An ABM of changes in individuals’ lifestyles which considers their
evolving behavioural choices. Individuals have a set of environmental behavioural traits that spread through a fixed Watts–Strogatz graph via social interactions with their neighbours. These exchanges are mediated by transmission biases informing from whom an individual learns and
how much attention is paid. The influence of individuals on each other is a function of their similarity in environmental identity, where we represent environmental identity computationally by aggregating past agent attitudes towards multiple environmentally related behaviours. To perform a behaviour, agents must both have
a sufficiently positive attitude toward a behaviour and overcome a corresponding threshold. This threshold
structure, where the desire to perform a behaviour does not equal its enactment, allows for a lack of coherence
between attitudes and actual emissions. This leads to a disconnect between what people believe and what
…

We provide a full description of the model following the ODD protocol (Grimm et al. 2010) in the attached document. The model is developed in NetLogo 5.0 (Wilenski 1999).

Digital social networks facilitate the opinion dynamics and idea flow and also provide reliable data to understand these dynamics. Public opinion and cooperation behavior are the key factors to determine the capacity of a successful and effective public policy. In particular, during the crises, such as the Corona virus pandemic, it is necessary to understand the people’s opinion toward a policy and the performance of the governance institutions. The problem of the mathematical explanation of the human behaviors is to simplify and bypass some of the essential process. To tackle this problem, we adopted a data-driven strategy to extract opinion and behavioral patterns from social media content to reflect the dynamics of society’s average beliefs toward different topics. We extracted important subtopics from social media contents and analyze the sentiments of users at each subtopic. Subsequently, we structured a Bayesian belief network to demonstrate the macro patters of the beliefs, opinions, information and emotions which trigger the response toward a prospective policy. We aim to understand the factors and latent factors which influence the opinion formation in the society. Our goal is to enhance the reality of the simulations. To capture the dynamics of opinions at an artificial society we apply agent-based opinion dynamics modeling. We intended to investigate practical implementation scenarios of this framework for policy analysis during Corona Virus Pandemic Crisis. The implemented modular modeling approach could be used as a flexible data-driven policy making tools to investigate public opinion in social media. The core idea is to put the opinion dynamics in the wider contexts of the collective decision-making, data-driven policy-modeling and digital democracy. We intended to use data-driven agent-based modeling as a comprehensive analysis tools to understand the collective opinion dynamics and decision making process on the social networks and uses this knowledge to utilize network-enabled policy modeling and collective intelligence platforms.

A general model of human mate choice in which agents are localized in space, interact with close neighbors, and tend to range either near or far. At the individual level, our model uses two oft-used but incompletely understood decision rules: one based on preferences for similar partners, the other for maximally attractive partners.

The model is an extension of: Carley K. (1991) “A theory of group stability”, American Sociological Review, vol. 56, pp. 331-354.

The original model from Carley (1991) works as follows:
- Agents know or ignore a series of knowledge facts;
- At each time step, each agent i choose a partner j to interact with at random, with a probability of choice proportional to the degree of knowledge facts they have in common.
- Agents interact synchronously. As such, interaction happens only if the partnert j is not already busy interacting with someone else.
…

We seek to improve understanding of roles enzyme play in soil food webs. We created an agent-based simulation of a simple food web that includes enzymatic activity. The model was used in a publication, Moore et al. (in press; Biochemistry).

In this agent-based model, agents decide to adopt a new product according to a utility function that depends on two kinds of social influences. First, there is a local influence exerted on an agent by her closest neighbors that have already adopted, and also by herself if she feels the product suits her personal needs. Second, there is a global influence which leads agents to adopt when they become aware of emerging trends happening in the system. For this, we endow agents with a reflexive capacity that allows them to recognize a trend, even if they can not perceive a significant change in their neighborhood.

Results reveal the appearance of slowdown periods along the adoption rate curve, in contrast with the classic stylized bell-shaped behavior. Results also show that network structure plays an important role in the effect of reflexivity: while some structures (e.g., scale-free networks) may amplify it, others (e.g., small-world structure) weaken such an effect.

This model builds on inquisitiveness as a key individual disposition to expand the bounds of their rationality. It represents a system where teams are formed around problems and inquisitive agents integrate competencies to find ‘emergent’ solutions.

Reducing packaging waste is a critical challenge that requires organizations to collaborate within circular ecosystems, considering social, economic, and technical variables like decision-making behavior, material prices, and available technologies. Agent-Based Modeling (ABM) offers a valuable methodology for understanding these complex dynamics. In our research, we have developed an ABM to explore circular ecosystems’ potential in reducing packaging waste, using a case study of the Dutch food packaging ecosystem. The model incorporates three types of agents—beverage producers, packaging producers, and waste treaters—who can form closed-loop recycling systems.

Beverage Producer Agents: These agents represent the beverage company divided into five types based on packaging formats: cans, PET bottles, glass bottles, cartons, and bag-in-boxes. Each producer has specific packaging demands based on product volume, type, weight, and reuse potential. They select packaging suppliers annually, guided by deterministic decision styles: bargaining (seeking the lowest price) or problem-solving (prioritizing high recycled content).

Packaging Producer Agents: These agents are responsible for creating packaging using either recycled or virgin materials. The model assumes a mix of monopolistic and competitive market situations, with agents calculating annual material needs. Decision styles influence their choices: bargaining agents compare recycled and virgin material costs, while problem-solving agents prioritize maximum recycled content. They calculate recycled content in packaging and set prices accordingly, ensuring all produced packaging is sold within or outside the model.

…

3spire is an ABM where farming households make management decisions aimed at satisficing along the aspirational dimensions: food self-sufficiency, income, and leisure. Households decision outcomes depend on their social networks, knowledge, assets, household needs, past management, and climate/market trends