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Displaying 10 of 25 results beliefs clear search
The model that simulates the dynamic creation and maintenance of knowledge-based formations such as communities of scientists, fashion movements, and subcultures. The model’s environment is a spatial one, representing not geographical space, but a “knowledge space” in which each point is a different collection of knowledge elements. Agents moving through this space represent people’s differing and changing knowledge and beliefs. The agents have only very simple behaviors: If they are “lonely,” that is, far from a local concentration of agents, they move toward the crowd; if they are crowded, they move away.
Running the model shows that the initial uniform random distribution of agents separates into “clumps,” in which some agents are central and others are distributed around them. The central agents are crowded, and so move. In doing so, they shift the centroid of the clump slightly and may make other agents either crowded or lonely, and they too will move. Thus, the clump of agents, although remaining together for long durations (as measured in time steps), drifts across the view. Lonely agents move toward the clump, sometimes joining it and sometimes continuing to trail behind it. The clumps never merge.
The model is written in NetLogo (v6). It is used as a demonstration of agent-based modelling in Gilbert, N. (2008) Agent-Based Models (Quantitative Applications in the Social Sciences). Sage Publications, Inc. and described in detail in Gilbert, N. (2007) “A generic model of collectivities,” Cybernetics and Systems. European Meeting on Cybernetic Science and Systems Research, 38(7), pp. 695–706.
This model makes it possible to explore how network clustering and resistance to changing existing status beliefs might affect the spontaneous emergence and diffusion of such beliefs as described by status construction theory.
The model simulates interactions in small, task focused groups that might lead to the emergence of status beliefs among group members.
How do bots influence beliefs on social media? Why do beliefs propagated by social bots spread far and wide, yet does their direct influence appear to be limited?
This model extends Axelrod’s model for the dissemination of culture (1997), with a social bot agent–an agent who only sends information and cannot be influenced themselves. The basic network is a ring network with N agents connected to k nearest neighbors. The agents have a cultural profile with F features and Q traits per feature. When two agents interact, the sending agent sends the trait of a randomly chosen feature to the receiving agent, who adopts this trait with a probability equal to their similarity. To this network, we add a bot agents who is given a unique trait on the first feature and is connected to a proportion of the agents in the model equal to ‘bot-connectedness’. At each timestep, the bot is chosen to spread one of its traits to its neighbors with a probility equal to ‘bot-activity’.
The main finding in this model is that, generally, bot activity and bot connectedness are both negatively related to the success of the bot in spreading its unique message, in equilibrium. The mechanism is that very active and well connected bots quickly influence their direct contacts, who then grow too dissimilar from the bot’s indirect contacts to quickly, preventing indirect influence. A less active and less connected bot leaves more space for indirect influence to occur, and is therefore more successful in the long run.
The model is based on Swann and Buhrmester’s Identity Fusion behavioural theory, which seeks to explain why an individual puts the group’s priorities above their personal expectations. In order to observe the theory and validate group behaviour, a case study was carried out focusing on scenarios of group violence in football stadiums in Brazil. For the modelling, each agent has a distribution of levels of identification with the group to which they belong, with their level of fusion varying between 1 and 5. According to behavioural theory, an individual’s degree of fusion with the group directly interferes with their behaviour of replicating actions and absorbing group beliefs.
The model presented here is extensively described in the paper ‘Talk less to strangers: How homophily can improve collective decision-making in diverse teams’ (forthcoming at JASSS). A full replication package reproducing all results presented in the paper is accessible at https://osf.io/76hfm/.
Narrative documentation includes a detailed description of the model, including a schematic figure and an extensive representation of the model in pseudocode.
The model develops a formal representation of a diverse work team facing a decision problem as implemented in the experimental setup of the hidden-profile paradigm. We implement a setup where a group seeks to identify the best out of a set of possible decision options. Individuals are equipped with different pieces of information that need to be combined to identify the best option. To this end, we assume a team of N agents. Each agent belongs to one of M groups where each group consists of agents who share a common identity.
The virtual teams in our model face a decision problem, in that the best option out of a set of J discrete options needs to be identified. Every team member forms her own belief about which decision option is best but is open to influence by other team members. Influence is implemented as a sequence of communication events. Agents choose an interaction partner according to homophily h and take turns in sharing an argument with an interaction partner. Every time an argument is emitted, the recipient updates her beliefs and tells her team what option she currently believes to be best. This influence process continues until all agents prefer the same option. This option is the team’s decision.
The my-side bias is a well-documented cognitive bias in the evaluation of arguments, in which reasoners in a discussion tend to overvalue arguments that confirm their prior beliefs, while undervaluing arguments that attack their prior beliefs. This agent-based model in Netlogo simulates a group discussion among myside-biased agents, within a Bayesian setting. This model is designed to investigate the effects of the myside bias on the ability of groups to reach a consensus or collectively track the correct answer to a given binary issue.
NeoCOOP is an iteration-based ABM that uses Reinforcement Learning and Artificial Evolution as adaptive-mechanisms to simulate the emergence of resource trading beliefs among Neolithic-inspired households.
This model simulates economic and epidemiological interaction between citrus production and the disease Huanglongbing (HLB), which is vectored by the Asian citrus psyllid. The model is used to evaluate area-wide coordinated spraying when free-riding is possible given individuals’ beliefs in other grower participation in area-wide spraying and in the information provided by extension on the threat as HLB spread.
The present model demonstrates that how two basic human features: (1) that in the human brain beliefs are interconnected, and (2) people strive to maintain a coherent belief system, gives rise to opinion polarization and the appearance of fake news.
Displaying 10 of 25 results beliefs clear search