requests. The experiment began with a group of 30 subjects playing a se t-up game four times, and then the subjects were divided into two sets, 15. The two players participated in hybrid setup B and C separately, and added bots to the team. This design allows each setup to maintain the network size to 30 (15 people, 15 bots, 15 bots, see Fig. 1). The agent appeared in the game with an indistingential distinction from the subject, and it was not announced that the players were playing with the agent until the experimental session was over, but this is the bias in the subject's decisio n-making. It was to reduce the potential of 12, 31, not as an intervention, but to evaluate "organic" adaptation. Subjects were effective in reaching the final configuration and the maximum score in each set up A game.
10. We found that the two hybrid setups performed weakly in team scoring. In particular, setup B had three game sessions and entered the final game before all players had earned the maximum payoff. This difference between the purely human setup A and the hybrid setups B and C suggests that the adaptation of the human subjects to the autonomous agents’ decisions was suboptimal, or that the autonomous agents’ strategies were suboptimal for reaching the objective of a given payoff function when played with human subjects. This incompatibility could be the result of fitting the model to data from a purely collective game, which resulted in the autonomous agents’ objectives differing from those of the current game. The games in setup B performed significantly worse due to the high number of interactions between agents resulting from the difference in the composition of agents and humans in the color groups (see Figure 3). Also, the autonomous agent model in our previous work10 included a stability rule that prevented agents from sending requests between two large clusters (i. e., clusters with a size larger than ⊖60⊖% of the maximum possible value). During the experiments, agents were generally more active in terms of sending requests, despite this stability rule being applied. This high activity caused instability, resulting in the breakup of profitable clusters.
11. We measure players' behavior using measures of activity, risk aversion, and rationality of the actions taken, based on an agent-based model implemented in a previous study10. Activity corresponding to requesting an exchange of places with a nearest neighbor or accepting one of such requests is measured as the proportion of performing that action whenever it is permissible for the focal player. Thus, we define the activity of player i as:
12. a_i = Γ
13. a_i = \frac
14. i＞, ＜i＞, ＜i＞, ＜i＞, ＜i＞, ＜i＞, ＜i＞, ＜i
15. Here, (N_i) is the actual number of times that player i chooses to interact with a neighbor, and (a_i) is the total number of times that player i could have chosen to interact. The quantity (⊖a_i⊖) is measured separately for requesting and accepting actions. For example, a requesting action of 1. 0 indicates that the player sent a request every time he had the option. The concept of risk aversion is very similar to activity, but it is measured as a function of the player's cluster size. The lower this value, the more the player is willing to risk losing the cluster size and ultimately the points that the player already has.
16. a_i(s) = Γ
17. a_i(s) = \frac
18. a_i(s)& gt;, \end
19. Here, (N_i(s)⒜) is the number of cases with cluster size s that interact with a neighbor, and (⒜mathcal _i(s)⒝) is the total number of cases with cluster size s that are ready to interact. Again, (a_i(s)⒜) is defined separately for requests and acceptances. We define player rationality as follows: We measure the amount of focal player i sending and accepting requests to neighbor j:
20. U
21. U_j=³³ s(c_j) ୧rangle - s_j. \end
22. where ∕∕ is j's color, ∕langle s(c_j) ∕rangle ∕ is the average cluster size of i's neighborhoods with the same color as ∕∕, and ∕∕ is j's cluster size. Note that all the cluster size information explained above is available to i but not to j. The larger the difference between (c_j) and (s_j), the greater the expected payoff for neighbor j if player i facilitates the exchange. Thus, a more positive value indicates more cooperation on the part of player i. In a purely cooperative setting, this quantity reflects the rational choices and cognitions of players10.
23. In the previous experiment, I realized that the closer to the purpose of forming the maximum cluster, the less activities in the sending and reception of players will decrease. If there is a different poin t-based payoff function, the player performed the same action when sending a request, but when accepting the request received after achieving a certain number of points, a more relaxed decision. Showed (see Fig. 4). After reaching the necessary cluster size, more strict acceptance actions indicate that the player understood the payoff functions and adjusted the decision after reaching a useful cluster size. Also, this increase in your actions can sacrifice your position to help other small clusters to optimize your points, and as a result, other groups. It indicates that if you reach a larger cluster, you can get more points. However, due to the game restrictions and the sample size obtained, there are some situations that rarely appear during the game, which can be seen from the fluctuations due to the lack of cluster size 8 players in setup B (Fig. 4). Evaluation of the individual activities of the players in each setup showed that there was no significant difference between the entire setup, and that there was no difference in the behavior of the players in terms of overall activities (see Fig. 4). 。 