This model examines language dynamics within a social network using simulation techniques to represent the interplay of language adoption, social influence, economic incentives, and language policies. The agent-based model (ABM) focuses on interactions between agents endowed with specific linguistic attributes, who engage in communication based on predefined rules. A key feature of our model is the incorporation of network analysis, structuring agent relationships as a dynamic network and leveraging network metrics to capture the evolving inter-agent connections over time. This integrative approach provides nuanced insights into emergent behaviors and system dynamics, offering an analytical framework that extends beyond traditional modeling approaches. By combining agent-based modeling with network analysis, the model sheds light on the underlying mechanisms governing complex language systems and can be effectively paired with sociolinguistic observational data.

Sahelian transhumance is a seasonal pastoral mobility between the transhumant’s terroir of origin and one or more host terroirs. Sahelian transhumance can last several months and extend over hundreds of kilometers. Its purpose is to ensure efficient and inexpensive feeding of the herd’s ruminants. This paper describes an agent-based model to determine the spatio-temporal distribution of Sahelian transhumant herds and their impact on vegetation. Three scenarios based on different values of rainfall and the proportion of vegetation that can be grazed by transhumant herds are simulated. The results of the simulations show that the impact of Sahelian transhumant herds on vegetation is not significant and that rainfall does not impact the alley phase of transhumance. The beginning of the rainy season has a strong temporal impact on the spatial distribution of transhumant herds during the return phase of transhumance.

Amidst the global trend of increasing market concentration, this paper examines the role of finance
in shaping it. Using Agent-Based Modeling (ABM), we analyze the impact of financial policies on market concentration
and its closely related variables: economic growth and labor income share. We extend the Keynes
meets Schumpeter (K+S) model by incorporating two critical assumptions that influence market concentration.
Policy experiments are conducted with a model validated against historical trends in South Korea. For policy
variables, the Debt-to-Sales Ratio (DSR) limit and interest rate are used as levers to regulate the quantity and
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This code simulates individual-level, longitudinal substance use patterns that can be used to understand how cross-sectional U-shaped distributions of population substance use emerge. Each independent computational object transitions between two states: using a substance (State 1), or not using a substance (State 2). The simulation has two core components. Component 1: each object is assigned a unique risk factor transition probability and unique protective factor transition probability. Component 2: each object’s current decision to use or not use the substance is influenced by the object’s history of decisions (i.e., “path dependence”).

Model implemented in Lammers, W., Pattyn, V., Ferrari, S. et al. Evidence for policy-makers: A matter of timing and certainty?. Policy Sci 57, 171–191 (2024). https://doi.org/10.1007/s11077-024-09526-9

Ferrari, S., Lammers, W., Wenmackers, S. (forthcoming) How the structure of scientific communities could impact the public uptake of uncertain science. Philosophy of Science.

This model proposes a new approach analyzing to the doctrinal paradox by considering a deliberative process (which can be represented by an agent-based model) in comparison with classical (binary) majority voting and an aggregation of (continuous) degrees of belief prior to majority voting. This model is a multivariate extension of the Hegselmann–Krause opinion dynamics model.

The purpose of the ABRam-BG model is to study belief dynamics as a potential driver of green (growth) transitions and illustrate their dynamics in a closed, decentralized economy populated by utility maximizing agents with an environmental attitude. The model is built using the ABRam-T model (for model visit: https://doi.org/10.25937/ep45-k084) and introduces two types of capital – green (low carbon intensity) and brown (high carbon intensity) – with their respective technological progress levels. ABRam-BG simulates a green transition as an emergent phenomenon resulting from well-known opinion dynamics along the economic process.

AgModel is an agent-based model of the forager-farmer transition. The model consists of a single software agent that, conceptually, can be thought of as a single hunter-gather community (i.e., a co-residential group that shares in subsistence activities and decision making). The agent has several characteristics, including a population of human foragers, intrinsic birth and death rates, an annual total energy need, and an available amount of foraging labor. The model assumes a central-place foraging strategy in a fixed territory for a two-resource economy: cereal grains and prey animals. The territory has a fixed number of patches, and a starting number of prey. While the model is not spatially explicit, it does assume some spatiality of resources by including search times.

Demographic and environmental components of the simulation occur and are updated at an annual temporal resolution, but foraging decisions are “event” based so that many such decisions will be made in each year. Thus, each new year, the foraging agent must undertake a series of optimal foraging decisions based on its current knowledge of the availability of cereals and prey animals. Other resources are not accounted for in the model directly, but can be assumed for by adjusting the total number of required annual energy intake that the foraging agent uses to calculate its cereal and prey animal foraging decisions. The agent proceeds to balance the net benefits of the chance of finding, processing, and consuming a prey animal, versus that of finding a cereal patch, and processing and consuming that cereal. These decisions continue until the annual kcal target is reached (balanced on the current human population). If the agent consumes all available resources in a given year, it may “starve”. Starvation will affect birth and death rates, as will foraging success, and so the population will increase or decrease according to a probabilistic function (perturbed by some stochasticity) and the agent’s foraging success or failure. The agent is also constrained by labor caps, set by the modeler at model initialization. If the agent expends its yearly budget of person-hours for hunting or foraging, then the agent can no longer do those activities that year, and it may starve.

Foragers choose to either expend their annual labor budget either hunting prey animals or harvesting cereal patches. If the agent chooses to harvest prey animals, they will expend energy searching for and processing prey animals. prey animals search times are density dependent, and the number of prey animals per encounter and handling times can be altered in the model parameterization (e.g. to increase the payoff per encounter). Prey animal populations are also subject to intrinsic birth and death rates with the addition of additional deaths caused by human predation. A small amount of prey animals may “migrate” into the territory each year. This prevents prey animals populations from complete decimation, but also may be used to model increased distances of logistic mobility (or, perhaps, even residential mobility within a larger territory).

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For deep decarbonisation, the design of climate policy needs to account for consumption choices being influenced not only by pricing but also by social learning. This involves changes that pertain to the whole spectrum of consumption, possibly involving shifts in lifestyles. In this regard, it is crucial to consider not just short-term social learning processes but also slower, longer-term, cultural change. Against this background, we analyse the interaction between climate policy and cultural change, focusing on carbon taxation. We extend the notion of “social multiplier” of environmental policy derived in an earlier study to the context of multiple consumer needs while allowing for behavioural spillovers between these, giving rise to a “cultural multiplier”. We develop a model to assess how this cultural multiplier contributes to the effectiveness of carbon taxation. Our results show that the cultural multiplier stimulates greater low-carbon consumption compared to fixed preferences. The model results are of particular relevance for policy acceptance due to the cultural multiplier being most effective at low-carbon tax values, relative to a counter-case of short-term social interactions. Notably, at high carbon tax levels, the distinction between social and cultural multiplier effects diminishes, as the strong price signal drives even resistant individuals toward low-carbon consumption. By varying socio-economic conditions, such as substitutability between low- and high-carbon goods, social network structure, proximity of like-minded individuals and the richness of consumption lifestyles, the model provides insight into how cultural change can be leveraged to induce maximum effectiveness of climate policy.