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.

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Model of influence of access to social information spread via social network on decisions in a two-person game.

The model is designed to simulate the behavior and decision-making processes of individuals (agents) in a social network. It aims to represent the changes in individual probability to take any action based on changes in attributes. The action is anything that can be reasonably influenced by the three influencing methods implemented in this model: peer pressure, social media, and state campaigns, and for which the user has a decision-making model. The model is implemented in the multi-agent programmable environment NetLogo 6.3.0.

This model has been developed together with the publication ‘Modelling Value Change - An Exploratory Approach’

Value change and moral change have increasingly become topics of interest in the philosophical literature. Several theoretical accounts have been proposed. Such accounts are usually based on certain theoretical and conceptual assumptions and their strengths and weaknesses are often hard to determine and compare, also because they are based on limited empirical evidence.

We propose that a step forward can be made with the help of agent-based modelling (ABM). ABM can be used to investigate whether a simulation model based on a specific account of value change can reproduce relevant phenomena. To illustrate this approach, we built a model based on the pragmatist account of value change proposed in van de Poel and Kudina (2022). We show that this model can reproduce four relevant phenomena, namely 1) the inevitability and stability of values, 2) how different societies may react differently to external shocks, 3) moral revolutions, and 4) lock-in.

A flexible framework for Agent-Based Models (ABM), the ‘epiworldR’ package provides methods for prototyping disease outbreaks and transmission models using a ‘C++’ backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents’ features, providing great complexity for the model dynamics. Furthermore, ‘epiworldR’ is ideal for simulation studies featuring large populations.

Sahelian transhumance is a type of socio-economic and environmental pastoral mobility. It involves the movement of herds from their terroir of origin (i.e., their original pastures) to one or more host terroirs, followed by a return to the terroir of origin.  According to certain pastoralists, the mobility of herds is planned to prevent environmental degradation, given the continuous dependence of these herds on their environment. However, these herds emit Greenhouse Gases (GHGs) in the spaces they traverse. Given that GHGs contribute to global warming, our long-term objective is to quantify the GHGs emitted by Sahelian herds. The determination of these herds’ GHG emissions requires: (1) the artificial replication of the transhumance, and (2) precise knowledge of the space used during their transhumance.
This article presents the design of an artificial replication of the transhumance through an agent-based model named MSTRANS. MSTRANS determines the space used by transhumant herds, based on the decision-making process of Sahelian transhumants.
MSTRANS integrates a constrained multi-objective optimization problem and algorithms into an agent-based model. The constrained multi-objective optimization problem encapsulates the rationality and adaptability of pastoral strategies. Interactions between a transhumant and its socio-economic network are modeled using algorithms, diffusion processes, and within the multi-objective optimization problem. The dynamics of pastoral resources are formalized at various spatio-temporal scales using equations that are integrated into the algorithms.
The results of MSTRANS are validated using GPS data collected from transhumant herds in Senegal. MSTRANS results highlight the relevance of integrated models and constrained multi-objective optimization for modeling and monitoring the movements of transhumant herds in the Sahel. Now specialists in calculating greenhouse gas emissions have a reproducible and reusable tool for determining the space occupied by transhumant herds in a Sahelian country. In addition, decision-makers, pastoralists, veterinarians and traders have a reproducible and reusable tool to help them make environmental and socio-economic decisions.

The present model was created and used for the study titled ``Agent-Based Insight into Eco-Choices: Simulating the Fast Fashion Shift.” The model is implemented in the multi-agent programmable environment NetLogo 6.3.0. The model is designed to simulate the behavior and decision-making processes of individuals (agents) in a social network. It focuses on how agents interact with their peers, social media, and government campaigns, specifically regarding their likelihood to purchase fast fashion.

FIsheries Simulation with Human COmplex DEcision-making (FISHCODE) is an agent-based model to depict and analyze current and future spatio-temporal dynamics of three German fishing fleets in the southern North Sea. Every agent (fishing vessel) makes daily decisions about if, what, and how long to fish. Weather, fuel and fish prices, as well as the actions of their colleagues influence agents’ decisions. To combine behavioral theories and enable agents to make dynamic decision, we implemented the Consumat approach, a framework in which agents’ decisions vary in complexity and social engagement depending on their satisfaction and uncertainty. Every agent has three satisfactions and two uncertainties representing different behavioral aspects, i.e. habitual behavior, profit maximization, competition, conformism, and planning insecurity. Availability of extensive information on fishing trips allowed us to parameterize many model parameters directly from data, while others were calibrated using pattern oriented modelling. Model validation showed that spatial and temporal aggregated ABM outputs were in realistic ranges when compared to observed data. Our ABM hence represents a tool to assess the impact of the ever growing challenges to North Sea fisheries and provides insight into fisher behavior beyond profit maximization.

Large outbreaks of Shigella sonnei among children in Haredi Jewish (ultra-Orthodox) communities in Brooklyn, New York have occurred every 3–5 years since at least the mid-1980s. These outbreaks are partially attributable to large numbers of young children in these communities, with transmission highest in child care and school settings, and secondary transmission within households. As these outbreaks have been prolonged and difficult to control, we developed an agent-based model of shigellosis transmission among children in these communities to support New York City Department of Health and Mental Hygiene staff. Simulated children were assigned an initial susceptible, infectious, or recovered (immune) status and interacted and moved between their home, child care program or school, and a community site. We calibrated the model according to observed case counts as reported to the Health Department. Our goal was to better understand the efficacy of existing interventions and whether limited outreach resources could be focused more effectively.

SeaROOTS ABM is a quite generic agent-based modeling system, for simulating and evaluating potential terrestrial and maritime mobility of artificial hominin groups, configured by available archaeological data and hypotheses. Necessary bathymetric, geomorphological and paleoenvironmental data are combined in order to reconstruct paleoshorelines for the study area and produce an archaeologically significant agent environment. Paleoclimatic and archaeological data are incorporated in the ABM in order to simulate maritime crossings and assess the emergent patterns of interaction between human agency and the sea.

SeaROOTS agent-based system includes completely autonomous, utility-based agents (Chliaoutakis & Chalkiadakis 2016), representing artificial hominin groups, with partial knowledge of their environment, for simulating their evolution and potential maritime mobility, utilizing alternative Least Cost Path analysis modeling techniques (Gustas & Supernant 2017, Gravel-Miguel & Wren 2021). Two groups of hominins, Neanderthals and Homo sapiens, are chosen in order to study the challenges and actions employed as a response to the fluctuating sea-levels, as well as probability scenarios with respect to sea-crossings via buoyant vessels (rafting) or the human body itself (swimming). SeaROOTS ABM aims to simulate various scenarios and investigate the degree climatic fluctuations influenced such activities and interactions in the Middle Paleolithic period.

The model focuses on simulating potential terrestrial and maritime routes, explore the interactions and relations between autonomous agents and their environment, as well as to test specific research questions; for example, when and under what conditions would Middle Paleolithic hominins be more likely to attempt a crossing and successfully reach the islands? By which agent type (Sapiens or Neanderthals) and how (e.g. swimming or by sea-vessels) could such short sea crossings be (mostly) attempted, and which (sea) routes were usually considered by the agents? When does a sea-crossing become a choice and when is it a result of forced migration, i.e. disaster- or conflict-induced displacement? Results show that the dynamic marine environment of the Inner Ionian, our case study in this work, played an important role in their decision-making process.