However, as can be seen from Fig. 4, the subject's activities were adapted to the setup D. < SPAN> In the previous experiment, I realized that the closer to the purpose of forming the maximum cluster, the less activities in the sending and reception of players will decrease. If there is a different poin t-based payoff function, the player performed the same action when sending a request, but when accepting the request received after achieving a certain number of points, a more relaxed decision. Showed (see Fig. 4). After reaching the necessary cluster size, more strict acceptance actions indicate that the player understood the payoff functions and adjusted the decision after reaching a useful cluster size. Also, this increase in your actions can sacrifice your position to help other small clusters to optimize your points, and as a result, other groups. It indicates that if you reach a larger cluster, you can get more points. However, due to the game restrictions and the sample size obtained, there are some situations that rarely appear during the game, which can be seen from the fluctuations due to the lack of cluster size 8 players in setup B (Fig. 4). Evaluation of the individual activities of the players in each setup showed that there was no significant difference between the entire setup, and that there was no difference in the behavior of the players in terms of overall activities (see Fig. 4). 。 However, as can be seen from Fig. 4, the subject's activities were adapted to the setup D. In the previous experiment, I realized that the closer to the purpose of forming the maximum cluster, the less activities in the sending and reception of players will decrease. If there is a different poin t-based payoff function, the player performed the same action when sending a request, but when accepting the request received after achieving a certain number of points, a more relaxed decision. Showed (see Fig. 4). After reaching the necessary cluster size, more strict acceptance actions indicate that the player understood the payoff functions and adjusted the decision after reaching a useful cluster size. Also, this increase in your actions can sacrifice your position to help other small clusters to optimize your points, and as a result, other groups. It indicates that if you reach a larger cluster, you can get more points. However, due to the game restrictions and the sample size obtained, there are some situations that rarely appear during the game, which can be seen from the fluctuations due to the lack of cluster size 8 players in setup B (Fig. 4). According to the individual activities of the players in each setup, it was suggested that there was no significant difference between the entire setup, and that there was no difference in the behavior of the players in terms of overall activities (see Fig. 4). 。 However, as can be seen from Fig. 4, the subject's activities were adapted to the setup D.
24. Since the subjects’ activities suggested that there were differences in decision-making between the purely human setup A and the hybrid setups B and C, we investigated the rationality of human players’ decisions. The average rationality of choices across possible cluster sizes is shown in Figure 5. Players’ strategies were individually heterogeneous, with some players adopting an average strategy with negative values ​​for both acceptance and request (see Figure 5). This suggests that during a particular setup, those players sent requests to neighborhoods with a larger cluster size than the average for that particular neighborhood. Such decisions are not necessarily irrational, since players may have information not obtained from their neighborhoods, but from the network structure or from actions previously taken by the players. Autonomous agents are excluded from this analysis, since their decisions are homogeneous in our implementation. However, the in-game environment may introduce variability into our measures of strategies, since agents make decisions based on probability functions over possible options.
25. In this study, we focused on human and agents experiments in order to insight coordination, cooperation, and complex dynamics in interaction with autonomous agents that imitate human and human behavior. Simulation using only autonomous agents gives a better or optimal performance, but c o-work that combines agents and humans, as it has been demonstrated in recent research 32, 33 using communication and video games. The result may be different in the decisio n-making setup. According to these studies, the model of the autonomous agent tends to decrease when combined with human decisio n-making, but unless the model itself has been specially trained to work well with the human subjects. 33 is shown that it is purely coordinated than a human group. However, the fundamental reason that the performance deteriorates when cooperating with the autonomous agent may be due to the fact that human players have not made decisions based on rules or unified decisions. 。 When comparing a human group and agent groups purely, these types of differences may be due to the highly operating ability of agents in the current environment. In this study, we conducted an experiment on cooperating groups in a hybrid group of humans and agents in order to evaluate the performance of these groups with various configurations and investigate the effects of unifying uniform payoff functions. These autonomous agents were modeled using the results of our previous research 10. < SPAN> In this study, we focused on human and agents experiments in order to insight, coordination, cooperation, and complex dynamics in interaction with autonomous agents that imitated human and human behavior. Simulation using only autonomous agents gives a better or optimal performance, but c o-work that combines agents and humans, as it has been demonstrated in recent research 32, 33 using communication and video games. The result may be different in the decisio n-making setup. According to these studies, the model of the autonomous agent tends to decrease when combined with human decisio n-making, but unless the model itself has been specially trained to work well with the human subjects. 33 is shown that it is purely coordinated than a human group. However, the fundamental reason that the performance deteriorates when cooperating with the autonomous agent may be due to the fact that human players have not made decisions based on rules or unified decisions. 。 