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Register a new userModeling Human Dynamics with Adaptive Interest
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Recently, increasing empirical evidence indicates the extensive existence of heavy tails in the interevent time distributions of various human behaviors. Based on the queuing theory, the Barabasi model and its variations suggest the highest-priority-first protocol a potential origin of those heavy tails. However, some human activity patterns, also displaying the heavy-tailed temporal statistics, could not be explained by a task-based mechanism. In this paper, different from the mainstream, we propose an interest-based model. Both the simulation and analysis indicate a power-law interevent time distribution with exponent -1, which is in accordance with some empirical observations in human-initiated systems.
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Human behaviors are often driven by human interests. Despite intense recent efforts in exploring the dynamics of human behaviors, little is known about human-interest dynamics, partly due to the extreme difficulty in accessing the human mind from observations. However, the availability of large-scale data, such as those from e-commerce and smart-phone communications, makes it possible to probe into and quantify the dynamics of human interest. Using three prototypical big data sets, we investigate the scaling behaviors associated with human-interest dynamics. In particular, from the data sets we uncover power-law scaling associated with the three basic quantities: (1) the length of continuous interest, (2) the return time of visiting certain interest, and (3) interest ranking and transition. We argue that there are three basic ingredients underlying human-interest dynamics: preferential return to previously visited interests, inertial effect, and exploration of new interests. We develop a biased random-walk model, incorporating the three ingredients, to account for the observed power-law scaling relations. Our study represents the first attempt to understand the dynamical processes underlying human interest, which has significant applications in science and engineering, commerce, as well as defense, in terms of specific tasks such as recommendation and human-behavior prediction.
The study proposes a modeling framework for investigating the disease dynamics with adaptive human behavior during a disease outbreak, considering the impacts of both local observations and global information. One important application scenario is that commuters may adjust their behavior upon observing the symptoms and countermeasures from their physical contacts during travel, thus altering the trajectories of a disease outbreak. We introduce the heterogeneous mean-field (HMF) approach in a multiplex network setting to jointly model the spreading dynamics of the infectious disease in the contact network and the dissemination dynamics of information in the observation network. The disease spreading is captured using the classic susceptible-infectious-susceptible (SIS) process, while an SIS-alike process models the spread of awareness termed as unaware-aware-unaware (UAU). And the use of multiplex network helps capture the interplay between disease spreading and information dissemination, and how the dynamics of one may affect the other. Theoretical analyses suggest that there are three potential equilibrium states, depending on the percolation strength of diseases and information. The dissemination of information may help shape herd immunity among the population, thus suppressing and eradicating the disease outbreak. Finally, numerical experiments using the contact networks among metro travelers are provided to shed light on the disease and information dynamics in the real-world scenarios and gain insights on the resilience of transportation system against the risk of infectious diseases.
We investigate the formation of opinion against authority in an authoritarian society composed of agents with different levels of authority. We explore a dissenting opinion, held by lower-ranking, obedient, or less authoritative people, spreading in an environment of an affirmative opinion held by authoritative leaders. A real-world example would be a corrupt society where people revolt against such leaders, but it can be applied to more general situations. In our model, agents can change their opinion depending on their authority relative to their neighbors and their own confidence level. In addition, with a certain probability, agents can override the affirmative opinion to take the dissenting opinion of a neighbor. Based on analytic derivation and numerical simulations, we observe that both the network structure and heterogeneity in authority, and their correlation, significantly affect the possibility of the dissenting opinion to spread through the population. In particular, the dissenting opinion is suppressed when the authority distribution is very heterogeneous and there exists a positive correlation between the authority and the number of neighbors of people (degree). Except for such an extreme case, though, spreading of the dissenting opinion takes place when people have the tendency to override the authority to hold the dissenting opinion, but the dissenting opinion can take a long time to spread to the entire society, depending on the model parameters. We argue that the internal social structure of agents sets the scale of the time to reach consensus, based on the analysis of the underlying structural properties of opinion spreading.
Communication is a cornerstone of social interactions, be it with human or artificial intelligence (AI). Yet it can be harmful, depending on the honesty of the exchanged information. To study this, an agent based sociological simulation framework is presented, the reputation game. This illustrates the impact of different communication strategies on the agents reputation. The game focuses on the trustworthiness of the participating agents, their honesty as perceived by others. In the game, each agent exchanges statements with the others about their own and each others honesty, which lets their judgments evolve. Various sender and receiver strategies are studied, like sycophant, egocentricity, pathological lying, and aggressiveness for senders as well as awareness and lack thereof for receivers. Minimalist malicious strategies are identified, like being manipulative, dominant, or destructive, which significantly increase reputation at others costs. Phenomena such as echo chambers, self-deception, deception symbiosis, clique formation, freezing of group opinions emerge from the dynamics. This indicates that the reputation game can be studied for complex group phenomena, to test behavioral hypothesis, and to analyze AI influenced social media. With refined rules it may help to understand social interactions, and to safeguard the design of non-abusive AI systems.
There is currently growing interest in modeling the information diffusion on social networks across multi-disciplines. The majority of the corresponding research has focused on information diffusion independently, ignoring the network evolution in the diffusion process. Therefore, it is more reasonable to describe the real diffusion systems by the co-evolution between network topologies and information states. In this work, we propose a mechanism considering the coevolution between information states and network topology simultaneously, in which the information diffusion was executed as an SIS process and network topology evolved based on the adaptive assumption. The theoretical analyses based on the Markov approach were very consistent with simulation. Both simulation results and theoretical analyses indicated that the adaptive process, in which informed individuals would rewire the links between the informed neighbors to a random non-neighbor node, can enhance information diffusion (leading to much broader spreading). In addition, we obtained that two threshold values exist for the information diffusion on adaptive networks, i.e., if the information propagation probability is less than the first threshold, information cannot diffuse and dies out immediately; if the propagation probability is between the first and second threshold, information will spread to a finite range and die out gradually; and if the propagation probability is larger than the second threshold, information will diffuse to a certain size of population in the network. These results may shed some light on understanding the co-evolution between information diffusion and network topology.
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