When comparing a human group and agent groups purely, these types of differences may be due to the highly operating ability of agents in the current environment. In this study, we conducted an experiment of cooperative groups in a hybrid group of humans and agents to evaluate the performance of these groups with various configurations and investigate the effects of unifying uniform payoff functions. These autonomous agents were modeled using the results of our previous research 10. In this study, we focused on human and agents experiments in order to insight coordination, cooperation, and complex dynamics in interaction with autonomous agents that imitate human and human behavior. Simulation using only autonomous agents gives a better or optimal performance, but c o-work that combines agents and humans, as it has been demonstrated in recent research 32, 33 using communication and video games. The result may be different in the decisio n-making setup. According to these studies, the model of the autonomous agent tends to decrease when combined with human decisio n-making, but unless the model itself has been specially trained to work well with the human subjects. 33 is shown that it is purely coordinated than a human group. However, the fundamental reason that the performance deteriorates when cooperating with the autonomous agent may be due to the fact that human players have not made decisions based on rules or unified decisions. 。 When comparing a human group and agent groups purely, these types of differences may be due to the highly operating ability of agents in the current environment. In this study, we conducted an experiment on cooperating groups in a hybrid group of humans and agents in order to evaluate the performance of these groups with various configurations and investigate the effects of unifying uniform payoff functions. These autonomous agents were modeled using the results of our previous research 10.
26. To understand the above aspects better, the experimental design was divided into three different setups, of which two experimental setups changed the configuration of autonomous agents. The contrast group of the agent was implemented only by autonomous agents using the same model as these two hybrid setups. The experimental design of this research is based on the experiments in accordance with the experiments that use network topology as a mesh with a periodic boundary, adding a small world link to make human players cognitive and moving easier. It is. However, since it is not necessary to form a maximum cluster to achieve the maximum payoff, the number of rounded rounds is reduced from 21 to 15, and each of the three color groups sends a place to exchange with neighbors. I gave the opportunity. The number of rounds was reduced because the agent was trained with the same poin t-based payoff function and the maximum payoffs were measured. The preliminary simulation of this possibility has reinforced the initial forecast that humans and agents would be more effective. The number of games was limited by the length of the experimental session. If the session is longer, human players may be tired and affect decision making. It should be noted that this is a restriction of game design, and it is impossible to make all possible conditions (that is, different nearby combinations) appear in the experimental session. < SPAN> In order to understand the above aspects better, the experimental design was divided into three different setups, of which two experimental setups changed the configuration of the autonomous agent. The contrast group of the agent was implemented only by autonomous agents using the same model as these two hybrid setups. The experimental design of this research is based on the experiments in accordance with the experiments that use network topology as a mesh with a periodic boundary, adding a small world link to make human players cognitive and moving easier. It is. However, since it is not necessary to form a maximum cluster to achieve the maximum payoff, the number of rounded rounds is reduced from 21 to 15, and each of the three color groups sends a place to exchange with neighbors. I gave the opportunity. The number of rounds was reduced because the agent was trained with the same poin t-based payoff function and the maximum payoffs were measured. The preliminary simulation of this possibility has reinforced the initial forecast that humans and agents would be more effective. The number of games was limited by the length of the experimental session. If the session is longer, human players may be tired and affect decision making. It should be noted that this is a restriction of game design, and it is impossible to make all possible conditions (that is, different nearby combinations) appear in the experimental session. To understand the above aspects better, the experimental design was divided into three different setups, of which two experimental setups changed the configuration of autonomous agents. The contrast group of the agent was implemented only by autonomous agents using the same model as these two hybrid setups. The experimental design of this research is based on the experiments in accordance with the experiments that use network topology as a mesh with a periodic boundary, adding a small world link to make human players cognitive and moving easier. It is. However, since it is not necessary to form a maximum cluster to achieve the maximum payoff, the number of rounded rounds is reduced from 21 to 15, and each of the three color groups sends a place to exchange with neighbors. I gave the opportunity. The number of rounds was reduced because the agent was trained with the same poin t-based payoff function and the maximum payoffs were measured. The preliminary simulation of this possibility has reinforced the initial forecast that humans and agents would be more effective. The number of games was limited by the length of the experimental session. If the session is longer, human players may be tired and affect decision making. It should be noted that this is a limit by game design, and it is impossible to make a sufficient number of possible states (that is, different nearby combinations) during the experimental session.
27. First, in the case of setups for human-human, the final result of each game was the formation of the largest group like a collective payoff game, but human subjects understand the fixing individual payoff functions. It was shown to be there. This special payoff expression is not a declining gradient such as a collective payoff game with a cluster size, but the percentage of the request for acceptance for each cluster size follows the cluster size. It was clearly seen in the action. The indicator of the human player's strategy is that this is considered by consolidating the game status into the combination of three variables. In other words, it is a cluster size (S_I) of the focus player, the selected alter ego cluster size (S_J), and the average cluster size (S_J) of the adjacent player that shares the selected alter ego. The payoff function affected the player's strategy by making the cluster size exceeding the required threshold. This suggests that the player's strategy is divided into maximizing cluster (request) and helping other groups to form a cluster. Such an imbalance for such a purpose was also proved in the following human and agents. Agent, who modeled a fully cooperative setting player, has achieved the following good results. < SPAN> First, in the case of a human-human setup, the final result of each game was the maximum group, such as a collective payoff game, but human subjects have a modified individual payoff function. It was shown to understand. This special payoff expression is not a declining gradient such as a collective payoff game with a cluster size, but the percentage of the request for acceptance for each cluster size follows the cluster size. It was clearly seen in the action. The indicator of the human player's strategy is that this is considered by consolidating the game status into the combination of three variables. In other words, it is a cluster size (S_I) of the focus player, the selected alter ego cluster size (S_J), and the average cluster size (S_J) of the adjacent player that shares the selected alter ego. The payoff function affected the player's strategy by making the cluster size exceeding the required threshold. This suggests that the player's strategy is divided into maximizing cluster (request) and helping other groups to form a cluster. Such an imbalance for such a purpose was also proved in the following human and agents. Agent, who modeled a fully cooperative setting player, has achieved the following good results. First, in the case of setups for human-human, the final result of each game was the formation of the largest group like a collective payoff game, but human subjects understand the fixing individual payoff functions. It was shown to be there. This special payoff expression is not a declining gradient such as a collective payoff game with a cluster size, but the percentage of the request for acceptance for each cluster size follows the cluster size. It was clearly seen in the action. The indicator of the human player's strategy is that this is considered by consolidating the game status into the combination of three variables. In other words, it is a cluster size (S_I) of the focus player, the selected alter ego cluster size (S_J), and the average cluster size (S_J) of the adjacent player that shares the selected alter ego. The payoff function affected the player's strategy by making the cluster size exceeding the required threshold. This suggests that the player's strategy is divided into maximizing cluster (request) and helping other groups to form a cluster. Such an imbalance for such a purpose was also proved in the following human and agents. Agent, who modeled a fully cooperative setting player, has achieved the following good results.
28. In summary, our game split payoff functions into three types, two of which are achievable from a player’s team-level performance (thresholds for the player’s own group size are small and large), and the third type depends on the performance of other teams. To achieve the third type of payoff, teams are required to cooperate with other teams, which also entails risk, since their own group size may fall below the threshold. Our previous experiment (incentivizing collective goals)10 demonstrated that if all teams cooperate, they can simultaneously achieve the maximum payoff. Therefore, we used an all-subjects setup to test whether a different payoff function that seems less cooperative would change the outcome of the game. We then focused on how the inclusion of agents changes the overall performance, and how the type of mixture (setup B vs. setup C) affects the outcome. First, we found that overall performance is best when teams are composed of only humans (setup A), and that performance may be worse when humans and agents are composed of homogeneous teams (setup B) as opposed to when humans and agents are composed of separate teams (setup C). From the plot of the average scores, we can see that setups A and C allow for a much faster increase.
29. The human team seems to use a flexible and different strategy in various stages of games. Therefore, giving a lot of humans (like a setup C) in hybrid systems, whether purely cooperative and slightly cooperative, can help maximize the overall performance of the system. There is a possibility. Alternatively, a hybrid setup with a small number of interactions between agents and agents can expect better performance. However, if the payoffs are further distorted and the team has less benefits to helping each other, the agent's coordination may help improve the performance of the entire system.
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33. Corthard, J. M., Baho, J., De Pass, Y. & Amp; Tapia, D. I. II. Intelligent environment for monitoring Alzheimer's patient, agent technology for health care. Decision Support Systems 44, 382-396 (2008).
34. Van Doorn, J. et al. Domo Arigato Mr. Roboto: Appearance of automated social beings and customer service experience at the forefront of organizations. J. Service Res. 20, 43-58 (2017). Https://doi. org/10. 1177/1094670516679272ARTICLEGLEGLEGLE SCHOLAR.

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Easy Communication

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Mobile phones can be used for Entertainment

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Camera in Mobile phones

すべての著者は、EU HORIZON2020 FET Open RIA プロジェクト（IBSEN）No. 662725、SoBigData からの支援を謝意する： Social Mining and Big Data Ecosystem, Grant agreement No： ソーシャルマイニングとビッグデータ分析のための欧州統合インフラ、 助成 契約 ID ： 871042, (http://www. sobigdata. eu)。

アールト 大学 理学 部 コンピュータ 学科 、 、 00076 、 エスポー 、 トゥオマス ・ タッコ 、 クナル ・ バッタ 、 ダニエル ・ モンシヴァイス ＆ キンモ ・ カスキ カスキ カスキ カスキ

GPS location

Finland, Espo, 00076 University of Ulto University, Faculty of Business Administration Kunal Batachariya

Flashlight

Alan Tuuling Research Institute, 96 Euston RD, Kings Cross, London, NW1 2db, UK Kinmo Kaski (Kimmo Kaski

Alarms and reminders

Tuomas Tacco

Calculator

Mobile phones (also called mobile celluler networks, mobile phones, and handphones) are electrical equipment used for total dual dual wireless communication via cellular networks called cell sites.

Contacts

This article explains the advantages and disadvantages of using a mobile phone. Before starting this topic, I would like to ask some questions.

Online banking

Do you use smartphones for a long time?

Do you feel frustrated without a mobile phone, or you can't live for a second without a mobile phone?

Disadvantages of mobile phones

Do you have a habit of using the Internet on your mobile phone for 6 to 10 hours every day?

Distraction

If your answer is "yes", this article will be very useful for you and will provide complete information on the advantages and limits of using a mobile phone.

Ear problems

I hope this article is useful to get information about mobile phones. This article is also useful for students to write and give presentations on the advantages and weaknesses of using mobile phones. Let's get into the main subject right away.

Wastage of time

In this era, few people do not have a mobile phone or not use. Now, mobile phones are the priority of all people's lives, and everyone uses mobile phones in their daily lives for communication, business, and other activities. In today's world, mobile phones have completely changed their personal life.

Addiction to mobile phone

Everything has a positive and bad effect. The same is true for mobile phones, and it is a great invention, but there are also bad aspects. From a right perspective, mobile phones are astounding invention for humans, but some problems can occur by continuing to use for a long time.

Cyberbullying

Let's explain in detail the advantages of mobile phones.

Security issues

The mobile phone has changed the way of communication. Before the mobile phone appeared, I used a landline or letter to contact people who live in the distance.

Loss of study

Mobile phones are not limited to communication, but also have various other advantages and restrictions. Most people know the benefits of mobile phones. The advantages of mobile phones are as follows.

Health problems

The main advantage of using mobile phones is to make communication means easier and cheaper. Because of its low price, mobile phones are reasonable, revolutionized in the telecommunications industry, and about 95 % of people use mobile phones for communication.

Sleeping issues

Pressing some keys on your mobile phone allows your friends, family, and colleagues at any time, making your mobile phone easier. In mobile phones, you can contact your friends in various ways, such as voice calls, video calls, text messages, and recorded calls.

Accidents

This is also a major advantage of mobile phones. Mobile phones can be used to get knowledge and information about various topics. Recently, most universities, educational institutions, and school offer online education that provides appropriate learning materials in forms such as images, photos, text, and PDFs. Corona's pandemic has confirmed that students are taking online classes provided by each educational institution to ensure student safety and health.

Distance from relatives

In this era, mobile phones are not used only for calls. Smartphones are said to be gifts for social media enthusiasts. Social media apps such as Twitter, Instagram, Snap Chat, Facebook are always on our fingertips. You can edit and share photos and posts on social media directly from your mobile phone. With mobile, you can always access social media.

Conclusion

Most people scroll the social media timeline on a mobile phone in spare time.

Mobile phones can also be used for business advertising. A mobile phone is perfect for entrepreneurs and businessmen to promote business through online or offline sources. Online can use social media websites and messaging applications such as telegrams, Instagram, Whatsapp, and Pinterest. Most of the major companies arrange meetings with video messaging applications like Skype.

One of the old offline methods to promote business using a mobile phone is text messages. Even in this era, most companies are promoting business with text messages. At the end of every message, links to product pages and business websites are affixed.

Elim Poon - Journalist, Creative Writer

Last modified: 27.08.2024

We studied macaque and human pairs in a coordination game Scientific Reports glfe.info “Human-agent coordination in a group formation game,” Scientific Reports, vol. 11, no. 1, pp. , [2]. A. Baird, and L. M. Maruping, “The Next. Here we report our findings from a group formation game in a small-world network of different mixes of human and agent players, aiming to.